dataesr/scientific-paragraphs-categorization huggingface dataset:
). This protection leads to coordinate cell proliferation, differentiation and apoptosis among other cellular responses  [10, 11]. It is not surprising, thereby, that mucins stay under intensive investigation as highly promising biomarkers and therapeutic targets in cancer and inflammatory diseases  [12, 13, 14]. Thus far, more than 20 mucin genes have been identified and characterized in higher vertebrates, but several mucins are likely waiting for discovery due to the technical problems associated to the large size and repetitive sequences of the mucin chain-peptide. Recently, it has become apparent that sequence databases can be useful tools to find new candidate genes. A better understanding of the molecular identity and functional regulation of mucins is, thereby, mandatory to assign specific roles to a given mucin gene or isoform within and among different vertebrate species.
This is especially relevant in the case of lower vertebrates and fish in particular. Thus, the first goal of the present study was to provide a comprehensive overview of the mucin gene family through searches in the updated cDNA repository database (http://www. nutrigroup-iats. org/seabreamdb) of gilthead sea bream (GSB) (Sparus aurata)  [15] , a perciform fish extensively cultured in the Mediterranean basin. The second goal was to underline the tissuespecific expression pattern of GSB mucins in skin, gills and the gastrointestinal tract. The third goal was to determine whether these mucins were altered by nutritional and pathogen challenges. To pursue this issue, the myxozoan parasite Enteromyxum leei was used as an intestinal infection model. This parasite causes severe desquamative enteritis, cachexia and eventually death  [16].
Molecular Identity and Structure Analysis The recently updated GSB cDNA transcriptome database (http://www. nutrigroup-iats. org/seabreamdb) was used to identify mucin-encoding genes. First, the database was term-searched for automatically annotated mucin genes. In a second step, mucinencoding genes were identified by BLAST queries using mucinsequence predictions derived from genome sequencing of tilapia and fish model species. When multiple GSB sequences were identified, they were manually curated for frame-shifting errors and a PCR approach was used to confirm that the construct belonged to the same gene transcript. For structure analysis, the edited sequences were blasted against the SMART database in the normal SMART mode, searching for Pfam domains and internal repeats. Transmembrane segments were predicted by the TMHMM2 server and those of mucin type GalNAc O-glycosylation sites by NetOGlyc 3.
Gene Expression Analyses Quantitative real-time PCR was performed using an iCycler IQ Real-time Detection System (Bio-Rad, Hercules, CA, USA) as described elsewhere  [21]. Briefly, diluted RT reactions were used for PCR reactions in 25 ml volume. Each PCR-well contained a SYBR Green Master Mix (Bio-Rad) and specific primers at a final concentration of 0. 9 mM were used to obtain amplicons of 50-150 bp in length (Table  1 ). The efficiency of PCR reactions varied between 90% and 99% and the specificity of reaction was verified by analysis of melting curves, serial dilutions of RT reactions, and electrophoresis and sequencing of PCR amplified products. Reactions were performed in triplicate and the fluorescence data acquired during the extension phase were normalized by the deltadelta method using b-actin as housekeeping gene  [22].
Statistical Analysis Data on gene expression are represented as the mean 6 SEM of 6-8 fish. For each mucin gene, the specific effect of tissue, pathogen exposure and dietary treatment on mucin mRNA levels were analyzed by Student t-test (when two groups were compared) or by one-way analyses of variance (ANOVA-I) followed by Student-Newman-Keuls test. When the test of normality or equal variance failed, a Mann-Whitney Rank Sum test or a Kruskal-Wallis ANOVA-I on ranks followed by Dunn's method was applied instead, respectively. The significance level was set at P,0. 05. All the statistical analyses were performed using Sigma Stat software (SPSS Inc. , Chicago, IL, USA).
Structure and Phylogenetic Analyses of Mucin Gene Candidates Searches in the GSB database recognized (E-value #1e-33) three contigs of 121-449 clones in depth with complete codifying sequences of 736 (Muc2), 434 (Muc13) and 643 (Muc18) amino acids in length (Table  2 ). Three additional non-overlapping contigs of 16-73 clones in depth and 1674-1849 bp in length were identified as partial-mucin mRNA sequences and annotated as intestinal mucin (I-Muc) (E-value 5e-33), Muc2-like (E-value 0) and Muc19 (E-value 0). These new GSB sequences were uploaded in GenBank with accession numbers JQ277712 (I-Muc), JQ277710 (Muc2), JQ277711 (Muc2-like), JQ277713 (Muc13), JQ277714 (Muc18) and JQ277715 (Muc19). As depicted in Figure  1 , the sequences annotated as I-Muc, Muc13 and Muc18 share the characteristic TM domain of the membrane-bound mucin subclass with a cytoplasmic tail of 26-52 amino acids in length and a strict conservation in the case of I-Muc and Muc13 of an extracellular proteolytic cleavage site (SEA Figure  S1  shows the deduced amino acid sequence of the reported GSB mucins together with sequence and domain alignments with orthologs from other species.
In fact, many mucins are used as prognostic and diagnostic markers in malignant diseases involving epithelial cells  [25, 26]. In most fish studies, immunocytochemical, cytochemical and biochemical techniques have been applied to determine the effect of environmental pollutants and pathogens on mucins and mucin producing cells (goblet cells, GC)  [27, 28, 29, 30]. However, fish mucin gene expression studies are very scarce in part due to the limitations imposed by the size and nature of the sequence of mucin genes. Thus, this is the first study which analyses in depth the gene expression profile of six mucins in fish tissues and how they are affected by nutritional and pathological challenges. First of all, it is noteworthy that the molecular identity of mucins categorized as SGFM (Muc2, Muc2-like and Muc19) was unequivocally established on the basis of Blast searches (Evalue = 0) and phylogenetic analysis of the GSB sequences annotated in our transcriptome database as complete or almost complete codifying sequences.
This work was funded by the  EU   ARRAINA  project ( Advanced Research Initiatives for Nutrition & Aquaculture ,  FP7/2007/2013 ; grant agreement no. 288925 ) and by the  Spanish Ministry of Science and Innovation (MICINN)  through the projects  AGL2009-13282-C02-01  and  AQUAGENOMICS  ( CSD2007-00002 ,  Improvement of Aquaculture Production  by the  Use of Biotechnological Tools). Additional funding was obtained from the  ''Generalitat Valenciana '' (research grants  PROMETEO 2010/006 ,  ISIC 2011/003 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To overpass these problems, salivary recombinant proteins have been used to develop standardized epidemiological serological tools to assess exposure to ticks  [26, 27] , sand flies  [28, 29] , triatomines  [30]  or to the afrotropical malaria vectors  [31]. Another approach, less demanding in terms of laboratory facilities and well adapted to the environment of research laboratories in Africa, has been to identify specific and immunogenic peptides from the salivary proteins sequences available in the data banks. This strategy was successfully applied to develop serological markers of human exposure to Aedes aegypti  [32]  and Anopheles gambiae  [33, 34]. In this paper we describe an ''in silico'' approach, in which several epitope prediction and protein conformation software's were used in combination with blast analyses to define synthetic peptides to assess human exposure to tsetse flies in West Africa.
Peptide identification Bioinformatics analyses were carried on the mature protein sequences (signal peptide removed) of the Glossina Ada (D3TR66), Tsgf1 (Q9U7C6), and Ag5 (D3TMF1) salivary proteins that were previously shown to react specifically with plasma from tsetse infested areas  [23]. Protein sequences were from G. morsitans morsitans as they are currently the only available tsetse sequences in the data banks. In a first step, each of the protein sequences was sequentially submitted to an array of four different epitope prediction algorithms. The identification of putative linear B-cell epitopes was performed on the NETSurfP  [41] , ABCpred  [42]  and Bcepred  [43]  servers using the B cell epitope database BCIPEP  [44] , which contains 3 031 entries that include 763 immunodominant, 1 797 immunogenic, and 471 null-immunogenic epitopes  [45].
MHC class2 binding regions were also searched with the Proped-2 online service  [46]. All epitopes (5-6 amino-acid length) that were identified by at least three out of four algorithms were selected for further analyses. In a second step, 10 amino acid sequences were added on each side of the selected epitopes and corresponding sequences were blasted on all nonredundant Genbank CDS databases  [47]. After this step the best candidate peptide (with highest specificity to Glossina) was selected for each of the study salivary antigens (Ada, Ag5 and Tsgf1). Accessibility of candidate peptides at the surface of the protein was then checked by performing 3D structure models of the proteins with Phyre  [48]  and verified with the molProbity server  [49]. Peptides were synthesized by the Genepep Company (Saint-Jean de Vedas, France) with a purity control by HPLC.
Peptide design In a previous immune-proteomic approach combining 2-D electrophoresis and mass spectrometry, we were able to identify three proteins (Ada, Ag5 and Tsgf1), from G. palpalis gambiensis saliva extracts, that reacted specifically with IgG from individuals exposed to tsetse bites  [23]. After the first screening step, where the sequence of each protein was submitted to four different epitope prediction algorithms, four to seven candidate epitopes (5 to 6 aa) were identified by at least three of the prediction algorithms for each of the study protein (Table  1 ). In a second step, candidate eptitope sequences (the 5-6 aa sequence added of 10 aa on each side) were submitted to Blast analysis with Genbank CDS non redundant databases. For each candidate sequence we calculated the difference between the Blast E-values for G.
It is however clear that all sources of human antigens have not yet been sequenced and are thus not present in the data bases that were used for our ''in silico'' approach. Nonetheless, Tsgf1 18-43 appeared to be a good  candidate to assess human exposure to tsetse flies. Anti-Tsgf1 18-43 IgG responses were low in all our control groups (from rural, urban Africa and France) and were highly and significantly elevated in plasma samples collected from our two populations from tsetse-infested areas (Guinea and SW Burkina Faso). Noteworthy an intermediate response was observed in one of the study control from Bobo-Dioulasso suggesting that anti-Tsgf1 18-43 antibodies may not be entirely specific of tsetse exposure; we can however also not completely rule out that this subject was truly exposed to tsetse bites in the near neighborhood of the city.
were identified by at least three out of four epitope prediction servers (NETSurfP, ABCpred, Bcepred and Proped-2) are listed for each protein. Within peptide sequences, amino acids forming the linear epitope are bolded. Each peptide was then blasted on all non-redundant Genbank CDS databases. Blast E-value for G. morsitans morsitans and the best match species are indicated. For each protein, the candidate peptide selected for biological validation is underlined. doi:10. 1371/journal. pntd. 0002455. t001.
Medical surveys and laboratory work were supported by the  French Ministry of Foreign Affairs (FSP/REFS  and  Aires-SUD  projects), the  World Health Organization (WHO)  and the  International Atomic Energy Agency (IAEA). ED was a recipient of an  Institut de Recherche pour le Developpement (IRD)  PhD fellowship. We would also like to thank the  Targeting Tsetse Project  funded by the  Bill and Melinda Gates Foundation  who paid for the synthetic peptides. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
were identified by at least three out of four epitope prediction servers (NETSurfP, ABCpred, Bcepred and Proped-2) are listed for each protein. Within peptide sequences, amino acids forming the linear epitope are bolded. Each peptide was then blasted on all non-redundant Genbank CDS databases. Blast E-value for G. morsitans morsitans and the best match species are indicated. For each protein, the candidate peptide selected for biological validation is underlined. doi:10. 1371/journal. pntd. 0002455. t001 Consensus peptides E-value G. m. morsitans Best match specie (E-value) ADA Seq1 71-96 : 2. 10 216 Burkholderia pseudomallei (0,96) HLSTGGEVQLNSKEIEVDRIFMKYKI Seq2 110-135 6. 10 217 Drosophila mojavensis (5. 10 213 ) AAGMHFFKAKPLIERSKVFRFLQQMP Seq3 168-193 6. 10 217 Leptospirillum ferrodiazotrophum (1,7) DQRGINILTFRENPERHKCTTQYVAV Seq4 174-199 4.
Sequence analysis The sequences obtained were aligned using Clustal W  19  and edited using Bioedit Software version 5. 0. 9. 20 Phylogenetic analysis was performed using the Neighbor-Joining algorithm (Kimura 2-parameter) and the MEGA version 5. 05 program. 21 The number of bootstrap replications was set to 1000, and bootstrap values greater than 60 were labeled on major tree branches for reference. The virus strains were clustered based on the nucleotides in their sequences, and only dominant clusters were used to infer phylogenetic relationships.
Accession numbers One hundred and seventy-one sequences have been deposited in Genbank under the accession numbers KF188234-KF188404. Susceptibility to neuraminidase (NA) inhibitors NA-inhibition (NAI) assays were performed using viruses standardized to equivalent NA activity and incubated with NAIs at concentrations of 5310  26  -500 mM for 30 min at 37  6  C and with 29-(4methylumbelliferyl)-a-D-N-acetylneuraminic acid (Sigma-Aldrich, St Louis, MO, USA) as a substrate for 30 min at 37 6 C before stopping the reaction and measuring the fluorescence. 22 Fluorometric determinations were quantified with a Synergy 2 multimode microplate reader (BioTek, Winooski, VT, USA) and were based on the release of the fluorescent product 4-methyl-umbelliferone using excitation and emission wavelengths of 360 and 460 nm, respectively.
Antigenic analysis of H9N2 influenza viruses The availability of effective prophylaxis and treatment options against emerging biological agents are among the important risk assessment factors considered by government agencies for emergency preparedness and biological containment. 30 Therefore, the characterization of antigenic cross-reactivity (for vaccine selection) and phenotypic and genotypic susceptibility to currently available antivirals of H9 viruses is of clear public health importance. To monitor the antigenic similarity among the H9N2 viruses, we generated post-infection ferret antisera and performed HI assays using chicken red blood cells. Titers against the homologous viruses ranged from 1 : 80 to 1 : 20 480 (Supplementary Table  S2 ). The shorebird/DE and chicken/Beijing viruses each reacted poorly against the heterologous antisera, thereby supporting the genetic data that these two viruses belong to distinct lineages.
As such, these internal segments may also contribute to emerging pandemics. This possibility has recently been realized in the emergence of the H7N9 strains infecting humans in mainland China. 2  Those viruses contain surface proteins previously found mostly in avian influenza viruses with internal genes of H9N2-lineage viruses. Intriguingly, none of the H9-lineage viruses that have been characterized in public databases appear to cluster closely with all of the internal gene segments from the H7N9 lineage. This finding indicates that an as-yet-undocumented event occurred among H9 strains to generate the internal gene cassette of the novel H7N9 lineage. Although swine viruses are often more intensely scrutinized due to the status of the pig as a mixing vessel, the chicken/HK/G9 virus stood out in comparison with the four other chicken viruses in our analysis, in terms of it growing reasonably well in multiple mammalian model systems.
ACKNOWLEDGMENTS We would like to thank  Pamela McKenzie  ( St Jude Children's Research Hospital ) for administrative support and helpful conversations and  Angela McArthur  ( St Jude Children's Research Hospital ) for manuscript editing. This work was supported by  NIH/NIAID  Contract  HHSN266200700005C  ( St Jude Center of Excellence for Influenza Research and Surveillance ) and  American Lebanese Syrian Associated Charities at St Jude.
The A. tumefaciens WRT31 genome consists of one circular chromosome containing 2,938,081 bp, one linear chromosome containing 2,176,692 bp, and two additional scaffolds containing 674,326 bp and 86,649 bp, respectively. The percentages of GϩC content are 58. 4%, 58. 6%, 57. 4%, and 54. 6% for the circular chromosome (scaffold 1), linear chromosome (scaffold 2), scaffold 3, and scaffold 4, respectively. A total of 5,718 putative coding sequences were predicted using the Rapid Annotations using Subsystems Technology (RAST) v4. 0 automated pipeline  (8). A BLAST search using scaffold 3 and scaffold 4 as queries indicated that both should be part of a plasmid related to the At plasmid of A. tumefaciens strain C58 encoding the lactonase BlcC. Nucleotide sequence accession numbers. The A. tumefaciens WRT31 genome sequence has been deposited at DDBJ/EMBL/ GenBank under the accession no.
Urogenital schistosomiasis, caused by Schistosoma haematobium, accounts for over 110 million of these cases of human schistosomiasis in SSA alone  [2]. Since 2002, large-scale mass drug administration (MDA) of preventative associated DNA extractions have been bio-banked within the Schistosomiasis Collection at the Natural History Museum (SCAN), which can be found and accessed at: https://www. nhm. ac. uk/our-science/ our-work/sustainability/schistosomiasis-collection. html and https://scan. myspecies. info/ The data that support the epidemiological findings of this study are openly available in ClinEpiDB. The dataset "Study: SCORE Zanzibar S. haematobium Cluster Randomized Trial" can be found at: https:// clinepidb. org/ce/app/record/dataset/DS_ eddb4757ba. Coding, combined with anonymised microsatellite data associated with the representative schistosome samples, are all available and on GitHub at: https://www.
github. com/minesneves/Zanzibar_population_genetics. chemotherapy with praziquantel (PZQ) has been implemented across much of SSA  [3, 4]. Morbidity control has been generally successful across many countries  [5]  and this has helped lead to a revision of the World Health Organization's (WHO's) strategic plan for a vision of "a world free of schistosomiasis"  [6, 7]. Most recently, the new WHO NTD-Roadmap and Guidelines both outline aims to achieve schistosomiasis elimination as a public health problem (EPHP, i. e. elimination of morbidity where prevalence of heavy infection intensity less than 1% in all sentinel sites) in all endemic countries by 2030, as well as a complete interruption of transmission (IoT, i. e. reduction of incidence of infection to zero) in selected regions by the same point  [8] [9] [10].
Data analyses Microsatellite multilocus genotypes (MLGS) for each miracidium were compiled by combining data from microsatellite panels 1 and 2 using R (v. 3. 2. 1. ) [28]. Data for each miracidium were associated with data from the parasitological surveys including ID, year of collection (baseline or year 5), whether the individual was a child (6-8 or 9-12 years old) or adult, infection intensity as S. haematobium eggs per 10ml of urine, sex, age, and shehia of residence (adult) or schooling (child), island (Pemba or Unguja) and intervention arm (MDA only, MDA + snail control or MDA + behaviour change). Data on MDA coverage, shehia prevalence and infection intensity were available from the main trial  [20]. Linear mixed-effects models with random intercepts (using the intensity in eggs per 10ml as the response variable, intervention arm and age as fixed effects, and shehias as random effects) were fitted to the 2012 baseline and 2016 Year 5 follow up data to explore differences in infection intensity between intervention arms, age and sex within and between islands.
Genetic diversity Summary statistics for allelic richness (Ar), expected heterozygosity (He) and observed heterozygosity (Ho), which are measures of genetic diversity, and the inbreeding coefficient (F ST ) for each infra-population (the parasite population of each child or adult at each time point) were created using Powermarker  [29]  and FSTAT  [30]. Allelic richness is suitable for the comparison of samples of different sizes since it rarefies to the smallest sample size in the dataset, and is thus particularly useful for schistosome population genetics where the number of miracidia collected is partly dependent on infection intensity. The alternative approach of excluding small sample size datasets would bias against lighter intensity infections, which are likely following successful PZQ treatment.
The inbreeding coefficient is a measure of the degree of similarity between parasites within an individual where a lower value indicates less relatedness. Differences in Ar, He, Ho and F ST between treatment arms and between baseline and Year 5 were investigated using linear regression models implemented in Stata (StataCorp LP, USA), controlling for age and sex of individuals, shehia/school, island and miracidia sample size. Models used robust standard errors to account for clustering of infra-populations/individuals within shehias. Two approaches to modelling were taken. Since there was minimal evidence of difference in genetic diversity indices between intervention arms at baseline, effects of the different intervention could be seen by direct comparison of the arms in the Year 5 dataset.
Population structure by island and persistent hotspot status To gain further insights into the population structure of the miracidia, the microsatellite data were subjected to Principal Component Analysis (PCA) using the "adegenet" v2. 1. 1 package  [38]  in R (version 3. 5. 2). Each miracidium MLG was assigned to its island of origin and as either from a hotspot versus non-hotspot using the definitions as above.
Results Whilst the infected participants matched exactly to those sampled within the main ZEST programme  [20, 21] , and hatching was attempted from all participants identified egg-positive by urine filtration, the total values regarding the number of participant samples obtained for genotyping were lower due to a combination of non-systematic drop-out from collection, lack of urines provided, hatching success, exclusion of baseline samples where fewer than five miracidia per participant were obtained, and/or poor-quality DNA for amplification. From the baseline collections in 2012, MLGs were obtained from 1,825 miracidia (see Tables A-D in S1 Text for details by island and intervention arm, and https://github. com/minesneves/Zanzibar_ population_genetics for full dataset and coding), 1,522 of which were from 176 children (mean of 8.
7 miracidia per child) and 303 from the 43 adults (mean of 7. 06 miracidia per adult). The age of the participants at baseline varied from 6 to 75 years with a mean of 12. 7 years (see Table  B  in S1 Text for details by sex). The 2016 Year 5 follow-up dataset comprised of MLGs from a total of 1,700 miracidia, 1,486 of which were from 146 children (mean of 10. 18 miracidia per child) and 214 from 25 adults (mean of 8. 56 miracidia per adult). The age of hosts varied from 9 to 46 years with a mean of 12. 4 years, and thus the age range was lower than the baseline collections (and hence whilst statistical analyses within year encompassed the full age spectrum, comparisons between years were restricted to matched 9-12-year-old age groups where applicable).