dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012)  and Arc958R  (Delong, 1992). PCR amplification with LA Taq polymerase (TaKaRa Bio Inc. , Otsu, Japan) was performed using a Veriti 96well thermal cycler (Applied Biosystems, Foster City, CA). The amplification conditions were as follows: 40 cycles of denaturation at 96 • C for 25 s, annealing at 50 • C for 45 s, and extension at 72 • C for 60 s for DNA fragments encoding 16S rRNA genes. PCR amplification of an extraction blank was used to assess experimental contamination. Cloning and sequencing of the PCR products were performed as described in  Igisu et al. (2012). The Mothur Utility package was used for statistical analyses of the 16S rRNA sequence data  (Schloss et al. , 2009). Partial 16S rRNA gene sequences with more than 97% similarity were assigned to the same operational taxonomic unit (OTU).
Representative sequences were aligned to the SILVA Reference Alignment using the NAST algorithm  (Pruesse et al. , 2007) , followed by manual alignment. Phylogenetic affiliations were assigned by the maximum parsimony method with the SILVA SSU Ref111 Database in ARB software  (Ludwig et al. , 2004). The 16S rRNA gene sequences in the subseafloor core samples that displayed greater than 97% similarity with those of the drilling fluids were defined as potential contaminants.
, 2003; Nunoura et al. , 2010; Yoshida-Takashima et al. , 2012). These results suggest that detailed contamination assessments, including the development of a library of potential contaminant phylotypes, are an important component of any investigation of potentially indigenous microbial populations and their functions. Most of the potential contaminant sequences were derived from Beta-and Gammaproteobacteria and Bacteroidetes (Figure  3 ) A few sequences were closely related (>97% sequence similarity) to previously reported contaminant phylotypes such as Pseudomonas, Halomonas and Vibrio  (Masui et al. , 2008; Santelli et al. , 2010). The remaining sequences have not been identified as contaminant sequences. Our knowledge of contaminant sequences is still lacking. As described above, large sequence databases based on pyrosequencing results obtained with various drilling/coring systems and drilling fluid components will be needed to discriminate contaminationspecific phylotypes from the indigenous microbial community.
ACKNOWLEDGMENTS We are deeply grateful to the  Integrated Ocean Drilling Program , the captains, offshore installation managers, operation superintendents, the scientific party, and all the crew, expedition staff and technicians on the  Chikyu from Mantle Quest Japan and CDEX, JAMSTEC , who assisted us with drilling, sampling, installation and measurements during IODP Expedition 331. This work was supported in part by the  Grant-in-Aid for Scientific Research : Project  TAIGA (New Scientific Research on Innovative Areas, 20109003 ).
The time of acquisition was between 30 seconds and 1 minute per spot. The 12 IIH2 T spectra were imported into the MALDI Bio Typer software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the main spectra of 8 Archaea (Natrinema gari, Natrinema pallidum, Haloterrigena thermotolerans, Haloterrigena. sp, Haloarcula. sp, Halopiger. sp, Haloferax mediterranei, Halogeometricum. sp) used as reference data (Figures  4  and 5 ). The method of identification included the m/z from 2,000 to 20,000 Da. For every spectrum, 100 peaks at most were taken into account and compared with the spectra in the database. The MALDI-TOF score enabled the predictive identification and discrimination of the tested species from those in a database: a score > 2 with a validated species enabled identification at the species level, and a score < 1.
Genome project history The organism was selected for sequencing on the basis of its phenotypic differences, phylogenetic position and 16S rRNA similarity to other members of the genus Halopiger, and as part of the study of archaeal diversity in hypersaline lakes of Algeria. It is the second genome of a Halopiger species and the first sequenced genome of H. djelfamassiliensis sp. nov. The EMBL accession number is CBMA010000001-CBMA010000055 and it consists of 6 scaffolds (HG315684-HG315689). Table  3  shows a summary of the project (PRJEB1777) information and its association with MIGS version 2. 0 recommendations  [24].
Genome annotation Open Reading Frames (ORFs) were predicted using prodigal  [36]  with default parameters. ORFs spanning a sequencing gap region were excluded. Assessment of protein function was obtained by comparing the predicted protein sequences with sequences in the GenBank  [37]  and the Clusters of Orthologous Groups (COG) databases using BLASTP. RNAmmer  [38]  and tRNAscan-SE 1. 21  [39]  were used for identifying the rRNAs and tRNAs, respectively. SignalP  [40]  and TMHMM  [41]  were used to predict signal peptides and transmembrane helices, respectively. ORFans of alignment length greater than 80 amino acids were identified if their BLASTP E-value was lower than 1e-03. An E-value of 1e-05 was used if alignment lengths were smaller than 80 amino acids. DNA Plotter  [42]  was used for visualization of genomic features and Artemis  [43]  was used for data management.
The mean level of nucleotide sequence similarity was estimated at the genome level between H. djelfamassiliensis and 5 other members of the Halobacteriaceae family (Table  6 ), by BLASTN comparison of orthologous ORFs in pairwise genomes. Orthologous proteins were detected using the Proteinortho software using the following parameters e-value 1e-05, 30% identity, 50% coverage and 50% of algebraic connectivity  [44].
0 and 40 °C, respectively. Magnesium is not required for growth. Cells are polymorphic (0. 9-2. 2 µm) and lyse in distilled water. Tween 80, gelatin and lipids from egg yolk are hydrolysed, D-glucose, Dmelibiose, L-rhamnose, D-xylose, D-galactose, Dmannose, D-ribose and D-sucrose are fermented. Cells are susceptible to bacitracin, novobiocin and tetracycline but resistant to ampicillin, cephalothin, chloramphenicol, erythromycin, gentamicin, kanamycin, nalidixic acid, penicillin G, streptomycin, and vancomycin. The G+C content of the genome is 64. 30%. The 16S rRNA and genome sequences are deposited in GenBank and EMBL under accession numbers KC430939 and CBMA010000001-CBMA010000055 respectively. The type strain IIH2 T (=CSUR P3035= DSM ongoing deposit) was isolated from the sediment border of the hypersaline Lake Zahrez Gharbi, located in the Djelfa region of Algeria.
After two weeks, a booster vaccination was given (day 14). EDTA blood samples were collected directly before the first (day 0) and second vaccination (day 14), as well as two weeks after the booster (day 28). Plasma samples were prepared and stored at -80°C until further analysis. Plasma anti-TT AB titers were determined in triplicate using a commercially available ELISA (RE57441, IBL International, Hamburg, Germany) according to manufacturer's protocol. Five-week-old piglets were vaccinated twice with TT. Directly before the first (day 0) and the second (day 14) vaccination as well at day 28 blood samples were collected and PBMCs were isolated. Anti-TT antibody titers were quantified by ELISA from day 28 plasma samples. Animals were then performance-tested. Performance data and AB titers were interpreted as a basis for the identification of divergent phenotypes of lean growth performance and humoral immune response, respectively.
Finally, expression profiles of divergent phenotypes for lean growth and humoral immune performance were compared. Juvenile animals (average age of 10 weeks) were performance tested and evaluated during fattening and at slaughter (final average weight of 110 kg). Performance test data were subjected to a principal component analysis, which identified the first component (factor 1) explaining 39% of the total variance (Supplementary Table  1 ). Among the animals there was no evidence for association between performance and immune traits. In fact, coefficients of correlation between performance traits and plasma anti-TT AB titers were low (r ≤ 0. 1), thus facilitating the selection of animals that represent divergent groups for both traits. Key parameters (loads) of factor 1 (Table  1 ) were taken as a basis for the identification of lean growth performance phenotypes.
DNA preparations were fragmented and labelled with the WT Terminal Labeling Kit (Affymetrix, Santa Clara, CA, USA). Labelled DNA was hybridized on snowball arrays, a recently developed and annotated whole genome array  [22, 23]. Further information about this platform is also available in the 'ArrayExpress' database (accession number A-AFFY-189, http://www. ebi. ac. uk/arrayexpress/ arrays/A-AFFY-189/). Following staining and washing, arrays were scanned and raw data generated using Affymetrix GCOS 1. 1. 1 software. Microarray data is MIAME-compliant and was deposited in the database of the National Center for Biotechnology Information Gene Expression Omnibus http://www. ncbi. nlm. nih. gov/geo [GEO: GSE47845].
Microarray data processing and functional analyses Quality-controlled raw data were normalized by the PLIER algorithm using Affymetrix Expression Console 1. 1 software (Affymetrix) and filtered by standard deviation (s ≤ 0. 2). According to the experimental design aiming at showing contrasts of the PBMC transcription due to antibody responsiveness and lean growth performance of the test subjects the factor of immune response group (`IR´) and lean growth performance group (`P´) were used as fixed effects in the analysis of variance. Since the genetic background and the age at sampling and expression profiling affect the gene activity `sire´ (`S´) and `time´ (`T´) were also fitted as fixed effects in a mixed model. So essentially, relative transcript abundance changes were determined using these fixed effects and the interactions between `IR´ and `P´ and between `T´, `IR´, and `P´ [v = μ + S + T + IR + P + (IR x P) + (T x IR x P) + ε].
The mixed model was combined with a repeated statement for the time component in order to take into account correlations among measurements made on the same subject by specifying heterogeneous covariance structure. Thus sampling time points were weighted equally while comparing lean growth and humoral immune response phenotypes. Mixed model analyses were performed with JMP Genomics 5. 0 software (SAS Institute, Cary, NC, USA). Annotation data for the snowball arrays were obtained from the developers  [22, 23]. Transcripts showing different abundance at nominal p-values < 0. 5 were assigned to annotated genes and bioinformatically analysed with Ingenuity Pathways Analysis (IPA) Software  [24]. At the levels of IPA's functional analyses for canonical pathways and biofunctions a correction for multiple testing was applied by use of IPA's B-H p-value correction procedure.
Differentially-expressed genes and assigned functions for divergent lean growth performance phenotypes Using Ingenuity Pathways Analysis (IPA) software  [24] , genes with significantly different transcript abundances (hereafter referred to as DE-genes) were assigned to affected canonical pathways and altered downstream effects (hereafter referred to as biofunctions). Comparison of hiLG and loLG revealed 574 DE-genes (Table  2 ) that were assigned to pathways predominated by the cellular immune response (Table  4a ). The most significant pathways, including T-cell receptor, PKCθ, and CD28 signaling, are responsible for co-stimulation during lymphocyte activation and showed higher transcript abundances in the hiLG group. Functional analysis predicted decreased organismal death and increased development, differentiation, and homeostasis of lymphocytes for the hiLG phenotype (Table  5a ).
It remains to be examined whether lower expression of other functions impair physiological or metabolic functions. AB divergent phenotype comparisons revealed integrin signaling as the most significant pathway. Integrin DE-genes ITGA2, ITGA2B, and ITGB3 increased 1. 2-to 1. 4-fold in loAB compared to hiAB; transcripts increased 1. 4-to 1. 6-fold in hiLG+loAB compared to hiLG+hiAB (Supplementary Table  2 ). Integrins are transmembrane molecules that enable attachment to and communication with other cells or the extracellular matrix. Although the IPA database does not attribute integrin signaling to the immune system, integrins may play a crucial role in the leukocyte immune response. Hogg and colleagues  [33]  showed that integrin-mediated leukocyte migration through the body and into lymph nodes is essential for immune responses.
PCNA purification, PIP and APIM peptides preparation and isothermal titration calorimetry PCNA purification was performed as described previously  (22). The synthetic peptides FBH1 PIP and APIM, and ABH2 APIM peptides were purchased from Genecust at 95% purity, and the concentrations of the stock peptide solutions were determined by amino acid composition. Binding between PCNA and the different peptides was determined by isothermal titration calorimetry (ITC) using a VP-ITC calorimeter (GE-Healthcare). Prior to measurements, all solutions were degassed under vacuum. The reaction cell was loaded with 20-130 mM PCNA solution. Control experiments were performed with peptide solutions injected into buffer. Thermodynamic parameters ÁH, N, and Ka were obtained by non-linear leastsquares fitting of the experimental data using the single set of independent binding sites model of the Origin software provided with the instrument.
FUNDING  Ministe`re de l'Enseignement Supe´rieur et de la Recherche  and the  Association pour la Recherche sur le Cancer  [ DOC20110603022  to A. B. ]. Funding for open access charge:  La Ligue Nationale contre le Cancer (Equipe labellise´e)  (to P. L. K. lab);  Institut National du Cancer  [ 2010-1-PLBIO-08 ];  Agence Nationale de la Recherche  [ ANR-09-PIRI-001 ].
References were composed of previously published sequences  [11, 15]  from GenBank, sequences derived from reference strains, and iteratively added patient sequences. Discrimination between the genotypically highly similar Leishmania braziliensis and L. peruviana, and between L. infantum and L. donovani, is not feasible for the mini-exon. The clinical relevance of such distinction is limited, as preferred treatment in the Netherlands is identical for both species, and only depends on the clinical presentation. Of note, discrimination between these species is impossible or difficult by other targets as well  [16, 17] , and therefore their taxonomic status has been a continuing matter of debate  [18]. We therefore refer to these species as L. braziliensis/peruviana and L. donovani/infantum, respectively.
Before DNA sequencing, the PCR products were excised from agarose gels and purified using the QIAquick Gel Extraction Kit (QIAGEN). The Big-Dye Terminator Cycle Sequencing Ready Reaction Kit V3. 1 and the automated ABI PRISM 377 DNA sequencer (Applied Biosystems) were used. Sequences obtained were analysed and edited using the software BioEdit Sequence Alignment Editor, version 7. 0. 9. 0  [26]. ITS1 and ITS2 sequences were compared with sequences of the ITS types described by Kuhls et al. [23] , these sequences were aligned with BioEdit Sequence Alignment Editor using ClustalW multiple alignment algorithm and were manually adjusted. For accurate estimation of the k26 PCR product size, the bands were excised from agarose gels, purified using the QIAquick Gel Extraction Kit (QIAGEN), and analysed by capillary electrophoresis using the Agilent 2100 Bioanalyzer and the Agilent DNA 1000 kit (Agilent Technologies).
Phylogenetic analysis Phylogenetic analysis based on the nucleotide sequences of the Leishmania ITS1 and ITS2 was performed based on maximum parsimony using PHYLIP (PHYLogeny Inference Package), version 3. 69  [30]. To test the robustness of the internal branches generated, we performed bootstrap analysis using 10,000 replications. We included DNA sequences retrieved from GenBank (AJ634341, AJ000288, AJ000294, AJ634356, AJ634367, AJ634373, AJ000297, AJ634376) that are representative for each of the ITS types A to H, respectively, described by Kuhls et al. [23]. According to these.
Results Analysis of the 12 microsatellite sites of the ITS1 and ITS2 concatenated sequences returned two different ITS types among the 73 isolates in the first assembly (isolates from the leishmaniasis outbreak area and other regions of Madrid from 2008 to 2012). ITS type A was present in 21 of the isolates; the remaining 52 isolates were of a type that had not been described previously by Kuhls et al. [23]. The concatenated sequence of both ITS1 and ITS2 corresponding to this second ITS type was subjected to a BLASTn search  [31] , which returned the best score with a sequence corresponding to the L. infantum strain MHOM/ES/87/Lombardi (Gen Bank Accession Number AJ000295). The 12 microsatellite sites found in MHOM/ES/87/Lombardi were to those observed in the ITS type we found (hereafter called ITS-LOMBARDI).
Comparison of ITS-LOMBARDI with the polymorphic microsatellite types described by Kuhls et al. for the L. donovani complex  [23]  is shown in Figure  3. ITS-LOMBARDI was common to all 31 isolates we analysed from the outbreak that affected Health Areas 9 and 10. These isolates were confirmed to be L. infantum by hsp70 PCR and DNA sequencing and further comparison to the DNA sequences of strains from the L. donovani complex (GenBank Accession Numbers FN395027-FN395033), described elsewhere  [24]. In both assemblies, ITS-A and ITS-LOMBARDI sequence types were found in isolates from CL and VL cases, as well as in patients who were HIV-positive and -negative. Both ITS types are widespread in Madrid, having being detected in almost all Health Areas, since 1988 (ITS-A) and since 1992 (ITS-LOMBARDI) (Tables  2  and 3 ).
This ITS type was also present in all of the MON-24 and 20% (11/55) of the MON-1 L. infantum isolates from the second assembly. It is noteworthy that this ITS type was also present in L. infantum MON-24 strain MHOM/ES/87/Lombardi, which was isolated in Spain (location unknown) in 1987, and its ITS DNA sequence (AJ000295) was submitted to the GenBank in year 1998, although no report on this ITS type was published. Unfortunately, neither this ITS sequence nor any other obtained from a MON-24 isolate were included by Kuhls et al. in their study of ITS sequence analysis of the L. donovani complex  [23]. Our phylogenetic analysis, following a similar procedure, revealed that isolates with ITS-LOMBARDI, in spite of being L. infantum (as shown by MLEE and hsp70 gene DNA sequence analyses), do not form a clear phylogenetic group with ITS types A, B (Mediterranean basin, Brazil, China) and C (China), and are not well separated from the second main group that includes all strains from East Africa and India.
Many MLEE-typed isolates were analysed with genetic methods as well. For 12 sequences, we relied on the species identification as listed in GenBank, where the typing method is not specified. Finally, no typing data were available for the remaining 57 sequences, which included those determined for diagnosis. For the sequences described in the paper by Zhang et al. [23] , we did not rely on the GenBank identification, as this was in conflict with data in the paper itself. Aligning was done manually, which was straightforward as no size variation was detected in 205 sequences, while two sequences showed a deletion of three nucleotides, corresponding to one amino acid. Species delineation was based upon the clustering of aligned sequences in a comparative dendrogram, which was constructed with the freely available software package MEGA5  [24].
Results The final alignment contained 207 sequences from 42 Leishmania-endemic countries, representing 18 species of which 15 are causing human disease (Figure  2  and supplementary dendrogram available at www. itg. be/LeishmaniaHSP70). As further detailed in the Discussion, four GenBank entries contained sequences that did not correspond to the indicated isolate, reducing the number of trustworthy sequences to 203 from 41 countries. The clustering of a representative selection of the hsp70 sequences is depicted in Figure  3. A dendrogram of the complete set of available hsp70 sequences can be found at www. itg. be/LeishmaniaHSP70. The medically relevant clusters indicated in bold in these figures could be easily discriminated, and were supported by bootstrap values between 89 and 99% (Figure  3 ).
Leptomonas-like (AFLP) analysis clearly identified this strain as L. braziliensis  [19]. (vii) The MLEE-typed L. braziliensis isolate MCAN/PE/91/LEM2222 clustered with the L. braziliensis outliers, confirming results from several other genes (data not shown). This is in line with the fact that MLEE does not separate both L. braziliensis groups. (viii) L. braziliensis isolate MHOM/PE/--/CU00181 clustered intermediate between L. braziliensis and L. braziliensis outliers, which agrees with AFLP data  [19]. (ix) Two sequences were found in an incorrect species cluster: MRHO/CN/60/Gerbilli and MHOM/FR/78/LEM75. According to GenBank data, JX021436 is the sequence from the WHO L. gerbilli reference strain MRHO/CN/60/ Gerbilli, but it clustered among L. donovani, apart from the other L. gerbilli sequences.
As the strain was not included in the publication describing related GenBank entries  [23] , there are no independent data to confirm the identity of the sequence. Moreover, several species designations in this set of GenBank entries (especially those listed as L. donovani) do not match those in the corresponding paper. MHOM/FR/78/LEM75 is a type strain of L. infantum, but it strongly grouped with L. tropica. Given that all 17 other L. infantum strains clustered correctly with hsp70, it is reasonable to assume that the sequence in GenBank is erroneous. (x) Finally, four isolates, MHOM/PE/95/LQ-8, MCAN/IR/96/LON-49, MHOM/--/94/CRE58, and IMON/CN/90/KXG-Y, did not group with any of the designated species complexes, the reason for which is unclear. The sequences reported for MHOM/PE/95/LQ-8 and MCAN/IR/96/LON-49 were found related to the Leptomonas sp.
sequence described in  [30] , and hence these do not match with the Leishmania isolates listed in GenBank (results not shown). MHOM/--/94/CRE58 and IMON/CN/90/KXG-Y were typed as L. naiffi and L. turanica, respectively, using several genes, and it is unclear why they did not group with their respective species. Taken all evidence together, of the 135 trustworthy sequences for which either MLEE or independent genetic species identification was done, 130 (96. 3%) grouped with the correct species in the hsp70 sequence dendrogram; two (1. 5%) did not group with any species; and three (2. 2%) were assigned to the correct species complex, but the wrong species. Since we started routine species typing on the basis of hsp70 sequences, we could type 33 clinical samples that were sent to our clinic for diagnosis, along with three samples sent to us by other institutes (accessions HF586387, HF586390, HF586391).
amazonensis; arc: L. archibaldi; bra: L. braziliensis; braO: L. braziliensis outlier; bra-braO: hybrid; cha: L. chagasi; don: L. donovani; gar: L. garnhami; ger: L. gerbilli; guy: L. guyanensis; inf: L. infantum; lai: L. lainsoni; maj: L. major; mex: L. mexicana; nai: L. naiffi; pan: L. panamensis; per: L. peruviana; sia: L. siamensis; tar: L. tarentolae; tro: L. tropica; tur: L. turanica. (iii) EBI/GenBank accession number if available. (iv) World Health Organization (WHO) code: Missing data are indicated by. (v) Clinical samples diagnosed in this study are indicated with + in front of the taxon name, and those from the Institute of Tropical Medicine Antwerp are identified by ITM without WHO code, whereby identical sequences are presented as one taxon. CL following the WHO code indicates that the sample was taken from a cutaneous lesion.