dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by  ANRS  ( Agence Nationale de Recherche sur le Sida et les Hépatites) ,  SIDACTION  and  CNRS  ( Centre National de la Recherche Scientifique ). We thank  David N. Levy  for  NLENG1-ES-IRES WT  and  NLENG1-ES-IRES D116N  plasmids. Received: 19 June 2013 Accepted: 2 August 2013 Published: 13 August 2013.
The study population The population we studied were residents of Prampram, a coastal fishing village of 7,000 inhabitants situated 50 km east of Accra and south of the Volta Basin in the Greater Accra region of Ghana. Malaria (98% Plasmodium falciparum) is endemic in Prampram, with a mean of 8. 5 infectious bites/person/year  [80]. In the course of a study to determine the patterns of malaria infection in children, samples of genomic DNA were obtained from 131 newborn infants and from 104 of their mothers  [80]. The subjects are from the Ga-Adangbe ethnic group, which currently comprises 2 million individuals in total. Archaeological data and accompanying historical accounts, combined with linguistic and genetic evidence indicate that Ga-Adangbe ancestors first lived in the region of present-day Nigeria or Burkina Faso before the Bantu expansion (,3000 years ago) and then migrated to the Volta Basin 750-1000 years ago  [88, 89].
High-resolution KIR genotyping Nucleotide sequences were determined for the exons of KIR genes from 16 Ga-Adangbe children who were chosen at random to represent the study population. The sequences of newly discovered alleles were confirmed by re-amplification, cloning and sequencing; or by direct sequencing of the PCR products obtained from homozygous and/or hemizygous individuals. When possible, new alleles were also confirmed by amplification and sequencing of the same gene from the mother. From this dataset of Ga-Adangbe KIR sequences, we developed a pyrosequencingbased method for KIR genotyping that distinguishes all known variants, including those detected in the 16 randomly selected children (Figure  S1  and Figure  S2 ). Pyrosequencing provides a semi-quantitative measure of SNP genotypes (the peak-height ratio) that determines both allele identity and copy-number genotype  [57].
PCR and DNA sequencing PCRs were performed using a Perkin-Elmer 9600 thermal cycler (or a Veriti 96-Well instrument using 9600 emulation mode) with a three minute denaturing step at 94uC, 10 cycles of (94uC 10 s; 65uC 60 s) and 20 cycles of (94uC 10 s, 61uC 50 s, 72uC 30 s). Standard DNA sequencing reactions were performed in forward and reverse directions using BigDye Terminator v3. 1 and analyzed using an ABI-3730 sequencer (ABI, Foster City CA). When required, PCR products were cloned using Topo-pcr2. 1 vector (Invitrogen, Carlsbad CA) and sequenced using M13 and internal primers. All of the newly-discovered alleles described herein were validated according to the guidelines recommended by the curators of the Immuno Polymorphism Database (IPD)  [12]. At least five clones of the desired allele were sequenced from each individual examined.
KIR nomenclature KIR genes and alleles were named by the KIR nomenclature committee  [92]  formed from the WHO Nomenclature Committee for factors of the HLA system, and the HUGO Genome Nomenclature Committee. A curated database is available at http://www. ebi. ac. uk/ipd/kir/  [12]. ,D. denotes the number of Ig-like Domains, ,L. a Long, inhibitory, cytoplasmic tail ,S. a Short, activating, tail and ,P. a Pseudogene. A unique DNA sequence that spans a KIR coding region is considered an allele and those that yield unique proteins are considered to define an allotype. The first three digits distinguish the allotypes, the fourth and fifth digits distinguish synonymous variation. To give an example: KIR3DL1*01501 and KIR3DL1*01502 are synonymous variants of the KIR3DL1*015 allele, and encode the KIR3DL1*015 allotype -an inhibitory receptor having three Iglike domains.
KIR and HLA class I haplotypes The high heterozygosity observed for each KIR and HLA class I gene in the Ga-Adangbe, coupled with analysis of mother-child pairs, allowed unambiguous deduction of HLA class I and KIR allele-level haplotypes. Core sets of 208 HLA and 208 KIR haplotypes were deduced by segregation analysis in 104 motherchild pairs. These sets of haplotypes were used as priors in PHASE 2. 1  [94]  analyses which deduced 54 HLA class I and KIR haplotypes from the remaining 27 unrelated individuals. The final data set consisted of 262 independent HLA class I and KIR haplotypes.
Analysis of HLA class I distribution The distributions of HLA-A, -B and -C alleles were compared in 108 populations, including the Ga-Adangbe, for which highresolution genotyping data were available. These comprised 103 of the 497 populations studied by Solberg et al. [13] , of which 11 are sub-Saharan Africans, and four additional sub-Saharan populations: Ugandans from Kampala  [95] , Yorubas from Ibadan in Nigeria  [96] , KhoeSan from Southern Africa and Hadza from Tanzania  [75]. Data from a total of 31,298 individuals were used in the analyses described here. Statistica 10 (StatSoft Inc. Tulsa OK) was used to perform principal component analysis on the frequencies of every HLA-A, Presence of leucine at residue 86 disrupts cell-surface expression of KIR3DL1  [57]  (Figure  S13 )). Orange indicates KIR-contacting residues, green indicates LILR binding residue.
Analysis of Tajima's D Tajima's D measures the impact on allele-frequency spectra of directional selection favoring a single allele (D,0), or balancing selection favoring multiple alleles (D. 0)  [98]. Tajima's D was calculated using DnaSP 4. 1  [99]. Statistical significance was assessed by comparing the observed values with those expected under neutral-drift equilibrium, in a range of demographic models generated using the program ms  [100]. When evidence remains significant under all reasonable demographic models, the allele distributions are unlikely to have arisen through neutral genetic drift. The demographic models were as described previously  [35].
Watterson's F test and normalized deviate (F nd ) Watterson's homozygosity F test provided the first evidence that balancing selection was acting on HLA molecules  [101]. The statistic, which is the proportion of homozygotes expected under Hardy-Weinberg equilibrium, was calculated from the frequencies of allotypes for given HLA class I motifs using the exact test described by Slatkin  [102]  and implemented in the Pypop software package  [103]. The reported p-value is the probability of obtaining an F statistic less than the observed value if the motif was evolving under neutrality. It is based on the null distribution of F values simulated under neutrality/equilibrium conditions and on the observed number of alleles (k) of any given motif and sample size (2N). In order to directly compare the magnitude of deviation from neutral expectations for motifs with differing numbers of alleles, we computed the normalized deviate of the homozygosity statistic (F nd ).
F nd is the difference between the observed homozygosity, divided by the square root of the variance of the expected homozygosity. This calculation is implemented in Pypop, with variance values obtained through simulations  [63]. Significant negative values of F nd indicate balancing selection, while significant positive values of F nd indicate directional selection. Analysis of diversifying selection of MHC class I in the hominid linage PAML 4. 5  [104]  was used to identify codons subject to positive diversifying selection. Neighbour-joining (NJ) and Bayesian phylogenetic analyses to provide input for PAML were performed as described previously  [35]  using Mega 5  [105]  and MrBayes 3. 2. 1  [106]. The MHC-C data set used corresponded to release 2. 21 of the IPD database  [12]  which included 340 alleles unique through exons 2 and 3 (a 1 and a 2 domains) of HLA-C, plus all unique chimpanzee and orangutan MHC-C alleles having sequences complete through these exons.
Mantel test for correlation between KIR and ligand genotypes Distance matrices (p-distance; number of SNPs which differ, divided by number of SNPs) between individuals in the study cohort (N = 131) were calculated from SNP genotypes using the 'dist. gene' function in the 'ape' (Analyses of Phylogenetics and Evolution: ver. 3. 0-6  [110] ), package for the R language for statistical computing  [111]. Mantel's permutation test for similarity of matrices  [70]  was implemented for pairwise combinations of distance matrices using the 'mantel. test' function of 'ape'. The function compares the observed value of the z statistic for correlation to a distribution obtained by permuting the rows and columns of data. 10,000 permutations were performed. The SNPs were phased and haplotypes concatenated prior to analysis.
The rows are: 1 The top row shows the accession numbers for those SNPs that are also available in dbSNP. 2 Indicates the exon number. 3 Nucleotide number from initiation codon of each SNP. Numbers highlighted in grey are SNPs that were not genotyped in all individuals, but were sequenced in ,20% individuals. 4 SNPs in universal code (Numbers highlighted in green are SNPs identified during this study). 5 Codon numbers (ATG = 1). 6 Codon numbers (mature protein). 7-9 Alternative residues. 12 onwards The SNP sequence for each of the alleles. The highlighting color for each SNP corresponds to the amino acid residue at that position (as identified in rows 7-9). SNPs filled in dark grey were not assayed. Green are alleles identified during the present study (+ denotes allele identified concurrently by Hou et al.
or Pyo et al. [93, 114] ). Grey are alleles not distinguished by genotyping (but could be identified during the sequencing round). The columns are:1 Genbank accession numbers for newlyidentified alleles. 2 Immunopolymorphism database (IPD) accession numbers. 3 Allele names. 4 Indistinguishable groups of alleles are shown in grey in column 3; the lowest number in series was reported for these groups. Brackets indicate rare variants, each detected only in the mother of one subject. Gene-absence was considered an allele for all variable-content KIR. 'Alleles' are the unique KIR coding-DNA sequences, 'allotypes' are the proteins with unique polypeptide sequences. D. Heterozygosity (H) for genome-wide (non-GATA) microsatellites from all populations described as West African and with N. 20  [83].
FigureS2KIR genotyping protocols. This is an excel spreadsheet with each of 10 KIR genotyping protocols on a separate sheet. Shown are all the alleles (as of Aug 2013) for each KIR gene represented by the different nucleotides that distinguish them. The rows are: 1 The top row shows the accession numbers for those SNPs that are also available in dbSNP. 2 Indicates the exon number. 3 Nucleotide number from initiation codon of each SNP. Numbers highlighted in grey are SNPs that were not genotyped in all individuals, but were sequenced in ,20% individuals. 4 SNPs in universal code (Numbers highlighted in green are SNPs identified during this study). 5 Codon numbers (ATG = 1). 6 Codon numbers (mature protein). 7-9 Alternative residues. 12 onwards The SNP sequence for each of the alleles. The highlighting color for each SNP corresponds to the amino acid residue at that position (as identified in rows 7-9).
SNPs filled in dark grey were not assayed. Green are alleles identified during the present study (+ denotes allele identified concurrently by Hou et al. or Pyo et al. [93, 114] ). Grey are alleles not distinguished by genotyping (but could be identified during the sequencing round). The columns are:1 Genbank accession numbers for newlyidentified alleles. 2 Immunopolymorphism database (IPD) accession numbers. 3 Allele names. 4 Indistinguishable groups of alleles are shown in grey in column 3; the lowest number in series was reported for these groups. (XLSX)FigureS3Low diversity of telomeric KIR genotypes in sub-Saharan Africa. Shown are the KIR gene-content genotypes detected in Ga-Adangbe. Presence of a gene is indicated with a black box. Shown at the right are the frequencies of the genotypes in the unrelated sample set (N = 131).
The highlighting color for each SNP corresponds to the amino acid residue at that position (as identified in rows 7-9). SNPs filled in dark grey were not assayed. Green are alleles identified during the present study (+ denotes allele identified concurrently by Hou et al. or Pyo et al. [93, 114] ). Grey are alleles not distinguished by genotyping (but could be identified during the sequencing round). The columns are:1 Genbank accession numbers for newlyidentified alleles. 2 Immunopolymorphism database (IPD) accession numbers. 3 Allele names. 4 Indistinguishable groups of alleles are shown in grey in column 3; the lowest number in series was reported for these groups. (XLSX)FigureS3Low diversity of telomeric KIR genotypes in sub-Saharan Africa. Shown are the KIR gene-content genotypes detected in Ga-Adangbe.
Methods Temprano ANRS 12136 Temprano (http://clinicaltrials. gov/show/NCT00495651) is a multicentre, randomized, open-label trial to assess the benefits and risks of initiating ART earlier than currently recommended by the WHO, with or without a six-month isoniazid preventive therapy (IPT). The trial was launched in March 2008 in Abidjan and is still on-going. It will end in December 2014. The trial inclusion criteria are as follows: HIV-  ]. IPT consists of a six-month course of isoniazide (300 mg once a day), starting at the Month-1 visit and culminating the Month-7 visit. The sample size required for this study was calculated to be 2072 participants. Each participant is followed for 30 months. The main outcome is the occurrence of a new episode of severe morbidity, including AIDS-defining diseases, non-AIDS-defining severe bacterial diseases, non-AIDSdefining cancers, and any events leading to death.
Some of them were explosive, making any drug intake impossible and leading patients to immediately stop taking the drug. Others were less severe and did not preclude patients from continuing the drug; however, these AEs did not resolve over time. Because the lingering nausea or vomiting could lead to poor patient compliance over the long term, these patients were switched to a better-tolerated regimen. Overall, these grade 3Á4 or persistent grade 1Á2 adverse events were significantly more frequent in patients on TDF/FTC'ZDV compared to those on TDF/FTC'EFV. After carefully reviewing the evidence, the Temprano trial data safety monitoring board recommended that TDF/FTC'ZDV should no longer be given as first-line treatment to new participants in the trial. From December 2008 onwards, all new patients included in the Temprano trial who could not receive EFV as part of their first line treatment were prescribed LPV/r.
Acknowledgements We are indebted to all patients who participated in this trial. We gratefully acknowledge the valuable contributions of the  SMIT, CeDReS, CEPREF, USAC, CIRBA, CNTS, La Pierre Angulaire, Hopital General Abobo, Formation Sanitaire Anonkoua Koute, Centre de santeEl Rapha ,  Programme PACCI  team and INSERM U897 teams:  Abanou Matthieu ,  Aman Adou ,  Anasthasie Yapo ,  Bombo Le  ´ontine, Celestin N'chot,  Christian Kouadio ,  Djetouan Hugues ,  Djobi-Djo Edouard ,  Goly Jocelyn ,  Kassi Marie-Ce  ´cile,  Koffi-N'Dri Aholi ,  Konan Sylvie ,  KonateMamadou ,  Kouadio Bertin ,  KouameMartin ,  Kouame Ferdinand ,  Kouadio Victoire ,  Kouakou-Aboua Adrienne ,  Kouakou Yao ,  Kouame Ántoine , KouameFerdinand, KouameGerald,  Labibi Georgette ,  Lokou Benjamin ,  Moh Jules ,  N'Dri Marie Julie ,  Nalourgou Tuo ,  N'Chot Celestin ,  N'Goran Brou ,  Nogbout Marie-Pascale ,  Orne-Gliemann Joanna ,  Kouadio Cheftin ,  Ouattara Minata ,  Oupoh Jose  ´phine, SidibeAbdelh,  SiloueBertine ,  Soro Adidiata ,  Tchehy Amah-Cecile , Yao Emile ,  Yao Juliette.
Acknowledgements We thank  Carole Peyssonnaux  for advice and gift of reagents and  Souhila Medjkane  and  Jonathan Weitzman  for growing BL3 and TBL3 cells. M. M. is a recipient of a PhD fellowship from the  University Paris-Diderot. N. E. is a recipient of a Wellcome Trust PhD training fellowship award and M. C. a  Wellcome Trust postdoctoral fellowship. This work was supported by a  Wellcome Trust   Special Initiative grant  ( 075820/A/04/Z  ) for an integrated approach for the development of sustainable methods to control topical theileriosis to G. L. and E. J. G. and by an  ANR  grant ( 11 BSV3 01602 ) to G. L. who also recognises  Inserm , the Cnrs and the  Labex ParaFrap   ANR-11-LABX-0024  for support. E. J. G. acknowledges a  BBSRC Institute Strategic Grant.
500 copies per mL; no previous exposure to PIs and/ or Integrase Strand Transfer Inhibitors. The study was approved by each site's Ethics Committee and was registered at Clinicaltrials. gov (NCT00931463). The cohort median (IQR) age was 38. 5  (32. 4-44. 4 ) years, 55% male, 42% Asian and 36% African, 73% heterosexual transmission, with an estimated duration of HIV infection of 6. 0 (3. 6-8. 7) years. The primary results of the Second Line study have been described  [37]. The protocol and analysis plan for this trial and supporting CONSORT checklist are available as supporting information; see CONSORT checklist (Checklist S1), Bone and Body Comp Substudy protocol (Protocol S1), and SECONDLINE w48 bone and body comp analysis plan (Analysis Plan S1). Of the 37 sites that participated in the Second-Line study 8 sites from 5 countries (South Africa, India, Malaysia, Thailand, Argentina) participated in the body composition sub-study (clinicaltrials.
gov identifier: NCT01513122) and analysed as a subgroup of the parent Second-Line study. These sites had access to a Dual energy X-ray absorptiometry (DXA) scanner and recruitment was open to all participants screened at these sites between July 2010 and July 2011. The sub-study was approved by each site's Ethics committee's and all participants gave written, informed consent. The specific ethic's committee's that gave approval for this substudy are:   22)  or Hologic (Thailand n = 26, South Africa n = 94) DXA scanners. Whole body composition was measured as per a standard protocol provided to all sites. We did not use phantoms for quality assurance and scans were not subjected to central interpretation, however scans were done on the same machine for each participant and all imaging centres had quality control measures in place.
Acknowledgments Thank you to the participants of the Second Line body composition substudy. Second Line body composition sub-study sub-committee:  Allison Martin ,  Cecilia Moore ,  Dr Patrick Mallon ,  Prof Jennifer Hoy ,  Prof Sean Emery ,  Dr Waldo Belloso ,  Prof Praphan Phanuphak ,  Dr Samuel Ferret ,  Prof David Cooper ,  A/Prof Mark Boyd. Second Line body composition sub-study investigators:  Dr Nagalingeswaran Kumarasamy ,  Dr Sharne Foulkes ,  Prof Robin Wood ,  Dr Ploenchan Chetchotisakd ,  Prof Praphan Phanuphak ,  Dr Lerato Mohapi ,  Dr Adeeba Kamarulzaman ,  Dr Oscar Messina. Second Line team:  Prof David Cooper ,  Prof Sean Emery , A/ Prof Mark Boyd ,  Allison Humphries ,  Natalie Espinosa ,  Hila Haskelberg ,  Maria Arriaga ,  Sally Hough ,  Cecilia Moore ,  Dr Janaki Amin ,  Andrea Redgrave ,  Rosemary Robson ,  Dr Steven Kerr ,  Kanitta Pussadee ,  Dr Marcelo Losso ,  Cecilia Abela ,  Mariana Valdivinos ,  Sylvia Pizzuto ,  HIV Immunovirology (Biobank) Laboratory St.
In order to estimate the peptide secondary structure content, an analysis of relevant CD spectra was carried out using the CDFriend program, developed by Dufourc and Buchoux  [18]  and CDPro software, developed by Sreerama and Woody  [19]. Similar results are obtained for both methods. Results are given using CDFriend, which uses standard curves for a-helix, b-sheet and random coil obtained from LiKj peptides of known length, secondary structure and CD spectrum and therefore is more relevant for our study  [20, 21]. The program implements a simulated annealing algorithm to obtain the best combination of a-helix, b-sheet, and random coil conformation that exhibits the lowest normalized root mean-square deviation (nrmsd) with respect to the experimental spectrum. The experimental error is estimated to be 6 5%.
Differential scanning calorimetry DSC experiments were performed on a high-sensitivity calorimeter (TA instruments, Guyancourt, France). The temperature range was 0-40uC with a scan rate of 1uC/min and a delay of 10 min between sequential scans in a series to allow thermal equilibration. Four heating and cooling scans were performed for each analysis. These experiments were performed on DMPC, DMPC/DMPG 7:3 and DMPG MLVs, with added or incorporated peptide. The lipid concentration was 1 mg/mL with a peptide/lipid molar ratio of 1:20. Data analysis was performed by the fitting program NanoAnalyze provided by TA instruments.
Incorporation method promotes a-helical conformation for Drs S9 Our data demonstrate that the membrane leakage and insertion is weaker in DOPG than in DOPC/DOPG and DOPC vesicles. A possible explanation is that this observed difference in Drs S9membrane damage could be related to differences in aggregational behavior, which in turn could be related to differences in conformational behavior of the peptide. To study the secondary structure of Drs S9 in the absence and presence of membranes, we performed CD spectroscopy. For these experiments phosphate buffer without sodium chloride was used in order to increase the signal-to-noise ratio, keeping in mind that the absence of salt may affect peptide-membrane interactions. Drs S9 freshly dissolved in 10 mM phosphate buffer at 25 mM displays a peak with negative ellipticity at approximately 220 nm and a peak with positive ellipticity at 195 nm, that are characteristic of b-sheet structures (Figure  4A ).
Acknowledgments Christophe Piesse ,  Geraldine Toutirais  and  Ghislaine Fre  ´bourg ( IFR83-UniversitePierre et Marie Curie, France ) are acknowledged for the Drs S9 peptide synthesis and the electron microscopy, respectively. 2H-and 31 P-NMR experiments were performed at the  IECB (Bordeaux, France ) and monolayer experiments were performed in the  Membrane Biochemistry & Biophysics group (Utrecht University , the Netherland). We thank  Cecile Courre  `ges and  Martijn Koorengevel  for helping in 2 H-and 31 P-NMR experiments and in monolayer experiments, respectively. We thank  Erick J. Dufourc  for valuable discussions.
Proteins and toxins Recombinant Env protein was produced for Geneuro by Protein'Expert (Grenoble, France) from plasmid pV14  [30]  encompassing the complete orf of HERV-W envelope protein (58 KDa, 542 aminoacids) cloned from MSRV virion RNA (Env; GenBank no. AF331500. 1). Env-SU is a 33-kDa and 293 amino acids fractions of the full-length MSRV-Envelope protein (542 amino acids). The recombinant Env-SU protein was produced and purified by Protein'eXpert (Grenoble, France) as previously described  (Rolland et al. 2006 ). The proteins were refolded and biologically active. As a negative control, a mock protein was synthesized and purified under the same conditions. Fiftymicrogram aliquots per -milliliter were flash frozen into liquid nitrogen and then stored at 280uC. Quality and purity of the recombinant proteins were assessed by mass spectroscopy and western-blot.
Cellular analysis For flow cytometry analysis, cells were harvested, washed in PBS, 3% FCS, 0. 16% sodium azide and immunostained for surface expression of distinct markers for 30 min at 4uC. The conjugated Ab used for staining were all obtained from BD Biosciences: IAB-PE, CD11c-APC. Data acquisition and analysis were performed with a FACS Aria flow cytometer and the software FACSDiva5 (BD Biosciences). A propidium iodide staining has been done in order to exclude dead cells.