dataesr/scientific-paragraphs-categorization huggingface dataset:
90745 Å at the ID23-1 beamline of the European Synchrotron Radiation Facility. Both datasets were processed using the XDS package  (35)  and were found to belong to the same crystal form despite the different crystallization conditions. SIRAS phasing was not attempted due to nonisomorphism. The structure was solved by SAD phasing with the KI soak dataset. SHELXD (36) could localize eight iodine atoms per asymmetric unit. After density modification using SHELXE, interpretable density maps were obtained, initially to 2. 8 Å resolution. The structure was refined and analyzed using the PHENIX (37) and CCP4 (38) suites and built using COOT  (39). Phenix. autobuild was able to place about 50% of the amino acids in the initial map. Despite significant data anisotropy, we gradually extended refinement resolution for subsequent refinement and building, following the current best practice  (40).
Thus, we included data according to CC1 ⁄ 2 and validated this choice by comparing CC(work) and CC(free) to CC* (Table  1 ). Coordinates were refined for the two 1600 -1733 molecules in the asymmetric unit with local NCS restraints. TLS and individual B-factors were refined with three TLS groups as follows: residues 1628 -1723 of both molecules, residues 1595-1627 of molecule B and residues 1597-1627 of molecule A (residues N-terminal to 1600 represent the residual affinity tag). In later stages of refinement, all iodine ions were identified in log-likelihood gradient maps. The asymmetric unit contains 42 iodine atoms (23 with occupancy of at least 50%). The final refined protein model was used as the starting point for refinement against the native dataset keeping the same test set. After rigid body refinement, the same refinement protocol was used as for the KI soak.
There is additional weak density in the KI soak that could be ascribed to residues 1724 -1725 stabilized by an iodine ion site. We did not include these residues in the final models as no density is apparent in the native dataset. The KI soak (respectively native) models have no Ramachandran outliers, and 98% (respectively, 97%) of residues are in the favored region of the Ramachandran plot. CC* (  40 ) is 0. 86 (respectively 0. 91) in the highest resolution shell. Crystal packing and interfaces were analyzed with PISA (41) and the NOXclass server  (42). Structure visualization was performed and structural figures made with PyMOL. Atomic coordinates and structure factors have been deposited to the RCSB Protein Data Bank under accession number 4TT0 (potassium iodide derivative) and 4TT1 (native crystals).
Homology modeling was performed with program MODELLER (43) release 9. 14 with default parameters using the alignments shown in Fig. 5A. Molecular dynamics simulations of a crystallographic monomer (residues 1600 -1722 of chain B) of the modeled folded-back monomer and of homology models corresponding to HSV-1 UL36 residues 1632-1723 were performed using the AMBER 9 program suite  (44)  with the Parm99SB force field. Hydrogen atoms were added, and proteins were neutralized with 2-4 Na ϩ cations and immersed in an explicit TIP3P water box with a solvation shell at least 12 Å-deep using the LEaP program. The systems were then minimized and used to initiate molecular dynamics. All simulations were performed in the isothermal isobaric ensemble (p ϭ 1 atm, T ϭ 300 K), regulated with the Berendsen barostat and thermostat (45), using periodic boundary conditions and Ewald sums for treating long range electrostatic interactions  (46).
The NH of the indole group is also involved in an intermolecular hydrogen bond with the carbonyl of Leu-1640 (Fig. 4C ). The NOXclass server (42) reports 2140 Å 2 of buried surface per molecule for this dimeric interface, accounting for 20% of the accessible surface. Taking into account the amino acid composition of the interface, the server assigns a 99. 99% probability that the crystallographic dimer is biologically relevant. How- ever, dimers can be either obligate or nonobligate, i. e. the dimer can be stable or it can be in equilibrium with monomers. Here, the calculated probability that it constitutes an obligate dimer is only 89. 6%. Thus, the dimer might be a transient species, sensitive to external parameters. In addition to this major interface, the PISA program  (41)  predicts no other contact in the crystals to be stable in solution.
A tryptophan fluorescence emission spectrum displays a maximum at a wavelength of 338 nm for fragment 1600 -1733 (Fig. 4B ) independently of the pH (between 6 and 8) and of the salt concentration (between 20 mM and 1 M NaCl). This is not consistent with a fully exposed tryptophan but is fully compatible with the environment observed in the dimer  (48). Thus, the tryptophan in the solution monomer is similarly buried as in the crystallographic dimer. The DALI server was used to detect whether the crystallographic and the model folded-back monomers were homologous to other proteins in the Protein Data Bank. The highest Z-score obtained for the crystallographic protomer corresponded to the copper-sensitive operon regulator (Z ϭ 5. 2). However, a detailed analysis reveals that the homology only extends to helix A, which suggests that it is not relevant.
DISCUSSION The structural data presented in this study reveal that a fragment of HSV-1 UL36 comprising residues 760 -1733 adopts a very elongated conformation. The major species is a stalk conformation with a length of 34 nm and a width of 2. 9 nm. A smaller subpopulation of 760 -1733 appeared to be even longer with an average length of 60 nm. It could either reveal a conformational change between an extended 60-nm monomeric species and a 34-nm hairpin conformation or a rare dimerization of two 34-nm monomeric fragments via one of their extremities. No significant width difference could be measured between the 34-and 60-nm species, arguing in favor of the second hypothesis. The measured lengths for UL36 residues 760 -1733 have to be compared with the dimensions of the elongated tufts detected at the surface of T36 particles (3).
Acknowledgments-We are grateful to the ESRF and SOLEIL Synchrotron facilities for allocation of beamtime and especially to Beatriz Guimaraes for help in data collection. We thank  Malika Ouldali  for help in electron microscopy imaging. We thank  Paloma Fernandez-Varela ,  Christophe Velours , and  Karine Madiona  of the  Structural and Proteomic Biology pole  of the IMAGIF integrated platform for the SEC-MALLS and analytical ultracentrifugation experiments. We acknowledge  Ines Gallay  of the core platform " Cristallisation et Determination de la Structure 3D " ( IBBMC, Paris Sud ) for crystallization of the 1600 -1733 fragment. We also acknowledge  Thibaut Leger  and  Camille Gracia  of the " Plateforme Proteomique Structurale et Fonctionnelle/Spectrometrie de Masse de l'Institut Jacques-Monod " for the mass spectrometry analysis of fragments  760 -1733  and  1600 -1733.
The percentage of infected cells (p24 + or GFP + cells) was determined by gating the living cell population on the basis of viability dye staining. Samples were analyzed on an LSRII cytometer (BD Biosciences), and data analysis was performed using FlowJo vX. 0. 7 (Tree Star). Measurement of serum-mediated ADCC was performed with a previously described assay (12, 15) after 48-72 h for in vitro infection or 6 d postactivation Fig. 5. The CD4mc JP-III-48 sensitizes ex vivo-expanded endogenously infected primary CD4 T cells from viremic antiretroviral therapy (ART)-naive individuals to ADCC-mediated killing by autologous sera. (A) The top panel show percentages of p24 + CD4 T cells expanded after activation from a representative HIV-1infected untreated viremic patient, while the bottom panel represents the percentages of p24 + CD4 T cells expanded after activation for four HIV-1-infected viremic patients (Table  S2 ).
ACKNOWLEDGMENTS. We thank  Yvette McLaughlin  and  Elizabeth Carpelan  for help in manuscript preparation. We also thank the  Centre de Recherche du CHUM Flow Cytometry Platform  for technical assistance,  Mario Legault  for cohort coordination, and  Mathieu Coutu  for producing sCD4. We thank  Kimberly Barbas  from  Children's Hospital Boston  for providing the samples from the uninfected women. We are thankful for all subjects' participation and collaboration. This work was supported by a  Canada Foundation for Innovation Program  Leader grant, by  Canadian Institutes of Health  Research operating Grants  119334  and  134117 , by  Fonds de Recherche Québec Santé Establishment of Young Scientist Grant   26702  (to A. F. ), and by the  FRQS AIDS and Infectious Diseases Network. A. F. is the recipient of a Canada Research Chair on Retroviral Entry.
Expression constructs: solubility and antigenicity The constructs and expression systems used are listed in Table  1. To maximize the chance of producing these cysteine-rich domains as correctly folded recombinant proteins, three expression systems were tested for some domains: i) baculovirus/insect cells (Sf9 or HiFive cells)  [39] , ii) Pichia pastoris  [42]  and iii) Escherichia coli  [16, 42]. All expression products were obtained as soluble proteins except for eDBL0, which was produced mostly as an insoluble protein in E. coli that was solubilised in urea. The proteins used for immunisation and ELISA assays were pure, as judged by absence of major contaminants in Coomassie blue-stained SDS-PAGE gels (Fig 1A ) as well as N-terminal sequencing (data not shown)  [16, 42]. Antigenicity of the constructs (except eDBL0 which was insoluble in the absence of urea) was assessed using immune sera from humans living in endemic areas and control sera from non-exposed adults living in France (Fig 1B, left panel).
All constructs were readily recognised by immune sera (Fig 1B, right panel shows details for some constructs). The Pichia pastoris NTS-DBL1α (pDBL1) construct strongly reacted with non immune sera, but this was not observed with the baculovirus (bDBL1) or the E. coli (eDBL1) constructs (Fig 1B  , left panel ). However, we established that the elevated reactivity of pDBL1 with non immune sera was due to O-glycoslyation by Pichia pastoris (data not shown) and the construct was therefore not studied further. The immunisation regimen used for all proteins is shown Fig 1C. For a given antigen, inbred (BALB/c) or outbred animals (OF1) were immunised with the same adjuvanted preparation (see Materials and Methods section). Antibody responses were assessed for each individual animal.
This work was supported by the  French National Research Agency  (www. agence-nationalerecherche. fr/)( Agence Nationale de la Recherche ,  Programme Microbiologie ,  Immunologie et Maladies Emergentes , grant  ANR-07-MIME-021-0 ,  Project Rosettes ,  PIs OMP  and GAB), which supported a fellowship to A. J. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement N°Evimalar  242095. OMP is a member of the Evimalar Network strong basis for a rational vaccination strategy targeting rosetting. of  Excellence  (www. evimalar. org). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This has led to clever strategies to statistically infer what the underlying HLA alleles are on the basis of SNP-level information, in a process known as HLA imputation which compares the target SNP data against a reference panel possessing both SNP and HLA allele data  18, 26. However, the accuracy of this imputation process has been shown to depend critically on whether the reference panel possesses populations that are genetically representative of the target population  27, 28  , as underlying haplotype differences between the target and reference populations can distort this process of statistical copying. In the absence of a comprehensive HLA and SNP reference database, one useful indicator of how successful the imputation will be at recovering the HLA alleles is thus to survey the extent of haplotype diversity at Class I and Class II gene regions between the target SNP data and the reference database.
In this study, we investigate the extent of haplotype diversity in HLA Class I and Class II genes across eight Japanese populations, comprising Ryukyu samples from the Okinawa prefecture and seven groups of samples from six prefectures, located in or close to the mainland of Japan. To benchmark the extent of genetic diversity seen in these eight populations, we performed the same analyses between: (i) East Asian samples with ancestries originating from North and South China; and (ii) South Asian samples with ancestries originating from the Gujarat state in North India and from the Tamil-Nadu state in South India (Fig. 1 ). In order to measure the impact of the genetic differences present between the eight Japanese populations, we also evaluated the accuracy of SNP imputation in these populations with existing data from the HapMap panel, as a surrogate inference measure of how well the imputation can recover the HLA alleles for these eight Japanese populations.
Four of the seven Japanese populations from the ADP (i. e. , Amagasaki, Ehime, Fukuoka and Kita-nagoya) were genotyped on the Illumina Omni 2. 5 M array, while samples from Shimane and Tokyo were genotyped on the Illumina HumanHap550, and samples from Okinawa were genotyped on the Illumina OmniExpress. The HapMap and SGVP samples were genotyped on both the Affymetrix SNP6. 0 and the Illumina Human1M. Only SNPs with call rates greater than 95% and with no departure from Hardy-Weinberg equilibrium (defined as P HWE > 0. 05) were retained in our analysis, while all samples were used as these data have been already subject to prior quality control in previous publications 5,  [29] [30] [31] [32] [33] [34] [35]. Summary of the details of the population data we have used can be found in Supplementary Table  1.
From this cohort, 12,569 subjects completed the questionnaire and also provided DNA for genotyping of SNPs to investigate lifestyle factors and genetic susceptibility of the so-called lifestyle-related diseases such as cardiovascular diseases, cancer, and diabetes mellitus. All participants provided written informed consent and this study was approved by the Ethics Committee of the Kyushu University Faculty of Medical Sciences  32. Kita-Nagoya. The Kita-Nagoya Genomic Epidemiology (KING) study (ClinicalTrials. gov identifier: NCT00262691) is an ongoing community-based prospective observational study of the genetic basis of cardiovascular disease and its risk factor  33. The study recruited 3,975 Japanese subjects aged 50-80 years, who underwent community-based annual health checkups between May 2005 and December 2007.
From this network, subjects were enrolled at separate sites in Japan including the Tokyo and Shimane districts. Subjects in the Shimane district are people who visited the Shimane Institute of Health Science for a health screening examination between July 2003 and March 2007. Subjects in the Tokyo district were selected from participants in the Hospital-based Cohort Study at the National Center for Global Health and Medicine (NCGM), Tokyo, to investigate lifestyle factors and genetic susceptibility for lifestyle-related diseases. All participants from these studies provided written informed consents, and the local ethics committees approved the protocols. Haplotype phasing. The genotype data for all the Japanese populations were phased with BEAGLE version 3. 3. 2  36 to obtain the haplotype data necessary for our analysis of haplotype diversity.
Although phased haplotypes for the HapMap and SGVP samples were available from the respective websites, these have been phased using PHASE and fastPHASE respectively. To avoid confounding the analyses due to the phasing algorithm used, the genotype data for the HapMap and SGVP samples were similarly phased with BEAGLE using the same settings. The analysis of haplotype diversity subsequently focused on a set of 1,607 SNPs between 25 Mb and 35 Mb on chromosome 6 (NCBI Build 37) that were present across all 12 populations studied.
Population structure analyses with principal component analysis (PCA). A series of PCAs were performed with different input. The first set of three PCAs were performed with smartPCA in the EIGENSOFT package  38  using the genotype data at 240,332 SNPs present across the 12 populations, to investigate the population structure of the: (i) 12 populations; (ii) eight Japanese and two Han Chinese populations; and (iii) seven Japanese populations from the six mainland regions. The second set of three PCAs were performed with an eigen-decomposition of a K × K distance matrix, where the (i, j) element in the matrix is given by the average SNP-level F ST between population i and population j, averaged across 1,607 common SNPs present in the interval between 25 Mb and 35 Mb on chromosome 6. The second set of three PCAs considered the same population set as the first set of three PCAs, between 12 (K = 12), eight (K = 8) and seven (K = 7) populations respectively.
The third set of three PCAs was similar in construct to the second set, except that the analysis considered the haplotype-based F ST at each of the six HLA genes. Evaluating imputation performance in the MHC region. We performed a series of SNP imputation in the different Japanese populations to evaluate the performance of the different population-specific and combined reference panels at the MHC. The target data to perform the imputation comprised 19 new subjects from each of the seven Japanese populations which similarly possessed the same 1,607 SNPs, although we masked 400 random SNPs and used the remaining 1,207 SNPs as input for imputation. Each population was imputed nine times, against the eight single-population reference panels and the combined East Asian reference panel which was derived from a combination of the CHB, CHS, JPT and Southeast Asian Malay samples from the SGVP or HapMap.
Results Measuring genetic distance at the MHC with SNP-level F ST. Between 25 Mb and 35 Mb on chromosome 6, a total of 1,607 SNPs were present in our data comprising the eight Japanese populations and the four HapMap and SGVP populations. The genetic distance between every pair of these 12 populations was measured by the average SNP-level F ST values across these 1,607 SNPs. Between the eight Japanese populations, Okinawa stood out as the most distinct population, showing a minimum F ST of 0. 6% with Ehime and a maximum F ST of 1. 0% with Fukuoka, Shimane and Tokyo (Supplementary Table  1 ). The remaining seven Japanese populations were comparatively more homogeneous, with genetic distances in the order of 0. 1% to 0. 3%; the latter figure was observed in the comparison of population pairs mostly involving Ehime.
The genetic distances calculated from the same 1,607 SNPs between North and South Chinese (CHB, CHS), and between North and South Indians (GIH, INS) were used to benchmark the distances seen in the Japanese populations. The distance between CHB and CHS was 0. 4%, while the distance between GIH and INS was 0. 5%, suggesting that the mainland Japanese populations were more homogeneous than Han Chinese from North and South China at the MHC region, whereas Okinawa was more distinct from the rest of the mainland Japanese populations than the case for genetic differences between the Gujarati and Tamil Indians. Principal component analyses of population structure. In a preliminary PCA of 1,833 samples with genomewide data across 240,332 common SNPs in the eight Japanese and four benchmarking populations, it was evident that the two South Asian populations (GIH, INS) were significantly distinct from the East Asian populations (CHB, CHS, JPT, seven Japanese populations), although it was also clear that there were three genetic sub-clusters that corresponded to the Okinawa samples, Han Chinese and mainland Japanese respectively (Fig.
Haplotype differences between populations. Haplotypes for the 1,607 SNPs were obtained by phasing the genotype data for the 12 populations with BEAGLE. This allowed us to examine the distribution of the major haplotypes at each of the six HLA genes in each of these populations (Table  1 ). The definition for major haplotypes is quite arbitrary. In our study, for HLA-A, HLA-B, HLA-C, HLA-DR, we defined a major haplotype as possessing a population frequency of at least 10% in any of the 12 populations. While, for HLA-DQ and HLA-DP, we defined a major haplotype as possessing a population frequency of at least 6% in any of the 12 populations. This is due to the large number of haplotypes found across a larger set of SNPs at HLA-DQ and HLA-DP. Unsurprisingly, there were ancestry-specific haplotypes that were found only in South Asians or in East Asians, and the majority of the major haplotypes in Japan were shared across the different Japanese populations except that the haplotype frequencies varied between the populations to some extent (Fig.
We investigated this in two manners: firstly, whether the accuracy changed when different single-population panels were used to impute SNP data for each Japanese population; and secondly, whether the use of a combined East Asian panel, which consists of Chinese, Japanese and Malays from public databases such as the HapMap and the SGVP, will yield better performance. The different reference panels except for the combined panel were deliberately chosen to be of comparable sizes in order to avoid any confounding due to sample size, to allow for investigation of the impact of haplotype diversity. Also, to avoid over-fitting, 19 additional samples from each of the Japanese populations (except HapMap JPT) were used as the target data for imputation. We observed that the use of either the HapMap JPT panel or the combined East Asian panel yielded marginally higher discordance rates, when compared to the use of most of the single-population panels (Fig.
Acknowledgements W. Y. S. , X. L. and Y. Y. T. acknowledge support from the  Saw Swee Hock School of Public Health , and  Life Sciences Institute  from the  National University of Singapore. Y. Y. T. also acknowledges support from the  National Research Foundation Singapore  ( NRF-RF-2010-05 ). N. K. acknowledges support from the  grant of National Center for Global Health and Medicine.
Imatinib increases myeloid progenitors in mouse and human marrow To further characterize the effects of imatinib on the expansion and differentiation of myeloid progenitors, bone marrow from imatinib-treated mice was plated in semi-solid media and analyzed by Colony Forming Cell (CFC) assay to detect and quantify colonies of granulocytemacrophage hematopoietic progenitors (CFU-GM). As shown in Fig. 4A , bone marrow derived from mice treated with imatinib at 66mg/kg/d or 200 mg/kg/d for seven days yielded ~34% more CFU-GM colonies compared to marrow from naïve mice. These data suggest that treatment with imatinib induces an irreversible commitment of HSCs into progenitors that can differentiate ex vivo into myeloid cells. To determine whether cells from naïve animals could likewise be induced to differentiate into myeloid-type colonies when treated with imatinib in culture, CFC assays were performed on naïve bone marrow cultured with various concentrations of drug.
As shown in Fig. 4B , addition of imatinib at 50 nM caused a 33% increase in CFU-GM, whereas concentrations exceeding 500 nM, were without effect. We also assessed the effects of PTK inhibitors in CFC assays using marrow derived from human donors. Addition of low concentrations of imatinib (50 nM) to the media maximally increased the number of CFU-GM by 42% compared to untreated marrow (Fig. 4C ). By contrast, and in accordance with previous reports  [46]  concentrations at or exceeding 500nM were without effect. Together, these data suggest that imatinib induced an irreversible differentiation of HSCs or progenitors into myeloid cells in a dosedependent fashion, and that imatinib effects on myelopoiesis in vivo are recapitulated in cultures of murine and human cells in vitro. Partial inhibition of c-Kit affects myeloid and lymphoid cells in the blood.