dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and unresolved issues regarding the formation of OC parent bodies We conducted an extensive spectroscopic survey of 83 main belt S-type asteroids and 3 new S-type families, obtaining the biggest spectral data set yet assembled for the largest Stype asteroids (95% of all objects larger than 60km). In parallel, we built up the existing database of ordinary chondrite laboratory spectral measurements, which now spans a much broader range of temperature history (from unheated to significantly metamorphosed) than previously analyzed. This allowed us to discover several unexpected and fundamental results. First, most S-type asteroids, including large ones (D≈100-200 km), are distributed into two well-defined compositional groups, Hebe-like and Flora-like (H-like and LL-like). This indicates that identical compositions among multiple asteroids are a natural outcome of planetesimal formation and makes it possible that meteorites within a given class originate from multiple parent bodies.
The spectra for equilibrated ordinary chondrites (EOCs; types 4-6) were retrieved from the RELAB database. (We display all EOCs on the same axis, as their breadth is homogenous across types 4-6 in terms of ol/(ol + low-Ca px) ratio; for clarity, the three classes are slightly shifted horizontally) UOCs for types 3. 0 to 3. 5 are olivine rich and their composition is relatively 'homogeneous' (same for H, L and LL). The same result was confirmed with 2 other approaches (Mössbauer spectra, XRD). to those acting on the Moon (e. g. ,  Pieters et al. 2000)  redden and darken the ordinary condritelike spectrum of a fresh asteroid surface, giving it the appearance of an S-type spectrum (e. g. ,  Vernazza et al. 2008 and references therein). Thus, for a quantitative comparison between the asteroid and meteorite spectra, we de-reddened the average asteroid spectra with the space weathering model developped by  Brunetto et al.
Acknowledgements We thank the referee for his pertinent and constructive remarks. The research leading to these results has received funding from the  European Community's Seventh Framework Programme. The work by the  MIT  co-authors was supported by the  National Science Foundation. The near-infrared data were acquired by the authors operating as Visiting Astronomers at the  Infrared Telescope Facility , which is operated by the  University of Hawaii  under Cooperative Agreement with the  National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program. We warmly thank the  Antarctic meteorite collection  for providing the ordinary chondrite samples. We thank  Cecilia Satterwhite  for preparing the samples. We thank  Zibulle  for producing Figure 6's artwork.
The prediction of open reading frames (ORFs) was achieved using FgenesB  (8)  via the SoftBerry web interface (http://linux1. softberry. com /berry. phtml), rRNA prediction was carried out via the HMMER Web server  (9) , and tRNA prediction was done using tRNAscan-SE via the WebMGA interface  (10). The assembled 248 contigs totaled 2,236,677 bp, with a GϩC content of 59. 8%. The genome was predicted to contain 4 rRNA operons, 47 tRNAs, and 2,095 protein-coding sequences (CDSs). The main recognized virulence factor of T. pyogenes is pyolysin (PLO), an exported, cholesterol-dependent, pore-forming cytotoxin (CDC)  (11) (12) (13) , and the PLO gene sequence was identical to that published for other strains of T. pyogenes. Additional virulence-associated factors in MS249 included fimbriae/pili (Fim/Pil), collagen-binding protein (CbpA)  (14) , and neuraminidases (NanH and NanP) (2).
, 2007). This work is primarily a methodological study to assess the reliability of a classical OMP analysis performed in the SAO to resolve its complex water mass mixing patterns. Several sensitivity analyses will be performed to check the stability of the resulting water mass distributions, which will be finally discussed. In addition, the meridional evolution of the thermohaline and chemical properties along the core-of-flow, i. e. the lines of maximum proportions, of the different SWT, will be examined. 2 Data set WOCE line A17 (Fig. 1 ) was occupied during the cruise CITHER (Circulation THERmohaline)-2, aboard R/V Maurice Ewing, from 4 January to 21 March 1994. WOCE line A14 (Fig. 1 ) was sampled during the first leg of cruise CITHER-3, aboard R/V l'Atalante, from 11 January to 16 February 1995.
The WOCE A17 hydrographic and biogeochemical parameters of the WOCE Hydrographic Programme were measured at 235 full-depth stations from 10ºN to 52ºS, at a nominal distance of 600 Km from the continental slope. A general presentation of the A17 cruise and the complete database can be found in volumes 1 to 3 of the CITHER-2 data report  (Groupe CITHER-2, 1995; 1996a; 1996b). In the case of WOCE A14, hydrographic and biogeochemical parameters were measured at 107 full-depth stations from 4ºN to 45ºS, along ∼9ºW. A general presentation of the cruise and the complete database can be found in volumes 2 and 3 of the CITHER-3 data report  (Groupe CITHER-3, 1996; 1998). The parameters used in the present work (salinity, temperature, dissolved oxygen, nitrate, phosphate, silicate and chlorofluorocarbon 11) were measured on both cruises at a maximum of 32 levels using standard methods, which are explained in detail in the data reports.
The original data can be found in the CCHDO (CLIVAR and Carbon Hydrographic Data Office) web site (http://whpo. ucsd. edu/). Both cruises were incorporated to GLODAP (Global Ocean Data Analysis Project, http://cdiac. ornl. gov/oceans/glodap/Glodap_home. htm) (Key et al. , 2004)  and subject to a careful quality control analysis  (Gouretski and Jancke, 2001). The CARINA (CARbon IN Atlantic ocean) data synthesis is a recent effort compiling old and recent CO 2 and CO 2 -relevant data from the Arctic, Atlantic and Southern Oceans  (Key et al. , 2010). Apart from retrieving and collecting previously unavailable data, the main aim is to obtain a consistent and high-quality data base. In order to do this, a careful crossover analysis was performed on the new and reference (some original WOCE lines) data and final adjustments were suggested for particular cruises and variables  (Tanhua et al.
The NNLS method used to resolve the set of mixing equations (lsqnonneg function in Matlab®) provides the contribution of the SWT introduced, the Total Residual from the system of equations, and the Individual Residual for θ, S, NO, SiO 4 , and mass conservation for each sample. The Individual Residuals are weighted and normalized as the equations, so they give a hint on the relative contribution of each equation to the Total Residual. The Total Residual provides an idea of how well the OMP resolves the mixing of water masses for each sample, being mathematically the squared sum of the absolute Individual Residuals: Total Residual j = Σ p (abs(A i • x ij -N j )) p 2 (4) where p stands for each mixing equation or variable, i for each SWT and j for each sample, A is the SWT matrix (Table  2 ), x the SWT fractions and N the measured variables.
The stability of the solutions was evaluated by examining the magnitude of the mean and standard deviation of the STD matrix for each SWT after the 100 (or 31) solutions per test. That is, being right or wrong, the SWT proportions given by the OMP solution can be more or less stable. Note that for any of the tests the mean or the standard deviation was less than 5% (Table  4 ). The stability of the Central Waters was practically unaffected, but the Intermediate (AAIW) and Deep (NADW and CDW) Waters presented differences depending on the test (Table  4 ). Particularly, perturbing the data and the SWT characteristics did have an impact in the stability of Intermediate and Deep Waters. The former analyses point to the perturbation tests on the SWT characteristics and measured data as the main sources of error and instability in the OMP analysis.
Acknowledgements The authors wish to thank the captain and the crew of  R/V Maurice Ewing  and  R/V L'Atalante  for their help during the cruise. We are very grateful to  C. G. Castro  and  M. J. Pazó  for their participation in nutrient measurements. Financial support from this work came from the  Spanish CICYT  contracts  ANT93-1156-E  and  ANT94-1168-E  and, and from the  IFREMER  contract No 94  1430 087. X. A. A. S. and M. A. were supported by the  Spanish Ministry of Science and Innovation (MALASPINA expedition , grant number  CSD2008-00077 ). M. A. was funded by the grant ORCASEX ( RYC-2006-001836 ), program Ramón y Cajal from the  Spanish Ministry of Science and Technology.
ChIP-seq Input or ChIP samples were converted to barcoded libraries by adaptor addition  (Quail et al. , 2008). In brief each sample was quantified, end-repaired (End-It kit, Epicenter Biotechnologies), an A-overhang added, and ligated to one of a set of 12 barcode-specific adaptor primers. The resulting libraries were size selected (600 ± 50 bp), amplified by 18 cycles of PCR, purified with SPRI beads (Agencourt AMPure XP; Fisher Scientific), quantified on a high-sensitivity DNA chip by 2100 bio-analyzer (Agilent Technologies, Inc. ), mixed at equivalent concentrations, and multiplexed at 12 samples per Illumina HiSeq 2000 lane. Data images from 100 bp runs (single or paired-end) were processed by the Illumina Sequence Control and Pipeline packages. ChIP-seq data was loaded into Genplay (v533) as individual tracks (50bp windows; normalized and INPUT subtracted) relative to the annotated fission yeast genome (Sanger Center release pombe090511)  (Lajugie and Bouhassira, 2011; Zang et al.
Isothermal Titration Calorimetry ITC measurements were conducted at 25°C with a MicroCal Auto-ITC200 in 1× PBS, pH 7. 6 (2. 7 mM KCl, 1. 5 mM KH 2 PO 4 , 140 mM NaCl, 8 mM Na 2 HPO 4 ). Recombinant forms of the Nrc1 bromodomains (residues S199-G526: see Figure  3A ) were dialyzed in 1× PBS, pH 7. 6 and brought to 30 μM. Peptides were lyophilized and resuspended in 1× PBS, pH 7. 6 at 400 μM. Titrations were performed with 20 × 2 μl peptide injections at 180-second intervals using a reference power of 8 μcal/s. Results were analyzed using the MicroCal Origin software, and the Isotherm (μcal/sec) and integrated data (Kcal/Mole of injectant) for each titration presented (Figure  3F ).
Microscopic analysis of mitotic chromosomes Cells were grown in rich media and briefly (< 30 sec) fixed in 100% methanol at -80°C. DNA and microtubules were visualized by DAPI staining and anti-Tubulin immunoblotting. Anaphases were scored as defective if chromatin was observed near the metaphase plate after the bulk of separation had occurred  (Kim et al. , 2009). Data was presented as the percentage (mean ± s. d. ) of late anaphase cells with chromosome segregation defects (counted > 100 cells per population: e. g. Figure  1D ).
5′). Samples were prepared from asynchronous cells and analyzed by pPCR (rather than duplex PCR as previously) with primers flanking the regions indicated in panels D & E. The enrichment of each factor at each location is normalized to GFR (set to 1 for each IP). Peaks were identified, localized and quantified by optimized parameters (see Table  S2 ). (C) 6,906* transcribed RNA species are annotated to the fission yeast chromosome arms (* excluding TEL3 and CEN1-3). These are classed by type and presented by percentage. (D-F) The structural RNAs are overrepresented as NCT peaks. Data is presented as the percentage of NCT peaks (major / all) per feature, and percentage coverage of each feature (specific NCT peaks were assigned to all overlapping features when required). For F the Fisher's exact test (by R) was used to estimate the probability of the observed enrichment at structural RNAs: 'Major NCT peaks', p-value 5.
Acknowledgments We thank  Steve Buratowski ,  Julien Lajugie ,  Andrew McLellan ,  Jamie Moseley ,  Charles Query ,  Mike Shales ,  Arthur Skoultchi ,  Jon Warner  and  Ian Willis  for help and advice;  Robin Allshire ,  Mark Bedford ,  James Bradner ,  Dan Finley ,  Keith Gull ,  Tetsuro Kokubo  and  Richard Maraia  for the generous supply of materials. C. J. R. was supported by  ICON  plc and the  UCD Newman Fellowship Program. J. F. was supported by start-up funding from  Ryerson University  and a discovery grant from the  Natural Sciences and Engineering Research Council of Canada. T. K. is a  Canada Research Chair in Proteomics of Cancer Research. This work was also supported in part by the  National Institutes of Health  ( ES019966 ,  GM085394 ,  GM68088 ,  GM85394 ); the  Canadian Institutes of Health Research  ( MT-6092 ); NYSTEM, the funding agency of the  Empire State Stem Cell Board  ( C024405  and  C024172 ); pilot funding from the  AECOM Epigenomics facility ; and an  NCI Cancer Center Support grant to Albert Einstein College of Medicine  ( CA013330 ).
Flow cytometry Flow cytometric data were acquired on a four-color FACS Calibur cytometer (BD Biosciences) and analysis was done with Cell Quest Pro software. For surface phenotyping, the following antibodies were used: anti-human CD45RA-FITC (MEM-56), CD11c-APC (BU15), HLA-DR-FITC (LT-DR), CD123-PE (9F5); anti-mouse CD11b-PE (M1/70), CD11c-APC (HL3) (BD Bioscience). Uptake of propidium iodide-stained DNA in CD11c-APC cells was performed on FL3 channel.
Immunofluorescence For DNA uptake experiments, BMDCs were plated on a m-slide 8 well ibiTreat (ibidi) then stimulated for 1 h with L. major or mouse DNA. DNA was stained overnight with propidium iodide (PI) and precipitated in ethanol. Cells were fixed with 2% paraformaldehyde for 20 min at RT. Images were taken using a Leica SP5 scanning confocal microscope with an 663 oil objective. DAPI and propidium iodide signal was acquired following respective excitation at 405 nm and 561 nm and respective emission at 420/465 nm and 577/ 690 nm, in sequential captures with optical sections of 0. 4-0. 8 mm. All microscope parameters were kept constant between experiments. Icy software (version 1. 3. 6. 0) was used for merge analysis and image presentation  [33]. ImageJ software (version 1. 47q) was used to calculate the average DNA uptake into cells.
Subcellular distribution of HMGB1 BMDCs (3610 6 ) were stimulated with CpG 1826 (1 mg), L. major or vertebrate DNA (20 mg) for 30 min and 1 h. The cells were lysed in buffer (20 mM TrisHCl (pH 7. 4), 10 mM NaCl, 3 mM MgCl 2 ) with anti-proteases for 30 min at 4uC. After centrifugation at 3000 rpm, the cytoplasmic fraction was harvested and the nuclear fraction extracted with the buffer from Subcellular Proteome Extraction kit (Calbiochem). The fractions were analysed by Western Blotting, run in a 10% SDS-PAGE, probed with antibodies against HMGB1 (1:1000, Abcam), actine (1:10000, Sigma), histone H3 (1:5000, Abcam) and revealed with an anti-rabbit or anti-mouse immunoglobulin-horseradish peroxidase conjugates (1:10000, Serotec). Quantification was performed with ImageJ software.
Genome analysis The genomes analysed are referenced in GenBank Assembly database as: GCA_000002725. 2 for Leishmania major genome strain Friedlin by the Friedlin Consortium, GCA_000001635. 4 for mouse (Mus musculus) genome by the Genome Reference Consortium Mouse and GCA_000001405. 13 for human (Homo sapiens) genome by the Genome Reference Consortium Human. The Trypanosomatidae genomes referenced T. cruzi strain CL Brener Esmeraldo-like, T. brucei strain TREU 927 and T. vivax strain Y486 (2013-01-16 versions for all) were taken from Trypanosomatidae database TritrypDB (http://tritrypdb. org/ tritrypdb/)  [35]. All bioinformatic analyses, including motif counts and determination of genome size, were made from this same dataset. The different motifs were searched in each chromosome or in the whole genome with in-house software (wcount).
Genomic or proteins sequences accession numbers or ID Here is the list of accession numbers/ID numbers for genomes mentioned in the text: N GCA_000002725. 2 for Leishmania major genome strain Friedlin by the Friedlin Consortium N GCA_000001635. 4 for mouse (Mus musculus) genome by the Genome Reference Consortium Mouse N GCA_000001405. 13 for human (Homo sapiens) genome by the Genome Reference Consortium Human. Genome sequences from the primary assemblies for the other Trypanosomatidae organisms were obtained in TriTrypDB (http://tritrypdb. org/tritrypdb/) and are publicly accessible in the cited link in Data Summary/Genomes and Data Types. The genomic sequences are available under: N the organism ID tcruCLBrenerEsmeraldo-like for the Try- panosoma cruzi strain CL Brener Esmeraldo-like N the organism ID tbruTREU927 for Trypanosoma brucei strain TREU 927 N the organism ID tvivY486 for Trypanosoma vivax strain Y486 The proteins studied in the text are listed below, publicly accessible in Uniprot database: N murine TLR9: Q9EQU3 N murine SLPI: P97430 N murine HMGB1: P63158 N human LL37, part of hCAP-18: P49913.
Stimulatory motifs (RRCGYY for [purin-purin-C-G-pyrimidin-pyrimidin]; HRWCGTTN for [(notG)-purin-(A or T)-C-G-T-T-any base] and inhibitory motifs (WKKVGGGG for [(A or T)-(G or T)-(G or T)-(A or C or T)-G-G-G-G]; CCNDDNNGGG for [C-C-x-(notC)-(notC)-x-x-G-G-G] , where 6stands for any base) were searched in whole genomes. For each motif, the counted number was indicated as the observed number. The expected motif number was calculated as (motif frequency 6 genome size). The motif frequency corresponds to the product of frequency for each nucleotide, which is different from one genome to another (Table  S1 ). The rO/E corresponds to the ratio between the observed number and the expected number of the motifs. doi:10. 1371/journal. pntd. 0003308. t001 The data represent the ratio of observed/expected number rO/E for each motif [stimulatory (RRCGYY and HRWCGTTN) or inhibitory (WKKVGGGG and CCNDDNNGGG)] from the analysis of Trypanosomatidae complete genomes (Table  1 ).
Acknowledgments We are especially grateful to  Pr J. Louis  ( Institut Pasteur (IP) ),  Pr J. M. Sallenave  (IP) and  Pr F. Strauss  ( Museum National d'Histoire Naturelle ) for helpful discussions. We are thankful to  S. Gangloff  (IP),  D. Eisen  and  A. Holland  for their critical reading and the English editing of the manuscript. We gratefully acknowledge the advice and the help of M. Parlato (IP),  S. Maschalidi  ( Hopital Necker ) and  R. Rua  (IP). We thank also  C. Fitting  ( IP ) for precious technical assistance. We thank  I. Schwartz  ( Institut National de la Recherche Agronomique ) for providing us vertebrate organs, and  T. Blisnik  ( Institut Pasteur ) and  S. Bonnefoy  ( Institut Pasteur ) for the cultures of parasites. We acknowledge the  Plateforme d'Imagerie Dynamique, Imagopole (Institut Pasteur).
Data Data included weather station records and P. falciparum malaria positive cases. Quality controlled daily records of maximum temperatures, minimum temperatures and rainfall totals, gathered at Kericho meteorological station  [18] , located at 33°21′E and 0°21. 6′S in the Kenyan highlands, were processed. Temperature records are available for the period spanning 1 January, 1979 to 31 December, 2009. Rainfall time series are available for the period spanning 1 January, 1980 to 31 December, 2009. Monthly malaria-positive cases from inpatient admission registers in Tea Plantation 1 in Kericho, spanning the period January, 1970 to October, 2004, were obtained from Figures four and six in  [23] , see Figure  1(A).
Set of simulations Simulations proposed here included six series of experiments, which were run using the user-friendly, onlinedownloadable, open source computer software Scilab® 5. 3. Codes developed for the analyses are available upon request. Experiments were designed to: 1) compare Scilab® 5. 3 simulation outputs with analytical solutions; 2) perform simulation runs for changes in initial conditions and for seasonal variations in climatic variables; 3) simulate actual climatic conditions and assess the role of climate long-term trends, inter-annual dependency and seasonality in malaria incidence; 4) assess models' sensitivities to changes in sets of parameters; 5) incorporate uncertainty in the predictability of malaria outbreaks; and 6) analyse the potential impact of anti-malarial drug resistance on morbidity profiles.
The 'most likely' models were then implemented to assess the impacts of changes in climatic conditions on P. falciparum malaria transmission dynamics in the highlands under study. The ensemble was run with and without long-term climatic trends, inter-annual dependency and historical seasonality, in order to address how much of a change in the size of epidemics could be attributed to changes in climatic conditions. The third set of experiments also included multi-model simulations to the end of the Kericho temperature and rainfall data (i e, retrospective period spanning January, 1979 to December, 2009), in order to understand whether or not climatic conditions have been less favourable to malaria transmission in recent years. A full certainty in the 'most likely' set of parameters was also assumed in these simulation runs.
Analysis of climate data Annual cycles of observed rainfall and minimum and maximum temperatures were calculated and compared with the annual cycle of P. falciparum malaria incidence. Annual values of several climatic variables were then computed. Climate variables included the diurnal temperature range, which has been suggested to be important in the analysis of malaria transmission dynamics  [14]. Total December-January-February, March-April-May, June-July-August, and September-October-November rainfall amounts, dry days and maximum dry spells were also processed to support the analyses. A total number of 24 climatic variables were analysed: 12 for rainfall, four for minimum temperature, four for maximum temperature, and four for the diurnal temperature range. Long-term linear trends in observed and simulated annual time series were identified using simple regression analysis, and trend magnitudes were calculated by the method of least squares.
Acknowledgements We thank  Samuel M Waweru  from the  Kenya Meteorological Department  for providing quality controlled daily records of maximum temperatures, minimum temperatures and rainfall totals gathered at Kericho meteorological station. DR was partially supported by the  Department of Earth and Environmental Sciences at Columbia University in the City of New York ; the  International Research Institute for Climate and Society, Lamont-Doherty Earth Observatory ; and the  Escuela de Ingeniería de Antioquia-Colombia.
Although many models have been developed to describe canopy structure using VIS-NIR imagery, varied vegetation types such as grasses, shrubs, trees reflect similarly, especially when leaf-on, and some reflectance based indices saturate above LAI of about 4  [25]. Other remotely sensed data, such as SAR (Synthetic Aperture Radar) and LIDAR (Light Detection and Ranging) data, offer a new opportunity to characterize hedgerows in a whole landscape. Indeed, LIDAR remote sensing has the ability to acquire three dimensional measurements of a study site, at a fine scale, which is useful for estimating a variety of tree features (tree height, volume, biomass)  [26] [27] [28]. However, LIDAR data are generally acquired in one shot because each data acquisition is very costly. Radar SAR data are easier to acquire and allow a priori to access to the inner structure of the hedgerows when the radar signal interacts with the tree structure.
To our knowledge, radar images have not been evaluated to characterize canopy fragmentation. In this study, we therefore investigated whether the spatial resolution of VHSR radar data like TerraSAR-X images allows the canopy structure to be taken into consideration. The aim of this study was to address the issue of evaluating VHSR radar SAR images to (i) detect hedgerows in order to characterize hedgerow networks; and (ii) characterize the canopy structure at the hedgerow scale.