dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank the team at the  IRD  for supporting this study, particularly Ms. Sandrine Dupe who was instrumental in formulating the questionnaire and assisted in all stages of the survey. Special thanks also go to the  ARS  and their team of coordinators, technicians, and agents. In particular, we would like to extend our gratitude to the coordinators,  Thomas Margueron  and  Manuel Rodicq  as well as the many technicians who helped in the implementation and organisation of the field-work. Finally, the study could not have been achieved without the dedication and patience shown by all the vector control officers who volunteered to conduct the interviews.
Results We describe in vivo genome-wide binding patterns of 84 Drosophila transcriptional regulatory factors (TRFs), using both previously published data sets and new data generated by the modENCODE Project  (Li et al. 2008; Zinzen et al. 2009; Negre et al. 2011 ; Supplemental Table  S1 ). Overall, 65 DNA-binding proteins and 19 non-DNA-binding proteins (cofactors, chromatinbinding factors) are represented; most factors were tested in one developmental stage or cell line, though multiple factors were tested in multiple contexts (Supplemental Table  S1 ). In total, these data, all of which are available through the modENCODE Data Coordination Center (http://intermine. modencode. org/) or Gene Expression Omnibus (GEO; see Supplemental Table  S1  for accession numbers), represent 171 separate genome-wide ChIP experiments performed in duplicate or greater, 413,743 TRF-binding sites, and 50,336 unique TRF-binding regions.
Data processing ChIP-chip peak calls are as previously described  (Li et al. 2008; MacArthur et al. 2009; Zinzen et al. 2009; Negre et al. 2011). For ChIP-seq experiments, biological replicates were scored against an appropriate input DNA control (from non-immunoprecipitated chromatin). The MACS (v2) peak caller was used to identify and score (rank) potential binding sites/peaks  (Zhang et al. 2008). For obtaining optimal thresholds, we used the irreproducible discovery rate (IDR) framework to determine high-confidence binding events by leveraging the reproducibility and rank consistency of peak identifications across replicate experiments of a data set  (Li et al. 2011a). Briefly, for individual replicates, peaks were called using MACS2 with a P-value threshold of 1 3 10 À3 to obtain a maximum of 30-k peaks, and peaks were ranked according to their P-value scores.
Replicates were then pooled and MACS2 was again used to call peaks at a P-value threshold of 1 3 10 À3. Peaks from the pooled set that overlapped at least one peak in both individual replicate sets were retained. From this set of replicate-reproducible peaks, we obtained two independent rankings based on the P-values from each replicate; this pair of ranked lists was used as input for the IDR framework. Cross-replicate and pseudoreplicate rank thresholds at an IDR of 5% were generated, and the better of the two was used to generate the final set of rank consistent and reproducible peaks. Details of the IDR framework are available at https://sites. google. com/site/anshulkundaje/projects/idr. All data sets are available through the modENCODE Data Coordinating Center (DCC; see Supplemental Table  S1  for DCC submission IDs).
Acknowledgments This work was supported by  U01HG004264  from the  National Human Genome Research Institute  to K. P. W. We thank the  University of Chicago IGSB High-Throughput Genome Analysis Core (HGAC)  for performing Illumina sequencing, and the  BAC-Recombineering Core  for generating GFP-tagged BAC constructs. We thank  Stacy Holtzman  and  Thom Kaufman  for performing BAC injections. We also thank the members of the  Drosophila community  who generously provided reagents for this work.
Data access Data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo/), modMine (http://intermine. modencode. org), or both under GEO accession nos. GSE49768-GSE49780, GSE49899, or the modMINE DCC IDs 2627, 2629, 2630, 2633, 2634, 2636, 3234, 3240, 3390-3396, 3399-3401, 3403. See Supplemental Table    S1  for all data set submission information.
http://www. earth-planets-space. com/content/66/1/122 The purpose of this paper is twofold. First, we analyze CHAMP and GRACE accelerometer data to reveal the full spectrum of non-migrating (i. e. , longitude-dependent) diurnal, semidiurnal, and terdiurnal tides in the upper thermosphere and the seasonal-latitudinal structure of each component. A new aspect of this tidal analysis is that we exploit the local time coverage afforded by combining CHAMP and GRACE data together and thus derive tides in terms of 72-day mean values with exosphere temperature being the dependent variable. Previous tidal analyses with CHAMP were performed in terms of perturbation density and consisted of 130-day mean values due to the slow local time precession of the CHAMP orbit. This type of averaging likely suppressed actual amplitudes and significantly smeared month-to-month variability.
In the context of tidal theory applicable to the thermosphere, we then attempt to ascertain to what degree the spectral components at 400 km arise due to vertical propagation from 110 km and, by extension, what components might be excited in situ within the IT system. The following section describes how we analyze the CHAMP and GRACE accelerometer data to derive the tidal spectrum as well as the elements of tidal theory employed in our analysis. The subsequent 'Analysis of CHAMP and GRACE data' section presents results for the diurnal, semidiurnal, and terdiurnal components of the observed upper thermosphere tides, including comparisons with TIMED/SABER at 110 km and discussions of possible origins of the illustrated waves. This is followed by presentations and discussions of the total latitude, longitude, and monthly variability of diurnal and semidiurnal exosphere temperature amplitudes in the 'Results and discussion' section.
Analysis of CHAMP and GRACE data As noted above, a hindrance of using CHAMP data to derive thermosphere tides is the 130-day time period it takes for the CHAMP satellite to sample 24 h of local time, even when considering sampling along both the ascending and descending parts of the orbit; over 130 days, tidal amplitudes and phases can vary considerably, and the 130-day mean tidal determinations can lead to a significant underestimate of the tidal amplitudes that occurred within the fitting interval. In  Forbes et al. (2009) , we took advantage of the relative precession rates of the CHAMP and GRACE satellites, and the fact that exosphere temperature is independent of height in the 350-to 550-km height regime, and demonstrated the feasibility of extracting 72-day running means of tidal components in exosphere temperature.
In the present paper, we apply this method to the CHAMP and GRACE data sets extending to 2010, with the intent of establishing the full spectrum of non-migrating (i. e. , longitude-dependent) diurnal, semidiurnal, and terdiurnal tides that exist at orbital altitudes in Earth's thermosphere. With a 72-day moving window, we are able to delineate tides with much improved resolution and variability compared with those obtainable Figure  2  Latitude vs. month depictions of tidal amplitudes. Sample plots providing latitude vs. month depictions of tidal amplitudes of exosphere temperature for the four time intervals (top to bottom) considered in this study: DE3, first column; DE2, second column; SE2, third column; SE3, fourth column. http://www. earth-planets-space. com/content/66/1/122 from CHAMP or GRACE alone.
The reader is referred to  Forbes et al. (2009)  for a detailed description of the method, error analysis, and validation. A brief summary is provided below. As a first step, the CHAMP and GRACE densities are converted to equivalent exosphere temperatures. CHAMP and GRACE densities are measured along noncircular orbits and at different altitudes within the 300to 550-km height regime. In order to combine these data together for a tidal analysis, the densities are first converted to equivalent exosphere temperatures. After the CHAMP and GRACE densities are inter-calibrated, the conversion to exosphere temperature is done empirically by iterating on the solar flux input into the NRLMSISE-00 empirical model  (Picone et al. 2002)  until the model and measured density agree. The parametric dependence between exosphere temperature and density makes this possible.
(2013). Experimental evidence for an in situ driven D0 was also found by  Lieberman et al. (2013). Results for terdiurnal tides are not shown in this paper. The non-migrating terdiurnal tides derived from CHAMP-GRACE data generally lacked well-defined seasonal-latitudinal structures and had maximum amplitudes less than 2 K, which appears to be the level of detectability for tidal components by this method. One impact of our results is illustrated in Figure  8. Here we show the total diurnal and semidiurnal exosphere temperature amplitudes, obtained by vectorally summing all of the derived migrating and non-migrating tidal components, at the equator and at ±40°latitude as a function of longitude and month. All of the longitude variability and much of the month-to-month variability illustrated here is due to the aggregate contributions of the nonmigrating tidal components, their interference with the larger migrating components.
Measurements of organic carbon and ancillary data The organic carbon was intensively measured at the two sites, in both the gaseous and particulate phases, using numerous offline and online instruments. All the instruments used in the present analysis and their performances are presented in Tables  1  and 2. At SIRTA, gaseous organic carbon measurements included C 1 -C 16 organic compounds. These compounds can be classified according to their saturation concentration, C * , which is an indicator of their volatility  (Robinson et al. , 2007; Epstein et al. , 2010). Thus, given their C * , C 12 -C 16 nalkanes are of intermediate volatility, as they are characterized by 10 3 µg m -3 < C * < 10 6 µg m -3 , while all other measured compounds were volatile (C * > 10 6 µg m -3 )  (Robinson et al.
Concentration levels of VOCs: Paris vs. other areas Table  4  compiles mean concentrations of toluene and ipentane, two markers of vehicular exhaust and evaporation emission sources, measured at different locations, including megacities, European urban and suburban areas, and different areas in France. As VOC emissions are rapidly decreasing  (Warneke et al. , 2012) , only observations performed within the last 15 years have been reported in Table  4. Mean concentrations of these VOCs observed at LHVP are also reported in Table  4. SIRTA exhibits the lowest VOC mean concentrations among all these data sets. Toluene mean concentrations in other megacities range from 0. 58 to 19. 44 ppb: the highest value, measured in summer at an urban site of Beijing  (Duan et al. , 2008) , is a hundred times as high as the summertime toluene mean concentration at SIRTA (0.
The scatterplot of SIRTA is color-coded as a function of the wind direction. When SIRTA is downwind of the 225-330 • sector, enrichment in either i-pentane (upper part of the envelope) or i-butane (lower part of the envelope) is observed. Plumes arriving from the northwest at SIRTA flew over the Seine basin. In spring 2007,  Gaimoz et al. (2011)  also observed enrichment in i-butane concentrations in urban Paris when sampling oceanic air masses which had crossed this region. The Seine basin is an important industrialized area including different industries: chemistry, surface treatment and oil and gas production  (iREP, 2012). When the upper and lower part of the envelope, which correspond to the data associated with the 225-330 • wind sector, are not taken into account, a good correlation is obtained at SIRTA (R 2 = 0.
, 2014). Besides, the higher C OA is, the higher the SOA yield is  (Hildebrandt et al. , 2009; Presto et al. , 2010; Aumont et al. , 2012; Tkacik et al. , 2012). 2. The VOC/CO emission ratios. The ER to CO are the ones determined for the Paris area based on the LHVP ground site and airborne data by  Borbon et al. (2013). While the SIRTA site was rarely downwind of the Paris plume,  Borbon et al. (2013)  and  Boynard et al. (2014)  showed that VOC/VOC ER were usually consistent between northern mid-latitude urban areas. However, the C 11 -C 16 alkanes were not measured at LHVP. For this reason, their emission ratios have been estimated from the ER determined at vehicle exhaust by Fontaine   (2000) , as previous studies state that volatile alkanes and alkanes of intermediate volatility are mainly emitted from vehicle exhaust  (Fraser et al.
Estimation of SOA from its gaseous precursors: the integrated approach The first step in the integrated approach consists of determining the value of the OM / CO enhancement ratio. Figure  5a  shows the scatterplot of OM to CO averaged to 1 h at SIRTA. The data points have been color-coded by the O 3 / CO ratio, which is used here as a proxy of photochemical processing. This indicator considers CO as purely of primary origin. In our case, as discussed in Sect. 3. 3, secondary CO might have been sampled at SIRTA on 21 July. However, this represents only 3 % of the summertime data set, allowing the O 3 / CO ratio to be used as a proxy of the photochemical processing. Hence, fresh air masses with low photochemical processing show the lowest level of ozone and the highest level of CO: the ratio O 3 / CO tends to zero.
Processed air masses show the highest level of ozone and the lowest level of CO: the ratio O 3 / CO increases. The data with a higher photochemical processing tend to follow a steeper line (highest O 3 / CO ratio) than the points with a lower photochemical processing (lowest O 3 / CO ratio). Part of the OM scatter can therefore be explained by the secondary formation of organic matter. As a consequence, the value of the OM / CO enhancement ratio is determined from the fit of the correlation of data with an O 3 / CO ratio higher than 0. 5 (ppb ppb -1 ), which corresponds to the upper limit of the scatterplot envelope, implying that a maximum OM enhancement is considered here. The OM / CO enhancement observed at SIRTA in summer was OM CO = (109. 2 ± 9. 1) µg m -3 ppmv -1. The POA emission ratio has been determined by plotting the HOA PMF component determined at SIRTA  (Freutel et al.
Conclusions Under the auspices of the MEGAPOLI project, measurements of gaseous organic carbon have been performed in the Paris megacity, resulting in a large and unique database (available at the French Ether database: http://ether. ipsl. jussieu. fr/megapoli/). Compounds of different sources and chemical and physical properties have been documented during a summer and a winter field campaign at SIRTA, a suburban site of the French megacity. The first goal was to analyze the temporal and spatial variability and origin of gaseous organic carbon in suburban Paris. The analysis showed that -The seasonal variation of anthropogenic VOCs is the one usually depicted in urban areas with higher winter-time concentrations as a result of a combination of (i) a shallower boundary layer in winter than in summer, (ii) photochemistry processes less active in winter and (iii) emissions sources of different nature and/or intensity.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Seventh Framework Program FP/2007-2011  under grant agreement no. 212520. Support from the  French ANR  project  MEGAPOLI -PARIS  ( ANR-09-BLAN-0356 ), from the  French ADEME  via the  CNRS-INSU/LEFE program  is acknowledged. This research work has been supported by the  European Community  within the  seventh Framework Program :  Eurochamp-2  ( EU-FP7  grant agreement no. 228335 ). The authors acknowledge ADEME and the  Fulbright Commission  for their financial support to W. Ait-Helal doctoral researches. The authors acknowledge  M. Haeffelin , the SIRTA and the LHVP team for their technical support. R. Sarda-Estève  and  J. Nicolas  are kindly acknowledged for the CO measurements at SIRTA.
The authors also thank  Tera Environnement  for lending the sampling systems. The authors thankfully acknowledge  A. Stohl ,  J. F. Burkhart  and  S. Eckhardt  for the FLEXPART simulations. Finally, the authors thank all the people who helped with their insights on the data and its analysis:  F. Oztürk  and  R. Bahreini  for all the discussions on the particulate matter and its measurement by  AMS ,  P. Ziemann  for the kind help on the SOA yield of IVOCs. The French ether database (http://ether. ipsl. jussieu. fr/megapoli/) is acknowledged for hosting the experimental data. Edited by:  C. Reeves.
A STAB902 strain grown in Todd-Hewitt medium supplemented with 0. 2% yeast extract (THY) and DNA for sequencing was extracted and purified using the phenol-chlorophorm technique. Genomic DNA was sequenced using HiSeq 2000 technology (Illumina, Inc. , San Diego, CA) and the paired-end library was built using the MGX facility of the CNRS in Montpellier, France. There are a total of 35,623,128 high-quality reads giving an average of 1,964-fold coverage of the genome, which was assembled using CLC Genomics Workbench v6 software (http:// www. clcbio. com). The resulting assembly consisted of 90 contigs, which were oriented and connected based on the two previously sequenced GAS M/emm3 strains (SSI-1 and MGAS 315)  (5, 6). In comparison with these two reference genomes, which both show large genomic rearrangements (5), the final chromosomal organization of the STAB902 strain is identical to that of the SSI-1 strain.
The multilocus sequence type  (10)  is determined to be ST406. In comparison with SSI-1 and MGAS 315 genomes  (5, 6) , almost all of the genes of known virulence factors (proteinases, superantigen, proteins adhesion, and gene regulator) are present. In addition, genomic comparison evidenced in the strain STAB902 in-frame deletions of sclB and graB  (11)  genes encoding two surface proteins anchored to the bacterial wall by an LPϫTG motif. The roles of these genes in the invasiveness of the GAS M/emm3 genotype are under investigation. Nucleotide sequence accession number. The S. pyogenes strain STAB902 genome sequence has been deposited in the NCBI database under the accession no. CP007041. The version described in this paper is the first version.
ACKNOWLEDGMENTS We thank  Dr. Jean Christophe Roux  for helpful discussion. This study was funded by  Fondation Lejeune  (to F. M. ) and the  Association Française contre les Myopathies. C. B. is the recipient of a fellowship from the  Algerian Ministry of Higher Education and Research. P. T. is the recipient of a fellowship from  Fondation pour la Recherche M édicale. G. M. was supported by a fellowship from the  French Ministry of Foreign Affairs.
We find no evidence of a gravitational-wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational-wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational-wave data is available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational-wave emission energy of 10 -2 M c 2 at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12 Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational-wave detectors, and a resulting examination of prospects for the advanced gravitational-wave detectors.
Long GRBs (duration greater than 2s; soft spectra) models are mostly related to the collapse of rapidly rotating massive stars. The extreme conditions encountered in such objects may make the system susceptible to a variety of rotational instabilities that may emit up to 10 -2 M c 2 through GW radiation  [7, 8]. Between 2005 and 2010, the first generation of large scale interferometric gravitational-wave detectors, LIGO, Virgo and GEO, were operating at, or close to, their design sensitivities. During these runs, the detectors had a sensitivity out to tens of Mpc for binary mergers  [9] [10] [11] [12]  and other transients emitting 10 -2 M c 2 in gravitational waves  [13] [14] [15]. Although it is expected that most GRB progenitors observed by gamma-ray detectors will be at distances too large for the resulting gravitational-wave signals to be detectable by initial LIGO and Virgo  [16, 17] , it is possible that in the GRB data set under study that one might be within the range of the detectors.
For example, the smallest observed redshift to date of an optical GRB afterglow is z = 0. 0085  ( 36 Mpc)  for GRB 980425  [18] [19] [20] ; this would be within the LIGO-Virgo detectable range for some progenitor models. Although GRB 980425 is a long duration soft spectrum GRB, observations seem to suggest that, on average, short-duration GRBs tend to have smaller redshifts than long GRBs  [21, 22]. We therefore search for evidence of a gravitational wave signal associated to any observed short or long GRB for which there is a sufficient duration of high quality data in at least two detectors. By making use of the known time and sky location of the observed GRB, it is possible to significantly reduce the parameters of the search and consequently improve the sensitivity over an all-sky all-time search of the data.
GRB SAMPLE The Interplanetary Network (IPN) is a group of spacecraft equipped with gamma-ray detectors used to localize gamma-ray bursts (GRBs) and soft gamma repeaters (SGRs, or magnetars). The IPN has contributed burst data to LIGO since the initial engineering runs in 2001. At the time of this combined search, nine spacecraft contribute their data: Wind, Mars Odyssey, MESSENGER, INTEGRAL, RHESSI, Swift, Suzaku, AGILE and Fermi. The astronomical locations of GRBs are determined by comparing the relative arrival times of the signal at the spacecraft. The precision is inversely proportional to the spacecraft separations, among other things, so that the localization accuracy of a network with baselines of thousands of light-seconds can be equal to or superior to that of any other technique.
In these cases, any burst with a T 90 under two seconds was classified as short. The unmodeled search uses many minutes of data around the given time of the burst whereas the modeled search uses a small sixsecond window around "short" bursts to search for the binary coalescence, due to the predicted time difference between merger and GRB emission. It is therefore important that the time of arrival at the Earth be calculated as accurately as possible for these bursts. When the burst is observed by a near-Earth satellite, this is straightforward and approximated to the spacecraft time. However, for some poorly localized GRBs only observed by distant satellites, the uncertainty in the Earth-crossing time can be up to approximately five seconds. Only the GRBs that occurred when two or more of the LIGO and Virgo detectors were operating in a stable configuration are analyzed.
GRAVITATIONAL WAVE DETECTORS In this paper, we discuss results obtained from analyzing data collected by the initial LIGO and Virgo gravitational wave detectors. There were three LIGO detectors: a 4 km and a 2 km detector, both at Hanford, WA (referred to as H1 and H2, respectively) and another 4 km detector in Livingston, LA (L1). The Virgo detector is a 3 km detector located in Cascina, Italy. Details of these detectors can be found in  [40]  and  [41]. The existence of multiple, widely separated detectors around the globe aids our ability to localize signals in blind, all-sky searches. Additionally, noise artefacts in the detectors caused by terrestrial disturbances are likely to be uncorrelated between the detectors, aiding the ability to distinguish true signals from the noise background.