dataesr/scientific-paragraphs-categorization huggingface dataset:
g. road dust) from being considered in our study. Fine aerosols (PM 2. 5 ) were collected every day from 00:00 to 23:59 LT, over 1 year, from 11 September 2009 to 10 September 2010. Two collocated Leckel low-volume samplers (SEQ47/50) running at 2. 3 m 3 /h were used for filter sampling. One Leckel sampler was equipped with quartz filters (QMA, Whatman, 47 mm diameter) for carbon analyses, the other with Teflon filters (PTFE, Pall, 47 mm diameter, 2. 0 µm porosity) for gravimetric, ion and metal measurements. Twenty-eight samples (i. e. 8 % of the data set) were discarded because of power failures, analytical problems, etc. (see Supplement Table  S1  for the detailed list).
Back trajectories were then defined according to their overall path in 1 of the 16 θ directions separated by 22. 5 • (i. e. N, NNE, NE, etc. ). This procedure allows curved back trajectories to be binned in the appropriate direction, but is laborious and prone to user approximations. Calm winds (i. e. wind speeds below 1 m s -1 ) were excluded from the data set, which represents 3 % of wind data. A total of 1417 air mass back trajectories were taken into account for CPF calculations. Different threshold criteria were tested, and the 75th percentile was retained as it better illustrates source locations. This threshold is in line with what is reported elsewhere (e. g. Amato and Hopke, 2012; Jeong, C. H. et al. , 2011; Kim et al. , 2004). Furthermore, a weighting function was empirically implemented to lower uncertainties associated with low n θ values (thus resulting in high CPF θ values, see Sect.
Acknowledgements. The  French Environment and Energy Management Agency (ADEME) , the  French Alternative Energies and Atomic Energy Commission (CEA) , the  region of Paris (Ile-de-France) , the city of Paris, the  National Centre for Scientific Research (CNRS)  and the  University of Versailles Saint-Quentinen-Yvelines  are acknowledged for their support. M. Reynaud  and  M. Artufel  are thanked for their technical and analytical help. The authors are grateful to the anonymous referees and the editor for their valuable comments. Edited by: B. Ervens The publication of this article is financed by  CNRS-INSU.
Conclusions: When compared to Saccharomyces cerevisiae experimental data, SPoRE predicts axis protein and DSB positions with high sensitivity and precision, axis protein density with an average local correlation r = 0. 63 and DSB density with an average local correlation r = 0. 62. SPoRE outbreaks previous DSB predictors, which are based on nucleotide patterning, and it reaches 85% of success rate in DSB prediction compared to 54% obtained by available tools on a benchmarked dataset.
Comparison with experimental data SPoRE has been constructed to predict DSB and axis proteins distribution along chromosomes, and to measure the importance of different factors in this prediction. To evaluate how accurate SPoRE modeling is, we performed four types of analysis: a. experimental data on Red1  [9]  and Spo11  [24]  proteins obtained for the S. cerevisiae genome were considered and the local/global Pearson and Spearman correlations between SPoRE modeling curves and experimental curves were computed. The distribution of peaks, characterizing sites of highest protein concentration, along the two curves was studied. Several models, characterized by different combinations of genomic signals, were tested to numerically evaluate the impact of each signal. b. Coherence of SPoRE predictions was tested on two experimental datasets  [24, 35]  related to axis proteins and DSBs.
Coherence of SPoRE predictions with two large-scale experimental datasets SPoRE modeling curves can be used for comparison with experimental data of different origin. In this respect, we considered two different datasets. First, as mentioned in the introduction, it has been shown previously that Red1 and Hop1 patterns are influenced by Rec8 (cohesin) patterns  [24]. Hop1, for instance, is distributed almost like Red1 (local correlation is r = 0. 92, global is r = 0. 88) with which it interacts  [37, 38]. On the other hand, Rec8 is more abundant around centromeres than Red1/Hop1, although local variations are the same. Therefore, Rec8 global correlation with Red1 is only r = 0. 57, while its local correlation is still r = 0. 83. Because of these correlations, we expect SPoRE to be locally well correlated with Hop1 and Rec8 (data from  [24] ).
Indeed, we find that SPoRE model has a local correlation of r = 0. 62 with Hop1 and r = 0. 60 with Rec8, compared to r = 0. 64 with Red1. This confirms that the three axial proteins share SPoRE local distribution patterns. Consistently, if we look at global correlation coefficients, SPoRE is well correlated with Hop1 (r = 0. 55) and Red1 (r = 0. 56) but weakly correlated with Rec8 (r = 0. 33). Second, we compared SPoRE curves to histone trimethylation data. It has been observed before that H3K4 trimethylation (H3K4me3) is linked to DSBs  [17]. Then, we computed correlations between H3K4me3 (data from  [35] ) and SPoRE modeling curve for Spo11. We find r = 0. 25, which is comparable to r = 0. 21 obtained when we correlate H3K4me3 and DSB experimental data. Similarly, with Spearman coefficients, we find ρ = 0.
We therefore enlarged the dataset by adding 452 randomly chosen coldspots in the same experimental data and on chromosome IV (see Methods). On this symmetric test set, the overall success rate of iRSpot-PseDNC falls to 54% against 85% for our model (see Methods), compared to an expected 50% for a random prediction. This is due to the fact that iRSpot-PseDNC detects 309 false positives (false positive rate is 68%) while we only detect 43 of them (false positive rate is 10%). This shows that iRSpot-PseDNC is little better than random in detecting DSB hotspots. It should be noted that comparison is realized on hotspot sites localization but that no prediction on protein density is made by iRSpot-PseDNC, contrary to SPoRE, where estimations of density can be directly inferred from the modeling curve.
We also extended this benchmark over the whole S. cerevisiae genome by considering all the 3600 hotspots discovered in  [9] , together with 3600 randomly chosen coldspots. The accuracy of SPoRE in that case is 84% (close to 85% for chromosome IV). Its predictive performance can also be measured with a ROC curve by varying the density threshold, in which case the area under the curve is 0. 90 (see Additional file 1: Figure  S11 ). iRSpot-PseDNC success rate on properly identifying hotspots and coldspots is 55% (comparable to the 54% obtained on chromosome IV; due to the nature of iRSpot-PseDNC output, no ROC curve can be produced). A second test was realized on the same dataset used in  [30]  to compare iRSpot-PseDNC to IDQD  [31]. This dataset, defined in  [31] , is composed of 490 hot ORFs and 591 cold ORFs, where the hot ORFs describe a set of recombination hotspots.
In yeast, our model highlights that axial chromosome structures and DSB distribution are governed by a rather simple combination of genomic signals. For other organisms, the model might be expected to become more complex. For the mouse, for instance, other factors such as DNA binding sites targeted by axial proteins have been demonstrated to play an active role in DSB localization  [42]. In this respect, SPoRE might be taken as a nutshell to add extra signals and reach appropriate descriptions of experimental data in other organisms, possibly multicellular ones. SPoRE software is provided to allow users for further development and testing of new genomic factors.
Visualization in a genome browser To allow biologists to visualize easily SPoRE modeling curves, SPoRE provides its results in the WIG file format. They can be loaded in the UCSC genome browser (http://genome. ucsc. edu/), in the genome browser available at http://yeastgenome. org/ and in the IGV software (see Additional file 1: Figure  S1 )  [43]. For the four yeast genomes that we analyzed, the corresponding wig files are available at http://www. lcqb. upmc. fr/SPoRE/. For convenience, we also provide the corresponding S. cerevisiae experimental data in the same format, to allow for easy comparison.
Annotation The reference strain we used to validate SPoRE is Saccharomyces cerevisiae S288C. The gene annotations were retrieved from the Saccharomyces Genome Database (http://www. yeastgenome. org/), release 64. We included 4879 "verified" ORFs and 895 "uncharacterized" ORFs in our set of coding genes, but not "dubious" ORFs. We also considered transposons by taking the 89 features labeled "transposable element gene", rRNAs (RDN37-1, RDN37-2, RDN5-1, RDN5-2, RDN5-3, RDN5-4, RDN5-5, and RDN5-6), and pseudogenes  (21). For Lachancea kluyveri and Kluyveromyces lactis, genomes and annotations were downloaded from Genolevures (http://www. genolevures. org/). Only features named "CDS" were taken into account in our models. For Schizosaccharomyces pombe, genome and annotation were downloaded from PomBase (http://www.
Protein density data used for SPoRE validation We use protein density data along the genome from Spo11 immunoprecipitation/454 sequencing for DSB  [9]  and from ChIP-on-chip for Red1, Hop1 and Rec8  [24]. They were mapped on the S. cerevisiae S288C genome, even though strain SK1 was used in the experiments. Raw data were used for computing all correlations reported in Table  1. They were retrieved from supplementary data in  [9]  for Spo11, and from the GEO dataset GSE29860 for Red1/Hop1/Rec8.
Comparison with iRSpot-PseDNC on DSB data Comparison between SPoRE DSB model and iRSpot-PseDNC  [30]  was realized on the dataset of 452 experimentally annotated  [9]  recombination hotspots for the S. cerevisiae chromosome IV. This set, originally used to evaluate iRSpot-PseDNC in  [30] , has been extended with 452 coldspots that we extracted from the same experiment  [9]. This extension was done in order to test both systems for false positives. More precisely, for each hotspot in the dataset, we randomly selected a fragment of DNA on chromosome IV with the same length as the hotspot, but without any experimentally detected DSB, and verified that these fragments do not overlap each other. (Notice that 17% of the S. cerevisiae genome is made of regions that are larger than 242 nt, that is the average size of a hotspot, and that contain no peak.
We have randomly selected coldspots within these regions). Hence, we obtained a set of coldspots with the same number of sequences and the same length distribution as the set of hotspots. We then tested iRSpot-PseDNC online by providing the server with the DNA sequences in the dataset (the file is available at http://www. lcqb. upmc. fr/SPoRE/). To test our model, we simply predicted as a hotspot any fragment on which the average of our curve is higher than the average over the whole genome. To generate Additional file 1: Figure  S11 , we considered hotspots and coldspots over the whole S. cerevisiae genome. We used the same process as explained above for choosing coldspots, with the only differences that, first, we repeated the process for each of the 16 chromosomes, and, second, that we allowed for at most 1 read to be present in a coldspot (requiring 0 reads is too stringent on some parts of the genome).
We then used the average of our curve over the hotspots and coldspots as a predictor, and varied the threshold to produce the ROC curve (instead of setting it to the mean as above). A second dataset was used for comparison with iRSpot-PseDNC and IDQD  [31]. It is defined in  [31]  and it is downloadable as SI of  [30]. This set is defined by ORFs, but since SPoRE uses information about intergenic regions instead, we benchmarked SPoRE on this dataset by predicting hotspots on the intergenic regions lying before the gene start. Namely, we compared the average of our modelling curve in this region to its mean μ and standard deviation σ by predicting hotspots when the average of the curve is ≥ μ + σ. When the GC-content curve has been tested as a predictor of recombination hotspots in this dataset, formally, we compared the maximum of the smoothed GC-content curve in the gene and intergenic region preceding it to μ GC + σ GC , where μ GC , σ GC are the mean and the standard deviation of the GC-content curve on the full genome.
Acknowledgements We would like to thank  F. Klein  for providing us the axis proteins chIP-on-chip raw data [24] and explaining how to parse it,  Bernard Dujon  and  Gilles Fischer  for insightful remarks on a previous version of the manuscript. R. C. has been supported by a  MENRT (Ministère de l'Education Nationale et de la Recherche)   doctoral fellowship. Research Grant  from the  Institut Universitaire de France  for A. C.
Material and methods Analysing and understanding the emerging structure of major knowledge databases constitutes the epistemic project of the field of "big data" that affects all disciplines. In this context, tracing and mapping the structure and organisation of knowledge represents a major challenge for social studies of science, where an understanding of the social and cognitive dynamics of research activities is a necessary gateway  (Cambrosio et al. , 2004 (Cambrosio et al. , , 2006;; Bourret et al. , 2006; Bonaccorsi, 2008). Today, evolutions affecting the analysis of scientific dynamics are to a great extent driven by the issue of characterising the cognitive dynamics of knowledge production  (Powell et al. , 2005). The question of the emergence of multi-or cross-disciplinary research fields is also crucial  (Lucio-Arias and Leydesdorff, 2007).
Thus, the theoretical issues underlying the reconstruction of evolutions in long-range scientific fields are obstacles that need to be overcome  (Chavalarias and Cointet, 2013). In this context, network analysis plays a very important role in the scientific characterisation of research fields such as ours. In order to model socio-semantic dynamics based on a corpus of texts  (Cointet, 2009) , it is necessary to link several instruments. A software platform called CorTexT was developed, (http://www. cortext. org) notably for that purpose, with an online interface: CorTexT Manager (http://manager. cortext. net/). Fig. 1  shows how the CorTexT Manager was used for the analyses. The R software (R Core Team, 2012) supplemented these analyses for certain types of data processing. Scientometric analysis can be described as having different successive phases.
Constitution of the corpus The resource used most in scientometrics is the Thomson-Reuters Web of Science portal. It provides access to databases with multidisciplinary coverage, including 12,000 journals and 148,000 reports from conferences in the different fields of science (Science Citation Index Expanded, SCIE), the social sciences (Social Sciences Citation Index, SSCI), and the arts and humanities (Arts & Humanities, A&H). We chose to use the three Web of Science databases. Given the existence of numerous synonyms for the concept of ecosystem services, the search term was constructed by crossing, one by one, the different synonyms existing for "service" and "ecosystem" (and variations in their spelling).
X Construction of the list of synonyms was based on different studies  (De Groot et al. , 2002; Lamarque et al. , 2011; Jeanneaux et al. , 2012; Le Roux et al. , 2012). The precise content of the search term is shown in Appendix A. The search term used for the Web of Science (SCI, SSCI, A&HCI) produced 12,184 references during the period 1975-2012. Taking account of the Convention on Biological Diversity  (1992) and Millennium Ecosystem Assessment (2005)  publications, this total corpus could be broken down into 11,915 documents covering the period from 1993 to 2012, and 9027 applicable to the period between 2006 and 2012. Four bibliographical fields used in this were the Title (TI), Author Keywords (DE), Abstract (AB) and Subject Category (SC) fields. The SC field corresponded to one or more disciplinary keywords attributed by the Web of Science to each reference in its database, based on a predefined list of 151 Research areas: (http://images.
Frequential description of occurrences The importance of the different synonyms for "ecosystem service" (Query field), the different themes studied (Themes field) and the different disciplines concerned (SC field) were measured by counting their occurrences. For example, a publication indexed under both ecology and conservation biology counted for one in each of these disciplines. The numbers obtained where then compared against the total number of publications in the corpus or the number of documents indexed, depending on the case. An example of this is shown in Fig. 2-A. These lexical analyses were performed using a script developed under R (R Core Team, 2012).
Acknowledgements We would like to thank  Virginie Lelievre  ( INRA ) for her bibliometric help at the beginning of this work;  Jacques Baudry ,  Isabelle Doussan ,  François Lefèvre ,  Danièle Magda ,  Sylvain Pellerin ,  Alban Thomas  and  Philippe Breucker  ( INRA ) for their availability and helpful contribution in processing datasets; and  Victoria Hawken  for the English translation of this work. This study also received a support of the LABEX SITES and the IFRIS institute.
In particular, the D816V and D816H mutants auto-activate substantially faster than native KIT in kinase activity assays (586-and 184-fold, respectively). The auto-activation rate was even higher (. 600 fold) for KIT mutant V560D. Auto-activated mutants of kinases define a clinically validated class of targets for cancer drug therapy  [31]. Therefore, enormous efforts are devoted to the development of small molecules (inhibitors) targeting oncogenic kinases activity. However, extensive studies have revealed that many kinase inhibitors are less selective than initially expected  [32, 33]. The kinase inhibitor imatinib (Gleevec (R) or Glivec (R) ) is highly specific for a restricted number of kinases (recombinant receptor Bcr-Abl, and receptors of type III KIT and PDGFR). Structural data revealed that imatinib binds to the active site of the receptor and maintains the native KIT in an inactive non-autoinhibited state (PDB ids: 1T46, 1.
Molecular dynamics simulations Initial coordinate files. The crystallographic coordinates for the wild-type (WT) KIT (PDB id: 1T45, 1. 90 A ˚resolution, no cocrystallized ligand)  [34]  was retrieved from Protein Data Bank (PDB)  [42]  and used as a template for modeling KIT V560G/D and D816H/Y/N mutants. The crystallographic structure contains residues from 547 to 935 with the exception of a large part of the KID (residues 693-753) that was replaced by a short linker. As reported in all crystallographic studies of type III RTKs, such a deletion was applied systematically to successfully achieve the protein crystallization, strongly affected by a high mobility of the KID domain  [43]. The shortened engineered KID, further referred to as ''pseudo-KID'', will be neglected in the analyses. All crystallographic water molecules were removed.
Five initial models were generated: (I) KIT V560G , (II) KIT V560D , (III) KIT D816Y and (IV) KIT D816N , were built with MODELLER 9v7  [44, 45] , and (V) KIT D816H , with Maestro 9. 1 from Schro ¨dinger Suite  [46] , using a protocol identical to that previously employed for the KIT WT and KIT D816V modeling  [40]. All models were prepared using the LEAP module of AMBER  10 [47]  with the parm99 parameter set: (i) hydrogen (H) atoms were added, (ii) covalent bond orders were assigned, (iii) Na + counter-ions were added to neutralize the system charge, (iv) the protein was hydrated in a periodic box of explicit TIP3P water molecules  [48]. A hydration shell of 9 A ˚was employed for D816H/Y/N mutants. This distance was increased up to 12 A for V560G/D mutants, as we anticipated for large motions of the JMR in these mutants, according to the experimental data reporting an increased solvent accessibility and fluctuations of the JMR in KIT mutated in this region  [49].
The preparation details of the MD simulations are given in Table  S1. Protonation state of H816. The histidine H816 in KIT D816H was protonated on the N d1 atom as this protonation state corresponded to an optimized H-bond network according to Maestro evaluation. A close inspection of H816 environment and solvent accessibility  [50]  further supported this choice. The KIT D816H model was then minimized using MacroModel  [46]. Set up of the systems. Set up of the systems was performed with SANDER module of  AMBER 10 [47]. First, each system was minimized successively by steepest descent and conjugate gradient algorithms  [51]  as follows: (i) 10 000 steps of minimization of the water molecules keeping protein atoms fixed, (ii) 10 000 steps of minimization keeping only protein backbone fixed, to allow protein side chains to relax, and (iii) 10 000 steps of minimization without any constraint on the system.
After relaxation, each system was gradually heated from 10 to 310 K at constant volume using the Berendsen thermostat  [52]  while restraining the solute Ca atoms by 10 kcal/mol/A ˚2. Thereafter, the system was equilibrated for 100 ps at constant volume (NVT) and for further 100 ps at constant pressure (NPT) maintained by a Langevin piston  [53]. Finally, the restraints were removed and each system was equilibrated for a final 100-ps run. Backbone deviations obtained after equilibration are reported in Table  S1. Production of the trajectories. For each equilibrated system (models I-V) two independent MD simulations were run with different initial velocities using the PMEMD module of AMBER 10. The temperature was kept at 310 K (Berendsen thermostat), and pressure at 1 bar (Langevin piston coupling algorithm).
The SHAKE algorithm was used to freeze covalent bonds involving hydrogen atoms, allowing for an integration time step of 2. 0 fs. Long-range electrostatic interactions were treated by the Particle Mesh Ewald method  [54]. Each simulation run was continued until a total time of 50 ns to 70 ns, depending on the Analysis of the trajectories. Unless otherwise stated, all recorded MD trajectories were analyzed with the PTRAJ module of AMBER 10. From the Root Mean Square Deviation (RMSD) profiles, 2 ns (for KIT D816Y/N ) or 5 ns (for KIT D816H and KIT V560G/D ) were found sufficient to achieve relaxation. To compare simulations of equal ''productive'' duration in all KIT D816V/H/Y/N mutants, we extended KIT D816H simulations until 53 ns. The last 48 (KIT D816V/H/Y/N ) or 65 ns (KIT V560G/D ) were consequently retained for further analysis.
These vectors were consequently used to apply the LFA formalism as described in details in  [41, 63]. To discriminate between correlated and uncorrelated residues, a threshold value P cut was arbitrary chosen for each model in such way that about 1. 0-1. 2% of all LFA cross-correlations were above P cut. P cut was 0. 030 for KIT D816H , 0. 033 for KIT WT , KIT D816V , KIT V560G and KIT V560D , 0. 040 for KIT D816N and KIT D816Y. Distance matrices consisting of the average smallest distances between each residue pairs were computed using the g_mdmat program of GROMACS 4. 5. 3 [65]. Two residues i and j were considered as neighbors if the average smallest distance between them was lower than a given threshold d cut of 3. 6 A ˚. To characterize communication pathways we used the concept of communication propensity  [66]  as described in our previous work  [41].
Results KIT models bearing a gain-of-function mutation either in the A-loop (D816H/Y/N) or in the JMR (V560G/D) were generated from the crystallographic structure of the cytoplasmic region of KIT WT in the auto-inhibited inactive state (PDB id: 1T45)  [34]. For each mutant model, MD simulations were carried out (Table  S1 ) in duplicates (replicas 1 and 2) for 50 or 70 ns. The KIT mutant variants are referenced as KIT D816H , KIT D816N , KIT D816Y , KIT V560G and KIT V560D. For an integral vision of mutation effects in KIT we took into consideration our previously published MD simulations of KIT WT and KIT D816V  [40]. The analysis of these proteins was complemented and the related results were commented together with the newly obtained data to achieve a generic description of the hot-spots mutations effects on KIT.