dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007). As opposed to this sensitivity, the total averaging kernel (dashed black lines) is calculated as i A ij and represents the contribution of each level to the sensitivity at a given altitude i. In order to have a global vision of the results, Fig. 3  shows the global distributions of the degrees of freedom for signal (DOFS, top panels) separately for January (left) and July (right) 2011. These represent the number of independent pieces of information in the measurements and give an estimation of the vertical sensitivity of the retrievals. On average, all DOFS values range between 0. 9 and 1. 2, further indicating that only one level of information can be extracted from the IASI data for HNO 3. However, there are some latitudinal differences, with the DOFS being generally larger in the intertropical belt, with values around 1.
Validation methodology The IASI derived HNO 3 profiles/columns are compared here with reference profiles/columns retrieved from measurements made by ground-based infrared Fourier transform spectrometers (FTIR) for the sake of validation. Note that the FTIR HNO 3 profiles have been used to validate other satellite data sets before  (Vigouroux et al. , 2007; Wang et al. , 2007; Wolff et al. , 2008; Wood et al. , 2002).
FTIR stations and instrument For our study, six stations routinely operating FTIR instruments, which are all part of the Network for the Detection of Atmospheric Composition Change (NDACC, http: //www. ndacc. org) and span a large range of latitudes, were selected for the validation: Thule (76. 5 • N, 69 • W), Kiruna (67. 8 • N, 20. 4 • E), Jungfraujoch (46. 6 • N, 8. 0 • E), Izaña (28. 3 • N, 16. 5 • E), Lauder (45. 0 • S, 169. 7 • E) and Arrival Heights (77. 8 • S, 166. 7 • E). The location of each station is displayed in Fig. 3  (red dots, upper left panel) and details on the operating instrument and retrieval algorithms are given in Table  2. The FTIRs provide data year round for all six stations, except at high latitudes (Thule and Arrival Heights in our case), where the lack of light prevents solar measurements during winter.
All instruments are Bruker spectrometers (120M for Thule and Arrival Heights, 120HR for Jungfraujoch and Lauder and 125HR for Kiruna and Izaña). The algorithms used for the retrieval of HNO 3 profiles are PROFFIT  (Hase et al. , 2004)  for Kiruna and Izaña, SFIT2  (Pougatchev al. , 1995; Rins-land et al. , 1998)  for Thule and Jungfraujoch and SFIT4  (Pougatchev et al. , 1995)  for Lauder and Arrival Heights. An updated Lauder and Arrival Heights HNO 3 data set was used in this study and the updates are detailed hereafter. The Lauder and Arrival Heights retrievals implemented SFIT4, closely adhering to the prescribed NDACC IRWG HNO 3 retrieval strategy (see http://www. acom. ucar. edu/irwg/IRWG_ Uniform_RP_Summary-3. pdf), with the following particularities: at both sites the spectral resolution is 0.
9 ± 0. 5 (Jungfraujoch) and 3. 1 ± 0. 4 (Thule). The a priori profiles are described individually at each station, independent of seasonality. It should be noted that, due to the lower degrees of freedom in the Lauder and Arrival Heights retrievals, there were numerical artefacts in the smoothing operation between the IASI and the FTIR ground-based measurements (see Sect. 4. 2). To reduce these artefacts, a smoothed IASI a priori profile was used as the a priori in the ground-based retrievals in this study. The associated groundbased retrieval a priori covariance matrices (S a ) were also constructed from a smoothed IASI covariance data set. Spectroscopic line parameters were all taken from the HITRAN 2008 database  (Rothman et al. , 2009). Note that IASI retrievals use spectroscopic line parameters taken from the HI-TRAN 2004 database  (Rothman et al.
This way, FTIR measurements with a high number of co-locations do not influence the statistics. The comparison is carried out next for the profiles (Sect. 5. 1) or partial columns (5-35 km; Sect. 5. 2). For the discussion, we rely on the relative difference between the quantities retrieved from IASI and the corresponding ones from the smoothed FTIR data following: with x being the relative difference. Standard deviations are also calculated as follows: x (%) = IASI -FTIR FTIR × 100, (5) σ x = 1 N -1 N i-1 (x i -x) 2 , ( 6 ) with N the number of observations, x i the difference between IASI and FTIR values for the ith observation and x the mean of the differences for all measurements. This allows for the characterization of the variability of the data set. 5 Validation results.
It should also be noted that the differences observed between the two data sets can also be attributed, at least partly, to the difference in the spectral region used by each instrument, the different line parameters  (HITRAN 2004 for IASI and HITRAN 2008  for FTIR) and codes used for the retrievals and the errors of each instrument. While no seasonality was observed in the biases, it could be interesting to conduct a similar study on a larger timescale to account for any potential trend in the bias. IASI data allows remarkable monitoring of year-round HNO 3 concentrations. The global distribution acquired with IASI showed, for instance, a clear latitudinal gradient, with low and relatively constant concentrations at tropical latitudes, and much higher and very variable concentrations at midlatitudes and polar latitudes.
The daily variability also highlighted by the IASI data set can be further investigated due to the capability of the IASI sounder to monitor the atmosphere at both day and night time. The polar processes, including the strong denitrification in the Antarctic, are well monitored with IASI, both spatially and temporally. Overall, the results presented here are extremely encouraging with regard to the use of the HNO 3 data set from IASI to investigate stratospheric processes on local-to-global scales, with particular interest for the polar regions. The long time series that are available from IASI, which will span more than 15 years and be extended with the IASI-NG instrument on EPS-SG  (Clerbaux and Crevoisier, 2013; Crevoisier et al. , 2014) , will be important for monitoring longer-term changes in stratospheric composition and its link to climate.
Acknowledgements. IASI has been developed and built under the responsibility of the " Centre National d'Etudes Spatiales" (CNES, France). It is flown on board the MetOp satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near-real-time data distribution service. The research was funded by the  F. R. S. -FNRS , the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex arrangement 4000111403 IASI. FLOW)  and  EUMETSAT  through the  Satellite Application Facility on Ozone  and atmospheric  Chemistry Monitoring  ( O3MSAF ). G. Ronsmans is grateful to the " Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture" of Belgium  for a PhD grant (Boursier FRIA). Cathy Clerbaux is grateful to  CNES  for financial support.
We thank  U. Raffalski  and  P. Voelger  for technical support at  IRF Kiruna. We would like to thank  Antarctica New Zealand  and the  Scott Base  staff for providing logistical support for the  NDACC-FTIR measurement programme at Arrival Heights. Measurements at Lauder and Arrival Heights are core-funded by  NIWA through New Zealand's Ministry of Business, Innovation and Employment. The contribution by the  University of Liège  has been primarily supported by the  F. R. S. -FNRS , the  Fédération Wallonie-Bruxelles  and the  GAW-CH programme of MeteoSwiss. We further acknowledge the  International Foundation High Altitude Research Stations Jungfraujoch and Gomergrat (HFSJG, Bern). E. Mahieu is Research Associate with F. R. S. -FNRS. Edited by: H. Worden Reviewed by:  E. Remsberg  and one anonymous referee.
INTRODUCTION Ultrafast Shape Recognition (1) (USR) is a molecular shape similarity technique enabling extremely rapid search for molecules with similar 3D shapes. In USR, each shape is encoded by a vector of 12 geometrical features derived from four strategically-located reference points with similar locations in similar molecules, thus providing a common framework for the comparison (2) (each point gives rise to a distribution of atomic distances, which is in turn characterized by its three first statistical moments acting as features). To date, USR has been successfully applied to a range of problems such as enabling real-time queries in integrative molecular databases  (3) (4) (5) (6) (7) ; tabu search for protein conformations  (8, 9) , atomic clusters  (10)  or protein-bound ligands  (11) ; searching for non-peptidic inhibitors of protein-protein interactions  (12) ; polypharmacology prediction  (13)  or enabling the development of other ultrafast methods based on the same principle  (14) (15) (16) (17) (18).
Consequently, searching a molecular database for molecules that most closely resemble the shape of a given query molecule with known activity for a target is a fruitful strategy to identify other molecules with activity for that target. Furthermore, similar molecular shapes can be supported by strikingly different chemical scaffolds, which would lead to the valuable discovery of new chemical series for the target of interest. In this context, USR has discovered a high proportion of innovative inhibitors for molecular targets (falcipain-2  (30) , arylamine NATs (31), DHQase2  (32) , PAD4  (33) , PRL-3  (34) ) and phenotypic targets (colon cancer cell lines  (35) ). In addition to these Nucleic Acids Research, 2016, Vol. 44, Web Server issue W437 prospective validations, USR has also been retrospectively validated for additional molecular targets  (2, 36).
However, the value of having alternative molecular similarity methods available is that these lead to the discovery of different active molecules for the same target  (40). This is also the case even for methods based on the similar principle and screening the same set of molecules, as shown prospectively in this study  (34)  using freely-available and commercial shape similarity methods. In addition, USR-VS has several substantial advantages over the UFSRAT web server. First, USR-VS screens a much larger database comprising 93. 9 million 3D conformers (23. 1 million purchasable compounds). Second, these compounds come from ZINC database, which is not screened by UFSRAT (there are other web servers mining the ZINC database, but these employ different types of virtual screening methods such as 2D fingerprints  (41)  or docking  (42) ).
USR-VS WEB SERVER The purpose of USR-VS is to provide a single resource where the user can bring a 3D conformer of a molecule with activity against macromolecular and/or phenotypic targets of interest and obtain a set of similar molecules ready to be purchased and experimentally validated on those targets. As the underlying ligand-based VS techniques have already been shown  (2, 16, (30) (31) (32) (33) (34) (35) (36)  to excel at identifying new active molecules with innovative chemical scaffolds for a range of targets, it is expected that a high proportion of USR-VS hits will have a similar profile of activity across targets than the query molecule. The description of USR-VS can be broken down into the following areas: preparing the query molecule selected by the user as input data, the employed similarity techniques to identify the most similar database molecules to the query molecule, the construction of the screening database, running the virtual screen on this infrastructure and visualizing the results of the virtual screen.
Input data from the user An SDF file specifying a 3D conformer of the query molecule must be provided by the user to run USR-VS. There are several ways to obtain these files. The user can search for a given molecule, e. g. a marketed drug, in Pub-Chem  (44)  and download the SDF with the modeled 3D conformer from this resource. To use the binding conformation of the query molecule, an SDF with a crystal pose of this molecule may be available for download at the Protein Data Bank  (45). If this is not the case, this binding conformation can be predicted using user-friendly docking tools such as the idock web server  (42). Another route is looking for active molecules for a given target in the ChEMBL database  (46)  and use software implementing a conformer generator, e. g. Frog2  (47) , to obtain their 3D conformers from the downloaded chemical structures.
Screening database A USR-VS run screens 93 903 333 low-energy 3D conformers generated from 23 129 049 purchasable compounds collected from the All Clean subset of the ZINC database  (48). The very large size and diversity of this screening database maximizes the likelihood of containing similar molecules to any given query molecule. We used a recent protocol for conformer generation based on RDKit (http://www. rdkit. org/), which was found to offer the best results in accuracy and second best in terms of efficiency  (49). This includes a post-processing algorithm to discriminate and keep only those conformers of a given molecule that are both energyminimized and conformationally diverse. As a result, an average of four conformers per molecule were generated, with only one conformer retained for more than six million molecules and a maximum of 35 conformers generated for some molecules.
Running a virtual screening This is performed in three simple steps. The first is to upload the SDF file containing the 3D conformer of the selected query molecule. Second, the user must select either USR or USRCAT as the similarity method providing the score with which to rank database molecules (as customary, the score between two compared molecules is defined as the highest score between conformers from each molecule). We recommend prospectively testing hits from both methods, as this typically leads to the discovery of different active molecules for the same target, e. g. (34). Third, clicking the submit button to launch the virtual screen with these choices, which will start as soon as any previous screens from other users have been completed.
These hits, but not all database molecules, were aligned with the query molecule by calculating the 3D rotation that superposes the four reference points of each molecule. In addition, any hit molecule can be interactively translated, rotated and zoomed in/out to change the relative orientation of the compared molecules by dragging and scrolling with the mouse (selected orientations of any molecule can be exported as a PNG image). This permits an appreciation of the degree of 3D similarity of the compared molecules, which is intended to help the user decide which hits to purchase and how to purchase them to experimentally measure their activity against selected targets of the query molecule. In addition, the chemical structures of the compared molecules are also shown in order to identify cases of chemical scaffold hopping among the hits.
Third, USR-VS employs a multithreading design running on just n = 12 CPU cores, with each core screening an average of P = 16 database molecule chunks of size N/(p*n), where N = 23 129 049 compounds and p was set as the lowest parameter value ensuring a dynamic balance load across the cores (unbalances are due to uneven number of conformers between chunks and uneven share of CPU time among threads). Lastly, a heuristic to reduce the time to process conformers while comparing two molecules. In this case, we compare the conformer of the query molecule with the c conformers of the database molecule (c ranges from 1 to 35). Each of these c dissimilarity scores takes the form of a sum of absolute differences (SAD) between the two 60dimensional vectors. Since only the conformer with the min-imum SAD is required (i.
EXAMPLES Example 1: Fluspirilene Fluspirilene is an antipsychotic drug contained in our screening database. Figure  1  shows the results of using USR to search for similarly shaped molecules to fluspirilene. This query molecule is shown on the left canvas, whereas information about the completed virtual screen is displayed in a table above. Hits can be interactively visualized with iview. On the right canvas, the top 100 USR hits from the virtual screen can be displayed by pressing the corresponding numbered button below (e. g. number 6 for the seventh most similar USR hit). The first USR hit is fluspirilene itself as indicated by a Tanimoto score of 1, which is one of the 23. 1 million molecules in the screening database. The view of the displayed molecules can be changed with the keyings located at the bottom left.
FUNDING French Government Investissements d'Avenir program  A* MIDEX grant  [ ANR-11-IDEX-0001-02 ];  Chinese University of Hong Kong Direct Grant ;  Research Grants Council of Hong Kong GRF  Grant [414413, 4055008, 4055046]. Funding for open access charge:  Research Grants Council of Hong Kong GRF Grant  [ 414413 ,  4055008 ,  4055046 ].
We first compared the reported perception of embodiment across our experiment (measured as the average score of the embodiment questionnaire, see the electronic supplementary material). Because some groups of data did not conform to a normal distribution (Shapiro-Wilk test of normality p ≤ 0. 05), we used the aligned rank transform (ART) for non-parametric factorial data analysis for a pre-processing step to align data before applying averaged ranks  [24]. After which, we conducted a 2 × 2 within subject ANOVA. The first factor was the visuo-tactile brushing synchrony, which had two levels: synchronous and asynchronous. The second factor was brushing type, which was either other or self. We observed a significant main effect of the visuo-tactile synchrony, F 1,56 = 102. 096, p < 0. 0001, but no main effect in regard to the brushing type, F 1,56 = 0.
It is though not clear how motor-sensory predictions for the real hand are modified by the RHI; whether the motor-sensory mappings corresponding to the real hand are maintained in parallel to new mappings to the rubber hand, or slowly lost. Recent observations of skin temperature decrease in the real hand during RHI  [46]  would suggest the latter to be true. But further studies are required to clarify this issue. Ethics. The study was conducted with ethical approval of the National Institute of Advanced Industrial Science and Technology (AIST) in Tsukuba, Japan. In both studies, participants gave their written informed consent prior to participating. Data accessibility. The datasets supporting this article are available at the Dryad Digital Repository: http://dx. doi. org/ 10. 5061/dryad.
Results A Bacterial TGT Variant, TgtA5, Must Be Involved in a PreQ 0 -Dependent Pathway Different from Q Synthesis. Analysis of the distribution of all Q synthesis genes in bacteria was performed using the "dpd cluster" subsystem in the SEED database  (23). Analysis of ∼12,000 bacterial genomes showed that some organisms, such as pathogenic strains of E. coli (strain E22) or Salmonella enterica subsp. enterica serovar Montevideo, contained two homologs of the tgt gene, whereas most other sequenced E. coli or Salmonella species contained only one (SI Appendix, Table  S1 ). Synteny analysis revealed that one of the two tgt genes clustered with the Q synthesis gene queA and encoded the experimentally characterized bTGT enzyme (24) (SI Appendix, Figs. S1  and S2A ), whereas the other, that we named tgtA5 (later changed to dpdA), was found in a different neighborhood context (Fig.
S11 ), a region that had been previously identified as highly variable  (31). A pair of 19-bp direct repeats flanking the dpd cluster was identified (SI Appendix, Fig. S11 ), again indicative of a genomic island  (32, 33). More recently, the sequencing of a large number of Salmonella strains confirmed this region as a novel variable island (SGI2) that contained different types of restriction systems, toxin/ antitoxin modules, and mobile elements  (34). Of the sequenced S. Montevideo strains analyzed, 92% contain the dpdA cluster at the SGI2 position. Unlike the dpd islands of S. Montevideo and E. coli, neither tRNA genes nor direct repeats were identified in the region surrounding the K. radiotolerans dpd cluster. However, the region is flanked by mobile element proteins (e. g. , a transposase-like protein and resolvase protein), and the IslandViewer software  (35)  identified this cluster as a genomic island.
S12 ). In the characterization of phage 9g, Kulikov et al. (38)  speculated that the restriction endonuclease-resistant nature of the phage DNA suggested it was heavily modified, and they proposed that tgt and preQ 0 synthesis genes were involved in inserting Q into the DNA. To evaluate the prevalence of tgt paralogs and PreQ 0 synthesis genes in phages, a similarity-based search was performed on all available phage genomes in the National Center for Biotechnology Information database. This revealed 36 bacteriophages and two archaeal viruses that encode a Tgt-like protein (SI Appendix, Table  S5 ). Multiple sequence alignments of the phage Tgt-like proteins allowed the identification of catalytic residues (two conserved Asp residues) and of the preQ 0 -binding pocket (SI Appendix, Fig. S13 )  (19).
ACKNOWLEDGMENTS. We thank  Rémi Zallot ,  Zden ek Paris , and  Juan Alfonzo  for help with the determination of the presence of queuosine in tRNA;  Sophie Alvarez  for LC-MS/MS analyses of tRNA;  Anne Oudard  for sharing her local database of complete and ongoing genome projects;  Max Teplitski  for the S. Montevideo strain; and  Paul Gulig  and  Kelly Rice  for use of laboratory facilities. Mass spectrometric analyses were performed in the Bioanalytical Facilities Core of the  MIT Center for Environmental Health Sciences , which is supported by  National Institute for Environmental Health Sciences  Grant  ES002109. This work was supported by the  National Institutes of Health  Grant  GM70641  (to V. d. C. -L. ),  Deutsche Forschungsgemeinschaft  (S. M. K. ),  French National Agency for Research  Grant  ANR-10-BINF-01-01  and the  Institut Universitaire de France  (to C.
33 We end up with approximately 14,000 firms over the period 1995-2007 (about 157,586 observations). The smallest observed unit of a firm is the plant, and we have about twelve percent multi-plant firms. However, the municipality information is available only for firms' headquarters. The employer-employee matched data allows us to gather substantial information on both firms' workforce characteristics and firms' characteristics, which we summarize in Table  1. Firm productivity is calculated in the main analysis as value added per employee in logarithmic scale (i. e. , labor productivity), but in the robustness checks, we also introduce a TFP measure based on Amiti and Konings (2007). 34 The available information is as good as it gets to control for both the composition and the quality of firms' workforce as well as for firm heterogeneity.
34 We use accounting data available in the Firm Statistics Registers to construct this measure. Customs data contains export sales and the number of exported products at the firm level, available at specific destinations and also aggregated over all destinations. Exports are recorded in Danish kroner (DKK) according to the 8-digit Combined Nomenclature. 35 To make the classification of products consistent across time and to minimize potential measurement errors, we use the product classification at the 3-digit level. We depart from the typical approach in the literature measuring the firm's export experience by means of the lagged export status, and compute, in any given year, the (cumulative) number of years a firm has been exporting for (export experience).