dataesr/scientific-paragraphs-categorization huggingface dataset:
Whereas the maximum bias is 'only' ∼ 3%, this is already significant compared to other AMS-02 uncertainties. Moreover, in AMS publication, the uncertainty associated with the conversion is estimated to range from 1% (lowest energy) to 4% (highest energy), which does not reflect our results. For all these reasons, we conclude that AMS-02 data, and in general all data, should be fit in their native energy scale in order to avoid a non-necessary bias introduced by converting the data to another energy scale. We E k/n [GeV/n] 4 3 2 1 0 1 2 3 4 Residuals [%] B/C converted from R to E k/n , using < A > B = 10. 7 < A > C = 12. 0 < A > B =.
Acknowledgements. We thank our 'cosmic-ray' colleagues at  LAPTh, LAPP, LUPM , and  Universidade de São Paulo , and in particular  P. Serpico  for stimulating discussions and feedback that helped us clarify and improve the presentation of the methodology presented in this paper. This work has been supported by the ' Investissements d'avenir, Labex ENIGMASS ' and by the  French ANR , Project  DMAstro-LHC ,  ANR-12-BS05-0006. The work of Y. G. is supported by the IISN, the  FNRS-FRS  and a  ULB ARC. The work of M. B. is supported by the  European Research Council (ERC)  under the  EU   Seventh Framework Programme  (FP7/2007-2013) /  ERC Starting Grant  ( Agreement No 278234 - " NewDark " project).
Secondly, select a dataset to fit (e. g. B/C AMS-02 data) and perform the fit. Thirdly, use the best-fit model to simulate data close to the real data in the following way: (i) interpolate model values at data energies, y model a normal distribution G(0, 1), and use the latter and σ data error) to form the mock data values k (data L. Derome et al. : Fitting B/C data in the AMS-02 era (a cookbook) and its input ingredients (e. g. a specific cross-section dataset). k (e data k ); (ii) draw at each e data k a value from.
Data Reference data. We used the data in the figures of  [18]  and  [17]. Usage of the data was kindly approved by the authors. In order to apply our model to this dataset, we suppose that color-tuned cells in the hypercolumns are of three types, each tuned to one particular 1D color axis, and that color matching is made independently on each axis. Because the data mainly involved s chromaticity, we applied the model only along the s axis independently of l and Y axes. Personal data. Throughout the experiments, we used two particular test and comparison backgrounds filled with Yellow (HSL = (60˚, 50%, 50%)) and Gray (HSL = (0˚, 0%, 50%)), respectively. A. S. performed color matching experiments as in Fig  3  (in a spirit similar to  [3]  who worked with achromatic colors). Experiments used a standard computer screen, in a dark room.
Squares were presented against a black background. Asymmetric color matching was repeated for several colors tested with the Yellow surround, at regularly spaced locations in the three-dimensional HSL color space. We obtained a vector field of color shifts in the HSL space associated to the pairs (c test , c match ), as seen in Fig  5  (note that representing shifts as a vector field is not new  [9] ). Our final data was obtained by averaging the shifts along the Luminance coordinate, and lived in the chromatic disk only (see the Results section), as we are mainly interested in chromatic shifts. The treatment of luminance seems to be more complex, and has to be separate from that of chromaticity. To apply our model to this dataset of 2D shifts, we suppose that color-tuned cells in the hypercolumns are of two types, one tuned to 2D HSL chromaticity, and the other to 1D Luminance.
Acknowledgments We thank  Patrice Jegouzo  for the head devices and headpost development and production. We also thank  P. Calvo ,  A. Mialot , and  E. Idoux  for their help in the development of previous versions of the device and VVM protocol. This work was funded by the  Centre National des Etudes Spatiales , the  CNRS , and the  Université Paris Descartes. J. C. and M. B. receive support from the  French   ANR-13-CESA-0005-02. F. F. B. and M. B. receive support from the  French   ANR-15-CE32-0007.
With the above settings, it was verified that the computational errors did not exceed 0. 1%. The input geometrical parameters were the same as those used for the experiment (Table  1 ). Although the relevant data can be found in the bibliography, the velocity and attenuation of Plexiglas were determined experimentally, in order to ensure the best possible predictive accuracy. The parameters used to compute the modeled acoustic field of the scanner are summarized in Table  2. Surfaces 1 S and 2 S , were approximated as the projections of emitter 0 S and receiver 3 S onto the surface of the Plexiglas solid. Since the emitter and receiver were located very close to the solid sample, they were considered to be circular (they are in fact slightly elliptical  [23, 24] ). See Eq. (A. 5) 1 0 s s s f     dB/cm   1 3.
The camera will comprise 265 modules, each consisting of 7 photomultiplier Tubes (PMTs) and a Front-End Board performing the data capture, sending the data over the Ethernet after the trigger decision at rates up to 10 kHz. This contribution provides an overview of the status of the first NectarCAM camera currently under integration in CEA Paris-Saclay (France). Furthermore, we will discuss the calibration strategies and present performance results from the CEA Paris-Saclay test bench and from the first data taken under a real sky on the prototype of medium sized telescope (MST) structure in Adlershof (Germany). 36th International Cosmic Ray Conference -ICRC2019-July 24th -August 1st, 2019 Madison, WI, U. S. A.
Dark room test bench The NectarCAM test bench consists of a ∼12 m long dark room at the NectarCAM integration hall at CEA Paris-Saclay. The camera is equipped with the full readout, data acquisition and trigger electronics as well as its mechanical structure and cooling system. The two light sources are a Light Emitting Diode (LED) pulser and a continuous Night Sky Background (NSB) emulating light controlled by an OPC Unified Architecture (OPCUA) server in the control room. The flatness of both light sources was evaluated for the full camera scale with the help of an x,y table.
Timing measurements in shower data During the observation campaign in Adlershof, the NectarCAM camera successfully observed several hundred thousands of atmospheric Cherenkov events. These data will provide precious information to understand the camera's performances. We will present here the steps of a simple atmospheric shower analysis, focusing on the value added by the timing information. Those steps are summarized in Fig. 7  During the observations, the TIB sends periodic trigger signals at a rate of 100 Hz. These events are used to estimate the baseline of the PMTs. The floating average of each 60 samples of the readout window for each pixels is subtracted to the signal from the real events. The baseline subtraction is actually done during offline analysis but it is planned to be done during the writing of the data.
ACKNOWLEDGMENTS This work was conducted in the context of the  CTA NectarCAM Project  and has been carried out thanks to the support of the  P2IO ,  OSUG2020  and  OCEVU Labex  ( ANR-11-LABX-0060 ) as well as the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR. We gratefully acknowledge support from the agencies and organisations listed in this page: http://www. cta-observatory. org/consortium_acknowledgments/.
OBSERVATIONS AND DATA REDUCTION NGC 1313 X-1 was recently observed in an extensive campaign with XMM-Newton (PI: Pinto), Chandra (PI: Canizares) and NuSTAR (PI: Walton) that is described in  Pinto et al. , submitted and Walton et al. , submitted. In this letter, we focus on the timing analysis of the XMM-Newton observations. While all 750 ks of data were analyzed, only the first half of obsid 0803990101 (70 ks) showed sufficient variability , and thus is the focus of this work. We analyze data from the XMM-Newton EPIC-pn camera  (Strüder et al. 2001 ) because of its superior effective area and time resolution over the MOS. We reduced the data using the XMM-Newton Science Analysis System (SAS v. 18. 0. 0) and newest calibration files. We begin with the observation data files and followed standard procedures.
The observations were taken in Full Frame Mode. We produce a  circular source extraction region of 35 arcsec in radius, and a background region of 80 arcsec. To avoid periods of high background, we removed time intervals at the beginnings or ends of the observation where the 12-15 keV light curve exceeded 0. 4 counts/s. The background was low throughout the remaining observation. The response matrices were produced using rmfgen and arfgen in SAS. The pn spectra were binned to a minimum of 25 counts per bin to allow for the use of χ 2 statistics for spectral modelling. For the timing analysis, we created background subtracted light curves in different energy bins with the same start and end times with the SAS tool epiclccorr in 1 second bins.
ACKNOWLEDGEMENTS Thank you to the referee  Thomas Dauser  for helpful comments that improved this work. EK acknowledges support from  NASA  Award Number  80NSSC19K0176. CP is supported by  ESA Research Fellowships. TPR thanks  STFC  for support as part of the consolidated grant  ST/K000861/1. CRC acknowledges support by the  Smithsonian Astrophysical Observatory (SAO)  contract  SV3-73016  to MIT, which is in turn supported by  NASA  under contract  NAS8-03060.
NODULE TRANSCRIPTOME DATABASES Transcriptional reprogramming associated with nodule formation has been studied in many legume species, including M. truncatula (see below), Medicago sativa  (O'Rourke et al. , 2015) , pea (Pisum sativum)  (Alves-Carvalho et al. , 2015) , chickpea (Cicer arietinum)  (Kant et al. , 2016; Kudapa et al. , 2018) , Trifolium pratense  (Trujillo et al. , 2019) , L. japonicus  (Verdier et al. , 2013; Høgslund et al. , 2009; Takanashi et al. , 2012; Colebatch et al. , 2004; Kelly et al. , 2018) , G. max  (Brechenmacher et al. , 2008; Severin et al. , 2010) , common bean (Phaseolus vulgaris)  (O'Rourke et al. , 2014) , Sesbania rostrata  (Capoen et al. , 2007) , Lupinus spp. (Keller et al. , 2017) , Aeschynomene evenia  (Gully et al. , 2018) , and peanut (Arachis hypogaea), Arachis duranensis, Arachis ipaensis  (Clevenger et al.
php ; Benedito et al. , 2008). MtGEA compiles a broad range of developmental and environmental conditions, including different plant organs and tissue types, biotic-, abiotic stress conditions, and hormone treatments and is therefore currently the richest M. truncatula resource for the analysis of expression patterns and specificity of genes of interest and the discovery of genes expressed under certain conditions or in organs of interest. Ordering the MtGEA dataset with a clustering algorithm results in the identification of tissue-specific gene sets (Figure  2A ). The heat map discloses large clusters of genes that are expressed in different plant organs, leaves, stems, flowers, seeds, hypocotyls, roots, mycorrhiza, and nodules. This analysis also revealed that nodules and mycorrhiza are primarily typified by the expression of different sets of genes (Figure  2B ), despite the shared CSSP module of regulators that controls both nodule and mycorrhiza formation and a number of common target genes that are activated immediately downstream of the CSSP during both types of interactions  (Hogekamp et al.
, 2011). For research on rhizobium-legume symbiosis, the MtGEA dataset can be complemented by expression data derived from laser-capture Because nodules form de novo on roots starting from fully differentiated cortical, endodermal, pericycle, and epidermal cells, the nodule transcriptome is usually defined by the genes showing differential expression in nodules versus roots. A recurring theme in each transcriptome study on legumes, Parasponia, and actinorhizal plants is that nodule formation is accompanied by the differential expression of hundreds to thousands of genes. These sets of nodule-enhanced genes are composed in part of genes that are more strongly expressed in nodules than in other organs but importantly, these gene sets also contain large numbers of absolutely nodule-specific genes (see below).
, 2014). As discussed below, the large majority of these genes are expressed in differentiating or mature symbiotic cells (Figure  3C ). Shannon entropy analysis of the AGED, PvGEA, LjGEA, and SoyBase datasets (Figure  3D ) confirmed the notion that nodule formation in M. sativa, P. vulgaris, L. japonicus, and soybean is also associated with the expression of large numbers of low Shannon entropy genes that are (nearly) uniquely expressed in that organ. Soybean/P. vulgaris, L. japonicus and M. truncatula/M. sativa belong to different clades of legumes: the Milletioids, Robinioids, and IRLC (inverted repeat-lacking clade), respectively. Thus, the reliance on large gene sets that are uniquely expressed in the nodule is not restricted to Medicago spp. and is not a specific feature of the particular nodule types formed by these species, which are of the indeterminate type and induce terminal bacteroid differentiation.
, 2009a). This systematic screening identified 39 genes that are upregulated during nodule formation. A gene encoding a MYB TF was found to be expressed in vascular tissues and not in symbiotic nodule cells. Although the remaining genes were not further characterized, they constitute a collection of putative regulators in symbiotic nodule cells. Overall, very little is thus known about how the massive transcriptome switch in symbiotic nodule cells is directly regulated. Libault et al. (2009b)  and  Young et al. (2011)  cataloged all TFs in the M. truncatula genome. Analyzing the MtGEA and Symbimics databases using nodule-specific or nodule-enhanced expression and expression in symbiotic cells (IZ and zone III) as criteria resulted in the identification of a set of interesting TFs that might be involved in gene-specific regulation in symbiotic cells (Supplemental Figure  1 ).
japonicus  (Holt et al. , 2015). The corresponding databases represent promising sources for the future discovery of novel RNAs that control gene expression in nodules. Finally, long non-coding and antisense RNAs (lncRNAs) might also be involved regulating nodule-specific gene expression. LCM RNA-seq analysis of M. truncatula nodules (SYMBIMICS dataset) has revealed a surprisingly large number (over 1000) of lncRNAs expressed in nodules. Most of these are active in differentiating or mature symbiotic cells. Many are located in SRIs, and the expression profiles of many of these lncRNAs were correlated with the expression of the closest mRNA-encoding genes in the genome, suggesting these lncRNAs play regulatory roles  (Pecrix et al 2018). For example, the TF EFD is expressed in young symbiotic cells in the distal and proximal infection zones of the nodule.
C. The SYMBIMICS dataset (Roux et al. , 2014) for nodule-specific genes from MtGEA (gene cluster labeled in red in panel A). The columns correspond to the nodule tissues: I, meristem; IId, distal infection zone or nodule zone II; IIp, proximal infection zone; IZ, interzone between nodule zones II and III; III, fixation zone or nodule zone III. Color-codes as in panel A. The data sets used to generate the heatmaps are available upon request.
C. The SYMBIMICS dataset (Roux et al. , 2014)  for nodule-specific genes from MtGEA (gene cluster labeled in red in panel A). The columns correspond to the nodule tissues: I, meristem; IId, distal infection zone or nodule zone II; IIp, proximal infection zone; IZ, interzone between nodule zones II and III; III, fixation zone or nodule zone III. Color-codes as in panel A. The data sets used to generate the heatmaps are available upon request.
ACKNOWLEDGMENTS Work in the laboratory of P. M. is funded by the  Agence Nationale de la Recherche , grants n°  ANR-17-CE20-0011-02  and  ANR-16-CE20-0013-03. The P. M. lab also benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). Research in the A. K. /E. K. laboratory is supported by the  Hungarian National Office for Research, Development and Innovation  through grants  OTKA 120120/119652  (to A. K),  GINOP 2. 3. 2-15-2016-00014Evomer  and  GINOP 2. 3. 2-15-2016-00015I-KOM  (to E. K) as well as by the Balzan research grant to E. K. Collaboration between the two institutions is supported by the  co-operative Balaton program  of  Campus France  ( n°39661TA ) and the  Hungarian National Office for Research, Development and Innovation  ( 2017-2. 2. 5-TÉT-FR-2017-00002 ).
Catch efficiency Catch efficiency data have been estimated in several studies on various flatfish species including Pleuronectes platessa and Solea solea. The catch efficiencies are most likely overestimated in these studies given the experimental protocols (e. g  Kuipers 1975). Consequently, fish abundances are probably underestimated, which fits with the conservative approach. First, available data have been compiled per study and then averaged over the different studies. Solea solea value has been attributed to Dicologlossa cuneata given their close phylogeny (Soleidae family). Data on roundfish species are scarcer but conservative data are suggested for Merlangus merlangius and Trisopterus luscus. This data has been associated to the roundfish Mullus surmulutus and Merluccius merluccius.
Energy density An investigation has been done on all articles of the journals "Aquaculture", "Aquaculture nutrition" and "Aquaculture research". A complementary research in all journals has been done with the keywords "Body composition" "energy density". The body composition is often described as a proportion of proteins, lipids, glycogen, and water. Energy conversion coefficients have been compiled from different studies  (Brett & Groves 1979 , Brafield 1985 , Cho & Kaushik 1990 , Jobling 1993). Proteins: 23. 7kJ. g -1 Lipids: 36. 3kJ. g -1 Glycogen: 17. 2kJ. g -1 Water: 0kJ. g -1 As adult body composition varies with the reproduction season, only juvenile data are used. Pelagic fish data are rejected as this kind of fish has a higher fat concentration. Data corresponding to fish fed with high fat food are rejected.
All data referring only to the flesh or the liver were also rejected. Among the 186 data collected, 62 referring to the fish taxa Gadidae, Perciformes, Pleuronectidae, and Soleidae were used. The average and the standard deviation of the corresponding taxa are attributed to each species. 25 references are used  (Pandian 1970 , Holdway & Beamish 1984 , Smith et al. 1986 , Hidalgo et al. 1987 , Costopoulos & Fonds 1989 , Marais 1990 , Imsland et al. 2000 , Lee et al. 2003 , Person-Le Ruyet et al. 2004 , Dias et al. 2004 , Rosenlund et al. 2004 , Tibbetts et al. 2005 , Rema et al. 2008 , Borges et al. 2009 , Silva et al. 2010 , Ding et al. 2010 , Gatta et al. 2011 , Li et al. 2012 , Marinho et al. 2014 , Guerreiro et al. 2015 , Bonvini et al. 2015 , López et al. 2016 , Zhang et al. 2016 , Li et al.
Gross conversion efficiency (growth-to-food intake ratio) An investigation has been done on all articles of the journals "Aquaculture", "Aquaculture nutrition" and "Aquaculture research". A complementary research has been done in all journals with the keywords "growth efficiency energy", "gross growth efficiency energy", "gross conversion efficiency energy", "food conversion efficiency energy", "feed conversion efficiency energy". Data collected in winter are rejected as lower temperature gives lower conversion efficiency (e. g. Zanuy & Carrillo 1985). When conversion efficiency data are estimated on a temperature gradient, we take only values around the optimal temperature. When data refer to weight conversion efficiency, we convert the production and the consumption in energy when possible.
Gross conversion efficiencies are higher for young fish as no energy is allocated to reproduction. Thus, we use only gross conversion efficiencies referring to fish juveniles  (Mateo 2007). We used 189 data referring to the fish taxa Gadidae, Perciformes, Pleuronectidae, and Soleidae. As these data mostly come from aquaculture studies, they are expected to be a bit higher than the gross conversion efficiency occurring in natural environment. The exploitation efficiencies are consequently likely underestimated. The average and the standard deviation of the corresponding taxa are attributed to each species. 31 references are used  (Pandian 1970 , Edwards et al. 1972 , Marais & Kissil 1979 , Hidalgo et al. 1987 , Fonds et al. 1992 , Hamerlynck & Hostens 1993 , Lankford & Targett 1994 , Buckel et al.
The multiplex PCR kit (Qiagen, Hombrechtikon, Switzerland) was used with the following primers: 0. 5 μl of 100 μM C1-J-1718 mod (5'-GGA GGA TTT GGA AAT TGA TTTG-3') and 0. 5 μl of 100 μM C1-N-2191 mod (5'-GTA AAA TTA AAA TAT AAA CTT CTGG-3') in final reaction volumes of 50 μl. A plasmid containing the target sequence of C. imicola was used as positive control and sterile water as a negative control  [31]. The Qiagen MinElute kit (Qiagen, Hombrechtikon, Switzerland) was used to purify amplicons, and sequencing was done at Synergene (Schlieren, Switzerland). Chromatograms were quality checked and edited with Finch TV (finchtv. software. informer. com) and compared against the GenBank database using BLASTn  [32]  and BOLD  [33]. Similarities higher than 97% were considered as a species match.
Interestingly, C. impunctatus Goetghebuer was found in Bulgaria and Greece, but not in our study, possibly due to the sampling period. Our results did not show the presence of C. imicola, the main BTV vector in the Mediterranean basin, which is in agreement with findings in Albania  [21] , Bosnia and Herzegovina  [22]  and Bulgaria  [20]. This implies the role of species other than C. imicola in the transmission cycle of BTV in the investigated locations in Serbia. Another study in northern Europe also identified that not a single specimen of C. imicola was detected amongst 100,000 Culicoides collected in France, Belgium and Luxemburg  [44]. To the best of our knowledge, we describe the first data of host analysis for Culicoides in Serbia. Our results suggest for most species identified in this study have a mammophilic feeding behaviour, but interestingly, blood of a bird was recorded in one of the samples.
Acknowledgments We thank  Jeannine Hauri ,  Anca Paslaru ,  Dr Felix Grimm  ( Institute of Parasitology, VetSuisse Faculty, Zurich, Switzerland ) for technical assistance and training. We are also very thankful to  Dr Samuel Furrer  ( Zoo Zürich, Switzerland ) and  Dr Andrea Vögtlin  ( Institute for Virology und Immunology (IVI), Bern, Switzerland ) for scientific cooperation and to  Dr Jolene Karlson  ( FLI, Greifswald, Germany ) for thorough revision of the English language. SCOPES AMSAR training group: 4  Adriana Aniță , 4  Ioana Alexandra Anton ,.
2% D 2 O solvent that cancels out MS (see above). Fig. 5(b)  shows the rocking curve of OA-4-4 fitted with a Lorentzian distribution, and, using equation (  11 ), a value of L z = 1. 49 mm has been found. L z corresponds to the ordering distance, or grain size, in the longitudinal direction, i. e. along the pore channel. This value is of the same order of magnitude as the transverse size D (0. 66 mm for OA-4-4) determined previously. Fig. 5(c ) shows the different L z obtained for OA-4-4, OA-10-4 and OA-22-4. L z appears to be independent of the real length L of the AAOs, an observation consistent with the superimposition of I(q) in Fig. 5 (a). Similar analyses have been performed for OA-18-4 and OA-18-11, and L z values of 1. 74 and 1. 33 mm, respectively, have been found. These results show that L z is a critical parameter to fully interpret the SANS data and, in the following fitting, we will use the values of L z as the length in equation (5).