dataesr/scientific-paragraphs-categorization huggingface dataset:
17  on six Ceroplastes species from China. For COI, we obtained for each species one distinct haplotype while for 28S only two species were successfully sequenced. Moreover, the interspecific distance within COI haplotypes of the three species ranged from 12. 5% to 17. 5% which is superior to the minimum interspecific distance observed (12. 5% -Fig. 3A ) and congruent with the average intraspecific distance (12. 2%) found by Deng et al. 17. Overall, the combination of these COI and 28S markers has repeatedly proved useful for routine identification and DNA barcoding of Coccidae, Diaspididae and Pseudococcidae  13, 15, 32  , with 28S providing high rates of successful PCR and signal to easily distinguish most species and COI providing more polymorphism to distinguish closely related species and feed large DNA-barcodes databases relying on the COI Folmer region.
A. nerii is a cosmopolitan and polyphagous pest that comprises multiple cryptic parthenogenetic and sexual taxa  34, 35. The Chilean diversity of A. nerii could be the result of local adaptation or of multiple introductions of the different lineages from various source populations. As parthenogenetic lineages are known to occur in the A. nerii complex  34, 35  , locating parthenogenetic lineages in Chile could be an advantage for national biocontrol manufacturers as rearing parthenogenetic scales generally enable higher population growth rates and subsequently more cost-efficient production of parasitoids. Further investigations should be dedicated to look for this trait within the Chilean lineages due to low-quality comparison with GenBank sequences. Direct observation or testing the presence of the endosymbiont Cardinium 35 could be two ways to detect parthenogenesis within A.
The group A had the greater distribution (20°S to 39°S) and number of host-plants (9 species) than the group B sampled from 29°S to 33°S on three host-plants. Furthermore, the unique haplotype of 28S matched at 99. 3% with a sequence from Coccus formicarii (GenBank accession: JX866687). The study of Lin et al. 31  suggested the genus Coccus might not be monophyletic. New studies of this group including South American samples may provide insights into the species delineation in this genus. The black scale, Saissetia oleae, also presented high diversity with six haplotypes for COI and nine haplotypes of 28S. The 28S intraspecific distance reached 0. 6% which was the maximum value for this distance (Fig. 3A ). The black scale is a polyphagous species, widely distributed around the world and one of the most notorious Coccidae pests in Chile  37, 38.
Methods Sample collection. In total, 116 populations of Diaspididae and 112 populations of Coccidae were sampled along a gradient running from Arica in northern Chile (18°S) to Frutillar in the south (41°S), between January 2015 and February 2016 (Fig. 4 ). The minimum distance between two sampled sites was 1 km. We collected up to 10 adult females from the Diaspididae or immature instars and young females from the Coccidae from crops, indigenous or endemic trees and shrubs and ornamental plants. The collected insects were preserved in 95% ethanol and stored at -20 °C for molecular analysis and morphological identification. Details about the samples collected including sampling locations, host plants, and dates, are available in Supplementary Data 1. DNA extraction and amplification. When possible, we analyzed two individuals from each sample collected.
The, PCR products were then sent to Genewiz (UK, Essex), for bidirectional sequencing by capillary electrophoresis on an ABI 3130XL automatic sequencer (Applied Biosystems, Foster City, CA, USA). The consensus sequences were provided by Genewiz. Chromatograms were visualized with SeqTrace 0. 9. 0  41  to check nucleotide variations. The sequences were deposited in GenBank (COI accession numbers: KY084933 to KY085394 and 28S accession numbers: KY085395 to KY085889 -Supplementary Data 1).
Phylogenetic analysis. We considered sequences differing by one or more nucleotides to correspond to different haplotypes. Haplotype alignment was performed with MEGA version 7  42  with the CLUSTALW method  43. Blastn queries against NCBI GenBank, neighbor-joining tree generation and bootstrapping were performed on the haplotypes of each gene with the R 44 : libraries "ape"  45  , "ade4"  46  , and "phangorn"  47. The intraspecific and interspecific pairwise distances were calculated with the R package 'adhoc'  48. The Kimura two-parameter nucleotide substitution model (K2P)  49  was used for all analyses. Species delineation was performed on the concatened 28S and COI sequences with the online version of Automatic Barcode Gap Discovery (ABGD)  50  , with a prior maximal distance P = 0. 001 and a Jukes-Cantor MinSlope distance of 1.
Morphological identification. In the laboratory, we mounted one to ten specimens per COI and 28S haplotype on slides, as follows: specimens were macerated in 10% KOH, washed in distilled water, stained in a mixture of fuschin acid, distilled water, glycerol and lactic acid, dehydrated in acetic acid and lavender oil and mounted in Canada balsam  32. All the slide-mounted specimens were deposited in the ANSES collection at Montferrier-sur-Lez and most were also deposited in the entomological collection of the Chilean National Museum of Natural History (MNHN) of Santiago, Chile (Supplementary Data 1). Several taxonomic and ecological articles were used for the identification of species and establishment of their distribution  37, 51-55.
Acknowledgements The authors would like to thank all the growers that allowed us to access into their orchards to collect samples. We are grateful to  Paula Molina  and  Angela Romo  for their technical help in the laboratory. We are also grateful to the persons who helped us collect samples through Chile:  Hugo Herrera ,  Francisco Bugueño ,  Sebastian Catalan  and  Isaac Vera  from  Xilema S. P. A. (Quillota) ;  Claudio Salas  from  Instituto de Investigaciones Agropecuarias (Intihuasi -La Serena );  Marta Godoy  from  Cabilfrut (Ovalle) ;  Leonardo Fuentes  from  Pontificia Universidad Católica de Valparaíso (Quillota) ;  Alda Romero  from  Pontificia Universidad Católica de Chile (Santiago) ;  Jean-Claude Malausa  from  Institut National de la Recherche Agronomique (Sophia-Antipolis, France ).
Despite several studies on temperature trends in the tropopause region, a comprehensive understanding of the evolution of temperatures in this climate-sensitive region of the atmosphere remains elusive. Here we present a unique global-scale, long-term data set of high-resolution in situ temperature data measured aboard passenger aircraft within the European Research Infrastructure IAGOS (In-service Aircraft for a Global Observing System; http: //www. iagos. org). This data set is used to investigate temperature trends within the global upper troposphere and lowermost stratosphere (UTLS, < 13 km) for the period of 1995-2012 in different geographical regions and vertical layers of the UTLS. The largest number of observations is available over the North Atlantic. Here, a neutral temperature trend is found within the lowermost stratosphere.
This contradicts the temperature trend in the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis, in which a significant (95 % confidence) temperature increase of +0. 56 K decade -1 is found. Differences between trends derived from observations and reanalysis data can be traced back to changes in the temperature difference between observation and model data over the period studied. This study underpins the value of the IAGOS temperature observations as an anchor point for the evaluation of reanalyses and its suitability for independent trend analyses.
Petersen (2016)  showed that at flight level the errors of temperature and wind in the 3-48 h forecast were reduced by nearly 50 % when assimilating data from passenger aircraft. However,  Ballish and Kumar (2008)  and  Drüe et al. (2008)  identified that the AMDAR aircraft temperature is strongly affected by a warm bias, which can fluctuate by altitude, aircraft type, and phase of flight, while the reason for this bias is not fully understood  (Ingleby et al. , 2016). Previous studies already used the passenger aircraft temperature measurements from MOZAIC (Measurement of ozone and water vapor by Airbus in-service aircraft  Marenco et al. , 1998)  for intercomparison with GPSRO measurements and ECMWF (European Centre for Medium-Range Weather Forecasts) analyses. Bortz et al. (2006)  analyzed MOZAIC temperature measurements at cruise altitude from 1994 to 2003 within the tropical region.
Schmidt et al. (2010)  characterized the tropopause inversion layer in the Northern Hemisphere with temperature profiles from in situ measurements and from the model for the period from 2001 to 2009 and concluded that the cold point at the tropopause agreed well. In this study, IAGOS (In-service Aircraft for a Global Observing System) temperature observations, which are available for almost 2 decades since 1994, are analyzed. The geographical coverage of the measurements is shown in Sect. 2, in which the reliability of the IAGOS observations and the data selection is discussed. The UTLS temperature distribution and derived temperature trends are presented in Sect. 3, and their robustness and suitability for the evaluation of global-scale reanalyses with the example of ERA-Interim (ERA-I) are discussed in Sect.
, 2015; Petzold et al. , 2015). Pre-and post-deployment calibrations from the laboratory are used to evaluate the temperature signal and to ensure the quality of the temperature measurement  (Helten et al. , 1998; Neis et al. , 2015). Typical deployment phases are in the range of 2-3 months. Accounting for corrections of adiabatic compression at the inlet part of the housing  (Stickney et al. , 1994; Moninger et al. , 2003) , the overall uncertainty of the ambient air temperature is ±0. 5 K. More details are given in the standard operating procedure (SOP) of the IAGOS Capacitive Hygrometer (ICH), available at http://www. iagos. org. For the purpose of this analysis the data set was reduced to 1 min averages. To ensure the reliability of the IAGOS temperature observations, we make use of temperature measurements from the AIRTOSS-ICE aircraft campaign (Aircraft Towed Sensor Shuttle -Inhomogeneous Cirrus Experiment), which focused on midlatitude cirrus clouds  (Neis et al.
5 K (sensor and adiabatic compression correction), which demonstrates the capability of the IAGOS temperature sensor to measure the ambient temperature at cruise altitude very precisely. 1 ). The total number of 1 min averages is 14. 8 million. Note, 1 % includes 0. 2 million 1 min mean data points within the UTLS region. The temperature measured from the aircraft (T AC ) is based on total air temperature (TAT) designed for subsonic aircraft (Goodrich Corporation, formerly Rosemount Aerospace). The total air temperature is defined as the ambient air temperature plus the temperature increase due to adiabatic compression in the Rosemount housing. Typically three TAT sensors (platinum resistance sensor) are installed at the nose region of the aircraft, but in general only one is used for the pilots and stored for IAGOS.
Spatial and temporal data coverage In this study all temperature measurements from the IAGOS-CORE flights at cruise altitude (p < 350 hPa) from January 1995 to December 2012 are used. More recent measurements are not yet validated and therefore not included in this study. Therefore, most of the IAGOS temperature observations rely on the former instrument design. Following previous IAGOS or MOZAIC analyses  (Thouret et al. , 2006; Dyroff et al. , 2015; Thomas et al. , 2015; Stratmann et al. , 2016) , we divide the data into 14 geographical regions (Fig. 3  and Table  1 ). Seasonal and regional differences in the temperature behavior can then be linked to the different dynamical patterns. The largest number of measurements is obtained in the midlatitudes (North America, North Atlantic, Europe, and Central Asia), whereas the number of measurements in the higher latitudes (northern Canada, Greenland, Scandinavia, northern Asia) and the tropical regions (Middle America, tropical Atlantic, North Africa, tropical Asia, South America, southern Africa) is much smaller and does not provide continuous coverage over the period presented.
Meteorological reanalysis ERA-I covers the period from 1979 until present, assimilating observational data from various satellites, radiosondes, buoys, commercial aircraft, and others  (Dee et al. , 2011; Simmons et al. , 2014). Note that the IAGOS temperature observations are not assimilated in any numerical weather prediction model or reanalysis product, which makes it unique for model evaluation. For this study, the 6-hourly outputs from ERA-I (0. 75 • × 0. 75 • ) were interpolated onto a 1 • × 1 • horizontal grid and on 60 vertical levels of constant pressure and potential temperature  (Kunz et al. , 2014). Additionally, the variables of the PV, and the pressure of the thermal tropopause (p TPHWMO ) based on the WMO criteria were calculated  (WMO, 1957; Reichler et al. , 2003). The ERA-I data were linearly interpolated (longitude, latitude, pressure, time) onto each flight track with 4 s resolution as described by  Kunz et al.
ERA-I reanalysis is widely used for intercomparisons and is currently the latest global atmospheric reanalysis product provided by ECMWF  (Dee et al. , 2011; Fujiwara et al. , 2017). Simmons et al. (2014)  indicated that there is a problem with the temperature near the tropopause in the tropics and extratropics. Here, we demonstrate the use of IAGOS observations to evaluate the ERA-I reanalysis within the UTLS region. Figure  9  shows the intercomparison between the temperature of the IAGOS observations and the ERA-I reanalysis of all 1 min mean data over the North Atlantic region at cruise altitude (below 350 hPa). The good agreement between both temperatures is reflected by the high correlation coefficient (R 2 = 0. 97) and the slope of 0. 94 from the regression fit. The bias between the temperature of the IAGOS measurements and ERA-I is -0.
02 K over the entire period. The absolute bias is 0. 81 (±0. 72) K, and 93 % of the data are close to the 1 : 1 line (±2 K). The spread of the correlation corresponds to a larger variability in the IAGOS temperature measurements when the aircraft flew through clouds, or from the interpolation of the gridded ERA-I temperature to the aircraft position. IAGOS measurements with a backscatter cloud probe have been available since 2011 and these measurements will be used in the future to distinguish between clear-sky and in-cloud measurements to further reduce the uncertainties. The good agreement between the 1 min mean temperature data from ERA-I and the IAGOS observation in the other regions is documented in Table  2 , in which R 2 is always larger than 0. 97, the slope of each regression fit is between 0.
05 (±0. 17) K decade -1 over the period analyzed (see Sect. 4. 1 above). We calculated the temperature trends in the lower (5th percentile, colder temperatures) and upper (95th percentile, warmer temperatures) ranges of the data to determine the robustness of these trends within the LMS. For the 5th percentile, the temperature trend from ERA-I remains significant (99 % confidence) at +0. 48 K decade -1 , while the trend from the IA-GOS observations still does not significantly decrease at -0. 22 K decade -1 (87 % confidence). In the upper range, the differences between the temperature trends diminish to +0. 12 K decade -1 (53 % confidence) using ERA-I and -0. 07 K decade -1 (73 % confidence) using the IAGOS observations. This shows that within the colder temperature regimes, the trend difference is significant, while for the warmest temperatures regimes, the variability is too large to obtain a significant temperature trend.
Possible sources of the differing temperature trends There is a remarkable coherence of the temperature time series and the anomalies of the observations and ERA-I in all regions and layers, but the linear temperature trends reveal a deviation between the two data sets, especially in the LMS. In order to investigate this in more detail, Fig. 10  shows the annual deviation between the IAGOS observations and ERA-I at the LMS over eight regions. From 1995 to 2002 the observations are always warmer than ERA-I within the Northern Hemisphere. The sign changes to neutral until 2008, and since 2009 the IAGOS observations have been colder than ERA-I. This temporally varying temperature bias is one reason why the temperature trends are not equal between the two data sets. The differences cannot be explained by the chosen definition to determine the different layers within the UTLS region.
The calculated temperature trends in the UT show all the same signs for each region, whereas only a significant trend is found over North America, with a large cooling rate of about -0. 92 K decade -1 (ERA-I) and -1. 08 K decade -1 (IAGOS) in the last 18 years. The large deviation between the different LMS temperature trends from IAGOS observations and ERA-I data is mostly related to the temporally varying bias between both temperature time series. As we have no reason to assume an evolving bias in the IAGOS observations, we conclude that ERA-I temperatures are too cold between 1995 and 2001 and too warm after 2007. These dates roughly correspond with changes in the data streams that were used in the ERA-I data assimilation. The evolution of ERA-I temperature can be explained by additional assimilation of GPSRO data and data from the AMDAR passenger aircraft network, which have been shown to be warm-biased.
These data sources play an increasing role in the ERA-I data assimilation after 2006. Our recommendation is therefore to include a bias correction for these temperature measurements in future versions of the European reanalysis and use IAGOS observations as an anchor point. The IAGOS temperature measurements highlight the need of independent global measurements with high and longterm accuracy to quantify long-term changes, especially in the UTLS region, and to help identify inconsistencies between different data sets of observations and models. Due to the expansion of the IAGOS aircraft fleet with airlines from other continents, we are looking forward to investigating temperature changes in the UTLS over several more regions in a few years from now.
Acknowledgements. We gratefully acknowledge all partners for their continuous support for more than 20 years:  Lufthansa ,  Air France ,  China Airlines ,  Cathay Pacific, Iberia, CNRS, University of Manchester ,  Meteo-France ,  Sabena Technics , and  Enviscope GmbH , including all former coordinators of MOZAIC and IAGOS:  Alain Marenco ,  Andreas Volz-Thomas  and  Jean-Pierre Cammas. We acknowledge  ECMWF  for providing the  ERA-Interim data  and  Nicole Thomas  for excellent programming support. Part of this project was funded by  BMBF  in IAGOS-D contract  01LK1301A. The IAGOS database is supported by  AERIS  ( CNES  and  INSU-CNRS ). The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association. Edited by:  Farahnaz Khosrawi Reviewed  by:  Heini Wernli  and one anonymous referee.
Details on the piezoceramics and configuration of transducers can be found in  Brantut et al. (2014a). All 16 transducers were consecutively pulsed with a voltage of 500 V (1 μs rise time), so that each sensor was used once as a source and recorded by the rest of the transducer network during what is referred to as a velocity survey. A stack of 10 signals was performed to enhance the signal-to-noise ratio. Ultrasonic signals were amplified to 45 dB and recorded on a 16-channel digital encoder at 10 MHz sampling frequency. We used these recordings to determine the P wave travel times in the sample along selected raypaths. Given the large amount of data to be computed, we applied a cross-correlation method  (Pettitt et al. , 2002)  to automatically pick travel time differences with a reference survey, which was performed after hydrostatic compression.
(2013). Elastic wave anisotropy and, in particular, shear wave splitting, the easiest anisotropic feature to be measured at the field scale, have become a primary tool for imaging the stress field  (Boness & Zoback, 2006; Crampin & Lovell, 1991; Mizuno et al. , 2005). Although we measured only P wave velocities, our data also provide important constraints on shear wave splitting. Indeed, within a transversely isotropic medium, if the sample exhibits radial P wave anisotropy, it will exhibit azimuthal S wave anisotropy as well (see   Guéguen, 2003). Accordingly, shear wave anisotropy (or splitting) is likely to exhibit time series variation similar to that of V p anisotropy, shown in Figures  4c  and 4d. We can therefore suspect that shear wave splitting may also rotate quite rapidly, both in time and space.
Acknowledgments This research was founded by the  National Natural Science Foundation of China  ( Major Program 51490651 ). We thank  Damien Deldicque  and  Yves Pinquier  ( ENS-Paris ) for their technical support, the Associate Editor, an anonymous reviewer,  Marie Violay , and  Harsha Bhat  for their insightful suggestions, which helped improve the quality of this manuscript. The data produced during our experiments are available in the supporting information.
Project consortium The use case is driven by the industrial end user Bouygues Construction, responsible for the clearance and refurbishment of a site and operating the system. Robotnik Automation SLL develops and manufactures mobile platforms and manipulators for service robotics applications and unmanned ground vehicles. indurad is a producer and integrator of optical and radar sensor technology with a unique data processing and visualization software framework. The robotic system including the developed operational process for the use case is integrated by the Fundacio eurecat, an experienced system integrator. The state-of-the-art technology in the areas of control, task planning and sensor data processing will be augmented by the research institution RWTH Aachen University. SIGMA Clermont brings research expertise in mechatronic design, modelling, perception, and collaborative control for simulating and piloting the robotic system in its environment.
Composition of the Trifolium root microbiome The 16S amplicon sequencing of 24 Trifolium root samples and 15 soil samples from climate chamber and natural site growth experiments (Fig. 1 , Table  1 , Additional file 1: Figure  S2 ,) yielded 9,923,925 high-quality, nonchimeric sequences across all samples, with a median of 153,072 (range 21,731-981,922) sequences per sample (Additional file 2). We rarefied the dataset to an even sequencing depth of 20,000 sequences and identified 3495 bacteria OTUs and one archaea OTU. We confirmed in the Trifolium root microbiome the typical patterns that are often observed in microbial ecology. The soil microbiome is richer and phylogenetically more diverse than the root microbiome (Additional file 1: Figure  S3 ; Table  S1 ). We quantified the major components driving differences between samples (ß-diversity) using unconstrained principal coordinates analysis (PCoA) on weighted UniFrac distances and found a clear separation along axis 1 (explaining 69.