dataesr/scientific-paragraphs-categorization huggingface dataset:
The crosscorrelation coefficient (r) and the time lag (τlag) were calculated for each sequence of motions (including 5 repetitions for each condition). These results were expressed as mean values for each condition. Cross-correlation aimed at measuring the similarity between two signals. Additionally, an intra-class correlation coefficient (ICC) was performed to evaluate the consistency of the joint torque results between those computed from Kinect data and those computed from Vicon data, for each sequence of motions. The maximal joint torques of each sequence were used as a criterion, in order to get discriminative values from one task to another. In a second time, the recorded motions were segmented in order to focus the analysis on the dynamic Getting and Putting tasks. Thus, static poses at the beginning and the end of each motion had been eliminated.
Results In order to quantify the severity of occlusion simulated in this experiment, we computed the joint reliability for each experimental conditions. The mean reliability scores for P1 and P2 placements are 0. 89 and 0. 91 for NB, 0. 78 and 0. 71 for B and 0. 70 and 0. 74 for B45, respectively. The first part of the results aims at evaluating the accuracy of the joint torques calculated with reference Vicon data. This validation step ensures that the resulting data is suitable to be considered as reference values. To this end, the normalized values of residual forces and torques (at the theoretical ground-to-pelvis joint) were calculated for all conditions. Table  2. Mean RMS ±SD (%), of the residual forces and torques applied to the 6 dof groundto-pelvis joint for each condition. Let us recall that the z-axis was placed along the vertical axis.
In this experiment, computation time for joint torques based on Vicon and Kinect data were measured. Recall that reconstruction of occluded trajectories was performed manually for the Vicon data, while the Kinect data were automatically corrected in real time. Mean computation times for inverse kinematics were 250 ms and 0. 09 ms for the Vicon-based and Kinect-based calculations respectively. Inverse dynamics has requested a mean computation time of 10 ms and 0. 67 ms when using the Vicon and Kinect data respectively. These values are consistent with the sampling frequency of the Kinect (30 Hz), opening perspectives for real-time calculation.
Discussion The current study aimed at evaluating the ability to obtain correct joint torques estimation from Kinect data, in an ergonomic context. To this end, we conducted an experiment consisting in Getting and Putting tasks under different experimental conditions, e. g. occlusion level (withbox/no-box) and Kinect orientation (Front/45°). The resulting joint torques were compared to those calculated from Vicon data Residual forces and torques in the 6 dof ground-to-pelvis joint were below 3. 5%, and the standard deviation of these values was below 1%. These values computed from Vicon data were similar to previously published ones  (Hansen et al. 2014). Moreover, ranges of shoulder flexion and elbow flexion torques were in accordance with those reported for reaching tasks in the literature  (Hollerbach et al.
1982). Such results allowed us to consider the joint torques obtained with the Vicon data as a reference, and could consequently be compared to the Kinect-based method. Remaining error is mostly explained by the scaling of the biomechanical model, BSIP estimation that is relatively rough, and the kinematic error generated by inverse kinematics. Kinect-based and reference-based estimated joint torques were compared qualitatively and quantitatively thanks to two criteria: cross correlation and RMSE respectively. Firstly, qualitative results show a high similarity of torque shapes among trials, excepted for the orientation of the shoulder elevation plane (Y1). These poor results along Y1-axis can be explained by the small variation of the computed joint torques for the studied situations. Indeed, variations in joint torques values along this axis are so small that the signal/noise ratio becomes very important, leading to poor statistical results, especially when trying to correlate noisy signals together.
This type of result is also found for the Y2-axis, even if the correlation values remained higher than for the Y1-axis. Indeed, Figure  6  shows the joint torques calculated from the Kinect data (in red) and reference data (in blue) along the YXY shoulder axis and the Z elbow axis. We noticed significant variations in joint torques values along X-axis, unlike Y1axis and Y2-axis. In an ergonomic context, the elevation of the shoulder is an extremely important rotation as it has been reported to be linked to many well-known diseases. This rotation is also the best computed in inverse dynamic process based on Kinect data for this type of motion. Secondly, quantitative results showed differences between the two methods that can become relatively high. For the X axis, the maximum RMSE was 2. 82 ±1.
45 N. m and 2. 54 ±1. 34 N. m for the Side-B45 and Front-B conditions, respectively. These absolute errors should be related to the high range of joint torque values calculated for this rotation axis (10. 1 ±1. 6 N. m). Thus, RMSE obtained from the Kinect data represents an average value of 17. 4% of the joint torque range for this rotation axis. On the opposite, for the Y2-axis, although the absolute RMSE were lower (less than 1 N. m), relative to the joint torques range for this axis (2. 7 ±0. 6 Nm), this represents an average relative error of 26. 3%. Again, one may consider the signal/noise ratio for such axis. Significant interaction between occlusion conditions and Kinect placements show the importance of the point of view according to the task performed. Indeed, the B45 occlusion led to significantly better results in P2 placement compared to B, whereas B led to significantly better results for P1.
This observation means that even if occlusion was more important for any placement in B45 than in B, the most occluded joints were not necessary the ones evaluated in the study. In conclusion, one has to carefully choose the Kinect placement with regard to the task to investigate and the environment in order to obtain the most reliable results. ICC results highlighted a very important feature that such a method has to exhibit. Indeed, the Kinect-based maximum torques were consistent with those obtained with the Vicon data for most of the trials. This leads to the conclusion that the method is able to discriminate properly two different work situations in terms of torque level. Thus, it could be used to discriminate discomfort between work situations. nRMSE reported in table 5 may show that the system estimated approximately the absolute values of the joint torques.
However, ICC proves that such a method may be used to assess the torque of a given task and compare it to another task. These results are promising, but it could be useful to evaluate the robustness of the proposed method according to different work task conditions (with various external forces and velocities). The correction method of Kinect data is based on parameters which may affect its performance, especially, such as the filtering step of the corrected data. Indeed, the filter used in the correction can introduce a delay that could lead to a time lag in the final forces and torques curves. However, the results presented in Table  4  show that the observed time lag between the results obtained using Kinect and Vicon data ranged from 0. 02 s to 0. 03s for the shoulder elevation, and from 0.
However, in a work context, execution speed is generally limited and could be compatible with this frequency for most of the tasks. However, it would be important to assess the impact of speed on joint torques estimation. To this end, further studies on a larger number of tasks are necessary, in order to evaluate the relevance of the approach for a wider range of real work conditions. The data provided by the Kinect does not contain all the information required to accurately compute all of the joint angles, as recommended by the ISB. A possible solution to tackle this problem is to develop a more complex kinematic model, as proposed by  Bonnechère et al. (2014). However, the estimation of new anatomical landmarks from available joints should be based on precisely reconstructed information.
Recent articles showed that the joints provided by the Kinect could introduce significant errors in specific postures when self-occlusions occurred  (Plantard et al. , 2015). Because of these large potential errors, it seems difficult to reliably estimate new landmarks and more research would be needed to improve data quality first. Indeed, although results reported in this study are promising, some errors remain for conditions where occlusions persist over a long period. Another limitation is linked to the ability of the various measurement systems to capture small details, such as the hand motion. Indeed, the Kinect data was not accurate enough to capture all the dof estimated by the Vicon system. Particularly, wrist motion was not taken into account. This involves limiting the use of this type of system for tasks where these motions are not relevant.
Conclusion This study presents an evaluation of joint forces and torques estimation based on corrected Kinect data. The results show that these data are accurate enough to compute reliable joint torques values in challenging experimental conditions (when occlusions occur or sensor placement is not optimal). Despite the reported usability limitations, the results of the current study are promising for the ergonomic evaluation of workstations and assessment of physical risks. Kinect has already been considered as a promising tool to evaluate ergonomics on-site  (Dutta 2012; Diego-Mas et al. 2014; Patrizi et al. 2015) , but only at a postural level. The current work shows a practical capacity to estimate dynamics in the same experimental conditions. Moreover, the proposed correction method allows performing such estimations in challenging environments, e.
To achieve the expected precision, our analysis did not include ELODIE spectra with S/N lower than 50 or SOPHIE spectra with S/N lower than 80. We did not include SOPHIE spectra with unusual ThAr flux since they could be polluted and could affect our RV determination. SOPHIE spectra in thosimult mode were reduced using the pipeline described by  Bouchy et al. (2009a). The cross-correlation process is the same as described above. In June 2011 (BJD = 2455730), the SOPHIE spectrograph was upgraded. In order to minimize a systematic effect produced by a seeing change at the fibre input, referred to as seeing effect  (Boisse et al. 2010 (Boisse et al. , 2011;; Díaz et al. 2012) , a piece of octagonalsection fiber was installed in the SOPHIE fiber link  (Bouchy et al. 2013). Therefore, we distinguish two different datasets: SOPHIE and SOPHIE+, before and after BJD = 2455730, respectively.
We applied a partial correction to the SOPHIE data set to account for the seeing effect, which is done by measuring the difference between the RVs on the blue and red parts of the spectrum and using this value to decorrelate the velocities measured using the entire detector  (Bouchy et al. 2013; Díaz et al. 2012). Due to the scrambling properties before BJD = 2455730, we quadratically added a systematic RV uncertainty of 5 ms -1 on the SOPHIE data set. This value corresponds to the RV precision measured on stable stars at that time  (Bouchy et al. 2013). Finally, the SOPHIE spectrograph is affected by additional instrumental drifts (e. g. due to interventions in the instrument, lamp replacements). With the precision of SOPHIE+ this effect became detectable, and is well characterized thanks to the systematic observation of RV constant stars.
The long-term variations of the zero point of the instrument identified by  Courcol et al. (2015)  added up to about ±10 ms -1 over 3. 5 years. To correct for these effects, they used data from the monitored constant stars HD185144, HD9407, HD221354, and HD89269A, and from 51 other targets from their sample with at least ten measurements. Then they recursively built a RV constant master with these two data sets, starting with the constants and recursively adding corrected targets (from the 51-star sample) with a root mean square lower than a certain threshold (3 ms -1 ). The complete process is detailed in  Courcol et al. (2015). We applied the RV constant master correction to all of our SOPHIE+ data, achieving a RV precision in HR mode close to 2 ms -1.
Data analysis 4. 1. Method ELODIE and SOPHIE radial velocities were fitted using the tools available in the Data and Analysis Center for Exoplanets (DACE 3  ) developed by the National Centre of Competence in Research PlanetS. A preliminary solution is found using a periodogram of RVs which, for all three targets, shows clear peaks at the periods of each orbit. The analytical method used for computing the orbital parameters from the periodogram of RVs is described in detail in  Delisle et al. (2016). The results of this (1) Parallaxes from Gaia DR1. Nominal uncertainties in the parallax, but adding a 0. 3 mas systematic error is recommended (see  Lindegren et al. 2016 ). (2) Parallax from Hipparcos archive, since no value is available from Gaia. Target T e f f log g Fe/H M * R * Age v sin i log R HK Name [K] [cgs] [dex] [M ] [R ] [Gyr] [km/s] HD17674 5904 ± 22 4.
Astrometric analysis We analysed the astrometric data available from the Hipparcos mission  (ESA 1997)  to search for signatures of orbital motion. We employed the new Hipparcos reduction (van Leeuwen 2007) and followed the procedure described in  Sahlmann et al. (2011b). No significant orbital signals were detected in the Hipparcos astrometry for these stars. As was done in previous works (e. g. Díaz et al. 2012 ) by using the Hipparcos data and the parameters found from the RV analysis, we were able to set upper mass limits for two of the companions. For HD17674, the upper mass limit of the companion is 89 M Jup , and for HD42012 it is 67 M Jup. No constraints could be established for HD29021 since the orbital period is not covered by Hipparcos data.
2011a). By the end of the mission, all three planets could be detected using a combined analysis with estimated values of S /N Nmeas = 8. 0, 19. 5, and 69. 2 for HD17674, HD42012, and HD29021, respectively. The Gaia DR1 released in September 2016  (Lindegren et al. 2016 ) contains provisional astrometric results for over one billion sources brighter than G magnitude 20. 7 for the first 418 days of the mission. Using DR1, we discuss a third indicator. An orbiting companion of planetary mass will produce a perturbation in the stellar motion, and this will be reflected A&A proofs: manuscript no. SOPHIE_XII Table  4. Data from Gaia DR1 and estimated values. The number of Gaia observations is determined for each target using the Gaia Observation Forecast Tool. The S/N is calculated for one measurement, the measurements available in DR1, and the expected values at the end of the mission.
Discussion and conclusions We have reported the detection of three new Jupiter-mass companions orbiting the solar-type stars HD17674, HD29021, and HD42012. We found no evidence of additional giant companions. As a reference for this population of objects, as of October 2016, the exoplanets. eu database  (Schneider et al. 2011 ) listed around 200 planets with masses between 0. 8 and 15 M Jup and periods longer than 600 days. The three host stars present a subsolar metallicity, which is unusual for stars with giant planetary companions  (Johnson et al. 2010) , but also reinforces the result found by  Adibekyan et al. (2013) , who showed that planets orbiting metal-poor stars have longer periods than those around metal-rich ones. The fact that these companions all make, as far as our analysis can tell, single giant-planet systems, may also be linked to the low heavyelement content of their birth nebulosity.
Acknowledgements. We gratefully acknowledge the  Programme National de Planétologie  (telescope time attribution and financial support) of  CNRS/INSU , the  Swiss National Science Foundation , and the  Agence Nationale de la Recherche  (grant  ANR-08-JCJC-0102-01 ) for their support. We warmly thank the  OHP  staff for their support on the 1. 93 m telescope. J. R. acknowledges support from  CONICYT-Becas Chile  (grant  72140583 ). J. S. is supported by an  ESA Research Fellowship in Space Science. A. S. is supported by the  European Union  under a  Marie Curie Intra-European Fellowship for Career Development  with reference  FP7-PEOPLE-2013-IEF , number  627202. This work has been carried out in the frame of the  National Centre for Competence in Research "PlanetS"  supported by the  Swiss National Science Foundation (SNSF).
D. S. , R. F. D. , N. A. , V. B. , D. E. , F. P. , and S. U. acknowledge the financial support of the SNSF. P. A. W. acknowledges the support of the  French Agence Nationale de la Recherche (ANR) , under programme  ANR-12-BS05-0012  " Exo-Atmos ". The IA team was supported by  Fundação para a Ciência e a Tecnologia (FCT)  (project ref. PTDC/FIS-AST/1526/2014 ) through national funds and by  FEDER  through  COMPETE2020 (ref. POCI-01-0145-FEDER-016886 ), and through grant  UID/FIS/04434/2013  ( POCI-01-0145-FEDER-007672 ). N. C. S. was supported by  FCT  through the Investigador FCT contract reference IF/00169/2012 and  POPH/FSE (EC)  by  FEDER funding through the programme "Programa Operacional de Factores de Competitividade -COMPETE ". This research has made use of the SIMBAD database and of the VizieR catalogue access tool operated at  CDS, France.
Ca. Fukatsuia's closest relative, by 16S rRNA gene sequence identity, would be Budvicia diplopodorum strain D9 (INSDC accession number HE574451. 1), with which it shares 93% sequence identity. The generic name "Fukatsuia" is in honour of Dr. Takema Fukatsu (Prime Senior Researcher at the National Institute of Advanced Industrial Science and Technology, Japan), who has enormously contributed to the study of aphid biology and that of their endosymbionts, with particular emphasis on his early work on secondary endosymbionts form Lachninae aphids. The specific epithet 'symbiotica' alludes to the symbiotic habit of Ca. Fukatsuia bacteria. In the Lachninae aphid Ma. submacula, "Ca. Fukatsuia" is found inhabiting the bacteriome tissue and its cell presents a filamentous shape of variable length. Its tissue localisation and cell shape in aphids other than Ma.
Next, mothur v1. 31. 2  (Schloss et. al. , 2009)  was used for merging of the paired-ends reads and taxonomic assignment of the resulting contigs. Briefly, reads where first quality trimmed using fastx_toolkit v0. 0. 14 (http://hannonlab. cshl. edu/fastx_toolkit/, last accessed November 9, 2016). Next, we joined the overlapping paired ends reads using the make. contigs function of mothur and filtered out all contigs shorter than 420 bps. Then, contigs were aligned to the SILVA NR99 v128 database  (Quast et. al. , 2013)  and those with less than 90% of their length aligned were filtered out. After another step of redundancy removal, rare sequences (possibly resulting from sequencing errors) were merged with frequent unique sequences with a mismatch no greater than 2 bp (precluster function).
Resulting contigs were then screened for chimeric sequences using (chimera. uchime function). Remaining contigs were then taxonomically assigned to genus level using the classify. seqs function and the SILVA NR99 v128 database (cutoff=90). Lastly, the remaining unclassified sequences (namely unclassified Enterobacteriaceae) that exceeded 1% of the total contigs were used for a MegaBLAST  (Zhang et. al. , 2004)  search against a database of representatives including all 16S aphid endosymbiont sequences available to date. The best hit for each read, if this surpassed 94. 5% identity, was used for genus-level assignment.
Visualisation and tree-editing was done in FigTree v1. 4. 1 (http://tree. bio. ed. ac. uk/software/figtree/, last accessed November 9, 2016) and Inkscape v0. 91 (http://www. inkscape. org/en/, last accessed November 9, 2016), respectively. For a full list of accession numbers of sequences used for phylogenetic analyses see Table  S3.
Acknowledgements The authors would like to acknowledge emeritus professor  José Manuel Michelena Saval , professor  Joaquin Baixeras ,  Nicolás Pérez Hidalgo ,  Christelle Buchard , and  Evelyne Turpeau  for their invaluable and expert help in collection and/or taxonomic identification of aphid samples. Also, we thank and acknowledge artist/scientist  Jorge Mariano Collantes Alegre  for the aphid cartoons in Fig 6. This work has been funded by the  Ministerio de Economía y Competitividad (Spain)  co-financed by  FEDER  funds [ BFU2015-64322-C2-01-R  to A. L. ]; the  European Commission [Marie Curie   FP7 PITN-GA-2010-264774-SYMBIOMICS  to A. M. M]; the  Consejo Nacional de Ciencia y Tecnología (Mexico)  [ Doctoral scholarship   CONACYT 327211/381508  to A. M. M. ]; and the  Austrian Science Fund  [project no.
| Springtail assessment Soil fauna studies often suffer from the inefficiency of sampling strategies and a strong taxonomic deficit  (André, Ducarme, & Lebrun, 2002). We therefore chose to use environmental DNA (eDNA) from the soil core samples to characterize springtail communities. eDNA is an emerging approach for biodiversity studies  (Thomsen & Willerslev, 2015)  that has already proved its efficiency, e. g. for earthworms  (Pansu et al. , 2015). with the dedicated OBITools package and according to an established workflow  (Boyer et al. , 2016). A complete description of data processing is proposed in Appendix S1. T A B L E.
2002). Moreover, we used model averaging to estimate parameter and associated unconditional standard errors based on the subset of top-ranking models for which the sum of the AICc weights reached ≥0. 95 (MuMIn package). To determine the relative importance of soil vs. stand models, soil vs. landscape models and stand vs. landscape models, we compared the sum of the AICc weights for three categories of models: for example models that accounted for soil variables only, for stand variables only and models that accounted for both types of variables  (Burnham & Anderson, 2002). To determine if species composition was influenced by forest continuity and/or stand maturity, we used multivariate GLMs. This approach has the advantage of being based on a GLM framework, which is typically better able to handle the strong mean-variance relationship in abundance data  (Warton, Wright, & Wang, 2012).
ACKNOWLEDGEMENTS We thank  Gilles Favier ,  Sophie Labonne ,  Fanny Lebagousse  and  Pascal Tardif  from  IRSTEA  for help in the field. We are grateful to  Delphine Rioux  and  Amélie Saillard  from LECA for DNA extraction and to  Frédéric Boyer  from  LECA  for his assistance in bioinformation processing. We also thank  Vicki Moore  for correcting the English manuscript. Financial support was provided by  IRSTEA , by the  LTSER Zone Atelier Alpes  (a member of the  ILTER-Europe Network ) and by  Labex OSUG@2020  ( ANR10 LABX56 ).
| Wing morphometrics We quantified flight-related wing characteristics using geometric morphometrics  (Rohlf & Slice, 1990). We photographed the left hind wing of all males, and digitized the images in the tpsDig2 software  (Rohlf, 2015). To capture a detailed wing shape, we placed seven landmarks along the wing outline where it is intersected by major wing veins, and five semi-landmarks where intersections were not consistent between wings (Fig. S2 ). The landmark coordinates were imported into the software tpsRelw  (Rohlf, 2015) , where they were subjected to a generalized procrustes analysis in order to remove any non-shaperelated differences due to variation in position, orientation and size  (Rohlf & Slice, 1990). During this process, semi-landmarks are allowed to slide along their tangent vectors to minimize the shape differences between specimens.