dataesr/scientific-paragraphs-categorization huggingface dataset:
We observed that this haplotype fell within the target region of one of the 218 ARTIC primers. It has been suggested that non-removal of primers from sequencing reads could lead to an underestimation of the frequency of iSNVs  [67]. Trimming of the ARTIC primers from the reads did not affect the identification and frequency of the N30G/S31Stop/F32Stop/R34P/G35R haplotype in the S gene (Table  S6 ). In order to evaluate a potential bias related to the ARTIC procedure, we analyzed 120 additional samples from another dataset obtained by metagenomics. In this dataset, we were unable to recover any of the mutations that are part of the proposed haplotype in the S gene. However, sequencing depth was much lower in these data, and we noted a significant number of iSNVs per sample in this cohort, suggesting a poor quality of sequencing data.
Acknowledgments: We are grateful to the COVID-19 genomics response team of  Melbourne  for generating the data used in the current analysis. This work was supported by the  Shanghai Municipal Science and Technology Major Project  (Grant No. 2019SHZDZX02 ) and this benefited from the  Montpellier Bioinformatics Biodiversity  platform supported by the  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ).
Material and methods All beetles for this study were collected with the naked eyes using an aspirator in dark zones of the caves and kept in vials with 50% ethanol before studying. One exemplar of each species (but three legs removed from the holotype of Wulongius qilinger gen. nov. and sp. nov. ) were kept into 95% ethanol for DNA sequencing. Dissections and observations were made by using a binocular Leica MZ75 dissecting microscope (Wetzlar, Germany). Dissected genitalia, including the median lobe and parameres of the aedeagus, were glued on small transparent plastic cards and pinned under the specimen from which they were removed. Digital pictures were taken using a Canon EOS 5D Mark III camera (Tokyo, Japan) and then processed by means of Adobe Photoshop CS5 software (Adobe System Incorporated, California, USA).
Acknowledgements First of all, we thank  Prof. Yuanhai Zhang  ( Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin ) and  Ms Lin Duo  ( Head of the Administrative Committee of Wulong World Natural Heritage, Wulong ) for their kind invitation to survey in Wulong. Our deep appreciations also extend to  Mr Lixin Chen ,  "Wu Ya", "Ye Gong", "Ye Po", and "La Pi" (Qilinger Cave Exploration Team, Nanning ) and  Ms Jia Liu  ( Chongqing Cave Exploration Team ) for their assistance during our survey in Wulong, to  Mr Tongwang Yang  ( Manager of Qianlong Dong Scenic Spot ) for his help dur-ing the investigation in  Qianlong Dong , and  Mr Wenlong Zhou  ( Guizhou Institute of Mountainous Region Resources, Guizhou Academy of Sciences, Guiyang ) for providing the cave map of  Qianlong Dong  and an important literature.
TECHNOLOGICAL ADVANCES TO UNDERSTAND THE RUMEN MICROBIOME The onset of next generation sequencing has represents an opportunity to better describe the rumen microbiome and to infer its function. Although these studies are of great value, interpretation of the results generated across studies remains a challenge due to differences with respect to DNA extraction, primers, downstream computational analyses, and databases used as reference. López-García et al. , conducted one of the few studies which compared the performance and robustness of two of the most common software (QIIME vs. Mothur) and databases (GreenGenes vs. SILVA) used in meta-taxonomic studies of the rumen microbiome. This study provides practical recommendations to future users and clearly indicated that further advances in computational tools and culturomics are needed to improve the completeness of the rumen databases.
The released compounds were focused in a cold trap (ID 1. 80 mm) at 0 °C that was filled with Tenax and charcoal. The volatiles were transferred in splitless mode to the analytical column (30 m × 0. 25 mm ID, 1 μm film thickness, DB-5, Phenomenex, Torrence, CA, USA) by flash heating the cold trap at 40 °C sec -1 to 280 °C (10 min hold). The temperature program of the oven started at 40 °C and immediately rose to 280 °C (4 min hold) at a rate of 5 °C min -1. Electron impact ionization at 70 eV was used to ionize the column effluent. Mass scanning was carried out from m/z 35 to 300 with 4. 70 scans sec -1. Compounds were identified by comparing the mass spectra with the one of Wiley libraries, NIST and the Wageningen Mass Spectral Database of Natural Products. Identified compounds (listed in Table  S1 ) were confirmed based on retention index using the literature  (Adams, 1995).
Peak area was calculated based on total ion chromatograms (TIC) or selected-ion chromatograms (SIC). The SIC integration technique has a better resolution than the TIC technique, therefore, SIC data were used to analyse the effect of treatment on the composition of the volatile blend. Ions were selected based on specificity and mass; their m/z values are listed in Table  S1. The TIC technique allows to cumulate peak area of the eluted peaks of a chromatogram and, thus, was used to calculate total volatile emission of plants. Results of this study are based on plant compounds that were detected in at least 50% of the replicates of one of the treatments, and whose peak area was 3. 5-fold higher than in background samples (volatiles collected from empty oven bags in which no plant was present) for peaks that were integrated based on TIC, or fivefold higher for peaks that were integrated based on SIC.
Statistical Analyses For the data on C. glomerata preference, the effect of plant pair on wasp first landing and oviposition choice was first tested for each combination of treatment using a Generalized Linear Model (GLM) based on a binomial distribution with logit as a link function, and a 0. 05 significance level. No effect of plant pair was detected, and wasp choice was then tested regardless of the plant pair. Thus, landing and oviposition preference of both C. glomerata and D. rapae was tested with a binomial test, with a probability of 0. 50 for a wasp to go to one of the plants. The effect of treatment on the number of mummies per plant was tested with a paired Student's t test at the 0. 05 significance level; requirements of normal distribution of the data and equal variances were met.
Parasitization of Aphids and Caterpillars The number of aphid mummies on all central plants and on two side plants of a given plot was counted at 19 days after infestation in field A and 42 days after infestation in field B. Plants were first harvested and then living aphids were gently brushed off the plants to uncover all mummies for counting. Brevicoryne brassicae are the main aphids developing on B. nigra in our field, and mummies are assumed to mainly belong to this species. Numbers of mummies per plant were averaged at the plot level. Numbers of mummies at day 19 were analysed with a Generalized Linear Mixed Model (GLMM) based on a negative binomial distribution and log as link function. Numbers of mummies at day 42 were normally distributed, and variances could be assumed as equal, therefore these data were analysed with a Linear Mixed Model (LMM).
Effect of Single and Dual Attack on the Seed Set of Brassica nigra in the Field We determined the seed set of B. nigra plants in field B after 42 days of exposure to the treatments. Siliques were stored to dry at room temperature in the dark in a farm building (Unifarm, Wageningen University) until seeds were processed and counted. The number of seeds was estimated by dividing the weight of the total number of seeds of a given plant by the weight of 100 seeds of this plant. Seeds were counted for the central plant and the two randomly selected side plants for each plot, and we calculated the average number of seeds produced per plant per plot. In four cases, the central plant had died during the experiment, and in three cases, the seed bag of the central plant could not be identified. Data related to these plots were not included in the analyses.
Statistical Software and Procedures We used respectively the default GENMIX, GENLIN, and MIXED procedures of SPSS (IBM Corp. , IBM SPSS Statistics for Windows, Versions 24, Armonk, NY: IBM Corp. ) to run GLMMs, GLMs, and LMMs. Chi-square tests and G-tests were performed in Excel (version 2016, for Windows, Microsoft® office, Redmond, Washington, USA). PLS-DAs were performed in SIMCA (Umetrics AB, Version 15. 0, Umeå, Sweden), and we used the default 7-fold cross-validation (CV) procedure to calculate model fit parameters: the number of significant components, the goodness of fit R 2 X and R 2 Y, and the predictive ability Q 2 Y. R 2 X and R 2 Y represent respectively the percentage of variation explained by the matrix of volatile data (X) and by the matrix of treatments (Y). In poor models, the order of rows in the original data set can affect the value of Q 2 Y  (Triba et al.
Acknowledgments We thank  Alexandre Villela  for fruitful discussions on chemical analyses, and  Marcela Aragón-Gómez  and  John Venegas-Molina  for their contribution to preliminary assays on D. rapae behaviour. We thank two anonymous reviewers for their valuable comments on a previous version of the manuscript. Author's Contributions Lucille T. S. Chrétien ,  Dani Lucas-Barbosa , and  Marcel Dicke  planned and designed the study. Lucille T. S. Chrétien ,  Hessel van der Heide , and  Liana O. Greenberg  collected the data. Lucille T. S. Chrétien  and  Dani Lucas-Barbosa  analysed the volatile data, and  Lucille T. S. Chrétien  analysed the behavioural and ecological data. Lucille T. S. Chrétien ,  Dani Lucas-Barbosa ,  David Giron , and  Marcel Dicke  interpreted the data and wrote the manuscript.
Phylogenetic Tree A phylogenetic tree was made for insect LGRs using published sequences (3) and those constructed here from L. migratoria as described. All sequences and their Genbank accession numbers, where available, are listed in the Supplementary Spreadsheet. The L. migratoria LGR sequences and their orthologs from S. gregaria are presented in the Supplementary Data. Transmembrane regions were aligned and those were used for tree construction using Fasttree  (37)  with the following command:. /FastTreeDbl -spr 4 -mlacc 2 -slownni -pseudo.
Ovary and Ovariole Size Examination and Data Analysis Ovaries and ovarioles were photographed with a Leica DVM6 digital microscope (Leica Microsystems, Wetzlar, Germany). The pictures were then used to measure width and length of ovaries and primary follicles to produce ovary and primary follicle size indexes. The ovary size index was obtained by multiplying the length by the width (mm 2 ) of each ovary. For follicles the index was defined as length (in mm) x width (in mm) 2 x p/4 which yields a volume in mm 3. The primary follicle index is the average of three randomly selected primary follicles of each insect.
The L. migratoria LGR3 has previously been suggested to be an LGR4 ortholog  (38). Genbank identifiers and sequences are listed in the Supplementary Spreadsheet. both species (Figures  1A, B  and Supplementary Figure  S1 ). The structure of IGF is typically considered to be made up of the A-, B-, C-, D-and E-domains, but it is not possible to delineate those in locust aIGF, even though its structure is very similar to IGF of which it is likely an ortholog. The sequence homology with the IRPs is insufficient to identify the limits of the C-domain. Also, there is a lack of a recognizable convertase cleavage site that would indicate the border between of the D-and E-domains, suggesting that the D-domain is very large and the E-domain might be lacking (Figure  1A ). The genomic L. migratoria sequence for aIGF has two sequence gaps (Figure  3 ), with the second one covering the four typical Cys residues in the A chain, which readily explains why this peptide was missed in previous analyses of the neuropeptide genes in this species  (21, 28) , while the S.
Discharge Estimate and Uncertainties The mass changes M(t) of the Antarctic Ice Sheet result from the difference between the cumulative surface mass balance SMB(t) minus the cumulative ice discharge over the grounding line D(t)  (Rignot et al. , 1997 (Rignot et al. , , 2008;; Shepherd et al. , 2018; Mankoff et al. , 2020). Here, we reorder the mass balance equation (Eq. 1), to estimate cumulative discharge as D(t) M(t) -SMB(t), where M(t) is obtained from GRACE/GRACE-FO, and SMB(t) is obtained from the snow accumulation product of the re-analysis data (ERA-5). In the following, we focus on the long-term temporal components for specific time periods; namely the rate and acceleration of cumulative discharge, _ D and € D, respectively, and likewise its rate and acceleration of cumulative SMB, _ SMB and € SMB, respectively.
For comparison, grey circles indicate direct estimates of the rate of cumulative discharge obtained from surface-ice velocities (R19)  (Rignot et al. , 2019a). Values for the entire Antarctic Ice Sheet are scaled by a factor of 10 -1 for plotting. Rates of mass change are represented as Gt yr -1 (bottom axis) and as integrated contribution to sea-level change by 2100 in cm (top axis). We assume 360 Gt of mass loss raise global mean sea level by 1 mm. Uncertainties for each component and the direct discharge estimate (Data availability) are shown in Supplementary Figure  S5. with published values of 2098 ± 133 Gt yr -1 for the time period 1979 to 2008 using a different regional climate model  (Rignot et al. , 2019a). Note that a two to three times larger relative uncertainty of SMB (10-15%; equal to ±88 to ±132 Gt yr -1 for the entire ice sheet, respectively) would reconcile the indirect and direct discharge estimates within uncertainties for all regions, as would seven times larger discharge uncertainties of the direct method (7%; equal to ±62 Gt yr -1 ).
Atmospheric Circulation Patterns and Snow Accumulation To show the impact of atmospheric circulation patterns in the Southern Hemisphere on the interannual mass changes presented earlier, we now calculate temporal correlations with climate indices. In particular, we investigate the Southern Oscillation Index (SOI)  (Parker, 1983)  and the Southern Annular Mode (SAM)  (Thompson, 2002) , reflecting atmosphere pressure differences, as well as the NIÑO 3. 4/4 indices, derived from gradients in sea-surface temperatures in the central Pacific  (Trenberth, 1975). Previous studies have shown correlation of these indices with monthly rates of precipitation in Antarctica  (Turner, 2004 ). As we analyze storage changes of the ice sheet represented by cumulative sums of monthly fluxes, we cumulate the indices analogous to the processing of the SMB data to enable a direct comparison.
For the Filchner-Ronne Ice Shelf region, we detect an acceleration of cD(t) of -1. 0 ± 0. 5 Gt yr -2 not present in the direct estimates (+0. 1 ± 0. 4 Gt yr -2 ). For both regions, cumulative SMB does not remove the decelerations and accelerations prominent in the GRACE data, leading to non-zero values for cumulative discharge in 2002-2017. We further investigate the robustness of our approach to recover the quasi-stationary accelerations of D(t) by prolonging the time period to 2020, now including GRACE-FO data. Figure  5  illustrates that values of € D only moderately change within their uncertainties, when including about three more years of data  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020). In contrast to the stability of the discharge estimates, accelerations of GRACE/GRACE-FO mass change M(t) and, prominently, SMB(t) show considerable variations when using different time periods.
, 2017). For the Antarctic Peninsula, our results show no significant change in ice-dynamic discharge, which is in contrast to the direct estimates, indicating moderate acceleration (Figure  5 ). At the basin scale, we identify discharge deceleration (+1. 3 ± 0. 4 Gt yr -2 ) in northern and acceleration (-1. 2 ± 0. 7 Gt yr -2 ) in the southern part of the Antarctic Peninsula (Figure  4 ). Evaluating GRACE/ GRACE-FO data over this elongated and narrow region is challenging and may be subject to biases introduced by processing choices, even though our analysis using published mass change products CSR RL06 Mascons and TUD COST-G yields consistent results. In addition, estimates of the SMB component could be biased due to the limited resolution of the ERA-5 re-analysis data. This could be clarified in future studies using higher resolution products.
CONCLUSION The mass evolution of the Antarctic Ice Sheet is a crucial factor for projecting future sea level rise. We have investigated accelerations of mass change € M from GRACE/GRACE-FO and the cumulative surface mass balance € SMB based on re-analysis data to determine, by differencing, acceleration in cumulative discharge, € D, in Antarctica between 2002 and 2020. We have shown that our acceleration estimates obtained by this indirect method, represent an independent alternative to direct estimates from surface-ice velocities  (Rignot et al. , 2019a) , producing consistent magnitudes and comparable uncertainties. We find that the main limiting factor of the indirect method is the systematic uncertainty in the surface mass balance estimates, exceeding GRACE/GRACE-FO uncertainties by a factor of about three.
However, the spatial resolution of the satellite gravimetry data poses limitations for estimates of smaller basins, such as the northern Antarctic Peninsula. Furthermore, we have shown that mass changes caused by snowfall variability strongly superimpose with signals from dynamic acceleration in the GRACE/GRACE-FO data. Separating the surface-mass balance and ice discharge variations in differential satellite measurements is a crucial first step for evaluating ice sheet models and extrapolating future sea-level change. Assuming that acceleration of cumulative discharge and current rates of mass loss are persistent features in Antarctica, we obtain, by extrapolation, a contribution of 7. 6 ± 2. 9 cm of the ice sheet to global mean sea level rise by the year 2100. Our extrapolation to 2100 falls within with the range of -5-14 cm of sea-level rise obtained from larger CMIP6/ISMIP6 ensembles of numerical projections  (Edwards et al.
The present study shows that excellent results are also obtained at a significantly larger non-linearity. In Figure  4 , our numerical results are compared to the available experimental data in the literature  [35, 23, 18, 14]. These experiments take advantage of the latest developments of experimental deformation at high P , T conditions. Pressure is created by compressing the sample assembly between two opposed anvils. This assembly contains an internal resistive heater to achieve high temperature conditions. Deformation in torsion is produced by rotating one anvil. Stress measurements are performed by X-ray diffraction using synchrotron radiation by measuring the orientation dependence and changes in lattice spacing for several diffracting planes. To ensure acquisition of a proper diffraction pattern, the grain size must be maintained small.
As the low-Qz method of NR to quantify the interfacial composition of binary mixtures at the air/water interface requires parallel measurements involving components in their native and deuterated forms, and as Fhexane has no hydrogen protons, we have performed NR and ellipsometry measurements using hydrogenous DPPC (h-DPPC) and its isotopic analogue bearing d62-perdeuterated acyl chains (d-DPPC), both in the LE and LC phases. NR and ellipsometry data were combined in order to determine the surface excesses of DPPC and F-hexane. This study is the first quantitative investigation of the adsorption and incorporation of a fluorocarbon in phospholipid monolayers and also, to our knowledge, the first quantitative assessment of a multi-component system involving adsorption of a component originating from the gas phase.
In the experiments reported here,  measurements were achieved using a phase modulation ellipsometer (Picometer Light Ellipsometer, Beaglehole Instruments, Wellington, NZ) in the Partnership for Soft Condensed Matter (ILL, Grenoble). [45]  The ellipsometer was equipped with a HeNe laser with a wavelength of 632 nm and the incidence angle was 50°. The change in phase shift was measured at 5 s time intervals, and then for presentation were averaged over a period of at least 300 s for experiment under air for which stabilization is fast. It took 1. 5 h for ellipticity to attain its equilibrium value in the experiments involving the fluorocarbon gas. Data were recorded on h-DPPC and d-DPPC monolayers deposited on the Hepes buffer solutions  (11 mL)  in Petri dishes (surface area ~30 cm 2 ) enclosed in a chamber designed at the ILL.
[30]  A beam of neutrons with  = 2-16 Å impinged upon the samples at  = 0. 62°, although a restricted range of  = 4. 5-12 Å was used in the data reduction process to generate reflectivity profiles with a Qz range of 0. 01-0. 03 Å -1 at a resolution of 8 %. The acquisition time for each sample was 5 min. The data analysis was performed using an arbitrary scattering length density r of 5 × 10 -6 Å -2 with the Motofit program in the Igor software, which allows to obtain the thickness value d. [53]  An investigation showed that fitting with refined r values instead of the arbitrary value led to small difference (< 3%) in the calculation of the surface excesses of DPPC and F-hexane. (see SI "Fitting parameter in low-Qz analysis", Table  S1 ). It was also verified that our treatment of the data was valid for layer thicknesses lower than 60 Å (see SI, Fig.
Interactions with h-DPPC and d-DPPC To confirm and explore the implication of the isotope effects suggested by the ellipsometry data presented above, neutron reflectivity profiles of h-DPPC and d-DPPC monolayers were measured in air and under F-hexane exposure in the low-Qz range (0. 01-0. 03 Å -1 ) (Fig. 2 ). First, for reference, the data were modeled using a standard treatment, that is, assuming that Fhexane interacts to the same extent with h-and d-DPPC monolayers, and that the resulting layers are homogeneous. The surface excesses of h-DPPC (Г h-DPPC ) and d-DPPC (Г d-DPPC ) and the surface excesses of F-hexane when interacting with h-DPPC and d-DPPC, ( F-hex/h-DPPC and  F-hex/d-DPPC ) are given in the following equations: It is observed that for monolayers in the LE phase under F-hexane exposure (Fig.
[59, 60]   Whilst it would be intuitive on the basis of the ellipsometry results above to attribute these physically unrealistic results to limitations of the analysis method applied in that F-hexane is interacting with h-DPPC and d-DPPC to different extents, we went on to scrutinize whether our assumption of modeling the low-Qz data as a single, uniform layer could instead explain, or at least contribute, to the unphysical nature of the results above. The possible effects of non-uniformity in the direction normal to the interface (i. e. different locations of F-hexane either in a layer above or mixed in with the lipid chains, themselves in a layer above solvated headgroups), or non-uniformity in the direction lateral to the interface (i. e. the possibility of domains of F-hexane sitting in between regions of lipid monolayer on a length scale above the coherence length of the neutrons) were examined.
Acknowledgements: The authors are grateful to various institutions for their financial support: the  French National Agency for Research (ANR ,  PatMol Project, Ph. D fellowship  for X. L. ), the  Strasbourg Foundation for Research in Chemistry  (icFRC, Ph. D fellowship for C. C. ); the  INTERREG V program  (Nanotransmed Project; Ph. D fellowship for D. S. ); the  CONACYT (Mexico, Ph. D fellowships for E. E. M. -O. (grant # 459199 ) and A. A. V. -G. (grant #  459202 ). X. L. , E. E. M. -O. are grateful to the  GIS Fluor  for travel grants. We also thank the  Institut Laue-Langevin  for beam time on FIGARO (DOI: https://doi. ill. fr/10. 5291/ILL-DATA. 9-13-772 ),as well as the Partnership for Soft Condensed Matter for use of the ellipsometer.