dataesr/scientific-paragraphs-categorization huggingface dataset:
The final PCR products were visualized by electrophoresis using the Tapestation 4200 device with the associated D1000 Screen Tape and reagents (Agilent). These PCR products were then purified using USB ExoSAP-IT PCR Product Cleanup (Affymetrix) and sequenced on a Genetic Sequencer 3500 (ThermoFisher) using the Big Dye Terminator Kit v3. 1 (Applied Biosystems). The generated sequences were aligned using the Clustal Omega software with default parameters. Second, the 2-plex ddPCR method was experimentally tested in duplicate on DNA from the gene-edited rice line and its parental line as well as on DNA from animal (Homo sapiens), bacteria (Bacillus subtilis) and fungi (Aspergillus niger) and plant (Beta vulgaris, Brassica napus, Glycine max, Gossypium hirsutum, Oryza sativa, Solanum tuberosum, Zea mays), including wild-type and transgenic crops.
The latter comprises at least all events currently authorized on the European market as well as unauthorized ones (Table  2 , Supplementary data 3). A minimum of around 2500 estimated haploid genome copies of non-targeted and targeted DNA were tested  (Arumuganathan & Earle, 1991; Fraiture et al. , 2021). The generated amplicons from the gene-edited rice line and its parental line were purified using USB ExoSAP-IT PCR Product Cleanup (Affymetrix) and sequenced on a Genetic Sequencer 3500 (ThermoFisher) using the Big Dye Terminator Kit v3. 1 (Applied Biosystems). The generated sequences were aligned to the reference sequences using the Clustal Omega software (Supplementary data 4).
In silico assessment First, the natural distribution of the OsMADS26 gene (locus: Os08g02070) was investigated (Supplementary data 1A). A total of 6 hits with a sequence coverage and identity ≥97% was observed from the megablast analysis. This gene was only found in rice species, including both O. sativa japonica and indica cultivars. Second, the natural distribution of the single variation point in the OsMADS26 gene was investigated using the PCR amplicon sequences from the gene-edited rice line (113 bp) and its parental line (112 bp) generated by the developed 2-plex ddPCR method (Supplementary data 1B-C). Among the entire NCBI nucleotide (nr/nt) collection, a total of 11 hits, all belonging to rice species, was observed and presented sequence coverage of 100% (Supplementary data 1B). All these hits showed a sequence identity of 100% with the parental rice line PCR amplicon and 99.
12% with the gene-edited rice line PCR amplicon. Such difference was always associated to the single nucleotide insertion region (Supplementary data 1C). In line with these results, no SNP and indel polymorphism in the region of interest (CCGTCGCATCGAGAACCCGGTTC) were found in the natural diversity of rice, using the Rice SNP-Seek Database containing more than 3000 rice accessions, suggesting that the region of interest is conserved across rice varieties  (Meunier A. C. et al. , unpublished; Wang et al. , 2018). To our knowledge, the present study is the first one using sequence databases to investigate the frequency of a single variation point introduced by gene-editing. Such investigation is a crucial step during the development of a detection method targeting a specific gene-edited organism line.
In vitro assessment First, the region amplified by the developed 2-plex ddPCR method in various transgenic and wild-type rice materials were compared (Supplementary data 2). The presence of the region of interest was confirmed in all these different rice materials and the single adenosine insertion was only observed in the gene-edited rice line, supporting the results from the in silico analysis (Supplementary data 1-2). Second, the specificity of the developed 2-plex ddPCR method was experimentally assessed using animal (human) and microbial (Bacillus subtilis and Aspergillus niger) materials as well as several plant materials including different transgenic and common wild-type crop species (Beta vulgaris, Brassica napus, Glycine max, Gossypium hirsutum, Oryza sativa, Solanum tuberosum, Zea mays).
ddPCR sensitivity Using different estimated haploid genome copy number of either the gene-edited rice line or its parental line  (100, 50, 20, 10, 5, 1, 0. 1 and 0) , the sensitivity of the developed 2-plex ddPCR method was investigated (Table  3 , Supplementary data 5). For all 12 replicates, a positive signal as low as 5 estimated haploid genome copies was observed for the mutant rice probe while a positive signal as low as 10 estimated haploid genome copies was observed for the wilt-type rice probe. For both probes of the developed 2-plex ddPCR method, a positive signal for few replicates was also observed as low as 1 estimated haploid genome copy. As expected, no positive signal was also observed at 0. 1 and 0 estimated haploid genome copy. In addition, the LOD 95% was determined at 4 estimated haploid genome copies for both probes of the developed 2plex ddPCR method.
ddPCR applicability The applicability of the developed 2-plex ddPCR method was assessed using several mixture samples. On the one hand, the mixture samples n • 1-11 contained different percentages of the gene-edited rice line and its parental line. These mixture samples were prepared to mimic a large spectrum of possible contamination levels of a gene-edited plant in its wild-type, and vice-versa (Table  4 , Supplementary data 7A). On the other hand, the mixture samples n • 12-13 were prepared to mimic either a maize or a soybean matrix contaminated with a low amount of both the gene-edited rice and its parental line  (Table 4, Supplementary data 7B). For all these mixtures, the presence, both at high and low percentages, of the gene-edited rice line and its parental line was detected by the developed 2-plex ddPCR method.
In addition, based on the natural distribution of the single variation point, such analyses allow to determine the discriminative power of this single variation in order to unambiguously identify the gene-edited organism line from all other lines, cultivars, varieties and species. This is essential to prove the presence of a gene-edited organism in particular. Such key analysis step could be facilitated by a future access to a genome database representative of the diversity, natural or through breeding programs, of all the cultivars and varieties for each species of interest. However, although it is essential for any statement on the frequency of a particular single variation point, such database is at our knowledge not currently available for crops, even for rice being one of the most sequenced crops.
This database should include at least all the whole-genome sequences from the authorized gene-edited organisms and their parental lines, both ideally provided by producers. For this reason, it can be difficult to prove the origin, natural or not, of a single variation point (ENGL, 2019; http://iric. irri. org/resou rces/rice-databases;  Ribarits et al. , 2021; Rice SNP-Seek Database). In case such specificity criteria are met, the second step of the proposed workflow is applied. It consists to assess, in agreement with the "Minimum Performance Requirements for Analytical Methods of GMO Testing" of the European Network of GMO Laboratories  (ENGL, 2015; ENGL, 2021) , the performance of the developed PCR-based method, including the specificity (inclusivity and exclusivity), sensitivity and applicability.
To overcome such issue, additional information on the surrounding sequences, genome background and off-targeting sites could for example be necessary in order to unambiguously prove the presence of the gene-edited organism. However, these information cannot be nowadays provided using such PCR-based strategy, amplifying only short fragments of few hundred base-pairs. Therefore, instead of PCR-based methods, the use of sequencing-based strategies supported by an appropriated database is needed. The comparison of a given sample to reference genome sequences will allow to verify the presence of SNP as well as genetic elements unique in the genetic background of a particular modified organism. Although sequencing-based approaches are promising, their implementation to control the food and feed chain are however currently highly challenging due to their expensiveness and technical bottlenecks related to the plant genome size and complexity as well as the need of specific reference genome sequences.
Acknowledgements The research that yielded these results was funded by the  Belgian Federal Public Service of Health, Food Chain Safety and Environment  through  GENEDIT  ( RF 20/6342 ). The Sanger sequencing was performed at the Transversal activities in Applied Genomics Service at Sciensano. The authors would like to thank  Emmanuel Guiderdoni  ( CIRAD ) for his kindness to provide seeds from the gene-edited rice line and its parental line. This material was produced in the frame of the  ANR-11-BTBR-0001_GENIUS  project.
Apparent Viscosity ASTM D2196 The apparent viscosity of a fluid is a measure of its resistance to deformation at a specified speed. It is obtained by measuring the torque of a modulus rotating in a material at a given constant rotational speed. The apparent viscosity can be designed to measure the frictional force between adjacent fluid layers in relative motion  [17]. It was determined according to ASTM D2196, which determines the thixotropic properties of non-Newtonian materials. Measurements were performed using a Brookfield Model LVDV-III U (made in France) viscometer (with a Spindle no. 1 and a torque between 10 and 20%) coupled to a computer. The temperature was controlled using a Heidolph EKT Hei-Con (made in China) digital thermometer. After stabilizing the measurement, Rheocalc software provided the calculated values in mPas.
Windshake The secondary mirror (M2) of ESO's ELT will be close to the dome aperture and thus undergo a rather strong wind in some cases, causing high-amplitude vibrations. ESO provided the firstlight instruments consortia with a data package,  25  in which one can find a 300-s time series of the wavefront TT due to such a windshake. This time series corresponds to a worst-case scenario: external wind 10 m∕s (scenario), wind in the dome 8 m∕s (scenario), frontal wind with respect to the telescope (worst condition), telescope zenith distance pointing 45 deg (worst condition for frontal wind), and wind screen deployed up not to vignette the telescope aperture (normal operating setup). The temporal power spectral densities (PSD) of the TT corresponding to the time series are plotted in Fig. 1.
Acknowledgments This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 893150. This work also benefited from the support of the  WOLF  project  ANR-18-CE31-0018  of the  French National Research Agency (ANR). This document has been prepared as part of the activities of OPTICON  H2020  ( 2017-2020 ) Work Package 1 (Calibration and test tools for AO assisted E-ELT instruments). OPTICON is supported by the  Horizon 2020 Framework Programme  of the  European Commission 's (Grant No. 730890 ). This work was supported by the  Action Spécifique Haute Résolution Angulaire (ASHRA)  of  CNRS/INSU  cofunded by  CNES. This work has been partially supported by the  LabEx FOCUS   ANR-11-LABX-0013. This work was also partially funded by the grant Premiale  ADONI Cram 1.
Finally, the reionization is modeled with a widely-used step-like transition between an essentially vanishing ionized fraction at early times, to a value of unity at low redshifts. The transition is modeled using a tanh function with a non-zero width fixed to ∆z = 0. 5  [15]. The reionization optical depth τ is then directly related to the redshift at which this transition occurs. The CMB power spectra are generated using the Boltzmann-solver code camb  [16, 17]. We sample the likelihood combinations using the cobaya framework  [18]  with fast and efficient Markov chain Monte Carlo sampling methods described in Refs. [19, 20]. All the likelihoods that we use are publicly available on the cobaya web site 1 and are briefly described in the next section.
PLANCK LIKELIHOODS We use the polarized likelihood at large scales, lowlEB, described in Ref. [12]  and available on github. 2 Specifically, it is a Planck low-polarization likelihood based on cross-spectra using the Hamimeche-Lewis approximation  [21, 22]. Using this formalism, the likelihood function consistently takes into account the two polarization fields E and B (including EE, BB, and EB powerspectra), as well as all correlations between multipoles and modes. It is important to appreciate that such correlations are relevant at large angular scales where cut-sky effects and systematic residuals (both from the instrument and from the foregrounds) are important. The cross-spectra are calculated on component-separated CMB "detset" maps processed by commander from the Planck PR4 frequency maps, on 50 % of the sky.
COMBINING PLANCK AND BICEP/KECK With the new BICEP/Keck data set, the uncertainty on r has decreased to σ(r) 0. 013, compared to the Planck uncertainty σ(r) = 0. 043 presented in Ref. [12]. As a consequence, we do not expect the addition of Planck data to significantly improve the upper limit on r. On the other hand, the addition of low-from Planck polarization modes allows the degeneracy with τ to be broken and also slightly shifts the peak posterior distribution for r. This is illustrated in Fig. 3.
ACKNOWLEDGEMENTS Planck is a project of the  European Space Agency (ESA)  with instruments provided by two scientific consortia funded by  ESA  member states and led by  Principal Investigators from France and Italy , telescope reflectors provided through a collaboration between  ESA  and a scientific consortium led and funded by  Denmark , and additional contributions from  NASA (USA). We gratefully acknowledge support from the  CNRS/IN2P3  Computing Center for providing computing and data-processing resources needed for this work. This research used resources of the  National Energy Research Scientific Computing Center , which is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Part of the research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
50 eggs) for 10 min at room temperature. Extracts were transferred into clean vials and stored at -20 °C. We used two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-TOFMS, Pegasus 4D Leco, St. Joseph, MI, USA), equipped with a combination of non-polar ZB-5MS (30 m, internal diameter 0. 25 mm, film thickness 0. 25 μm, Phenomenex, Torrance, CA, USA) and medium polarity BPX-50 (1. 3 m, internal diameter 0. 1 mm, film thickness 0. 1 μm, Restek, Bellefonte, PA, USA) columns. The temperature program for the primary column was 50 °C (1 min) to 320 °C (20 min) at 8 °C/min; the secondary column was set 10 °C higher. We identified RNERO based on Kovats retention index (1566), elution time comparison with synthetic standard and EI-MS fragmentation pattern  39. Relative RNERO abundance was compared by simultaneous visualization of all chromatograms from a given colony on an identical scale of detector intensities in Leco ChromaTOF software.
Origin of chemicals. (3S,6E)-nerolidol, (E,E)-α-farnesene, and (E,E)-farnesol were purchased from Merck (Darmstadt, Germany). RNERO was obtained from our previous synthesis and enantiomeric purity of (3S,6E)-nerolidol and RNERO were established by chiral GC  39. Statistics and reproducibility. The results presented here originate from the data on 10 different field colonies. For Experiment I, six colonies was used and each of them was treated as one replicate in a comparison of three dispersing vs. three nondispersing colonies. For Experiments II and III, each colony was considered as an independent unit and statistically evaluated separately from other colonies, with five replicates per each treatment within each colony: replicate groups originated through random sampling of individuals belonging to appropriate castes during the dissection of the colonies.
Details on statistical evaluation are given below together with the design of individual experiments. All calculations were performed and graphs were generated in GraphPad Prism v 8. 0 (GraphPad Software Inc. , San Diego, CA, USA). Bioassays. Bioassays were conducted in Laboratory Hydreco at 28 °C, elevated humidity and permanent darkness, dim laboratory light was used during the regular census of experimental groups. Experiment I-readiness of NY4♀ to molt into NQs. The design of the experiment is summarized in Fig. 2a. We established six groups of 17 NY4♀, originating from six different colonies (A-F). Three of these colonies (A-C) were considered nondispersing since they contained only a few tens of NY4 (51-168) and a low proportion of male NY4 (2-5%). The other three colonies (D-F) were apparently nearing dispersal and contained numerous male and female nymphs (stages 2-4).
New Pasteur pipettes were prepared for each series, and the data from each series was related to mean responses to hexane stimulations. The datasets were log 2transformed to reduce heteroscedasticity and comply with assumptions of variance equality for parametric testing (Brown-Forsythe test and Bartlett test for equal variances, and Shapiro-Wilk normality test) and then compared among treatments using ANOVA followed by Tukey post-hoc test. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Jarmila Titzenthalerová  for her help with electrophysiological measurements. We are grateful to the  Laboratoire Environnement HYDRECO  ( Emmanuel Vigier ,  Régis Vigouroux ) and  AgroParisTech-Centre de Kourou  ( Stéphane Traissac ,  Dominique Volland  and  Gaëlle Jaouen ) for logistical support in French Guiana. This research was supported by  Czech Science Foundation  ( 22-28470S ),  Institute of Organic Chemistry and Biochemistry, CAS  (RVO:  61388963 ), EVA 4. 0 Project (No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000803 ) financed by  OP RDE  (to A. R. ) and the  Grant Agency of the Charles University in Prague (580413)  (to N. H. )We also thank  Lindsay Seba  for proofreading.
Data quality check and cleaning Sequencing reads were processed using proprietary DArT analytical pipelines  (Jaccoud et al. 2001 , Kilian et al. 2012 ). In the primary pipeline, poor-quality sequences are filtered, applying more stringent selection criteria to the barcode region in comparison to the rest of the sequence. Identical sequences were then compiled into. fastq files and cleaned through the secondary pipeline using the DArT algorithm (DArTSoft14,  Kilian et al. 2012). This pipeline workflow first calls sequence clusters for all pooled samples and then for each individual. The DArT pipeline retained SNPs on the basis of the balance of average count for each SNP allele, the reproducibility values (> 95%), the average count for each se quence or row sum (sequencing depth: ≥ 5), and the call rate (proportion of samples for which the marker is scored: > 95%).
Potential contamination was identified by comparing all reads to bacterial and viral sequences from GenBank and a custom DArT database. Lastly, we applied the following filters to generate the final dataset: < 5% of individuals with missing data (call rate of > 95%), average reproducibility of alleles at a locus (> 95%), across SNPs that share a sequence tag, only 1 random SNP was retained per locus, and monomorphic loci were removed. Individuals with a call rate < 80% were also removed. After this filtering process, average heterozygosity per locus per individual was plotted and visually inspected for potential outliers indicative of sample contamination. The fine-scale analysis of structure focussed on the cleaning station sites, to the exclusion of Pouembout. This is because the relationship between cleaning and feeding aggregations is unclear, and feeding aggregations could contain manta rays from multiple cleaning stations and vice versa, decreasing any population genetic signals.
Two datasets were generated, one including the outgroup (East Australia), referred to as the regional dataset, and the other excluding this outgroup, called the local dataset (New Caledonia only). For the analysis of genetic diversity only, loci with minor allele frequencies > 0. 05 were filtered out of these 2 datasets. All filters were applied using the 'gl. filter' functions in the R package 'dartR'  (Gruber et al. 2018 ).
Genetic diversity We calculated genetic diversity for each sampling site in New Caledonia, for New Caledonia as a whole, and for East Australia using the statistical programming language R version 3. 6. 2 (R Core Team 2019). Specifically, we estimated observed (H o ) and expected (H e ) heterozygosity  (Nei 1987)  with the function 'gl. basic. stat' in the R package 'dartR'  (Gruber et al. 2018). The inbreeding coefficient (F IS )  (Weir & Cockerham 1984)  for each sample partition was also calculated using the 'gl. basic. stat' function. The rarefacted allelic richness (A R ) was estimated for each sample partition using the function 'allelic. richness' in the 'adegenet' package in R  (Jombart 2008). Since sample partitions have different sample sizes, the function used the minimum number of individuals (n = 19 for the regional dataset and n = 18 for the local dataset).
Population differentiation and structure We inferred genetic differentiation and population structure at a regional scale between East Australia and New Caledonia and then at a local scale among our study sites. We also compared the genetic diversity at both regional and local scales. Firstly, we estimated pairwise genetic differentiation indices for sample partition (F ST and Nei's genetic distance D), and statistical significance through permutation tests (10 000)  (Wright 1951) , using the 'stamppFst' function within the R package 'STAMPP'  (Pembleton et al. 2013). We conducted genetic clustering using 2 complementary methods: discriminant analysis of principal components (DAPC)  (Jombart et al. 2010 ) and the program TESS3  (Caye et al. 2016). The outcome of the DAPC depends on the number of principal components (PCs) used in the analysis and the number of clusters in the analysis.
To assess the number of PCs to be used for each dataset, we examined the mean alpha score (difference in reassignment probabilities between observed and permuted clusters), using the function 'a. score' and 'optim. a. score' of the R package 'adegenet'  (Jombart 2008). Alpha scores were calculated using the highest recommended number of PCs (N/3, were N is the total number of individuals) as recommended by the developer of 'adegenet'  (Jombart 2008). The DAPC was then performed with the 'dapc' function in the R package 'adegenet' (Jombart 2008) without assigning individuals to clusters a priori. This method derives a number of potential populations from the analysis and produces membership probabilities for each sample based on the data from all samples. Classification of a given sample to a cluster is based on the highest assignment probability.
The expected classification rate for each population (prior) was compared to the number of correct classifications (posterior) generated using the DAPC. Population genetic structure was further assessed using Bayesian clustering analysis of individual geno types with the 'TESS3R' package  (Caye et al. 2016 ) in R to estimate individual ancestry coefficients assuming admixture of K ancestral genetic clusters in our data set. TESS3 implements a spatially explicit Bay esian clustering admixture model. By including information on individual geographic coordinates, TESS tends to perform better than non spatial models implemented in programs using a similar algorithm, such as STRUCTURE  (Pritchard et al. 2000) , when the ancestral level of differentiation is low  (Durand et al. 2009 , François & Durand 2010).