dataesr/scientific-paragraphs-categorization huggingface dataset:
(D) Results obtained for the 36 tested chimeras distributed according to the L1 subfamilies. The TSS of each chimera is defined according to the PCR results obtained with the L1 downstream and upstream primers. Chimeras are defined as LCTs when no amplification was obtained with the L1 upstream primer on the tumor cDNA samples. (E) The long-read SMRT sequencing dataset was used to generate a heatmap showing TSS occurrence along the full-length L1 element associated to the 226 putative LCTs defined by CLIFinderin GSCs. Twenty-two LCTs were not detected in the long-read SMRT sequencing dataset and are shown as white horizontal lines. Among the other 204 LCTs, 84. 80% had at least one read with a TSS included in the 0-500 bp 5 UTR region, and this percentage increased to 98% when considering TSS in the whole 5 UTR.
Acknowledgements We thank the  Clermont-Ferrand hospital Neurosurgery department  ( Prof. J. J. Lemaire ) for help in completing this study, the  Platform 'Gentyane'  (http://gentyane. clermont. inra. fr/)for technical help with the Fluidigm 96. 96  Dynamic Arrays  and the  SMRT Sequel II  sequencing and the  Bioinformatic Platform from GReD institute. Conf lict of Interest statement. The authors delcare no competing or financial interests.
The chimera Id_2900 (L1PA8 subfamily) initiates upstream the two L1-ASP regions and therefore, it is not considered to be an LCT. (D) Results obtained for the 36 tested chimeras distributed according to the L1 subfamilies. The TSS of each chimera is defined according to the PCR results obtained with the L1 downstream and upstream primers. Chimeras are defined as LCTs when no amplification was obtained with the L1 upstream primer on the tumor cDNA samples. (E) The long-read SMRT sequencing dataset was used to generate a heatmap showing TSS occurrence along the full-length L1 element associated to the 226 putative LCTs defined by CLIFinderin GSCs. Twenty-two LCTs were not detected in the long-read SMRT sequencing dataset and are shown as white horizontal lines. Among the other 204 LCTs, 84. 80% had at least one read with a TSS included in the 0-500 bp 5 UTR region, and this percentage increased to 98% when considering TSS in the whole 5 UTR.
Fish sampling The temporal and spatial sampling design consisted of wild and farmed individuals collected in the western and central Mediterranean Sea between 2013 and 2021 (Table  1 , Fig. 1 ). Two sets of samples from the Balearic and the Adriatic Sea were included in the study: a historical dataset from 2013 to 2018, collected prior to the development of commercial aquaculture of S. dumerili, and a contemporary dataset from 2020 to 2021 from wild and farmed stocks. Samples of farmed fish were therefore represented only in the contemporary dataset and their collection coincides with the beginning of commercial farming in the Mediterranean as previous attempts were capture based. Wild fish were mainly collected from landings of the artisanal fishing fleet, including both juveniles (35 ± 3.
Effective population size and outlier tests The NeEstimator V2 program  (Do et al. , 2014)  was used to estimate contemporary effective population size (N e ) by the linkage disequilibrium method for populations with sample sizes greater than 30 individuals. A cut-off for alleles with low frequency ≤0. 02 was applied to minimise potential bias caused by rare alleles. Recent declines in N e over a relatively short period of time, e. g. , several N e generations, were tested using BOTTLENECK 1. 2. 02  (Piry et al. , 1999)  under the two-phase model (TPM), which includes 85% single-step mutations and 12% variance among multiple steps with 20,000 replications. As.
Differentiation and population structuring Population differentiation was assessed with pair-wise and global F ST values as a weighted average across 15 loci in ARLEQUIN v. 3. 5, and statistical significance was tested with 10,000 permutations. The D est measure based on allele frequencies  (Jost, 2008)  was also estimated using GENODIVE  (Meirmans and Van Tienderen, 2004). To assess the impact of null alleles at the population differentiation level and possible overestimation, FreeNA was used to calculate F ST statistics with both exclusion and inclusion of the correction method (ENA, Excluding Null Alleles). A total of 50,000 bootstrap replicates were set to calculate the 95% confidence interval for the global F ST values. Two approaches were used to evaluate population structuring, namely the Bayesian clustering program STRUCTURE 2.
3  (Pritchard et al. , 2000)  and the non-model-based Discriminant Analysis of Principal Components (DAPC) method implemented in the Adegenet package  (Jombart, 2008)  for R (R Core Team, 2017). STRUCTURE parameters were set as follows: admixture ancestry model, correlated allele frequencies, a burn-in period of 50 3 iterations followed by 50 4 MCMC steps, and K values from 1 to the maximum number of groups sampled with 20 replicates each. The most likely number of clusters was assessed using ln P(D) and deltaK values estimated in Structure Harvester 0. 6. 93  (Earl and Von-Holdt, 2012)  and visualised using CLUMPP  (Jakobsson and Rosenberg, 2007)  and DISTRUCT  (Rosenberg, 2004). When necessary, hierarchical population structuring was applied (e. g. ,  Vähä et al. , 2007)  to examine additional clustering of genetic groups found in the first run.
Geometric morphometrics The shape of each fish was characterized with a total of 20 landmarks and semi-landmarks altogether (Fig. S1 ) and digitised using tpsDig Dig 2 software  (Rohlf, 2010). The configurations of points extracted from each fish image (i. e. , x,y coordinates) were subjected to a generalised Procrustes analysis (GPA) in tpsRelw software  (Rohlf, 2010) , removing the variability irrelevant to shape associated to the scale, orientation and position of the fish in the picture. Semi-landmarks, as points that are not homologous but maintain a position correspondence, were allowed to slide along their tangent direction using the Procrustes distance criterion with the ten iterations setting. The allometric relationship of body shapes and log-transformed centroid size (CS) was examined using the procD.
lm function of the R package geomorph  (Adams and Castillo, 2013)  to determine whether populations exhibit isometric or allometric growth by fitting an individual regression of size on shape for each population. Additionally, the homogeneity of slope (HOS) was tested using the advanced. procD. lm function in geomorph to calculate and display pairwise comparisons of slope angles (direction of shape change with size) and slope lengths (amount of shape change with size). Variation between and within sample populations was investigated using a set of complementary approaches: exploratory ordination and supervised machine learning (See further down section: Assignment of escaped labelled individuals to wild or farm origin). Variation in body shape. Between-group principal component analysis (bgPCA) was performed to examine variation and to assess betweengroup structure and associated shape trajectories without distorting the shape space.
Assignment of escaped labelled individuals to wild or farm origin The discriminative power of genetic and body shape approaches was investigated with the R package assignPOP  (Chen et al. , 2018)  using morphometric, genetic and their combined datasets in a supervised machine learning framework. Prior to analysis, several steps were taken to ensure computational uniformity in the evaluation of the training dataset and assignment of escaped individuals: (i) alleles with low variance were removed from the dataset with reduce. allele function to more accurately determine the origin of the studied individuals; (ii) before performing PCA and cross-validation, the entire dataset was centred and scaled (mean and standard deviation of each feature were set to 0 and 1, respectively) because the integrated dataset contains different scales for genetic and morphometric traits; (iii) through the PCA dimensionality reduction procedure, the variables were transformed into PCs summarizing the mixed variations of the genetic and morphometric data, keeping those with an eigenvalue greater than 1; and (iv) the linear discriminant classification function (LDA) was used to build the predictive models for fish origin.
Wild (20BAW, 20BAWj, 20ADWj, n = 144) and farmed fish (20BAF, 20ADFj; n = 60) were used as training data, while escaped fish (20BAE; n = 49) were considered unknown individuals and tested for their assignment to wild or farmed origin. Monte Carlo cross-validation (MC) was performed to estimate the predictive accuracy of the training data by reassigning individuals with known origins to determine statistical accuracy for the membership of escaped individuals. The most informative loci were determined using the check. loci function for all proportions of the training individuals by counting the frequency of occurrence of each locus in the MC analysis to evaluate whether a subset of loci could perform as well as when all loci were included. After evaluation, the datasets were used as input files for the assign.
Among-population genetic structure ARLEQUIN and BayeScan analyses revealed an outlier locus (Sdu39) in the full dataset (Table  S4 , Fig. S2 ). The influence of this candidate locus was tested by performing F ST and STRUCTURE analyses with and without it (Table  3 , Fig. S3 ). Because inclusion or exclusion of this locus had no effect on the resulting genetic pattern of structuring and limited effect on overall and pairwise F ST values, Sdu39 was retained in all subsequent analyses. Assessment of statistical power for the dataset in POWSIM revealed that it was possible to detect genetic divergence up to F ST = 0. 005 with 100% confidence (χ2, Fisher's test) and up to F ST = 0. 001 with 76% confidence (Table  S5 ). Global genetic differentiation estimated using F ST with the inclusion and exclusion of the Sdu39 locus was 0.
053 and 0. 045 (p < 0. 0001), demonstrating a moderate level of differentiation among all populations. The high F ST differentiation index of 0. 114 was observed among farmed populations and was significantly different from the low index observed in the wild populations (0. 008). After Bonferroni correction, 33 of 45 pairwise F ST comparisons were statistically significant when permuted by Fisher's exact test (Table  3 ). Farmed samples from the Adriatic region (20ADFj and 21ADFj) showed high and significant pairwise differentiation in relation to all populations included in the dataset. A similar pattern was observed for the samples of farmed and escaped fish from the western Mediterranean (20BAE and 20BAF), but with a lower degree of genetic differentiation compared to the samples from the Adriatic Sea.
Assignment of escaped fish Prior to the MC test, Sdu8 was removed as a low variance locus and the remaining 14 loci were used along with 40 Procrustes coordinates as an integrated dataset for evaluating individuals from known populations, as the basis for assigning escaped amberjack to wild or farmed origin. Overall assignment accuracy was approximately 95% (Fig. 5A ), with a smaller proportion of the training loci (0. 25 and 0. 5) with the highest F ST values yielding similar overall assignment accuracy as when all loci were included (Table  S8 ). Consequently, seven loci (Sdu46, Fig. 2. Bayesian STRUCTURE clustering of the greater amberjack Seriola dumerili from the Adriatic and Balearic Seas, including wild, farmed, and escaped origins, assuming two and five inferred clusters (K = 2; K = 5) based on 15 microsatellite loci.
Abbreviations for populations are indicated in Table  1. Sdu44, Sdu39, Sdu21, Sdu5, Sdu27, Sdu7) were identified as most informative in the MC assignment test and were used for subsequent assignment of escapees. Of the total 49 escapees, 36 (73. 5%) were assigned to farmed and 13 (26. 5%) to wild origin (Fig. 5B ; Table  S9 ). When comparing these wild-assigned individuals with those identified by Bayesian cluster analysis in the group, both analyses depicted the same misclassified individuals, although the AssignPOP analysis detected three additional wild-like individuals based on the combined morphometric and genetic datasets (Table  S9 ).
A combined approach to fish origin assessment The use of multiple integrated biomarkers to distinguish and assign individuals to a population has been successfully used in fisheries conservation and management  (Nikolić et al. , 2020; TinHan et al. , 2020). Indeed, each marker has discriminatory power that may vary depending on species, farming intensity, or the methods used, i. e. , the assignment accuracy of genetic markers may be affected by high gene flow in fish or by farmed introgression while morphometric traits are affected by fish plasticity and feralisation in the case of older escapees. In the present study, integration of both genetic and morphometric data using the R package assignPOP  (Chen et al. , 2018)  improved assignment results of the escaped group with unknown origin. The baseline source populations used for training were independent of the test population, and a high assignment accuracy (95%) was achieved with a proportion of 0.
25 and 0. 5 of the training loci. Assignment of individuals to a source population with a threshold above 80% can be considered reliable since genetic introgression of farmed fish and the presence of hybrids in the wild are not yet expected due to the relatively recent onset of the marine net-pen production of the species. Still, further fish sampling and genetic studies at a larger spatial scale are recommended to allow sufficient discriminatory power of the genetic dataset to predict the source population(s) of individuals prior to an increase in greater amberjack production and likely widespread genetic introgression of escaped fish in the wild.
Acknowledgements This research has been funded by the projects AquaPop  IP-2014-09-9050  ( Croatian Science Foundation ) and  GLORIA (GLObal change Resilience in Aquaculture)  supported by the  Biodiversity Foundation of the Spanish Ministry for the Ecological Transition and Demographic Challenge , through the  Pleamar Program  and co-financed by the  European Maritime and Fisheries Fund (EMFF). Eventually the authors would like to thank the personnel of the Santa Pola Fish market and the rearing facilities of  CROMARIS (Croatia)  and the  Spanish division of the ANDROMEDA group.
The reference electrode was a sharpened tungsten wire (0. 125-mm diameter, 99. 98% purity, Advent Research Materials Ltd, UK), inserted into the eye with the help of a micromanipulator (UM-3C, Narishige, Japan). The recording electrode was filled with the stimulus solution just before stimulation, it was discarded after 2 minutes and replaced by a new one if needed. The signal from the recording electrode was pre-amplified (10x) and filtered (10 Hz high-pass) using a Taste Probe amplifier ( 65 ; Syntech, Germany) and further amplified (50x) and filtered (3000 Hz low-pass filter) (AC/ DC differential amplifier, A-M systems Inc. , WA, USA). The signal was digitalized and analyzed (Micro 1041-3 and Spike2, respectively, Cambridge Electronic Design Limited, UK), and spike detection was performed using dbWave software  66.
Data analysis. Statistical analyses were run in R 4. 1. 2 software  67. Generalized lineal models (GLM) were used for all the analyses with specific error families and link functions where needed. Pairwise comparisons used the Tukeys´s test of the package "emmeans", unless specified otherwise. To analyze length and width of the apical segment of the labial palp a GLM with Gaussian error distribution was used. The total number of sensilla in the apical segment of the labial palps and the distribution of sensilla, within each species, among the delimited areas (Supplementary Fig. S1 ) was analyzed with GLM with Poisson error distribution and a logit link. For spike counts analysis only the response to stimuli, and not to the 1 mM NaCl electrolyte, was analyzed. A quasi-Poisson error distribution was used because overdispersion was detected.
Acknowledgements CA was supported by a Ph. D. fellowship  BES-2017-081131  from the  Ministerio de Economía, Industria y Competitividad (MINECO, Spain)  joint financing by the  European Social Fund. This study was supported by research grants  AGL2016-77373-C2-2-R MINECO  and  PID2019-107030RB-C22   MICINN. We thank  Carla Gomez  for the labial palp 3D models.
6 ). To access the haploid induction rate (HIR), haploid plants were screened among 251~301 offspring from selfpollinated plants from both RNAi lines and edited lines. The RNAi lines produced 1. 12~1. 66% of haploid seeds during self-pollination and the edited sgpPLAIIγ lines 1. 45~1. 59% (Fig. 1j ). This result clearly shows that the loss-of-function mutants of pPLAIIγ is able to induce haploid plants in vivo. The Arabidopsis FAST-Red (promOLEO1:OLEO1-RFP) marker has been used as a simple and e cient method for high-throughput haploid identi cation, with >90% accuracy 8. To investigate the HIR and the origin of haploid induction, crosses were carried out using pPLAIIγ:RNAi as either female or male parents, and FAST-Red marker was used to rapidly differentiate haploids (Extended Data Fig. 7 ).
Acknowledgments This study was supported by grants from  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT, & Future Planning  (Grant No. :  2019R1A2C1004140 ) and from the  New breeding technologies development Program  (Project No. PJ01652301 ),  Rural Development Administration, Republic of Korea. T. W. was supported by  ANR  grant (no. ANR-19-CE20-0012 ), and by pack ambition recherche from the  Région Auvergne-Rhone-Alpes  ( HD-INNOV ).
Epitopes in close proximity (<40 nm) form a pair because two secondary antibodies, each coupled to a single-strand DNA probe, are revealed by fluorescently labelled oligonucleotides detected as a single fluorescent dot. PLAs were counterstained by immunodetection of CycA and His::YFP. Nota were dissected and fixed as previously described. Immunostained nota were incubated in PBS-glycerol (80:20, v/v), then mounted in PBS-glycerol-N-propylgallate (16:80:4, v/v/w). For immunofluorescence and PLA experiments, images were obtained using a spinning disk coupled to an Olympus BX-41 microscope (Roper Scientific, 40× or 60×, NA 0. 75 objective, CoolSnapHQ2 camera) and processed with Fiji software and Adobe photoshop CS6. Stimulated emission depletion (STED) microscopy was performed on immunostained samples as described above except that the secondary antibodies recognized rat and rabbit immunoglobulins (Sigma Aldrich, Anti-Rabbit Abberior® Star 635 and Anti-Rat 580, both at 1/100) and the mounting media was Prolong Gold (ThermoFisher P36930).
69). h-k Dsh localization is not impaired in CycA LOF (j, k) compared to the control (h, i) in pupae 17 h APF. Cells in late G2 phase (h, j) and during the metaphase (i, k). Cut immunostaining (blue); PON::GFP (green) and Dsh immunostaining (red). Anterior is to the left. Significance was determined by an unpaired two-tailed Wilcoxon test (c and g). * p ≤ 0. 1, **p ≤ 0. 05, ns, not significant. Scale bars, 5 μm. Source data are provided as a Source Data file. Division duration was calculated from recorded movies and corresponds to the time between the beginning of DNA condensation and the separation of the two daughter nuclei. Quantification and statistical analyses. Normalized Mud intensity for Fig. 6  was determined by measuring the intensity of fluorescence of Mud crescents using ImageJ or Fiji.
For each crescent, the three brightest z-slices (steps of 0. 5 μm) were chosen. The Raw-Intensity of the pixels (lassoed by hand in the software, excluding the centrosomes) was measured and then compiled. For fixed samples, the anterior of the cell was defined according to the PON::GFP staining observed. Anterior and posterior centrosomal Mud immunostaining was quantified by calculating the correlated total cell fluorescence (CTCF), where CTFC = integrated density-(area of selected cell × mean fluorescence of background). Ratios of anterior to posterior intensity were plotted with Kaleidagraph software. Statistical significance was calculated with an unpaired two-tailed Wilcoxon test. For each STED quantification, the signal was extracted along a line passing through the CycA crescent (freehand line tool, 2 μm width at the apical pole) of pI cells using the plot profile plugin from ImageJ software.
Then data were normalized to the highest value, and distances and pixels were correlated for each image. The data were plotted using Excel software. To test whether the distributions of division angles differ significantly, an unpaired two-tailed Mann-Whitney test was performed for α A/P angles (as angles were normally distributed according to the Shapiro & Wilk test), and for α A/B angles a Wilcoxon test was used (as angles were not normally distributed according to the Shapiro & Wilk test). Tests were performed using the Kaleidagraph software. For each curve, the mean ± the standard error is indicated in the main text.
Acknowledgements We specially thank  Y. Bellaïche ,  F. Schweisguth ,  M. H. Verlhac  and  P. O'Farrel  for antibodies and plasmids. The fly community for fly strains. Shelagh Campbell  and  Rachel Carol  for critical reading. Funding was provided by  Gefluc  ( Subvention 2018-2019 ), the  Centre National de la Recherche Scientifique and Sorbonne University. Pénélope Darnat was financed by grants from  la Ligue Nationale Contre le Cancer, France (Allocation doctorale   2016/2020 ;  MA/CD/SC-12836  and  JG/IP/SC-15958 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data monitoring by citizen scientists in the field of biodiversity is mainly achieved for free, since citizen science is primarily carried out by amateurs who are not paid for what they report. This lack of remuneration has been claimed to be either exploitative by those who study inequality of access and participation in amateur circles, or empowering by those who promote new digital solutions for naturalist reporting. In that respect, Faune France, a major citizen fauna database in France, represents a renewed relationship between amateurs and the general public through their contribution to this digital initiative. In contrast with explanations in terms of personal motivation or social stratification, participation in Faune France reveals a special attachment to the subject -birds, butterflies, frogsand the obligation toward nature felt by contributors, shown by their desire to pass on their observations digitally to recipients who are for the most part unknown.
Rather than a means of 'luring' amateurs when they give away information, commitments such as Faune France reporting is based on attachments to nature and others, and cautiousness in information disclosure. Data are given to an indeterminate other, be it a distant scientist or other participants on the internet. Hence the stress in this study on data sensitivity (given that the final destination of the data is unknown and there is a risk of it being misused), and on responsibility seen from the point of view of the contributing naturalist.
Introduction There are conflicting social explanations for participation in digital participatory science initiatives, ranging from the view that citizens are hired hands or cheap labor for scientists lacking other resources, to the perspective that these initiatives allow 'local voices' and provide an equal opportunity to contribute to effective knowledge production in society  (Gooding 1998 , Lawrence 2006). This article seeks to tackle these contrasting interpretations of the general development of participatory science by examining how people actually participate in them using the case of Faune France -the biggest citizen science biodiversity database in France. The paper aims to challenge the contrasting explanations for motivations that on the one hand highlight the creativity of amateurs  (Jennet et al.