dataesr/scientific-paragraphs-categorization huggingface dataset:
Culture and in situ tomography of Chlamydomonas reinhardtii cells The in situ of FIB-milling of C. reinhardtii centrioles was performed in the mat3-4 strain, as previously described (Le  Guennec et al, 2020). In brief, 4 ll of C. reinhardtii cells was deposited onto 200mesh copper EM (R2/1, Quantifoil Micro Tools) and vitrified using a Vitrobot Mark 4 (FEI Thermo Fisher Scientific). Cryo-FIB sample preparation was performed as previously described  (Schaffer et al, 2015 (Schaffer et al, , 2017)). The FIB-milled EM grids were transferred into a 300-kV FEI Titan Krios transmission electron microscope, equipped with a post-column energy filter (Quantum, Gatan) and a direct detector camera (K2 Summit, Gatan). Tomogram were acquired using Seri-alEM software  (Mastronarde, 2005) , with tilt series between À60°a nd +60°(bidirectional, 2°steps, separated at À0°or À20°) and a total dose around 100 electrons/A ˚2.
Multireference alignment was performed on this region, allowing us to classify our set into two classes: the "most proximal" class (n = 1,042) and the "most distal" class (n = 899), as depicted in Appendix Fig S7. For each set, the average was generated as a reference for the next alignment step. Each set was then divided into two independent halves and aligned for a few iterations to produce two averages. The resolution was estimated by generating the Fourier shell correlation (FSC) curve from the 2 averages using the EMAN2 package and choosing a cutoff at 0. 143  (Appendix Fig S6). One of the averages was bandpass filtered at this resolution, and the two half-sets were aligned on this filtered map to generate the final map. The new aligned set was then split again into two halves; each half was locally aligned on the A-C linker region of the final map.
Dehydration in ethanol, infiltration with Epon 812 (Serva Electrophoresis, Heidelberg, Germany), and final embedding were performed following standard procedures. Ultrathin serial sections (nominal 60 nm thickness) were cut with a diamond knife (type ultra 35°; Diatome, Biel, Switzerland) on an EM UC6 ultramicrotome (Leica, Wetzlar, Germany) and mounted on single-slot Pioloformcoated copper grids (Plano, Wetzlar, Germany). Sections were stained with uranyl acetate and lead citrate  (Reynolds, 1963)  and viewed with a JEM-2100 transmission electron microscope (JEOL, Tokyo, Japan) operated at 80 kV. Micrographs were acquired using a 4K charge-coupled device camera (UltraScan 4000; Gatan, Pleasanton, CA) and Gatan Digital Micrograph software (version 1. 70. 16. ).
Acknowledgements We thank the  BioImaging Center at Unige. We thank  Jürgen Plitzko  and  Wolfgang Baumeister  for providing support and instrumentation. We thank  Chandler Fulton  and  Lillian Fritz-Laylin  for providing N. gruberi cultures and advice. This work was supported by the  Swiss National Science Foundation (SNSF)   PP00P3_187198  and by the  European Research Council   ERC ACCENT StG 715289  attributed to P. G. , as well as the  Helmholtz Zentrum München  and the  Max Plank Society. Open  access funding enabled and organized by Projekt DEAL.
Data availability Subtomogram averages have been deposited at the Electron Microscopy Data Bank (https://www. ebi. ac. uk/pdbe/emdb/) with the accession codes EMD-10726, EMD-10727, EMD-10728, EMD-10729. Correspondence and requests for materials should be addressed to P. G. (paul. guichard@unige. ch). Expanded View for this article is available online.
Protein-protein interactions Further database mining screening for speci c protein interactions on the String server revealed few other possible pairs of associations (n = 8): ROBO3-CHN1, ROBO3-PRKCQ, ROBO3-LRRC3, DLG1-NCBP2, FURIN-PLG, PLG-MMRN2a, CELF-RRM2, and CRTC2-DENND4B (see Additional File 6). The FURIN-PLG and DLG1-NCBP2 associations were also detected in our literature survey. The rst one will be presented in the 'Discussion' (see Additional File 3 for the second association). However, most of these associations retrieved only poor information relative to the stress response. ROBO3 and CHN1 have been shown to interact in the brain with poorly-understood implications of CHN1 in neurological disorders [98] (CHN1 may have other stress-related functions, see Additional File 3). CELF2 (CUGBP Elav-like family member 2) and RRM2 (Ribonucleotide reductase M2 polypeptide) are both known to participate to messenger RMA (mRNA) metabolism  [99].
Acknowledgements Data used in this work were partly produced through the GenSeq technical facilities at  ISEM  and bene ted from the  Montpellier Bioinformatics Biodiversity (MBB) facility , both supported by the  LabEx CeMEB , and by  ANR  " Investissements d'Avenir" program  ( ANR-10-LABX-04-01 ). MGX acknowledges nancial support from the  France Génomique National infrastructure , also funded as part of " Investissement d'Avenir" program  managed by  ANR  ( ANR-10-INBS-09 ). We thank  N. Wagemaker  at the  Radboud University (Nijmegen, The Netherlands ) for his support, and  Prof. C. Grunau  at the  University of Perpignan Via Domitia (France)  for hosting M. V. K at various steps of this work. S. Ben Chehida  helped during the last stages of manuscript preparation.
Funding This research was funded by the  EU ERA-NET   FP7  programme for the  Cooperation in Fisheries, Aquaculture and Seafood Processing (COFASP)  and the  Agence Nationale pour la Recherche  (grant number:  ANR-16-0001-COFA ). These funding sources had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
This study investigates the magnitude of the noise introduced by the co-registration and interpolation in computing Stokes vector elements from observations by the Multi-viewing, Multi-channel, Multi-polarisation Imager (3MI). The 2-D polarimetric measurements from the Second-Generation Global Imager (SGLI) are weighted and averaged to produce two proxy datasets of the 3MI measurements, with and without considering the effect of the satellite motion along the orbit. By comparing these two datasets, we estimate the magnitude of the noise introduced by co-registration and interpolation that are necessary to offset the satellite's motion along the orbit. The results show that the noise is not symmetric about zero and not negligible when the intensity variability of the observed scene is large.
Data In this study, we produce 3MI proxy polarimetric measurements from the actual high-resolution polarimetric measurements obtained by the Second-Generation Global Imager (SGLI) aboard the Global Change Observation Mission -Climate (GCOM-C) satellite  (Imaoka et al. 2010). The SGLI sensor provides polarized normalized radiances at 1 km nadir resolution within a 1150 km-wide swath. As the SGLI is a push-broom sensor, the interpolation noise is expected to be negligible compared to that introduced by the 3MI's more complex co-registration and interpolation. We treat the SGLI measurement as truth and evaluate the magnitude of noise introduced by the interpolation and co-registration by producing 3MI-proxy data. One week of global SGLI Level 1B data near the 2008 September equinox (September 20-26) is used.
Estimation of the noise As the nominal resolution of the 3MI is 4 km and that of the SGLI is 1 km, 4 × 4 SGLI pixels are aggregated to produce a 3MI pixel. We repeat the aggregation for every 4 × 4 SGLI pixel blocks in three original ("Lt_P2_m60", "Lt_P2_0", and "Lt_P2_p60") radiance data in the SGLI Level 1B product. These three datasets correspond to the radiance measured at 0. 869 µm with polarizers at three different directions at -60°, 0°, and 60° with respect to the along-track axis of the satellite (i. e. ,  781 ,  1 ,  %81 ). From the aggregated data, we compute the normalized radiance and polarized normalized radiance by Eqs. (4) and (  5 ). The results are referred to as "reference data" hereafter. A similar method is applied to produce the 3MI "proxy data" which is compared to the reference data to estimate the magnitude of the noise.
The proxy data is different from the reference data in that it incorporates the effects of satellite's motion and interpolation. To simulate the motion of satellite between the acquisition of each image (1. 8 km), we compute the contribution of every SGLI pixel to the shifted 4 × 4 grid as in Table  1. The contribution factors are multiplied to the measured radiance in every SGLI pixel to perform the weighted average. After computing the weighted average, we obtain the shifted, aggregated images of "Lt_P2_p60" and "Lt_P2_m60" from which unshifted (with respect to the "Lt_P2_0" image) aggregated images are produced by linear interpolation. The linear interpolation is selected for the simplicity and the locality, but the noise estimation described in this subsection as well as the noise simulation detailed in Subsection 2.
Stratification of data To further the analysis of the noise, we classify pixels into four categories: coastlines, clear sky over land, clouds over land, along-track directions by 20 SGLI pixels to cover all SGLI pixels used for the noise estimation and to minimize the pixels on boundaries. A pixel is classified as "clear-sky over land" when it is flagged as "confidently clear" and "land cover 100%", "cloud over land" when it is flagged as "confidently cloudy" and "land cover 100%", "cloud over water" when "confidently cloudy" and "land cover 0%", and "coastline" when land cover is between 5% and 95%. Only pixels with glint angle greater than 35° are collected for coastlines and cloud-over-water classes to avoid contamination by sun glints. In every class of data, the aforementioned differences (Δ % and Δ) are stratified by the along-track Laplacian that is defined as follows: 𝐿 FG = ' ) * (2𝑋 1 -𝑋 IJ -𝑋 KJ ), (6) where  IJ is the  1 of the adjacent pixel in the negative along-track direction, and  KJ is the  1 of the adjacent pixel in the positive along-track direction.
Prediction of noise Figure  8  shows the predicted magnitude of noise based on the Monte Carlo simulation for cloudy pixels over water. The simulation predicts the median (red curves) that matches well to the estimation based on the SGLI data (dotted curves). As the empirical 95th percentile of the  FG / is 0. 39 and that of  FG is 0. 070, the abscissas of Fig. 8  cover the large portion of the plausible range of the data. A slight overestimation of the DOLP difference occurs in the entire range, but the maximum difference is 0. 0010 (i. e. 2. 2%) in the bin 0. 36 <  FG / < 0. 365. The difference between the predicted and estimated normalized polarized radiance is less than 10 I: in  FG < 0. 12. The difference reaches 1. 5 × 10 I: (i. e. 5. 4%) in the bin 0. 16 <  FG < 0. 17. Overall, the simulation can predict the median of the noise estimation at better than 5% accuracy.
The successful prediction of the noise by the statistical model implies that the noise distribution in the cloud-over-water class inferred from the SGLI data is a result of the inherent horizontal structure of clouds. The method is likely be applicable to other spatial scale to predict the magnitude of noise of the past, current, and future polarimetric instruments sharing similar designs. In the application to other satellite data, it is necessary to collect the several statistics that are used in the simulation, namely, normalized radiance distribution, normalized-radiance-stratified sub-pixel inhomogeneity (weighted variance), normalized-radiance-stratified DOLP, and normalized-radiance-stratified AOLP. However, most of these statistics are readily available from observations (past and current sensors) or simulations (future sensors).
The noise statistics based on the SGLI data and Monte Carlo simulation agree within 5% in terms of median bias, implying the predictability of the noise for cloudy pixels at an arbitrary spatial scale. The sensitivity study on the assumed power-law coefficient proved the need for the spatial correlation to be included in the noise prediction. The method of prediction is applicable to past, current and future missions with a polarimetric instrument based on a similar design, paving a way to better predict the performance of the instrument on orbit at the stage of planning. Finally, although simulations presented here are limited to cloudy scenes, similar diagnosis might be made for land surfaces under clear sky conditions for use in aerosol remote sensing applications.
and clouds over water. The classification is based on the land-water flag in the SGLI Level 1B dataset and the cloud flag algorithm developed for this study. The activity diagram (flow-chart) of the cloud flag algorithm is shown in Fig. 1 while individual test conditions are listed in Tables 3 (pixels over water) and 4 (pixels over land). To compute cloud flag in the SGLI Level 1B POLDK product's coordinate, other SGLI L1B products (VNRDK, VNRDL, IRSDK, and IRSDL products) are projected onto the POLDK grid. Once the cloud flag is derived, both land-water flag and cloud flag are extended into the.
Acknowledgements We appreciate  Japan Aerospace Exploration Agency (JAXA)  for providing the GCOM-C/SGLI data needed for the research. This research is conducted in a framework of jointly-funded postdoctoral fellowship by  Centre National d'Études Spatiales (CNES)  and the  Make Our Planet Great Again  (MOPGA) program by  Campus France. The authors are grateful for the financial support and various encouragements by  CNES ,  MOPGA , and the  Laboratoire d'Optique Atmosphérique. https://doi. org/10. 5194/amt-2020-407Preprint. Discussion started: 20 October 2020 c Author(s)  2020. CC BY 4. 0 License.
Some studies address the whole biodiversity (i. e. , all species present independently of the taxonomic group) from environmental samples of water, biofilms and preservative solution from field samples (e. g. , ethanol from macroinvertebrate samples). Great advances were acknowledged in the special session, namely in the use of metabarcoding for detecting hidden biodiversity, juvenile stages, low-abundance species, non-indigenous species and toxicity potential, and ultimately for ecological monitoring of diatoms and invertebrates. Yet, several drawbacks were highlighted and need further work, which include: taxonomic gaps in the reference databases (including gaps at species level and on intraspecific variability) or absence of public databases (e. g. for meiofauna), still high sequencing costs, the need of a substantial bioinformatics effort, difficulties in establishing the amount of environmental sample necessary for a good DNA extraction and the need for testing different genetic markers to obtain accurate results.
, 2014; diatom barcode database, Rimet et al. , 2016; DNA extraction, Vasselon et al. , 2017a; quantification bias, Vasselon et al. , 2018; bioinformatics treatment, Coissac et al. , 2012). In France, diatom metabarcoding has been applied successfully at small (80 samples,  Vasselon et al. , 2017b; Rivera et al. , 2020)  and larger monitoring networks (447 samples). In rivers of Central Portugal, the comparison between the Portuguese official monitoring index for diatoms (IPS -Indice de Polluosensibilité Spécifique), calculated based on morphological identification data and on Operational Taxonomic Units (OTUs) converted into species data, showed a high correlation Feio et al. Molecular tools for aquatic ecosystems' assessment  (Mortágua et al. , AIL2018). Besides, more than half (ca. 56 %) of the samples shared the same water quality class either using the conventional or the molecular approach.
However, the ecological status of the lake varied between these two methods since floristic inventories differed significantly  (Rivera et al. , 2018; Rivera et al. , AIL2018). The main reason for this discrepancy was the incompleteness of the diatom reference database (Diat. barcode, formerly called R-Syst::diatom in  Rimet et al. , 2016). In Cantabria, five mountain lakes were sampled for molecular analysis of environmental samples (water and sediment). The first is located at ca. 1870 m of altitude in the Liordes Valley, a unique ecosystem in the Picos de Europa massif, located in a glacial-karst depression surrounded by calcareous walls. The Lloroza lakes (ca. 1800 m of altitude) are small lagoons of karstic nature located in Picos de Europa National Park in the Cantabria province. Finally, the Enol and Ercina (at ca.
COI The DNA barcode region elected most frequently for the identification of individualized specimens is a fragment of the mitochondrial COI gene  (Herbert et al. , 2003). The Cytochrome c (COI) is an amino acid sequence that is highly conserved  Feio et al. in eukaryotes, differing by only a few residues. There are robust universal primers for it that recover most animal phyla, and thousands of reference sequences are available in public databases such as BOLD and GenBank (Ratnasingham  & Hebert, 2013; Hebert et al. , 2013). However, the high variability in the third position of the COI codons makes it difficult to design universal primers for metabarcoding DNA studies  (Ficetola et al. , 2010). For fish identification, most used barcode markers in DNA reference collections are the COI and cytochrome b (Cytb) genes, other mitochondrial genes, which can confirm taxonomic identification at the species level.
In the Lima estuary, DNA from meiofauna communities was extracted from intertidal sediments. In this case, the target genes were the COI and 18S ribosomal RNA (18S rDNA) gene. MiSeq amplicon sequences were processed in mothur (version 1. 39. 5,  Schloss et al. , 2009)  by using appropriate bioinformatic procedures; while the taxonomy of the processed sequences were assessed by blasting against the full ntNCBI database  (Fais et al. , AIL2018). This database was chosen due to the lack of adequate reference sequences in better-known databases, such as BOLD  (Ratnasingham & Hebert, 2007)  and Silva  (Pruesse et al. , 2007). In the French coastal lagoons, the invertebrate communities were as well analysed from eDNA with COI marker.
, AIL2018, Mortágua et al. , 2019 , Rivera et al. , 2018; Rivera et al. , 2020). Sample sequences obtained from metabarcoding were then analysed using the software mothur (version 1. 39. 5,  Schloss et al. , 2009). Taxonomic assignment of OTUs was based on the Diat. barcode library (available at: https://www6. inra. fr/carrtel-collection_eng/Barcoding-database). In French lagoons, the process was similar, but the DNA extraction was done with the kit Power Water DNA Isolation MOBIO ® and sample sequences obtained from metabarcoding were then analysed using the software QIIME (https://qiime2. org). In Azorean lakes and thermal springs, DNA was extracted up to 24h after sample collection, according to the gram-negative bacteria protocol of PureLink TM Genomic DNA Mini Kit (Invitrogen, Carlsbad, CA, USA), followed by amplification of genes targeting 16S rDNA and cyanotoxins (Microcystin, Saxitoxin and Anatoxin-a) using conventional PCR and electrophoresis protocols  (Cordeiro et al.
Then a Denaturing Gradient Gel Electrophoresis (DGGE) was run for each community, conducted in a DCode system (Bio-Rad, Hercules, CA, USA). DGGE images were converted, normalized, and analysed with the software BioNumerics 7. 6 (Applied Maths, Sint-Martens-Latem, Belgium) to obtain the relative abundances according to gel band intensity (OTUs). In the Russian lake Oiskoe, planktonic microeukaryotes were assessed before and after ice-and-snow cover formation  (Díaz-Quijano et al. , AIL2018). The focus was set on phytoplankton and general protists, but other eukaryotic actors of the microbial loop, such as ciliates and fungi were assessed as well. General eukaryote primer pair targeting the V4 region of the small subunit 18S rRNA gene was used  (Balzano et al. , 2015). This is a modification of Stoeck's primer pairs  (Stoeck et al.
Fish and invertebrates For marine life, core COI reference databases for the most prominent groups of Portuguese and Iberian fish and macroinvertebrates were made publicly available on BOLD systems. Regarding fish, in addition to the Portuguese marine ichthyofauna  (Costa et al. , 2012) , reference databases have been generated for the Mediterranean  (Landi et al. , 2014) , the North Sea and British Isles species  (Knebelsberger et al. , 2014). A published compilation for all European marine  Feio et al. fish species is available as well  (Oliveira et al. , 2016). For freshwater fish species, the reference database for European species is almost complete concerning standard DNA barcodes (COI) and public data can be found in GenBank and BOLD databases. However, there is only very limited 12S sequence data available that can be used as a reference to taxonomically annotate eDNA derived OTUs.
Among the invertebrates there are published databases and other scattered DNA barcode contributions available for annelids, namely Polychaeta  (Lobo et al. , 2016; Ravara et al. , 2017) , for molluscs (Gastropoda:  Borges et al. , 2016; bivalve woodborers: Borges et al. , 2012) , and for crustaceans (e. g. Amphipoda;  Lobo et al. , 2017b).
Meiofauna Concerning meiofauna, to the best of our knowledge, there are no specific databases. Yet, Tang and collaborators (2012) gathered a total of 12 000 sequences (generated and retrieved from GenBank) across 55 meiofaunal datasets comprising 3 taxonomic ranks (15 species complexes, 26 genera, and 14 higher taxa above the genus level, including orders, classes, and phyla), using either 18S or COI markers.
Diatoms For diatoms, Diat. barcode library (formely called R-Syst::diatom in  Rimet et al. , 2016)  (https://www6. inra. fr/carrtel-collection_eng/ Barcoding-database) and was used in the studies presented at AIL2018 conducted in Portugal by  Mortágua et al. (AIL 2018; 2019)  and in France by  Rivera et al. (AIL 2018). This database is open access and contains 18S and rbcL barcodes. In addition, R-Syst::diatom provides information concerning morphological diatom features (e. g. , biovolumes, chloroplasts, etc. ), ecological features (taxa preference to pollution) and life-forms (mobility, colony-type). The database is uploaded and curated every six months. The sequences obtained in the Russian study are not attributed to any taxocenose-specific database but should be made available to the builders of a cryophyllic diatom and green algae ribosomic RNA database at the Helmholtz Centre for Polar and Marine Research in Potsdam, Germany (shuang@awi.
V. Multidisciplinary international networks The metagenomics is an area where extended networks tend to be formed in order to easily tackle all the fields involved, encompassing fieldwork and sample collection to laboratory procedures, taxonomic expertise and molecular analyses. This need is clear in the global distribution of authors of the SS (Fig. 1 ). The University of Minho team (Portugal) has integrated the Consortium for the Barcode of Life (CBOL) from early stages and later the International Barcode of Life (iBOL) and in collaboration with the Museu Nacional de História Natural e Ciência, Instituto Português do Mar e da Atmosfera, the Portuguese Institute of Malacology, the research institutes IMAR, CIIMAR and CNC/Biocant, and the Universities of Guelph (Canada), Bangor (UK) and Vigo (Spain) works to build core reference databases for marine life.
Ecology and conservation Molecular analyses in aquatic ecosystems brought not only new information but also new questions. DNA barcoding studies on Portuguese marine life have been revealing numerous cases of comparatively high intra-specific divergences, suggesting the existence of considerable hidden diversity and putative cryptic species across diverse marine taxa, including fish and major groups of invertebrates  (Costa et al. , 2012 , Landi et al. , 2014 , Lobo et al. , 2016 , 2017a , Oliveira et al. , 2016). These findings suggest that populations of marine organisms may be much more structured than previously thought, calling for a continuous effort on the description of the hidden diversity and further completion of the reference databases. In order to improve the efficiency of amplification of COI barcodes from marine macrozoobenthos,  Lobo et al.