dataesr/scientific-paragraphs-categorization huggingface dataset:
[4] , but on Kinetics400 -1. 9p. p for 69. 6%. We have the same computational budget as they use 4 views for pretraining. Using 8 frames decreased performance by -2. 0p. p. , -1. 2p. p. and -4. 2p. p on Kinetics400,UCF101 and HMDB51. It maintains results that outperform on the three benchmarks ρMoCo and ρBYOL with 2 views. It suggests that SCE is more efficient with fewer resources than these methods. By comparing our best with approaches on the S3D backbone that better fit smaller datasets, SCE has slightly lower performance than the state of the art: -1. 0p. p. on UCF101 and -0. 3p. p. on HMDB51. Video retrieval We performed video retrieval on our pretrained backbones on the first split of UCF101 and HMDB51. To perform this task, we extract from the training and testing splits the features using the 30-crops procedure as for action recognition, detailed in Appendix D.
, Standford Cars [99], CIFAR-10 [90], CIFAR-100 [90], DTD [100], Oxford 102 Flowers [101], Food-101 [102], Oxford-IIIT Pets [103], SUN397 [104] and Pascal VOC2007 [105]. These datasets cover a large variety of number of training images (2-75 k) and number of classes. We report the Top-1 classification accuracy except for Aircraft, Caltech-.
2 Comparison with the state of the art We use the ResNet3D-18 and ResNet3D-50 network [110] and more specifically the slow path ofFeichtenhofer et al. [111]. We kept the best hyperparameters from Sect. 4. 2. 1 which are λ = 0. 5, τ m = 0. 05, RGB difference with probability of 0. 2, and color strength = 1. 0 on top of the strong -α and strong -β augmentations. From the randomly sampled clips, we specify if we keep 8 or 16 frames. Pretraining dataset To compare SCE with the state of the art, we perform pretraining on Kinetics400 [107] introduced in Sect. 4. 2. 1. Evaluation datasets UCF101 [108] and HMDB51 [109] have been introduced in Sect. 4. 2. 1. AVA (v2. 2) [112] is a dataset used for spatiotemporal localization of humans actions composed of 211k training videos and 57k validation videos for 60 different classes.
| INTRODUC TI ON The use of environmental DNA (eDNA) has revolutionized biodiversity studies  (Taberlet et al. , 2012; Yoccoz, 2012) , culminating with the development of high throughput sequencing (HTS) technologies  (Shokralla et al. , 2012). The application of HTS to amplicon sequencing (i. e. , metabarcoding) allowed the retrieval of massive amounts of genetic data from many different organisms. This breakthrough has contributed significantly to academic research, especially in the fields of ecology, evolution or biogeography  (Beng & Corlett, 2020; Boenigk et al. , 2018; Gaither et al. , 2022)  and, in applied research, to environmental quality assessment  (Lezcano et al. , 2017; Pawlowski et al. , 2021) , among others. Nevertheless, to draw sound ecological or biogeographical conclusions based on metabarcoding data, an accurate reference taxonomic database is required.
This database must be as exhaustive as possible, with representatives of the main clades that constitute the surveyed groups. This is the case for the most popular eukaryotic metabarcoding marker, the gene coding for the small subunit of the ribosome known as 18 S rRNA gene  (Berney et al. , 2017; del Campo et al. , 2018). The eukaryotic 18 S reference database PR 2  (Guillou et al. , 2013)  is very large and currently comprises (last accessed 06 June 2022) almost 200,000 taxonomic annotated sequences, which makes possible a relatively accurate placement of newly obtained environmental sequences into known clades. This extensive taxonomic database and the universal presence of 18 S rRNA in all eukaryotes has permitted the development of generic eDNA PCR protocols to retrieve a large part of environmental biodiversity without requiring prior knowledge of the taxonomic composition of communities (but see  Vaulot et al.
, 2022). However, despite these attributes, the 18 S rRNA gene is too conserved to be used for species identification in both micro-and macroscopic sized eukaryotes  (Heger, Pawlowski, et al. , 2011; Lara et al. , 2022; Pinseel et al. , 2020; Tang et al. , 2012). Due to this the taxonomic resolution of the 18 S rRNA gene is usually treated as operational taxonomic units (OTUs), which clusters the sequences with regards to their similarity, but lumps organisms with divergent ecological functions  (Lara et al. , 2022). As species are the only truly biologically and ecologically meaningful (and comparable) units of diversity in eukaryotes  (Bickford et al. , 2007; de Queiroz, 2005; Wiley, 1978) , specific protocols designed to discriminate down to species-level and curated databases are necessary in eDNA biodiversity studies.
, 2011; Kostka et al. , 2017; Macher et al. , 2021; Moniz & Kaczmarska, 2009). Despite the proven success of these approaches for (organism) barcoding, protocols have been seldom transferred to environmental studies. The exceptions have been ecological research involving diatom rbcL barcodes  (Hamsher et al. , 2011; Kermarrec et al. , 2013) , for which there is an extensive database  (Rimet et al. , 2019)  and, most recently, COI in foraminiferans  (Girard et al. , 2022). Both of these groups are excellent ecological and paleoecological bioindicators in marine environments. In addition, diatoms are also studied in freshwater systems, including streams and a variety of shallow lotic settings. Diatoms are also widely used as important proxies for ecological status assessment (e. g. , the European Water Framework Directive; WFD; European  Commission, 2000).
Finally, we describe a bioinformatic pipeline to analyse the resulting data. As a case study, we applied this protocol to a collection of samples from the Western Palearctic region covering sites with naturally high (peatlands) and low (estuaries) diversity/density of Arcellinida. We subsequently generated an environmental sequences database, which consists of a collection of ASVs that can be used in future applied studies. This database is built with both Sanger sequencing of cloned environmental amplicons as well as Illumina sequencing, HTS. We also compared our metabarcoding data with morphological observations to explore the capacities of our polyphasic methodology to draw conclusions on the environmental diversity of Arcellinida.
| Data curation The resulting cloning PCR sequences were checked and trimmed from cloning vector sequence and primer using the software Geneious Prime version 2019. 0. 4. We only considered sequences which were obtained from at least three independent colonies. To corroborate the identity of the sequences as Arcellinida, we performed blastn analyses  (Altschul et al. , 1990)  against both the whole GenBank database and our own database (González-Miguéns,  Todorov, Blandenier, et al. , 2022)  (accessible in supplementary material as "pipeline. rar"). The Illumina reads were curated using the following pipeline: (1) Primers trimming and demultiplexing was done with Cutadapt version 2. 8  (Martin, 2011) , following the pipeline "Cutadat_Piepline. bash" (provided as supplementary materials "pipeline.
rar"). (2) Reads were ordered in the same direction using MAFFT version 7. 490  (Katoh & Standley, 2013) ; (3) the resulting reads per sample were analysed with the dada2 R package  (Callahan et al. , 2016) , following the tutorial inhttps://benjj neb. github. io/dada2/ tutor ial_1_8. html, with minor modifications (supplementary material as "pipeline. rar"). First, we visualized the quality profiles of the forward and reverse reads, to filter and trim them, truncating the reads after positions 210 and 200 for the forward and reverse, respectively. We dereplicated the reverse and forward reads, and then merged the paired reads, generating amplicon sequence variants (ASV). Then, we removed chimeric sequences. We only considered the ASV that were composed of at least 10 reads to reduce the consequences of methodological errors such as "tag jumping"  (Schnell et al.
, 2015). (4) For the taxonomic assignation we used VSEARCH version 2. 14. 1  (Rognes et al. , 2016)  using a curated database from González-Miguéns,  Todorov, Blandenier, et al. (2022) , including COI sequences from GenBank public database (Amoebozoa, Archaeplastida, Excavata, Opisthokonta and SAR) (supplementary materials "pipeline. rar"). The ASV with an identity value higher than 0. 82 with the Arcellinida database were checked in GenBank to verify their taxonomic assignation.
| Seqlogo graphs We used the ggseqlogo R package  (Wagih, 2017)  to create sequence logos, a graphical representation of the sequence conservation of nucleotides for COI and SSU specific Arcellinida primers across the groups molecular diversity. These illustrations allow to graphically visualize mismatches between primers and binding sites that can generate potential amplification biases. We implemented these representations for our COI specific primer ArcCOIR for each Glutinoconcha infraorder, as well as for the outgroup (Tubulinea amoebae). Arcellinida COI sequences were retrieved from (González-Miguéns,  Todorov, Blandenier, et al. , 2022)  as well as from the GenBank database, from which we extracted the ArCOIR primer region. Moreover, we also represented potential mismatches with specific primers published in the literature that target the SSU gene.
We also created Seqlogo graphs for each Arcellinida infraorder and suborder. The primers evaluated were 2345 and 2088  (Ruggiero et al. , 2020) , each one in its corresponding binding site. Arcellinida SSU sequences were retrieved from "Supplementary data 5" of González-Miguéns,  Todorov, Blandenier, et al. (2022) , as it includes the most comprehensive database for this gene, including both barcoding and transcriptomics data.
, 2012). Often, however, eDNA sequences cannot be assigned to known groups/species, because databases are incomplete and no close related organisms has been characterized molecularly. These sequences resulting from lack of taxonomic information on microbes form so-called "environmental clades", whose number and importance depend on the degree to which biodiversity has been barcoded. In our data, we observed only one deep branching eDNA clade (to the level of an infraorder), which cannot be assigned yet to any known Glutinoconcha infraorder. We designated this clade as incertae sedis, pending morphological characterization of these organisms (Figures  1  and 4 ). Here, we provide a large dataset of Arcellinida environmental COI sequences (obtained through both cloning/sanger sequencing and Illumina) which includes all infraorders described as for now within Glutinoconcha, except Volnustoma and Hyalospheniformes, which are exclusively soil-dwelling lineages.
, 2023). In protists, primers located in the most conserved regions of the SSU can be even used to retrieve sequences from totally unknown groups, which are absent from molecular databases. For this reason, SSU has been (and still is) the principal marker gene for protist metabarcoding. In the other hand, COI does not contain regions that are conserved enough to capture all eukaryotes, which implies that general primers need to be highly degenerated  (Collins et al. , 2019). This generates limitations for most metabarcoding applications  (Deagle et al. , 2014) , preventing the development of general protocols for all eukaryotes. However, when the aim is to develop a clade specific approach, this weakness can be turned into a strength. Indeed, less unwanted taxa are amplified which increases the amount of information about the target organisms; this allows studies focussed on alpha diversity of protist groups, an approach that cannot be followed using SSU as a metabarcoding molecular marker.
, 2020). In contrast, there is a need for faster evolving genes, and, among them, only COI can rely on a sufficiently extensive database from Arcellinida to design primers and identify ASVs (González-Miguéns,  Todorov, Blandenier, et al. , 2022). Mitochondrial editing, which has been characterized in Arcellinida, increases the amount of insertions and deletions in COI  (Yang et al. , 2017) , which facilitates specific primers design. In Arcellinida, concretely, two codons have been deleted with respect to other Tubulinea (Figure  5 ). This is the region where the primer ArCOIR has been designed, allowing a high degree of specificity with respect other eukaryotes, and reducing the co-amplification of other taxa, especially when Arcellinida are numerous (Figure  2 ).
g. , lakes and reservoirs) and soil ecosystems, as their heterotrophic condition allows them to live where light does not reach. In addition, Arcellinida behave like microbial top predators in the ecosystems where they live, which means that they resume all the bioindicative information from the rest of the foodweb. Further studies that expand the reference COI database will improve the interpretative power of Arcellinida metabarcoding studies, and for example provide a deeper understanding of many areas of research with the group, such as the link between Arcellinida test traits and ecological functions  (Marcisz et al. , 2020). Expanding the database of barcoded species will provide researchers with a more accurate taxonomical identification of the ASVs, which will in turn allow for better ecological interpretations.
48 C and 38 I) and Incertae Sedis 4 (4 C and 0 I) (Figures 1, 3 and 4). Some of these ASVs, 12 in total, corresponded to organisms that had been previously isolated and barcoded (González-Miguéns, Soler-Zamora, Useros, et al. , 2022; González-Miguéns, Soler-Zamora, Villar-Depablo, et al. , 2022). These taxa were Galeripora bufonipellita (3 ASVs assigned), Centropyxis F I G U R E 3 Detail of the phylogenetic tree of Arcellinida based on cytochrome oxidase subunit I (COI) (Figure 1). The symbols on the map of the Iberian Peninsula represent the different localities where sequences of the group of Galeripora arenaria species complex have been found. At the bottom are SEM images for Galeripora bufonipellita and G. sitiens. The bold words at the tip of the tree represent the GenBank accessions for the eDNA ASVs obtained in this study.
sequences of the infraorders Volnustoma (within Glutinoconcha) and of the suborders Organoconcha and Phryganellina are not yet barcoded. The expansion of the barcode database will allow finding the limits of our protocol at deeper taxonomic levels. The situation is different with SSU; indeed, the specifically designed primers 2435 and 2088 are located in a highly conserved region which does not vary between the different infra-and suborders.
Detail of the phylogenetic tree of Arcellinida based on cytochrome oxidase subunit I (COI) (Figure 1) and the bottom part of the Figure 3. Symbols on the map of Europe represent the different localities where species from the Plagiopyxis clade have been found. At the bottom are SEM images for Plagiopyxis callida and Golemanskia viscidula. The bold words at the tip of the tree represent the GenBank accessions for the eDNA ASVs obtained in this study.
F Detail of the phylogenetic tree of Arcellinida based on cytochrome oxidase subunit I (COI) (Figure 1) and the bottom part of the Figure 3. Symbols on the map of Europe represent the different localities where species from the Plagiopyxis clade have been found. At the bottom are SEM images for Plagiopyxis callida and Golemanskia viscidula. The bold words at the tip of the tree represent the GenBank accessions for the eDNA ASVs obtained in this study.
Boundaries The upstream boundary conditions consist of measured hourly discharges imposed at Tan Chau (the Tien River) and Chau Doc (the Hau River). The tidal data base of TPXO (https://www. tpxo. net/global) is applied as a real sea water level dataset in both the West and East Seas for the downstream boundaries. A coefficient to calibrate sea level and a coefficient to calibrate tidal range are also adjusted to transfer the information between a large-scale model and the boundaries of a local model  (Pérez-Ortiz et al. , 2013).
Parameters of hydrodynamic simulation The bed friction is parameterized by having recourse to the Nikuradse roughness length scale,    (Nikuradse, 1950) , as shown in  Tassi et al. (2014)  and  Julien (2010) : 𝑘 𝑠 ≈ 3𝑑 50 (7) where  50 is the median grain diameter (m) The simulation uses a constant (horizontal) viscosity throughout the domain equal to 10 -6 m 2 /. The default value incorporates both molecular viscosity and eddy viscosity in the constant viscosity model. Wind is variable in time and space. Hourly wind data at 10 m from NCEP NOAA (https://www. ncep. noaa. gov/) are used in the simulation. According to the introduction by the Institute of Oceanographic Sciences of the United Kingdom, the wind infleunce coefficient is set to 0. 565 x10 -6 because most of the wind velocity is < 5 m/s (TELEMAC -2D User Manual).
Thus, four additional scenarios are established corresponding to the two main scenarios with the projected relative sea level rises of 30 cm in 2050 and 100 cm in 2100  (MONRE, 2016). These scenarios are summarized in Table  2. In Scenario S1, the initial set-up of the model is based on the current status of the VMD, the flood data is in the year 2011. The flood event in 2011 is one of two recent severe floods in Vietnam  (Triet et al. , 2020 and Chen et al. , 2020). The measured daily discharge at Tan Chau and Chau Doc stations are used as the upstream boundary conditions, and the hourly sea levels at the East Sea and the West Sea are used as the downstream boundary condition. In Scenario S2, the VMD consists of only Tien River and Hau River with eight estuaries, which correspond to the configuration of the delta before human intervention.
The data for simulation is the flood event in 2011. The measured daily discharge and the hourly measured sea levels are the same with the previous scenario. The model set-up for Scenarios S3 and S4 is similar to Scenario S1 and S2, respectively, with the same upstream discharges. However, the hourly sea levels at the downstream boundary are increased by 30 cm in both the East and West Seas, corresponding to the rising sea level scenario in 2050. This value of 30 cm is extracted from the report of Climate changes and sea level rise scenarios for Vietnam, which was conducted by the Ministry of Natural Resources and Environment (MONRE) in 2016. Scenarios S5 and S6 have the same procedure as the previous scenarios with the rise of 100 cm sea level at the downstream boundary condition, corresponding with the rising sea level scenario in 2100.
Inflorescence Meristem Pipeline This pipeline can be executed by running the script main_script_inflorescense_meristem, which consists of the following main stages (Fig. 2 ): 1. -Automatic detection of the expression domain in the inflorescence meristem. This pipeline executes some pre-processing steps as mentioned above, followed by an Otsu thresholding, producing a binarised image, with domains having a value of 1 when the pixel intensity is above the threshold, and 0 when it is below. As the floral primordia usually also express the reporters present in the inflorescence meristem, the binarised image shows multiple domains. Then, the code assumes that the binarised domain corresponding to the inflorescence meristem is the one that is the closest to the geometric centre of the ensemble of the domains' centroids in each image (see Note Overall, this pipeline enables to quantitatively characterise in a high-throughput manner the features of fluorescence domains in the inflorescence meristem.
Floral meristems pipeline This pipeline can be executed by running the script main_script_floral_meristems, which consists of the following stages (Fig. 4 ): 1. -Manual outlining of the different floral meristems in a Gaussian blurred sum slices projection of the autofluorescence channel image or a fluorescence channel image that shows the floral meristems (see Notes 15 and 16). In a later stage of the pipeline, such primordia outlines will be paired with automatically found fluorescence domains, and the domains remaining unpaired with outlined primordia will be discarded. Hence, this first stage will help determining the floral fluorescence expression domains of interest. This stage also allows to compute the projected area of the floral primordia and their distance from the centre of the inflorescence meristem (see Note 17).
3. For simplicity, we assume that different images of an experimental set have the same resolution in the z direction. If it is not the case, the g factor needs to be the resolution of the 3D image (in µm 3 ) to also take into account different resolutions in the z direction. 4. The experienced Matlab coder is encouraged to extract further observables from the fluorescence domains. See Readme file of the described pipelines in the RegionsAnalysis code. Also, we encourage the reader to push such code changes back to the RegionsAnalysis repository in GitLab, so that others can benefit from them. For this reason, we have included the Otsu prefactor, which can help to better detect all the domains of interest. 9. To find the fluorescence domain of the inflorescence meristem, depending on the images and the fluorescence reporters under study, the Otsu prefactor may need to be adjusted.
12. Note that the R-square coefficient of the nonlinear fit will not be a very good indicator for our purpose  [33] , although we provide it as an outcome in the pipeline; usually, the discrepancies of the fit with the data will be more in the fluorescence decay tail and in the fluorescence maxima, and this may be difficult to grasp with this coefficient. 13. We invite the reader to also compute the autofluorescence mean levels through an alternative method, e. g. by measuring it with Fiji [34], or by executing the script main_script_ROIs from RegionAnalysis. 14. When fittings are showing a low degree of variability on the profile shape parameters nX, one can find the average of these parameters and perform again the fittings, but having the exponents fixed by these averages. 15. To outline the floral primordia, the sum slices projection of the autofluorescence image (i.
Acknowledgements BL and PFJ thank  James Locke ,  Elliot Meyerowitz  and  Henrik Jönsson  for fruitful discussions and their support. PFJ would like to thank  Michael Elowitz  and  Pulin Li  for discussions about the quantification of fluorescence domains that had an influence on the elaboration of the presented method,  Hugo Tavares  for feedback on the data analysis. PFJ also thanks  George Coupland  and  Martina Cerise  for fruitful discussions and  Gabriel Rodriguez Maroto  and  Pau Casanova-Ferrer  for a critical feedback on the manuscript and for having contributed to the current version of the RegionsAnalysis code; the pipelines they have contributed to will be discussed elsewhere. PFJ. is grateful to the  Herchel Smith Foundation  for funding his postdoctoral fellowship. PFJ and BL thank the  Gatsby Charitable Foundation  for the provided support and PFJ thanks the  Max Planck Society  for its support as well.
( https://gitlab. com/slcu/teamHJ/pau/RegionsAnalysis, see Notes 4 and 5). The relevant pipelines can be executed by means of the different scripts located in the RegionsAnalysis/Applications/ShootMeristemsAnalysis subfolder, with the help of the Readme file found in the same folder. Such pipelines will enable to analyse at once all the multichannel microscopy z-stacks in either lsm or czi formats that are located in a specific folder. After executing the pipelines of interest, different outputs will be generated in the containing folder of the microscopy data, including a csv file with the main descriptors of the studied fluorescence domains (see Readme file). Available microscopy data of shoot meristems with fluorescence reporters can be found at [16]. As a start, we suggest the reader to use the script main_script_basicpipeline_fittings, which enables the user to define the fluorescence domains under study by manually selecting the different domains of interest (see Readme file).