dataesr/scientific-paragraphs-categorization huggingface dataset:
Retrieval of 18S sequences We searched for 18S sequences of at least 1,500 base pairs in length for each data set in the following order. If no sequence, or only a partial sequence was retrieved in a given step, we moved on to the next step. Details specific to each data set are included in the EukProt metadata. 1. Search GenBank  (Sayers et al. , 2020)  for an 18S sequence for the species and strain. When more than one sequence was available for the strain, we chose the one containing the largest quantity of high-quality nucleotides (after identifying and eliminating sequencing errors, chimeras, etc. ). 2. If the data set was sequenced as part of the MMETSP, use the 18S sequence from MMETSP (if it was provided in the project metadata, and also was not already present in GenBank). 3. Search GenBank for an 18S sequence for another strain of the same species.
4. Using the 18S sequence for the most closely related species in GenBank as a query, search the contigs associated with the data set (either genome or transcriptome) using blastn  (Altschul, 1997). 5. Assemble the 18S sequence from the raw sequence reads associated with the data set, using phyloFlash version 3. 4 (Gruber-Vodicka et al. , 2020) with the SILVA database version 138. 1  (Quast et al. , 2012)  and SPAdes assembler version 3. 14. 1  (Prjibelski et al. , 2020) , with default parameter values except for the number of reads used, and with read length set to match the read length of the data set. For each data set, we initially ran phyloFlash with all sequence reads. If we observed that there was evidence for reads mapping to the 18S of the target lineage, but that did not result in a contig assembled by SPAdes, we ran phyloFlash again with only the first 10 million reads.
If this run showed a similar result (reads mapping but no contigs), we ran again with only the first 1 million reads. When all three runs (all reads, first 10 million reads, first 1 million reads) did not result in a SPAdes assembly, we selected the reads mapping to the 18S of the target lineage and performed an assembly using CAP3 version date 02/10/15 (Huang, 1999) with 'relaxed' parameter values from  (Close et al. , 2009) : -p 75 -d 200 -f 250 -h 90. A selected subset of EukProt for phylogenetic studies Single-copy orthologs were identified in EukProt v3 proteomes using BUSCO v5. 2. 2  (Manni et al. , 2021)  with the Eukaryota Odb10 database. BUSCO searches were run in protein mode with the default E-value cutoff of 1e-3. These BUSCO scores were visualized using Plotly  (Plotly Technologies Inc.
The EukProt database distribution on FigShare The database is distributed in five files. One file contains 993 protein data sets, for species with either a genome (375), a single-cell genome (56), a transcriptome (498), a single-cell transcriptome (47), or an EST assembly (17). A second file contains assembled transcriptome contigs, for 126 species with publicly available mRNA sequence reads but no publicly available assembly. The proteins predicted from these assemblies are included in the proteins file. A third file contains GFF annotations for 40 genomes lacking publicly available predicted protein annotations. Finally, the database metadata are distributed as two files: one file for the data sets included in the current version of the database (993), and a second file for data sets not included (163), accompanied by the reason they were not included.
The EukProt database EukProt (currently in version 3; 22 November, 2021) contains 993 eukaryotic species from 5 different sequence data-types (Figure  1 ): genome (375 species), single-cell genome (56), transcriptome (498), single-cell transcriptome (47), and expressed sequence tag (EST; 17). The data sets were downloaded from 37 different sources (Figure  2 ), with the two principal sources being NCBI  (Sayers et al. , 2020)  and the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP)  (Keeling et al. , 2014). The database contains broad taxonomic representation, with species from 34 of 36 major deeply-branching eukaryotic lineages defined in the UniEuk database  (Berney et al. , 2017). Within these major groups, however, the representation remains uneven, which results from several factors: the difficulty of discovering, culturing or sequencing species from many lineages; a bias towards sequencing either macroscopic, multicellular species or unicellular species that are parasites (e.
g. , Apicomplexa), photosynthetic (such as diatoms, which are part of the ochrophyte lineage), or are otherwise economically important  (del Campo et al. , 2014) ; and very uneven evolutionary histories among groups, with some having undergone major diversification with many different morphologies, and others having diversified to a lesser extent (or with only a few surviving lineages). , 2014)  for which a procedure to remove cross-contamination was applied  (Marron et al. , 2016). Individual URLs for each data set are listed in the metadata files available on FigShare. * indicates that the URLs for one or more data sets point to servers that are no longer responding or are otherwise inaccessible. We have implemented in version 3 several changes proposed by the community of EukProt database users after version 2 was released: we have included (i) the 18S ribosomal DNA sequence corresponding to each data set (when available), (ii) a measurement of the completeness of the data set as estimated by BUSCO  (Simão et al.
Breadth of species phylogenetic diversity The objective of the database is to include as many lineages as possible from the broad diversity of eukaryotes. For the majority of lineages, at least one representative from all sequenced genera is included. The exceptions are lineages with hundreds of sequenced representatives, or lineages whose sequenced species are relatively closely related, for which we included only a subset of species selected to represent phylogenetic diversity in order to balance the overall size of the database versus broad representation of eukaryotes. These well-sequenced lineages are Chloroplastida (containing the land plants and green algae), Metazoa (animals), Amoebozoa, Fungi, and some parasites/pathogens (members of Apicomplexa, Peronosporomycetes, Metakinetoplastina and Fornicata).
Convenience of access The full data set and its associated metadata can easily be downloaded from FigShare  (Richter, Berney et al. , 2022a). For 624 of the total 993 species in the database, publicly available protein sequences were ready to be included directly. For the remaining 369 species, additional processing steps were required to produce protein sequences for inclusion in the database. The most common bioinformatics processing steps were de novo assembling and translating transcriptomes from raw read data (120), translating mRNA sequences from publicly available assembled transcriptomes or EST projects lacking protein predictions (102), merging independent sets of protein predictions for the same species (85 species) and, newly added in version 3 of the database, producing de novo gene predictions for unannotated genomes (40).
A unified taxonomic framework We placed all species into the taxonomic framework generated in the UniEuk project  (Berney et al. , 2017) , which is based on the most recent consensus eukaryotic classification  (Adl et al. , 2019)  and phylogenomic evidence. This will maximize interoperability of the database with other resources built upon to the UniEuk taxonomic framework (e. g. , EukBank and EukMap  [Berney et al. , 2017] , and EukRibo  [Berney, 2022] ), and ensure that results arising from EukProt can use a common set of identifiers to describe analyses at different taxonomic levels. For example, this might include labeling groups in a phylogenetic tree, or summarizing read placement data by taxonomic group. Knowledge of the taxonomy can also facilitate the detection of mis-identified gene sequences within a data set, via comparison of the topology of gene trees to the taxonomy.
To assist in species identification and to provide a convenient link to EukRibo (a manually curated database of 18S rDNA sequences, part of UniEuk, aimed at taxonomic annotation of metabarcoding data sets), we also supply in version 3 the 18S sequence corresponding to the sequenced strain (or, if an 18S for the sequenced strain is not available, an 18S sequence from the same species; see Methods). In addition to the complete taxonomic pathway containing all intermediate nodes, we provide for each species three sequentially nested groupings ("taxogroup2" within "taxogroup1" within "supergroup"; see Methods) that can help to judge the redundancy of data sets, or conversely their lack of representation, within different groupings. The taxogroup1 level is used as the leaves in Figure  1. Finally, for each data set we include a list of previous species identifications, if any, to prevent issues resulting from revisions to species names or lineages and corrections of species misidentifications, which have occurred frequently with improvements in sequencing and phylogenetic techniques and the discovery of new eukaryotic organisms.
Reusability, persistence and replicability The database will be released in successive versions, each of which will be permanently stored and accessible at FigShare  (Richter, Berney et al. , 2022a). Thus, analyses using the database will need only to specify which version was used, enabling follow-up analyses or replications to begin with the identical database. In addition, each individual data set within the database is assigned a unique, permanent identifier. When a new data set becomes available for a given species, it is assigned a new unique identifier (and the identifier of the data set it replaces, if any, is indicated in the database metadata). These and all other changes between versions of the database are recorded as appropriate.
Additional proteomes were chosen to maximize phylogenetic diversity and based on their completeness as estimated by BUSCO  (Manni et al. , 2021)  scores. Updates to the TCS will be based on availability of new proteomes from phylogenetically important lineages currently underrepresented, and on community feedback. Please send any comments or suggestions for the TCS to Jeremy. Wideman@asu. edu. The complete EukProt database and the TCS are available for download and also available for BLAST searches at http://evocellbio. com/eukprot/ using Sequenceserver  (Priyam et al. , 2019). with relationships based on the UniEuk taxonomy  (Berney et al. , 2017; Adl et al. , 2019). The position of the eukaryotic root, indicated with a dashed line, is currently unresolved. Group names shown in gray are not officially recognized.
Current limitations of the EukProt database In the course of constructing successive versions of EukProt, we have observed several areas that we believe to be current limitations of the database: 1. As can be observed in Figure  1 , representation of species is highly uneven among taxonomic groups. This is largely due to the difficulty in identifying, cultivating and sequencing species from underrepresented groups; however, some representatives of key taxa have been published but are not publicly accessible (a list of these species can be found in the "not included" metadata table of our FigShare distribution). 2. We currently lack an automated, consistent method to identify and remove contaminated data sets or individual contaminant sequences. Currently, contaminated data sets are identified by end users and removed in subsequent versions.
Growing the EukProt database with community involvement The core functionality of the database is the distribution of genome-scale protein sequences across the diversity of eukaryotic life and within the UniEuk framework. However, we anticipate that numerous other features might be useful to the community, and we hope to involve the community in suggesting and adding new features in successive versions. These may include information or analyses on full data sets, such as the sequencing technology that was used (e. g. , Illumina, PacBio), or the estimation of potential contamination levels (e. g. , from mixed cultures or prey species) with non-target species, as inferred using systematic sequence homology searches. Additionally, we propose to use our GitHub  (Richter, Berney et al. , 2022b)  as a flexible repository to disseminate community-contributed annotations on the level of individual protein sequences, such as protein domains from Pfam  (El-Gebali et al.
, 2019) /Interpro  (Mitchell et al. , 2019) , gene ontology (The Gene Ontology Consortium, 2019)/eggNOG  (Huerta-Cepas et al. , 2019) , which may be updated in between EukProt releases. As new genome-scale eukaryotic protein data sets become available, we plan to add them to the database. As yet, we do not have a formal mechanism to accomplish this, and will instead depend on monitoring the literature and assistance from the community. As an example, for version 3, we relied on PhycoCosm  (Grigoriev et al. , 2021) , MycoCosm  (Grigoriev et al. , 2014)  and PhyloFisher  (Tice et al. , 2021)  to add data sets that became recently available. Because the rate of genome sequencing is increasing exponentially, we will prioritize adding species that are incertae sedis and those from taxonomic groups with currently limited representation in EukProt, and only later would include new proteomes from currently well sampled groups.
Funding This work was supported by the  French Government ' Investissement d'Avenir' program   OCEANOMICS  ( ANR-11-BTBR-0008 ), the ABiMS computing cluster at the  Station Biologique de Roscoff, France , and received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 949745 ). DJR was supported by postdoctoral fellowships from the  Beatriu de Pinós programme  of the  Government of Catalonia's Secretariat for Universities and Research of the Ministry of Economy and Knowledge , and from  "la Caixa" Foundation  ( ID 100010434 ), with the fellowship code  LCF/BQ/PI19/11690008. CB was supported by grants from the  Gordon and Betty Moore Foundation  ( GBMF5257  &  GBMF8908 / UniEuk  project) and is grateful to the  International Society of Protistologists  for additional support.
JGW is supported by the  National Science Foundation  under Grant No. DBI-2119963. FB was supported by a grant from  Science for Life Laboratory , and by grants from the  Swedish Research Council VR  ( 2017-04563  and  2021-04055 ) and  Formas  ( 2017-01197 ). JFHS acknowledges support from the  German Research Foundation (DFG ; Grant  STR1349/2-1  Project No. 432453260 ). Research,  49 ,  D1004-D1011. https://doi. org/10. 1093/nar/gkaa898  Grigoriev IV ,  Nikitin R ,  Haridas S ,  Kuo A ,  Ohm R ,  Otillar R ,  Riley R ,  Salamov A ,  Zhao X ,  Korzeniewski F ,  Smirnova T ,  Nordberg H ,  Dubchak I ,  Shabalov I  (2014) MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Research, 42, D699-D704. https://doi. org/10. 1093/nar/gkt1183  Gruber-Vodicka  HR,  Seah BKB , Pruesse E (2020) phyloFlash: Rapid Small-Subunit rRNA Profiling and Targeted Assembly from Metagenomes ( M Arumugam , Ed,).
After each agitation, flies fell to the bottom of the tubes and then, due to their natural negative geotaxis, flies climb up the tube against gravity and crossed the 17 IR beams. We have optimized the Forced-Climbing System to monitor not only fly moves but also speed and positions in the tubes (representing how far can flies climb up). Specifically, we developed a MATLAB routine that allows us to record the flies' average moves per hour, speed per hour and positions. In short, first, we retrieved the raw files from MB5 software after each run. Then, we applied the following equations: Hourly locomotor movement (moves) is calculated by averaging the hourly beam-to-beam transition recorded generated from the flies' activity in the course of the experimental time The length between each IR is 3 mm.
CSPRC virgin males were collected and transferred into the tube in the same way mentioned above. We recorded their general activity, mean activity and activity counts/30 min all for five days. For the crossed flies with GAL4, we used normal food and flies aged was 1-5 days before experimentation, unless otherwise mentioned. Finally, all monitors were placed in the incubator for five days at 25 • C, and 60% humidity on a 12:12 light:dark cycle. On the sixth day, the data for the flies' general activity for five days were collected from the DAMSystem software and analysed using MATLAB, and GraphPad Prism.
iii. Chloride channel particles counting The grid for each group obtained from immunogold labelling TEM was divided into an area of 33 × 10 6 pixels (8. 7 × 10 9 micron). Next, we quantified the ClC-a gold particle in each area using ImageJ software (version 1. 53Ki, National Institutes of Health, Bethesda, MD, USA) and calculated the statistical difference using GraphPad Prism. The type of the test is indicated in the caption of each figure. iv. Measuring the sarcomere length TEM micrographs with a scale bar of 2 or 5 µm have been selected randomly from the groups. The length between Z-lines of the sarcomere was measured in µm using ImageJ (version 1. 53Ki, National Institutes of Health, Bethesda, MD, USA) from the top, middle and bottom of the sarcomere. Then, the significant difference was calculated using GraphPad Prism.
Each plate well contained a total volume of 20 µL of the following: 11. 52 µL Milli-Q ® water, 3. 60 µL Taq buffer, 0. 20 µL dNTPs, 0. 1 µL primers, 1 µL DMSO (1:20), 0. 5 µL SYBR Green (1:50,000) and 3 µL of cDNA of the gene of interest as described in Williams et al. [62]. All qPCR experiments were carried out in triplicates. The qPCR data were analysed using MyIQ 1. 0 software (Bio-Rad, Hercules, CA, USA) and values of fold changes (ratio of delta CT value) for the samples were normalized with RpL32 s values. Outliers of CT ratio were checked out by Grubbs' test and the significant outliers (p > 0. 05) were excluded. GraphPad Prism was used to calculate the significance. The type of the test is indicated in each figure's caption. iii. Western Blotting For fluvastatin groups, 10 virgin-CSORC males of one-two-day-old per each group were transferred onto a vial containing 5 mL of food mixed with a subsequent volume of fluvastatin stock solution sufficient to make 1 mM or 0.
By using the mild stripping protocol from https://www. abcam. com/protocols/western-blot-membrane-stripping-forrestaining-protocol, accessed on 1 August 2022, the blots were stripped and re-probed in the same PVDF membrane. We used the secondary antibody Rabbit IgG (H + L) Cross-Adsorbed Secondary Antibody (ThermoFisher, HRP #A16104) at 1:1000 dilution. Enhanced reagent ECL kits from Biorad were used for detecting the signals. Signal detection was performed using ChemiDoc MP Imaging System, Bio-Rad. Relative intensity was calculated by normalizing the absolute intensity of the experimental group to the absolute intensity of β-actin using Image Lab and GraphPad Prism software. The p-value was calculated using GraphPad Prism. The type of the test is indicated in each figure's caption. Table  6.
These methods aim at determining the full conformational distribution (also called conformational space, landscape, or manifold), based on which the images with similar conformations could be assembled in 3D reconstructions and, optionally, a displacement of a 3D model can be animated in this space without calculating 3D reconstructions  (Jin et al. , 2014; Harastani et al. , 2020). The problem of determining the particle conformation, orientation, and shift from images is an ill-posed inverse problem because the number of unknowns to be determined (the conformation, orientation, and shift for each particle image) is larger than the number of input data (the number of particle images), which combined with a low signal-to-noise ratio (SNR) of cryo-EM images makes the problem very challenging.
The third stage consists of projecting the predicted conformational parameters onto a low-dimensional space and exploring this space, using a HEMNMA module. In this low-dimensional space, which could be considered as an essential conformational space, close points correspond to images with similar conformations and distant points to images Hamitouche, I. , Jonic, S doi: 10. 3389/fmolb. 2022. 965645 8 with different conformations. The exploration of this space includes (1) generating animations of the displacement of the reference along the data distribution directions and (2) interactive grouping of images with similar conformations and calculating 3D reconstructions from these groups. In DeepHEMNMA, the parameters predicted by the network and those estimated by HEMNMA can optionally be combined into a single conformational space.
To prevent overfitting, one half of nodes in the MLP layers were randomly dropped out in each epoch and a weight decay of 10 -5 (L2 regularization term  (Krogh and Hertz, 1991) ) was added to the gradients. In the test phase, the trained entire network model (ResNet and MLP blocks) predicts the pose and motion parameters of the particle from the remaining input particle images (Image set 2). The network is implemented using Python 3. 8 and PyTorch 1. 8. DeepHEMNMA uses a unit quaternion representation for the orientation in 3D space, meaning that the three Euler angles estimated with HEMNMA for each single-particle image are converted into the corresponding quaternion and these quaternions are used to train the network. Similarly, the quaternions predicted by the network are converted back to the Euler-angle representation, for use with methods based on the orientation representation with Euler angles (the majority of cryo-EM methods), as the 3D reconstruction method used at Stage 3 of DeepHEMNMA.
RESULTS To evaluate the performance of DeepHEMNMA thoroughly, we carefully designed and run several experiments with synthetic datasets of the chain A of adenylate kinase (AK) from the PDB database (PDB:4AKE)  (Müller et al. , 1996)  and with the experimental cryo-EM dataset of yeast 80S ribosome-tRNA complexes from the EMPIAR database (EMPIAR:10016)  (Svidritskiy et al. , 2014). In this section, we describe these experiments and show their results. Hamitouche, I. , Jonic, S doi: 10. 3389/fmolb. 2022. 965645 16.
Performance of DeepHEMNMA with synthetic data In this section, we present results obtained with synthetic single particle images affected by noise and contrast transfer function (CTF) of the simulated microscope, to demonstrate the entire DeepHEMNMA protocol and show its accuracy and speed. The dataset was obtained by randomly sampling synthetic continuous conformational transitions, orientations, and positions of AK. The parameters of the synthetic AK conformation, orientation, and position were used as the ground-truth parameters to assess the accuracy of the prediction of these parameters by the neural network. As the network was trained using HEMNMA-estimated parameters, the accuracy of the neural network prediction was also assessed with respect to the HEMNMA-estimated parameters.
The distances between the inferred and ground-truth values of parameters obtained using the large test set (50,000 images) and these distances expressed in RMSD terms are shown in Table  3  and Table 4 , respectively. These tables show the same range of the parameter inference errors for the small and large test datasets, which indicates that the network model has successfully generalized during the training. Figure  7  shows a 2D conformational space obtained by PCA of the inferred normal-mode amplitudes. In this space, it is possible to calculate 3D reconstructions either from the reduced-size images (128 × 128 pixels), which the reconstructions from the original-size images (using the inferred shifts, after their multiplication by 2, and the inferred angles). Ten 3D reconstructions were calculated from the images in the corresponding ten dense regions of the 2D PCA space.