dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and future works We presented decision tree algorithms using local SVR models that achieve high performances for the non-linear regression of large datasets. The training task of tSVR is to partition the full training dataset into k terminal-nodes. This aims at reducing data size in training local SVR. And then it easily learns k non-linear SVR models in a parallel way on multi-core computers in which an SVR model is trained in each terminal-node to predict the data locally. The algorithm rtSVR is to learn the random forest of decision trees using local prediction SVR models for improving the prediction correctness against the tSVR model alone. The performance analysis and the numerical test results on datasets from UCI repository showed that proposed algorithms tSVR and rtSVR are efficient in terms of training time and prediction correctness compared to the standard learning algorithm LibSVM for global SVR models.
The learning time improvements of tSVR and rtSVR versus LibSVM are 1282. 66 and 482. 29 times; Furthermore, tSVR and rtSVR also improve 59. 43%, 63. 70% of the relative prediction correctness compared to the standard LibSVM. An example of tSVR's effectiveness is given with the non-linear regression of YearPredictionMSD dataset (having 400,000 datapoints, 90 dimensions) in 3. 297 min and 7. 38 mean absolute error obtained on the testset. In the near future, we intend to provide more empirical test on large benchmarks and comparisons with other algorithms. A promising avenue aims at improving the prediction correctness and automatically tuning hyperparameters of tSVR.
Image analysis Muscle fiber degeneration was quantified by counting the number of muscle segments with degeneration per embryo and calculating the percent of myotomes with muscle degeneration. MTJ angles were analyzed in FIJI by using the angle tool to measure the angles formed by the MTJ. For laminin and beta-DG staining analyses, images were blinded using a Perl script and embryos were scored according to their relative staining intensity (Additional file 1: Figure  S1 ). Intersegmental vessel (ISV) lengths were measured in FIJI using the segmented line tool. For anisotropy analysis, a polygon was drawn in eight myotomes per image analyzed using the polygonal lasso tool in Adobe Photoshop. The x,y coordinates of each polygon were recorded. Masks were generated from the coordinates, and 2DWTMM analysis was performed on each of these masks as previously described  [24, 25].
For analysis of xbp1 transgenic embryos, fluorescence intensity was quantified using FIJI. A polygon was drawn around the dorsal muscle above the notochord for all embryos in the trunk region. The average pixel intensity (mean gray value) was calculated. The mean average pixel intensity for all controls was calculated. These values were used to determine the percent average pixel intensity for all embryos. Images for all xbp1 transgenic embryos were blinded using a Perl script prior to analysis. For NMJ analyses, confocal images were processed into maximum intensity projections of fast-twitch (distributed) and slow-twitch (myoseptal) innervation using FIJI and were subsequently blinded using a Perl script. Masks for the fish, horizontal myoseptum, and myoseptal innervation were generated using FIJI.
The masks were imported into MATLAB (Mathworks) and used to segment acetylcholine receptor (AChR) and Synaptic vesicle protein 2 (SV2) fluorescence channels. The fluorescence images were enhanced using adaptive histogram equalization (MATLAB "adapthisteq" function) and then denoised with a 1-pixel radius gaussian filter (MATLAB "imguassfilt" function). Single masks of the fluorescence channels were then generated using a threshold of 30 on each channel, which were then combined with an "or" statement. Thresholds up to 58 and down to 10 yielded the same trend in the data, indicating that the measurements chosen were not overly sensitive to this parameter. The masks were skeletonized, and subsequently cleaned and despurred using the MATLAB "bwmorph" command. Branchpoints were also identified using the MATLAB "bwmorph" command.
NAD+/EmergenC are not sufficient to improve abnormal vascularization, midbrain-hindbrain development, or the increased unfolded protein response in fkrp morphants Fkrp is required for normal zebrafish vascularization: fkrp morphants and the recently described fkrp mutant exhibit shorter intersegmental vessel (ISV) lengths compared to wild-type embryos  [63, 78]. Fli1:EGFP transgenics injected with fkrp MOs exhibited shorter ISV lengths (including truncated vessels) than uninjected controls (Fig. 2E, F, H ). NAD+ supplementation had no significant effect on ISV length in fkrp morphants (Fig. 2G, H ). We also analyzed ISV length when normalized to the width of the embryos because muscle development is disrupted in fkrp morphants. The same result was observed with normalized data (not shown).
5B ). Supplementation of NAD+ or EmergenC improved NMJ length, although NMJs were not fully restored (Fig. 5C, D ). In order to quantify innervation, we developed a semi-automated technique in MATLAB to skeletonize NMJs (see methods). This analysis showed that although branching frequency was normal, skeleton length was reduced in fkrp morphants (Fig. 5E, F ). NAD+ or EmergenC supplementation at 6 hpf slightly, but significantly, improved skeleton length in fkrp morphants (Fig. 5E ). These results suggest that glycosylated DG is necessary for innervation, but not branching. As mentioned above, DG contributes to NMJ maturation and stabilization  [27]. Given the above data showing that initial NMJ development is disrupted in fkrp morphants, we asked whether DG is also required for NMJ development.
Acknowledgements The authors would like to thank the  Henry lab , especially  Dr. Michelle Goody  for technical assistance and critical reading of the manuscript,  Mary Astumian  for technical assistance and generation of transgenic fish,  Amneh Wise  and  Ethan King  for deyolking assistance, and  Maggie Pasquarella  for her assistance with preliminary experiments. We also thank  Wyatt Austin  for insight regarding image analysis and  Dr. Benjamin King  and  Dexter Canning  for insight on statistics. We also thank  Mark Nilan  for outstanding fish care and maintenance.
Norine, the unique resource dedicated to nonribosomal peptides (NRPs), is now updated with a new pipeline to automate massive sourcing and enhance annotation. External databases are mined to extract NRPs that are not yet in Norine. To maintain a high data quality, successive filters are applied to automatically validate the NRP annotations and only validated data is inserted in the database. External databases were also used to complete annotations of NRPs already in Norine. Besides, annotation consistency inside Norine and between Norine and external sources have reported annotation errors. Some can be corrected automatically, while others need manual curation. This new approach led to the insertion of 539 new NRPs and the addition or correction of annotations of nearly all Norine entries.
These molecules have in common that they are synthesized by nonribosomal peptide synthetases. As their name suggests, they are not synthesized following DNA transcription through translation by ribosomes. Nonribosomal peptides synthetases form huge enzymatic complexes that select amino acids or other monomers and connect them with several types of bonds  (2). Norine is a platform dedicated to these compounds. The database stores only natural nonribosomal peptides and it is complemented with analysis tools. In recent years, Norine has been extended and improved to address the needs of distinct scientific communities (mainly biologists and biochemists but also pharmacists among others). In particular, we developed a new pipeline in order to (i) massively add new NRPs and new annotations from external databases and (ii) enhance the quality of these annotations via automatic validation procedures.
We also developed new tools processing NRP chemical structures as well as mass spectra. Furthermore, the web interface has been upgraded to ease the retrieval and the reading of the data. A powerful D466 Nucleic Acids Research, 2020, Vol. 48, Database issue query builder now allows combining multiple criteria to optimize the database search. A query is built dynamically by the user that can search for any term in any NRP annotation. In the case of annotations with a limited number of possible terms, the term list is displayed and one or several values can be selected. In other cases, an auto-complete feature is provided. Several criteria can be combined using Boolean operators such as AND, OR and AND NOT. The NRP description page has been refactored. The different categories of annotations are accessible by tabs, allowing more detailed annotation display.
AUTOMATIC AND SOURCING In 2015, Norine was opened to crowd-sourcing through the creation of MyNorine (1) a tool that grants the scientific community access to Norine for submitting new NRPs or suggesting modifications of existing entries. NRP annotation is undoubtedly best achieved by experts. Submissions are manually verified and validated by the Norine team to ensure correctness and quality of data. About 50 contributors registered to MyNorine up to now, and ∼90 new NRPs or modification of annotations have been submitted and validated. Our next aim was to automatically and massively fill the Norine database with new NRPs or to refine annotations using external resources, without quality loss. To reach this goal, we have developed a new pipeline (see Figure  1  and a detailed description in the supplementary material).
• BIRD (4) (Biologically Interesting Molecule Reference Dictionary) is a resource that provides information about biologically interesting peptide-like antibiotics and inhibitor molecules in the PDB (5) archive. The entire BIRD resource can be downloaded from the wwPDB (World Wide Protein Data Bank) server in CIF format. • StreptomeDB (  6 ) is a database of molecules produced by bacteria in the Streptomyces genus, well known for their prolific production of NRPs. Again, StreptomeDB provides a link to download a file containing all entries. In the following paragraphs, we describe the pipeline through which data is fetched and selected for insertion in the Norine database. This pipeline is composed of Python scripts that are sequentially executed. For each database, the last version is downloaded and the files are checked before executing the update process.
The scripts parse all entries to extract the putative nonribosomal peptides from the external sources. Keywords distinguish the NRPs from the other types of secondary metabolites stored in the external entries. Then, several checks verify if these NRPs are already in Norine or not (see annotation quality enhancement 1 section and supplementary material for more details). Moreover, the source field is automatically instantiated with the name of the source database, the status field is set to unreviewed value and a link to the source entry is recorded. These annotations associated with each newly and automatically added NRP guarantee data traceability. MIBiG is the first external resource that is parsed. At this point in time, the archive contains ∼1800 entries. Thus, the first script parses all JSON entries and fills the database with the following annotations about the new NRPs: peptide name, family, synonyms, structure type, accession number of the corresponding MIBiG entry and PubChem ID when available, in order to reference MIBiG and PubChem  (7)  in the corresponding NRP entry of Norine.
Finally, the SMILES are fetched for these new Norine entries but not necessarily added. The selection process is detailed in the annotation quality enhancement section 1. As MIBiG is more focused on gene clusters, some NRP structures are incomplete and do not reach Norine quality criteria. A second script handles entries of BIRD. The CIF files contain information about the chemistry, the biology and structures of ∼1300 molecules. As for MIBiG entries, the script parses these files for finding new NRPs and annotations. The BIRD repository already references 12 Norine entries and, with the script, the cross-links between Norine and BIRD could be extended up to 171 entries. Classic annotation (name, type, synonyms, SMILES, etc. ) is completed with formula, molecular weight and monomer composition generated by using an in-house conversion table.
Indeed, Norine relies on its own monomer notation which can differ from that of PDB. The addition of new NRPs from StreptomeDB follows the same process. Then, a dedicated script completes general NRP annotations with chemical activity, producing organisms and references to PubMed IDs (https://www. ncbi. nlm. nih. gov/pubmed/). Once these scripts are executed, additional annotations are extracted from other sources. When PubMed IDs are fetched, the E-Utilities (Entrez Programming Utilities) API gives access to the entire references associated with NRPs (both existing and new ones) such as article title, full journal name and DOI. In the same way, the PubChem IDs give access to the SMILES of the compound. Also, links and chemical annotations from ChEMBL (8), a manually curated database of bioactive molecules with drug-like properties, are retrieved using a REST service  (9).
Unfortunately, not all SMILES of the three databases could be compared to the SMILES of Norine, because most NRPs entries in Norine did not contain SMILES information. In order to solve this discrepancy, all NRP entries lacking SMILES data were manually searched by name in chemical databases to retrieve the missing information. The execution of the pipeline led to the insertion of 539 new NRPs in Norine. Table  1  shows the contribution of the main external databases and the 666 external links that have been updated or added. Also, about 527 SMILES and 393 references were updated or added.
ANNOTATIONS QUALITY ENHANCEMENT The quality of NRP annotation in Norine has been and remains our top priority. Since its creation in 2006  (10) , Norine NRP curation has relied on manual extraction from scientific literature as well as meticulous validation prior to insertion in the database. Of course, some errors or incorrect information may occur but these are removed through regular checks. As cited in the first section, the MyNorine tool has created to boost precision. Nonetheless, automatic massive sourcing from external databases is needed to boost the number of entries, but it also increases the risk of introducing incorrect data. That is the reason why a strict validation pipeline was created. By strict validation, we mean that no entry is added if validation filters fail. Figure  1  shows some validation filters used during the execution process of the pipeline.
The following section provides details of some of these filters. First of all, we obviously verify that any external NRP or particular annotation (i. e. synonym, reference, access code, etc. ) is not already present in Norine before adding it. If a peptide name is already in Norine, missing for this NRP are added. When a graph (monomer composition of the NRP) is available, the Norine and external graphs are compared. If differences are detected, a failure report is generated. Various reports are created during the process to highlight inconsistencies or errors. These reports are intended to be manually analysed to curate the data. A second verification filter targets the new SMILES that were missing for many NRPs of Norine and that are fetched from external databases. For that purpose, we use Smiles2Monomers (s2m)  (11)  and rBAN  (12) , in-house tools that check the consistency of the graph.
They infer the monomeric structure of an NRP from its SMILES with two distinct strategies (see next section for description of rBAN). Thus, the new SMILES is only added if the inferred graph is the same as the given NRP graph. s2m and rBAN are used to detect and correct potential errors in the SMILES or monomeric structures recorded in Norine. More than 50 structures were corrected in that way. Another filter compares all SMILES to check if two NRPs are identical but registered with different names. SMILES are canonized prior to this comparison. Other filters not detailed in this article were developed to enhance D468 Nucleic Acids Research, 2020, Vol. 48, Database issue the quality of annotation in order to rebuild the taxonomy tree of the producing organisms, remove duplicates, convert PDB monomers notation, etc.
NEW SOFTWARE Norine has extended its range of NRP-dedicated software by including two additional tools. rBAN  (12)  (retroBiosynthetic Analysis of Nonribosomal peptides) infers the monomeric structure of a NRP from a SMILES code. This process can also be qualified as simulating the retro-biosynthesis of NRPs. The first step in rBAN is the fragmentation of a molecule that is broken following a set of pattern bonds. Then, the resulting fragments are matched to Norine monomers. The tool can be run in a 'discovery mode' when a monomer cannot be matched to Norine. Then, missing substructure(s) are searched in PubChem so as to suggest potential new monomers. All results are displayed in a directed graph format highlighting the bond types between monomers. rBAN addresses some of the limitations of s2m cited above.
URL: http://bioinfo. cristal. univ-lille. fr/rban The Kendrick Formula Predictor is a tool that uses the Kendrick mass defect (KMD)  (13)  to predict the chemical formula from the mass-to-charge ratio of a NRP. The required data for the development of the method is extracted from Norine and PubChem. The software was tested with high resolution mass spectrometry data from the surfactin family and the results confirmed the capacity of the tool to successfully predict NRP molecular formulae. Note that this is the first tool in Norine specifically dedicated to the mass spectrometry of NRPs. URL: https://bioinfo. cristal. univ-lille. fr/kendrick-webapp/ Both tools have contributed to the curation/extension of the Norine database. A pipeline combining PubChem and rBAN led to the validation of 97.
CONCLUSION This paper describes a substantial update of the Norine database. Almost 500 new NRPs and hundreds of annotations for existing Norine NRPs (SMILES, chemical formulas, synonyms, references, external links, etc. ) have been added. The quality of annotation was significantly enhanced. To achieve this goal, we developed a pipeline that relies on three main databases that potentially contain NRPs, namely, MIBiG, BIRD and StreptomeDB, and other resources such as PubMed, PubChem or ChEMBL to complement the fetched data. It should be noted that the status of these new NRPs is tagged as unreviewed. Our pipeline checks the data before insertion into the database. For example the monomeric composition is inferred from a SMILES using tools such as rBAN or s2m. If in doubt, a failure report is generated during the execution process of the pipeline to facilitate manual verification by experts.
In that sense, the process also enhances the quality of manual annotation that is in turn validated or not by automatic checking. Finally, Norine benefits from two complementary data sources: expert annotations input through the MyNorine tool and automatic annotations produced with the pipeline. In a virtuous circle, data is entered manually by experts, is verified and possibly completed and corrected automatically. Alternatively, data is entered automatically by the pipeline, is verified and possibly completed and corrected manually. The history of all changes for each NRP is kept and easily available from the NRP description page for traceability.
ACKNOWLEDGEMENTS We thank all scientists that have contributed to Norine by inserting new peptides or new annotations related to an already known NRP. AF is the main developer of Norine and supervises other developers. E. R. has developed rBAN, the  Kendrick Formula Predictor , and parts of the pipeline in relation to peptide structures and also participates in data correction. C. C. participates in the development of Norine. M. C. participates in the design of the Kendrick Formula Predictor, and in data correction. Y. D. and J. M. participated in the development of the pipeline and Y. D. in data correction. P. J. , C. F. , F. L. , V. L. and M. P. supervise Norine database and tool design as well as data curation.
FUNDING CPER  Alibiotech  project and the  INTERREG V France-Wallonie-Vlaanderen Project SmartBioControl/BioScreen ;  Institut Francais de Bioinformatique  [ ANR-11-INBS-0013  to J. M. ];  SIB Swiss Institute of Bioinformatics Fellowship Programme  (to E. R. ); Mobility between the CRIStAL and PIG teams is supported by the  Germaine de Stael French-Swiss cooperation programme Nucleic Acids Research ,  2020 , Vol. 48, Database issue D469 [39517NH]. Funding for open access charge:  University of Lille  ( SmartBioControl/BioScreen  project).
Apparatus The dominant eye's gaze position was recorded using a SR Research EyeLink 1000 Desktop Mount eye tracker (Osgoode, Ontario, Canada) at a sampling rate of 1 kHz. The experiment was controlled by an Apple iMac Intel Core i5 computer (Cupertino, CA, USA) and the experimental software was implemented in MATLAB (MathWorks, Natick, MA), using the Psychophysics  (Brainard 1997; Pelli 1997)  and EyeLink toolboxes  (Cornelissen, Peters, & Palmer, 2002). Stimuli were presented at a viewing distance of 60 cm on a 21-in. gamma-linearized SONY GDM-F500R CRT screen (Tokyo, Japan) with a spatial resolution of 1,024 by 768 pixels and a vertical refresh rate of 120 Hz.
Stimuli The conditions differed both in the visual characteristics and in the presentation mode, which could either be static or dynamic (Figure  1C ). The mask items of static stimuli (digital letters, Gabors, and crosses) changed into one static test target and static distractor items for duration specific to each stimulus. In contrast, dynamic stimuli (pink noise, RDK, and Gabor streams) continuously alternated (Gabor streams) or changed (pink noise and RDK) at a refresh rate specific to the stimulus. See Table  1  for a summary of stimuli properties. Supplementary Movie S1 illustrates a typical trial of the main task for each stimulus. We provide simplified MATLAB codes to produce the six stimuli used in our experiment (https://github. com/ nmhanning/StimTest), together with the collected datasets (https://osf.
Data preprocessing We scanned the recorded eye-position data offline and detected saccades based on their velocity distribution using a moving average over 20 subsequent eye position samples  (Engbert & Mergenthaler, 2006). Saccade onset and offset were determined when the velocity exceeded or fell below the median of the moving average by 3 SDs for at least 20 ms. We included trials if correct fixation was maintained within 2. 0 dva from the fixation target until cue onset, if the saccade landed within 2. 0 dva from the cued item center no later than 500 ms after cue onset (except no cue trials), and if no blink occurred during the trial. Finally, we excluded trials in which the saccade started before the presentation of the test target had ended. In total, we included 75,132 trials in the analysis (85.
Acknowledgments The authors are grateful to the current and former members of the  Deubel lab  for helpful comments and discussions. This research was supported by grants of the  Deutsche Forschungsgemeinschaft (DFG)  to HD ( DE336/5-1  and  GI964/1-1 ) and MS ( SZ343/1 ) as well as a  Marie Skłodowska Curie individual fellowship  to MS ( 704537 ). The authors declare no potential conflict of interest, financial or otherwise. *HD  and MS contributed equally to this work. Commercial relationships: none. Corresponding author:  Nina M. Hanning. Email: hanning. nina@gmail. com. Address: Allgemeine und Experimentelle Psychologie,  Department Psychologie ,  Ludwig-Maximilians-Universität München, München, Germany.