dataesr/scientific-paragraphs-categorization huggingface dataset:
DH acknowledges support from the  National Technology and Science Council of Taiwan  through grant number  111B3005191. M. N. D. acknowledges the  Swiss National Science Foundation (SNSF)   Ambizione  grant no. 180079 , the  Center for Space and Habitability (CSH) Fellowship , and the  IAU Gruber Foundation Fellowship. I. J. -S. acknowledges financial support from grant No. PID2019-105552RB-C41  by the  Spanish Ministry of Science and Innovation/State Agency of Research   MCIN/AEI/10. 13039/501100011033. This work was supported by a grant from the  Simons Foundation  ( 686302 , KIÖ) and an award from the  Simons Foundation  ( 321183FY19 , KIÖ). JKJ acknowledges support from the  Independent Research Fund Denmark  (grant number  0135-00123B ). ZLS acknowledges financial support from the  Royal Astronomical Society  through the  E.
Data Analysis First, a descriptive analysis was done with the quantitative traits to see the effectiveness of the data. Then a hierarchical ascending classi cation (HAC) based on principal component analysis (PCA) was carried out to establish the relationships between the accessions. All analyzes were done with R software v4. 1. 2 (R Core Team 2021). All codes and command lines are shown in Additional le All genotyping analyzes were performed according to the methodological approach described in  Barro et al. (2021). First, data were ltered out of missing data up to 10% to retain good quality SNPs; a second cleaning occurred then for retaining only sites with less than 45% heterozygous sites. A principal component analysis (PCA) based on the ltered dataset and including the corresponding 3k core set was performed; the IR841 samples were superimposed on the PCA set afterward.
After clustering accessions with PCA, we used the adegenet (http://adegenet. r-forge. r-project. org/ ) DAPC (Discriminant Analysis of PCA) package to identify discriminant functions, and then applied them to the IR841 samples to affect individuals to representative diversity clusters. For this we rst run the DAPC on the identi ed diversity groups and evaluated the performance of the grouping. Finally, we used this discriminant analysis to assign IR841 genotypes to genetic clusters with correct precision. All analyzes were executed using R v4. 1. 2 software. Phylogenetic classi cation was done using the distance value computed from the PCA and the Phangorn 2. 8. Declarations Figures Figure 1 HAC of the 72 accessions based the 80% of semi-heading cycle character. IR841 rice samples are boxed in color Figure  2  DAPC showing the variation of the genetic background within the indica subspecies with the IR841 accessions tested.
1 (https://github. com/KlausVigo/phangorn) and Ape 5. 6-2 (http://ape-package. ird. fr/) packages on R v4. 1. 2. Tree ltering and visualization was performed through the interactive TreeOfLife 6. 5. 6 (Letunic and Bork 2021) Abbreviations GxE = P Phenotype is the interactions between genotype and environment INRAB National Agricultural Research Institute of Benin DNA deoxyribonucleic acid NGOs Non-Governmental Organization NPK Nitrogen, Phosphate and Potash (NPK fertilizers) IRRI International Rice Research Institute SNP Single Nucleotide Polymorphism.
| Patients and intracerebral recordings Patients with drug-resistant focal epilepsy were selected from the database of the epileptology unit of La Timone     S2 ). The epileptologist team performed a visual evaluation of the SEEG traces before the analysis to identify interictal periods with a limited amount of artifacts (see Table  S1 ). The median length of the interictal SEEG traces among the 35 patients in the awake state was 28. 2 min (range = 23. 5-31. 6 min) (see Table  S1 ). The classification of the SEEG contacts in the EZ, propagation zone (PZ), and non-involved zone (NIZ) was made according to the sites of seizure initiation (seizure-onset zone [SOZ]) based on visual analysis and on the epileptogenicity index (EI). 4, 5, 21.
| Data and statistical analysis To systematically study the variety of transient waveform shapes emerging in the interictal SEEG dynamics, we developed the Nested Outlier Detection (NODE) algorithm as a general data-mining method. The NODE algorithm uses the local false discovery rate (LFDR) method  22, 23  to define the interictal events by detecting anomalies (i. e. , outliers) of amplitude across the frequency bands of interest. Figure  1A  schematizes the main processing steps associated with the NODE algorithm. Briefly, the NODE algorithm assigns to each event a four-digit label. Each label digit represents the proportion of the detected anomalies that can be expected to be true outliers in the frequency band associated with that digit. The four frequency bands range from the high-delta band corresponding to the first label digit from the left, to ripple band corresponding to the last label digit from the left.
AUTHOR CONTRIBUTION Damián Dellavale contributed to the conceptualization, methodology, formal analysis, writing the original draft, and figure preparation. Francesca Bonini, Francesca Pizzo, Christian-George Bénar, and Fabrice Bartolomei contributed to the data acquisition and visual analysis of the recordings, and review and editing of the manuscript. Julia Makhalova contributed to the acquisition and curation of the data set. Fabrice Wendling, Jean-Michel Badier, Fabrice Bartolomei, and Christian-George Bénar contributed to the funding acquisition.
ACKNOWLEDGMENTS This work was supported by the  Agence Nationale de la Recherche (ANR)  Neurosense  ANR-18-CE19-0013  grant, and by the  Recherche Hospitalo-Universitaire EPilepsy  surgery management and progNOsis using Virtual brain technology [A* MIDEX  project ( ANR-17-RHUS-0004 ) funded by the  'Investissements d'Avenir' French Government ].
DATA AVAILABILITY STATEMENT The data and code that support the findings of this study are available from the corresponding author, upon reasonable request. In particular, the source code for the computation of the NODE algorithm together with test script examples are freely available at: https://github. com/damia n-della vale/node/ We are willing to provide technical support to investigators who express an interest in implementing the NODE algorithm in other programming languages, integrating it in open-source software toolboxes, or use it for non-profit research activities.
This activity of NATs UGT73C6 relies on its RNA sequence and, although modulation of UGT73C6 in cis cannot be excluded, the observed phenotypes are not a consequence of the regulation of UGT73C6 in trans. The NATs UGT73C6 levels were shown to affect cell proliferation and thus individual leaf size. Consistent with this concept, our data suggest that the NATs UGT73C6 influence the expression levels of key transcription factors involved in regulating leaf growth by modulating cell proliferation. These findings thus reveal an additional regulatory layer on the process of leaf growth. In this work, we characterized at the molecular level two long non-coding RNAs (NATs UGT73C6 ) that are transcribed in the opposite direction to UGT73C6, a gene encoding a glucosyltransferase involved in brassinosteroid homeostasis in A.
, 2020). In the present work, we show that alterations in the transcript levels of two UGT73C6-overlapping lncNATs (NATs UGT73C6 ) lead to changes in rosette size of A. thaliana. Microscopic analyses indicate that these differences are mainly due to changes in cell number, suggesting that NATs UGT73C6 levels affect cell proliferation. These phenotypic effects were shown to be due to NATs UGT73C6 acting as bona fide non-coding RNAs. Our data suggest that NATs UGT73C6 exert their function in trans and independently of the sense gene in a mechanism that excludes downregulation of the UGT73C subfamily members but involves changes in the expression of important transcription factors from the GRF-GIF module.
RESULTS NAT1 UGT73C6 and NAT2 UGT73C6 , two lncRNAs overlapping the UGT73C6 gene, are expressed in diverse organs and at different developmental stages of Arabidopsis thaliana Analyzing the transcripts reported in the TAIR10 database, we identified two natural antisense transcripts (At2g36792. 1 and At2g36792. 2) that overlap the UGT73C6 gene. In silico analysis of the protein-coding capacity of both transcripts, hereafter referred to as NAT1 UGT73C6 and NAT2 UGT73C6 (for At2g36792. 1 and At2g36792. 2, respectively) and collectively NATs UGT73C6 , revealed the presence of several open reading frames (ORFs) potentially encoding peptides of at least five amino acids. Most of the predicted peptides were smaller than 30 amino acids in length and none of them showed similarity to peptides present in the databases, nor did they have functional domains as determined using Pfam and PROSITE.
The data suggest that the NATs UGT73C6 (mainly NAT2 UGT73C6 according to the pattern detected with the reporter gene line showing activity in aerial organs) were expressed at all analyzed times. Except for the last time point included in the study (45 days after stratification; DAS), the transcript levels of NATs UGT73C6 were observed to be higher than those of UGT73C6 (Figure  1c ). Consistent with these results, analysis of publicly available RNA-seq data showed that NATs UGT73C6 were detected in a larger number of libraries (Figure  S2a ) and that their expression is higher than that of UGT73C6 in all analyzed tissues and studied conditions (Figure  S2b ). We identified the ends of the transcripts by circular 5 0 RACE and conventional 3 0 RACE. Sequence analysis of the clones containing the generated amplicons (Figure  S3a, b ) indicated a single transcription initiation site (TIS) for UGT73C6 that matches the one reported in the databases (G 1 ), and five transcription termination sites (TTS), one major (G 1668 ) and four less frequent ones, located upstream of the reported TTS (Figure  S3c, d ).
For NATs UGT73C6 , we found one main TTS, which coincides with that reported for NAT2 UGT73C6 (C À98 ; Figure  S3c, d ) and three less frequent ones. Concerning the NATs UGT73C6 5 0 -ends, we identified three positions with a higher frequency (G 1255 , U 926 and C 866 ) and three less frequent ones (Figure  S3c, d ), suggesting the presence of different TIS, which is consistent with the existence of two transcripts and with the different localization of the promoter activity in the reporter lines. We confirmed these data by RT-PCR, and obtained successful amplification with all the primers flanking the TIS determined by 5 0 RACE for NATs UGT73C6 but not when using an oligo located close to the TIS reported in the database (Figure  S3e ). Altogether, these results suggest that the promoters of both NATs UGT73C6 transcripts are active, that the expression of the RNAs is developmentally regulated and that it occurs independently of the transcriptional activity of the UGT73C6 gene in cis and that it is initiated at different TIS.
NATs UGT73C6 transcripts are alternatively spliced and stable According to the TAIR10 database, the transcripts encoded by the NAT UGT73C6 gene contain an intron (Figure  1a ). To determine the splicing pattern of the NATs UGT73C6 transcripts, we extracted total RNA from shoots of 9-day-old seedlings grown in long day (LD) conditions and  performed RT-PCR. The NAT2 UGT73C6 amplicon (Figure  S4a ) was cloned, and analysis of individual clones by colony PCR revealed the presence of inserts of different sizes (Figure  S4b ). Sequence analyses of the clones demonstrated that the NAT2 UGT73C6 transcripts consist of splicing variants in which half of the population retains the intron. The other half of the transcripts was shown to consist of three splicing variants with lengths of 1013, 1005 and 985 nucleotides, which are suggested to be generated using alternative, canonical splicing donor and acceptor sites (Figure  S4c ).
Overall, these data showed that the NATs UGT73C6 transcripts are alternatively spliced and exhibit relatively high stability suggesting that it may function at the posttranscriptional level. NATs UGT73C6 downregulation leads to a reduction in the rosette area by decreasing leaf size and the number of mesophyll and epidermal cells Considering a potential trans-activity of the NATs UGT73C6 transcripts, we decided to analyze the effects of their downregulation. To achieve this, we first tested the earlier reported ugt73c6 ko mutant line (SAIL_525_H07;  Jones et al. , 2003) , which contains a T-DNA insertion at position 1440 downstream of the reported UGT73C6 TIS (Figure  S5a ). The insertion generates a premature stop codon at position 472 leading to a protein that is truncated at its C-terminal end.
Such a situation has been described for HIDDEN TREASURE 1 (HID1; Ó 2022 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2023), 113, 460-477  Wang et al. , 2014) , a lncRNA containing four main stemloops of which two are essential for its function, the regulation of PHYTOCHROME-INTERACTING FACTOR 3 (PIF3) gene expression. On the other hand, these data may be indicative of interaction with RNA-binding proteins that may be required for the functionality of the lncNATs and that bind exclusively to full-length NATs UGT73C6 molecules. TWISTED LEAF (TL), an endogenous lncRNA from rice, was previously reported to influence leaf blade shapee by modulating the expression of the gene encoding the R2R3 MYB transcription factor with which it overlaps  (Liu et al.
Generation of transgenic lines For the generation of NAT1 UGT73C6 and NAT2 UGT73C6 overexpressing plants, the sequences reported in the TAIR10 database were PCR amplified from genomic DNA extracted from A. thaliana plants. The same template was used for the amplification of the promoter regions of NAT1 UGT73C6 and NAT2 UGT73C6 (2045 and 2580 bp starting immediately upstream of the respective reported TIS), and of the promoters of UGT73C6 (bases À1687 to +4 considering the translation initiation site) and UGT73C5 (759 bp upstream of the translation initiation site). The amplicons, starting with a non-template CACC sequence, were directionally cloned into the pENTR TM /D-TOPO vector (Life Technologies, Carlsbad, CA, USA). The pENTR clone containing the genomic NAT2 UGT73C6 sequence was further used as template for start codon(s) removal by site-directed mutagenesis in PCR reactions with AccuPrime Pfx DNA polymerase (Thermo Fisher Scientific, Waltham, MA, USA) and overlapping primers in which the ATG codons were replaced by the CCC trinucleotide.
Rosette and leaf area measurements Complete plant images were taken with a digital camera (Sonyâ DSLR, Sony, Tokyo, Japan). For determination of the rosette area, digital images of 20-30 plants per line were analyzed with the Easy Leaf Area program  (Easlon & Bloom, 2014)  and the green area quantified. The area of individual leaves was measured with ImageJ  (Schneider et al. , 2012).
Cell borders were manually drawn on the photo using the Vectornator program (https://www. vectornator. io), or analogously after printing the photos on transparent sheet, scanning and processing them with the Fiji processing package (https://fiji. sc/; Schindelin et al. , 2012)  to obtain individual cell area. A lower limiting size of 2000 lm 2 was used to quantify only the fully developed palisade mesophyll cells. The total number of palisade mesophyll and abaxial epidermal cells on leaf 6 was estimated by dividing the determined area of individual leaves by the mean cell area of each cell type. For epidermal cells, an upper limit of 15 000 lm 2 was set to exclude abnormally large cells (less than 4% for all lines except for OE-NAT2 UGT73C6 -6, where they represent 5. 7%).
In silico and statistical analyses The potential protein-coding capacity from both NATs UGT73C6 was determined with the ExPASy translation tool (https://web. expasy. org/translate/), and protein domains were analyzed with the Pfam database web tool (www. pfam. xfam. org) and PROSITE (www. prosite. expasy. org). Data were tested with unpaired two-tailed Student's t-test or one-way analysis of variance and Tukey's honestly significance test using GraphPad Prism 7 software (GraphPad Software, San Diego, CA, USA). Graphs were generated with the same software.
ACKNOWLEDGEMENTS The authors thank  Alberto Carbonell  ( IBMCP, Spain ) for the design of the amiGUS constructs,  James Carrington  ( Donald Danforth Plant Science Center, USA ) for providing vectors for the expression of amiRNAs,  Brigitte Poppenberger  ( TUM, Germany ) for seeds of the OE-UGT73C5 line, and  Silvestre Marillonet  ( IPB, Germany ) for providing the p19 clone. The authors thank  Gary Sawers  ( MLU, Germany ) for reading the manuscript, and  Sabine Rosahl  ( IPB, Germany ) for conceptual input and comments. The authors thank  Kristin Eismann  and  Michael Andr e Fritz  for technical assistance and initial data, respectively. This work was supported by grants from the  Deutsche Forschungsgemeinschaft (DFG)  [ RTG1591  (projects  C2  to S. E. B. and  C3  to S. G. -Z. )], the  European Union  [ ERASMUS MUNDUS Action 2 program  ' BRAVE '], the  State Research Priority Program Saxony-Anhalt Fkz  [ ZS/2016/ 06/79740 ] (to S.
Sequencing and Phylogenetic Analysis of 16S rDNA Gene The amplicons were directly sequenced using a 373A model sequencer and the PRISM Ready Reaction Dye Deoxy Terminator Cycle sequencing kit. The results were converted into sequence information with the help of William Pearson's lalign programme (http: //www. ch. embnet. org/software/lalignform. html, access on 10 October 2023), and the partial sequences were combined into a single sequence using the results. The partial sequences were processed into sequence information with a sequence analysis chromas software (version 2. 33; Technelysium Pvt. , Ltd. , Brisbane, QLD, Australia; http://www. technelysium. com. au/chromas. html, access on 17 September 2014). The isolates were found using a single sequence analysis of the generated data that was dynamited through the NCBI database.
In order to find the closest neighbours to the cloned sequences, a BLAST search of the National Center for Biotechnological Information (NCBI) was conducted after the sequences were modified using Vecscreen. The Ribosomal Database Plan (RDP) classifier tool was used to carry out taxonomic assignments (http://rdp. cme. msu. edu/, access on 17 September 2014). The greatest likelihood approach was used to infer a phylogenetic tree. MEGA 6. 0 software was used to run the tree-creating algorithm and analysis  [29].
FT-IR and FT-R Spectro Photometry Data Analysis The Staphylococcal hyaluronidase was dissolved in D 2 O with guanidine hydrochloride (Gdn HCl) for measurements of FT-IR spectra (Fourier Transform-Infrared) using an infrared spectrophotometer (NEXUS 670, Thermo Nicolet) through 64 times of scanning  [32]. Using the BRUKER RFS 27 and Fourier Transform-Raman (FT-R) spectrometer, which are available at the high-end analytical instrument facilities in Indian Institute of Technology, Chennai, India, a FT-R spectrum was acquired in the range of 500-3000 cm -1. The spectra were examined with the aid of the program ORIGIN 6. 0.
The tree is constructed according to scale, and the length of the branches is measured by the number of substitutions per site. There were 934 positions in total in the final dataset. Further, the 16S region of rRNA from the strain (CASMTK1) was sequenced. According to NCBI BLAST analysis, the marine strain of Staphylococcus sp. (CASMTK1) (Accession No. JX435813) was found 98. 40% closer to Staphylococcus aureus RA 20 (AB634830). The phylogenetic analyses also confirmed the taxonomic position of the strain (Figure  2 ). The evolutionary history was inferred by utilizing the greatest likelihood method. The tree with the highest log probability (-3661. 8091) is shown. The percentage of trees, in which the associated tax is aggregated is shown beside the branches. The initial trees for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of estimated pairwise distances using the Maximum Composite Likelihood Approach (MCLA).
(CASMTK1) by sequencing its 16S region of rRNA and the sequence had a high similarity to other Staphylococcus species sequences in GenBank data base. Phylogenetic study verified the relationship between the species of the genus Staphylococcus. The technique of 16S rRNA gene sequences for classifying and identi-fying prokaryotes depends primarily on comparing the database of relevant species of known sequences in particular, sequences of typical strains of 99% of prokaryotic species with available NCBI databases  [45]. In general, hyaluronidase producing bacteria belong to the genus of Streptococcus, Streptomyces and Staphylococcus  [46]. The enzyme aids in invasion of bacteria by degrading hyaluronic acid-rich host tissue. Furthermore, the hyaluronan oligomers of the enzymes are powerful inflammatory agents that promote a microbial friendly environment  [24].
Systematic search strategy This systematic review followed the PRISMA 2020 guidelines for the reporting of systematic reviews  [34]. Search terms were developed around four key topics: (i) livestock and poultry, (ii) networks, (iii) models, and (iv) disease. Four databases-Medline, Embase, Web of Science and Scopus-were queried using title, abstract and keyword searches on 22 January 2021 and no date limits. Database searches were repeated on 27 January 2023 to cover all records published up to this date. Relevant subject headings were applied to databases using subject heading indexing (i. e. Medline and Embase; electronic supplementary material, table  S1 ). Search terms within the 'networks' topic were informed by previous reviews of the use of network simulation models in different contexts  [35] [36] [37] [38] [39].
However, broad terms were also included to ensure identified records were not restricted to known model types. Within each search topic, Boolean 'OR' operators were used to combine search terms and subject headings, while different topics were combined using 'AND' operators (electronic supplementary material, table  S1 ). Wildcards, truncations and adjacency searches were applied using the relevant syntax for each database. Peer-reviewed papers and conference proceedings were all eligible for inclusion. The screening process was expanded to include the reference lists of the included publications, as well as any papers that cited them. For full search terms see electronic supplementary material, table  S1.
Data extraction Information from each study was systematically recorded in a data extraction table. This was designed to record information about: (i) the type of model used; (ii) the applications of models; (iii) characteristics of the empirical network under study (livestock type, geographical location and disease focus); (iv) definition of network nodes and edges; (v) data types and variables used for model fitting, and (vi) how the performance of models was assessed (table  2 ). Descriptive analyses and visualizations of the frequency of key study characteristics were conducted using R v. 4. 2. 0  [42].
Screening process Database searches retrieved 12 226 publications of which 7981 (65%) were unique. Title, abstract and keyword screening excluded 7521 (94%) unique records (figure  1 ). A further 418 (5%) were excluded after screening full texts, mostly because they did not simulate a livestock contact network but presented descriptive analyses of empirical networks or simulated infectious disease transmission on empirical networks (figure  1 ). Six additional publications were identified from the citations of included papers. A single additional publication citing these publications was then identified. Therefore, a total of 52 publications published between 2009 and 2022 were eligible for inclusion (see electronic supplementary material, table S2 for all exclusion reasons). To identify the number of different models used across all included studies, we considered a model to be 'distinct' from others when a specific framework was applied to a particular dataset.