dataesr/scientific-paragraphs-categorization huggingface dataset:
This is powered by specific tools to perform the necessary pre-processing tasks, such as eliminating redundant information, correcting synonyms, normalising spelling and parsing (i. e. , breaking-down) the list of ingredients. But these tools are dependent on the platforms for which they have been designed, i. e. for databases not specifically dedicated to food innovation; and have not been benchmarked. Some scripts were released on well-known web-based version-control and collaboration platforms such as GitHub  4  , but some fell short because (i) they had not been benchmarked; (ii) they did not catch the attention of the scientific community (no reuse in research projects). Finally, INRAE (the French National Research Institute for Agriculture, Food, and Environment) has also developed a web-platform dedicated to the study of food supply, ODALIM  5  , from which an API offers a similar service.
Presentation of materials Materials used to develop this algorithm and the benchmark method were sourced from the Mintel GNPD for the Global New Products Database. Mintel is an international company that has been curating a large food database since 1972. It indexes all food products launched (i. e. , food production innovations) on every market covered by the company. The database now surveys product innovations in 86 countries and the European market has been covered entirely since 1996 ( Fig. 1  ). The Mintel database records visual observations made by hired "Mystery Shoppers"  6. Information on product packaging is recorded through an extensive network of expert shoppers in each country covered. 80 different data fields are considered. The Mintel-GNPD currently listed 3,6 6 6,585 products as of July 2021.
We chose to use this database because (i) , it is an innovation-oriented database -only new launches are registered; and (ii) , for the high level of details available for the products included  [12]. Indeed, other well-known accessible food databases such as the USDA database or OpenFoodFacts do not make this distinction between product innovations and well-established food products (i. e. , most consumed products). Therefore, with other databases it is not possible to assess how market supply is trying to change (whether or not those food innovations are a success). They also register less entries than the Mintel-GNPD. The OpenFoodFacts database listed 1,887,966 products and the USDA branded food database listed 1,142,610 as of July 2021. The source corpus was obtained thanks to the Boolean query search engine proposed by the GNPD platform ( Fig.
Benchmark method As mentioned in the section "Data Description", we propose a benchmark method to assess the effectiveness of parsing algorithms having to deal with that sort of data structure. We give, as additional materials  9  (see "Benchmark Table  Results "), the results of this method applied both for our parsing algorithm and the ODALIM webservice dedicated to decoding ingredients ("Simple Decoding Ingredients " 10  -now called "SDI") on a random dataset of 100 ingredient lists extracted from the "Filtered Corpus". Special attention was paid to this proximity calculation method, as in our particular case we had to take into account that: -Ingredient lists obtained thanks to the different parsers could be of varying lengths. -The order of ingredients (i. e. , their rank in the ingredient list) and depth (whether the ingredient is a sub-ingredient of a complex ingredient) is extremely important when computing similarity or dissemblance scoring between ingredient lists.
Materials and methods Specimens of Striastigmus, Vitreostigmus spp. , Bortesia mirostigmus and some of B. similis were swept in different Australian localities (for details see respective species). A majority of our specimens of B. similis were reared from buds, leaves and twigs of Hakea rostrata (Böhmová and Janšta, pers. obs. ). Hakea parts were stored in sealed plastic bags at room temperature and checked every two days. All specimens, both emerged and swept, were stored in 96% EtOH. DNA of some specimens (those labeled as JRAS or JBOH) were extracted using the Qiagen DNeasy kit following the manufacturer's protocol and were subsequently sequenced for the COI (barcode fragment) following  Cruaud et al. (2010)  or ultraconserved elements (UCE) following  Cruaud et al. (2021). All COI sequences are deposited at NCBI under the accession numbers: ON007286-88.
Vitreostigmus kangarooislandi Böhmová & Janšta, sp. nov. http://zoobank. org/0858BAAA-ED54-49B7-AFB2-C9BA18E7EB06 Figs 3A-G, 4A-D, 5A-I Material examined. Holotype: Australia • ♀; South Australia, Kangaroo Island, Flinders Chase NP, Gosse lands; 35. 93325°S, 136. 9326°E; 16 Jan. 2019; P. Janšta, J. Böhmová leg. , sweeping Allocasuarina sp. ; deposited in SAMA (JBOH0037_0101); NCBI accession number ON007288. Paratypes: Australia • 3 ♀♀; same collection data as holotype; CUPC (JBOH0035_0101 (CUPC000156), CUPC000157 (NCBI accession number ON007286), CUPC000162) • 1 ♀; same collection data as holotype; SAMA (CUPC000159) • 2 ♀♀; same collection data as holotype; SAMA (CUPC000159, CUPC000161) • 1 ♂; same collection data as holotype; SAMA (CUPC000158); NCBI accession number ON007287 • 1 ♂; Kangaroo Island, near Seal Bay turning; 35.
Acknowledgements This work was supported by The  Charles University Grant Agency  ( GAUK ) -project n. 1122218  (to JB) and  SVV 260571/2022  (to PJ). We are grateful to  Chris Burwell  and  Susan Wright  (both  QMB ), and  Ondřej Barda  for help with gathering material. We thank the following institutions: the  Queensland Government, Department of National Parks, Sport and Racing  for delivering permits to sample in Queensland National Parks (permits # WITK18248017-WITK18278817  to JYR);  Government of South Australia, Department of Environment, Water and Natural Resources  for pro-.
2015)  and was exacerbated for sRNA-dependent IESs (retained in DCL2/3 or DCL5 KDs)  (Lhuillier-Akakpo et al. 2014; Sandoval et al. 2014 ). In the DCL RNAi data sets, IESs from the very early cluster are totally absent, whereas IESs from the intermediate and the late clusters are overrepresented. In contrast, very early excised IESs are strongly enriched (∼60%) among the 12,414 IESs that are excised independently of the above factors ("excision complex only"). Considering the overlap between IES dependencies (Fig. 4E ), our data indicate that IESs depending on known heterochromatin-targeting factors tend to take longer to be excised during MAC development. We observed that, consistent with late IESs being error- prone (Fig. 4B ), there were more errors for Dcl2/3-or Ezl1-dependent IESs than for IESs depending on the "excision complex only" (Supplemental Fig.
S12D , boxplot). A similar size bias was reported for the Ezl1-dependent IESs from the reference set  (Lhuillier-Akakpo et al. 2014). For their related encompassing IESs (n = 223), we observed that 94% are significantly retained in Ezl1-depleted cells, consistent with their late excision timing. Our results suggest that internal IESs show similar characteristics to those of the reference IES set in terms of length and epigenetic control and, therefore, might share the same evolutionary history. With regard to the excision mechanism, molecular data indicate that IESs can be excised while retaining a nested IES  (Bétermier et al. 2000; Gratias and Bétermier 2001). This suggests that excision of internal IESs is not a systematic prerequisite for the elimination of their encompassing IESs, reminiscent of the sequential splicing of introns inserted within introns  (Hafez and Hausner 2015).
We determined at an unprecedented resolution the timing of IES excision and imprecise DNA elimination genome-wide, across successive endoreplication cycles (Fig. 5A ). Our data show that DNA elimination is an ordered process. We found that most IESs are excised between DEV1 ∼ 32C and DEV3 ∼ 64C under standard conditions (see Methods), whereas imprecise elimination only starts at DEV3 ∼ 64C. We established four classes of IESs according to their excision timing (Fig. 4A ). We also showed that the progression of IES elimination, once it has started, is independent of replication, suggesting that the excision machinery is recruited to its chromatin targets independently of replication fork passage. We observed that little IES excision has already taken place at the earliest stage of our study (median ES = 0.
2014). The stimulatory contribution of iesRNAs might explain why excision of late IESs precedes imprecise elimination of TEs and other MIC-limited sequences during MAC development. We also report that early excised IESs tend to be the oldest and are enriched for smaller sizes (54. 5% belong to the 25-to 33-bp peak). They are also mostly independent of sRNAs and heterochromatin marks and tend to be the least errorprone. Our data therefore provide experimental support to the proposed evolutionary scenario of Paramecium IESs, showing that their excision timing reflects their evolutionary age (Fig. 5B ). We provide evidence that IESs have evolved through optimization for efficient excision, combining an early and accurate excision process. Closer analysis of the intrinsic properties of very early excised IESs furthermore revealed a strong nucleotide sequence signal at their ends, which varies according to IES size (TATAG for 25-to 33-bp IESs, TACAG for 42-to 140-bp IESs).
Phosphate buffered saline-like (puraflow sheath fluid, Beckman Coulter) was used as sheath and run at a constant pressure of 10 or 25 PSI. Frequency of drop formation was 26 or 43 kHz. Purify mode was used for sorting in order to reach a maxi-mum rate of purity (>95%). The instrument used a 100-μm nozzle. A threshold on the PI signal was optimized to increase collecting speed (about 1000 events per second). Data were collected using Summit software (Beckman Coulter). Nuclei were first gated based on their side scatter (SSC-area) and high PI signal (PI-area) and sorted according to their AF488 signal. AF488-positive events were backgated onto SSC versus PI to optimize the gating. Doublets were discarded using PI-area and PI-height signals. Nuclei (fewer than 30,000) were collected into 100 μL of buffer AL (QIAamp DNA micro kit, Qiagen) and immediately lysed by pulse-vortexing.
Software and R packages Sequencing reads were mapped on genome references using Bowtie 2 (v2. 2. 9 --local --X 500)  (Langmead and Salzberg 2012). The resulting alignments were analyzed using SAMtools (v1. 9)  (Li et al. 2009) , ParTIES (v1. 05) (Denby  Wilkes et al. 2016 ; https ://github. com/oarnaiz/ParTIES), and BEDTools (v2. 26)  (Quinlan and Hall 2010). R (v4) packages were used to generate images (ggplot2 v3. 3. 5; ComplexHeatmap v2. 6. 2; GenomicRanges v1. 42)  (Lawrence et al. 2013; Gu et al. 2016; Wickham 2016; R Core Team 2021). The IES sequence end logos were generated using WebLogo (v3. 6. 0 --composition 0. 28 --units bits)  (Crooks et al. 2004 ).
Reference genomes and data sets Paired-end sequencing data were mapped on P. tetraurelia strain 51 MAC (ptetraurelia_mac_51. fa), MAC + IES (ptetraurelia_mac_51_ with_ies. fa), or MIC (ptetraurelia_mic2. fa) reference genomes  (Arnaiz et al. 2012; Guérin et al. 2017). Gene annotation v2. 0 (ptetraurelia_mac_51_annotation_v2. 0. gff3), IES annotation v1 (internal_eliminated_sequence_PGM_ParTIES. pt_51. gff3), and TE annotation v1. 0 (ptetraurelia_mic2_TE_annotation_v1. 0. gff3) were used in this study  (Arnaiz et al. 2012 (Arnaiz et al. , 2017;; Guérin et al. 2017). All files are available from the ParameciumDB download section  (Arnaiz et al. 2020 ; https://paramecium. i2bc. paris-saclay. fr/download/Paramecium/tetraurelia/51/). DNA sequencing data of Paramecium cells depleted of Ezl1, TFIIS4, Dcl2/3, or Dcl5 were previously published  (Lhuillier-Akakpo et al.
2014; Sandoval et al. 2014; Maliszewska-Olejniczak et al. 2015). ParTIES (MIRET module) was used to determine the IESs that were significantly retained compared to the control (Denby  Wilkes et al. 2016 ). An IES is considered to be dependent on the depleted factor for its excision  (31,505, 20,524, 3439, and 2475  IESs sensitive to EZL1, TFIIS4, DCL2/3, and DCL5 RNAi, respectively), if at least one IES boundary in at least one replicate shows significant retention.
IES-IES junctions The ParTIES concatemer module, developed for this study, was used with default parameters to identify concatemers of excised IESs. Reads were recursively mapped to the IES sequences, as shown in Supplemental Figure  S5A. At each round, reads are mapped to IES sequences and selected if the alignment begins or ends at an IES extremity. If the read is partially aligned, then the unmapped part of the read is reinjected into the mapping and the selection procedure continues until the entire read has been mapped.
IES excision errors The ParTIES MILORD module was used with the MAC + IES reference genome to identify IES excision errors. Only error types described in Supplemental Figure  S7A  were considered. The number of nonredundant errors was normalized by the number of mapped reads. PCR duplicates were removed using SAMtools rmdup. of the Imagerie-Gif core facility, supported by the Agence Nationale de la Recherche (ANR-11-EQPX-0029/Morphoscope, ANR-10-INBS-04/FranceBioImaging, ANR-11-IDEX-0003-02/ Saclay Plant Sciences). We acknowledge the sequencing and bioinformatics expertise of the I2BC High-throughput sequencing facility, supported by France Génomique (funded by the French National Program "Investissement d'Avenir" ANR-10-INBS-09). Author contributions: C. Z. , M. Bourge, M. Bétermier, O. A.
Acknowledgments We thank  Cindy Mathon  and  Pascaline Tirand  for their technical assistance and all members of the  Bétermier laboratory  for stimulating and fruitful discussions. We give special thanks to  Joël Acker ,  Julien Bischerour , and  Linda Sperling  for critical reading of the manuscript. This study was supported by intramural funding from the  Centre National de la Recherche (CNRS)  and by grants from the  Agence Nationale de la Recherche  (LaMarque  ANR-18-CE12-0005-02  and  CURE ANR-21-CE12-0019-01  to  M. Bétermier ,  POLYCHROME ANR-19-CE12-0015  to O. A. ) and the  Fondation pour la Recherche Médicale  ( FRM EQU202103012766  to  M. Bétermier ). The present work has benefited from the expertise.
Data access Genes used in this study are accessible in ParameciumDB (https ://paramecium. i2bc. paris-saclay. fr/) as follows: PGM (PTET. 51. 1. G0490162), PGML1 (PTET. 51. 1. G0110267), EZL1 (PTET. 51. 1. G1740049), TFIIS4 (PTET. 51. 1. G0900102), DCL2 (PTET. 51. 1. G0210241), DCL3 (PTET. 51. 1. G0990073), DCL5 (PTET. 51. 1. G0 070121), and ND7 (PTET. 51. 1. G0050374)  (Arnaiz et al. 2020). The sequencing data generated for this study have been submitted to the ENA database (https://www. ebi. ac. uk/ena/browser/home) under accession number PRJEB49315. The statistical data, scripts (Supplemental Codes), and raw images have been deposited at Zenodo (https://doi. org/10. 5281/zenodo. 6534539). The cytometry data generated in this study have been submitted to the FlowRepository database (http://flowrepository.
46 The carrier was Millipore water containing 0. 2 g L -1 sodium azide. Two detectors were coupled with AF4: a Dawn® Heleos™ MALLS, fitted with a K5 flow cell and a GaAs laser, (λ = 658 nm), from Wyatt Technology Corporation (Santa Barbara, CA, USA) and a refractometer (ERC-7515A, Erma, Tokyo, Japan). Solubilization and elution recoveries were calculated comparing the mass before and after filtration, and the mass eluted in the AF4 channel as previously described. 46 The weight-average molar mass (M ˉw) and z-average radius of gyration (R ˉG) were established using ASTRA® software from Wyatt Technology Corporation (version 6. 0 for PC), as previously described. 46 Branching characteristics were calculated as described by Rolland-Sabaté and co-workers. 33, 34, 46, 47 The average number of branching points per macromolecule (B ˉ) and the average number of glucosyl units in a linear chain per branching point ðDP w =BÞ were obtained from the molar mass and radius of gyration data through the average shrinking factor g M (g M = R ˉ2Gw(br) /R ˉ2Gw(lin) , where R ˉGw(br) and R ˉGw(lin) are the weight average radii of gyration of the branched molecule and of its linear equivalent at the same molar mass).
Fermentation is a natural process without inoculum; the bacterial flora comes from the processing of the roots and the washing and sieving of the pulp. The first subsample was oven-dried at 40  Table  1  Averages for the specific volume of the three main treatments and their combinations in Experiments 1 and 2. Averages (within each box) followed by the same letter are not statistically significant at the 1% probability level based on the least significant difference. On top of the table are the averages for the main sources of variation (genotypes across starch type and the drying method; starch type across genotypes and the drying method; and the drying method across genotypes and starch type). Data from a 10-day fermentation process was used for the fermented starches.
Acknowledgements The present research was carried out within the framework of a Memorandum of Understanding signed between CIRAD:  French Agricultural Research Centre for International Development, Montpellier, France  and  ABC: The Alliance of Bioversity International  and the  International Center for Tropical Agriculture (CIAT) , formerly  CIAT, Cali, Colombia. The authors thank  Max Reynes ,  Antoine Collignan ,  Dominique Pallet  in charge of the agreement at  CIRAD ,  Joe Thome  and  Clair Hershey  at  CIAT  for their administrative support. Financial support from the authors' respective organizations (CIAT, CIRAD and INRAe) made the reported research possible. CIRAD has contributed financially toward the salaries and benefits of the French food scientist researchers posted at  CIAT, Cali, Colombia.
X-ray Analysis on Compound 15 Colourless blocky crystals of compound 15 were grown from the slow evaporation of an ethanol solution. Single crystal X-ray diffraction analyses were carried out on a Bruker APEX Kappa Duo Diffractometer Mo-Kα radiation (λ = 0. 71073Å), and a graphite monochromator. Data collection and unit cell refinement were carried out with SMART  [42]  and data reduction with SAINT  [43]. The structure was solved by direct methods and refined by least-squares techniques with SHELXS and SHELXL  [44] , respectively, using OLEX2  [45]  as an interface. Hydrogen atoms were placed in calculated positions and refined using a riding model with their displacement parameters equal to 1. 2 Uiso of the non-hydrogen atom to which they are attached. X-ray crystallographic data for this structure has been deposited at the Cambridge Crystallographic Data Center under code CCDC 2184667.
Cell Cycle and Apoptosis Analysis CCRF-CEM cells were seeded at a density of 1 × 10 6 cells per one mL in 6-well plates (TTP) and were cultivated with compounds at concentrations corresponding to 1 × or 5 × IC50 value. Together with the compounds-treated cells, a vehicle-treated sample was harvested at the same time point. After 24 h, the cells were washed with cold phosphatebuffered saline (PBS) and fixed in 70% ethanol added dropwise and stored overnight at -20 °C. Afterward, cells were washed in hypotonic citrate buffer, treated with RNase (50 µ g mL -1 ), and stained with propidium iodide. Flow cytometry using a 488 nm single beam laser (Becton Dickinson) was used for measurement. The cell cycle was analyzed by the software ModFitLT (Verity), and apoptosis was measured in a logarithmic model expressing the percentage of the particles with propidium content lower than cells in the G0/G1 phase (<G1) of the cell cycle.
We reasoned that if the nucleocapsid packaging (envelopment) depended on YBX1, we would observe greater degradation of C protein by PK in the absence of detergent in YBX1 KO cells relative to WT cells. Consequently, lysates from WT and YBX1 KO cells were treated with PK in the presence and absence of Triton X-100 and the levels of structural viral proteins were determined by Western blotting (Fig. 4C ). We quantified the ratio of protein protected from PK-degradation by dividing the densitometry values of the PK-treated samples by that of the nontreated control. We found a slight reduction of the fraction of C that was protected from PK degradation between WT and YBX1 KO cells; however, this difference was not statistically significant (Fig. 4C , lanes 3, 6 and 9, and Fig. 4D ). Although this assay suggested that C is equally protected from PK-mediated degradation in all cell lines, the noisy quality of the data did not unambiguously rule out an effect of YBX1 in envelopment.
ACKNOWLEDGMENTS We thank members of the  Garcia-Blanco-Bradrick Laboratory (UTMB) and Garcia-Blanco-Pompon Laboratory (Duke-NUS)  for their insightful suggestions and support. We also thank  Ms. Zhixia Ding  for her expert assistance in TEM. We also would like to acknowledge  Dr. Michael Sheetz  and  Dr. Pei-Yong Shi  ( UTMB ) for their suggestions to the manuscript. MDT was the recipient of a  Rubicon fellowship  from  The Dutch Research Council (NWO). The project was supported by the  Duke-NUS   Signature Research Program in Emerging Infectious Diseases  funded by the  Agency for Science Technology and Research (A*Star) , and  NIH/NIAID   P01 AI150585  to M. A. G-B, and  NMRC (Singapore)  grant  NMRC/ZRRF/0007/2017  to J. P. The funders had no participation in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Heritable DNA methylation imprints in LEC2-induced clonal progeny We postulated that the phenotypic and transcriptional changes found in plants regenerated with embryonic transcription factors may be caused by the inheritance of heritable tissue-specific epigenetic imprints. To test this hypothesis, we studied the methylome of LEC2 regenerants that had been propagated for two successive generations (G2 and G3). To map methylome data we generated a de novo assembly of the Ws-2 genome (see methods). In an all-against-all comparison of RO-LEC2 and LO-LEC2 plants, we found 226, 120 and 386 Differentially Methylation Regions (DMRs) in CG, CHG and CHH contexts, respectively (S3 Table ). Principal component analysis (PCA) of the 226 CG-DMRs showed that the main source of variance in our dataset was the tissue analyzed (69% of variance), with methylome profile of leaves of RO-LEC2 plants being slightly skewed towards root samples (Fig  3A ).
The DNA library was prepared using the Accel-NGS XL Library Kit (Swift Biosciences, United States). The library was size-selected with a minimum fragment size of 20 kb using a BluePippin (Sage Science, Beverly, United States and a High-Pass v3 cassette. The library was sequenced on the Pacific Biosciences (Menlo Park, United States) Sequel platform (ICS v5. 0. 0) with the v5. 0. 1 chemistry, which produced 5. 09 Gb of long read sequences with a subread N50 of 15,750 bp. The genome was assembled into 120 contigs of total length of 123. 5 Mb with an N50 of 6. 57 Mb using a custom Snakemake pipeline, "auto-asm" (https://github. com/weigelworld/auto-asm). Briefly, long reads were assembled into contigs using canu  [57]  and scaffolded without polishing steps into chromosomes against TAIR10 using reveal  [58].
Transcriptome analysis For mRNA-seq analysis, total RNA (10 μg) for each sample was used to purify polyA+ mRNA that was used for synthesis and amplification of cDNA. The RNA-seq libraries were prepared using TruSeq RNA Sample Preparation Kit from Illumina (San Diego, CA). RNA libraries were sequenced on an Illumina HiSeq2000 instrument (100 bp single-end). Read quality was assessed using FastQC and trimming of low-quality bases at the 3' end of reads and adapter removal was done using Trimmomatic. Reads were mapped to the TAIR10 reference genome using Tophat (parameters -i 20 -I 30,000) with on average 85. 4% unique mappings (min = 82. 2, max = 88. 8). The read counts for these libraries are given in Dataset S1. We used the R package DESEQ2 (version 1. 10. 1) 8. RNA-seq libraries from samples Control-LEC2 root 2 and Control-RKD4 root 3 were discarded for further analysis due to bad quality and low replication fidelity.