dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 2  (Langmead and Salzberg, 2012). Then we employed LACHESIS  (Burton et al. , 2013)  software (clus-ter_min_re_sites = 100; cluster_max_link_density = 2. 5; clus-ter_noninformative_ratio = 1. 4; order_min_n_res_in_trunk = 60; order_min_n_res_in_ shreds = 60) to cluster, reorder, and orient the contig-scale genome assembly, and manually checked the placement and orientation errors apparent in chromosomes using the Hi-C heatmap. To evaluate the newly assembled genome, the completeness of the genome assembly was assessed using BUSCO  (Simao et al. , 2015)  against the Embryophyta_odb10 database. Quality was assessed using '72L' genetic maps of carnation published  (Yagi et al. , 2017)  using ALLMAPS  (Tang et al. , 2015). Finally, we judged the assembly quality by mapping our Illumina and ONT reads back to the genome.
Annotation of repeat elements and genes Before gene prediction, repetitive sequences were masked by an extensive de novo TE annotator pipeline  (Ou et al. , 2019). Transcriptome-based, homology-based, and de novo approaches were used to predict high-quality protein-coding genes (Supplementary Notes). For homology-based prediction, we selected transcript protein sequences from eight species, including A. thaliana, Oryza sativa, Rosa chinensis, V. vinifera, Carica papaya, D. caryophyllus_draft _r1. 0, Solanum lycopersicum, and B. vulgaris. For ab initio annotation, Augustus  (Stanke et al. , 2004) , SNAP (https://github. com/KorfLab/SNAP), and Glim-merHMM  (Majoros et al. , 2004)  were employed. For transcriptome-based prediction, the transcriptome data were produced by Illumina sequencing of materials including leaves, shoots, and roots of 'Scarlet Queen' and RNA-seq data of flowers from NCBI.
RNA-seq alignment files were generated using HISAT2  (Kim et al. , 2015)  and the PASA program was used to align spliced transcripts and annotate candidate genes. Evidence Modeler v1. 1. 1  (Haas et al. , 2008)  was used to generate the final consensus set of gene models obtained using the three approaches and results were updated using PASA. Functional annotation of protein-coding genes was performed by BLASTp (evalue 1e-5 cutoff) using the SwissProt, NR, and KOG databases. InterProScan  (Jones et al. , 2014)  was used to annotate the protein domains by searching the InterPro database. GO terms for each gene were obtained from the corresponding InterPro entries. Non-coding RNAs were identified using Rfam and Infernal software.
Phylogenetic analyses To investigate the evolutionary history of D. caryophyllus, eight other species with complete genomes, B. vulgaris, S. oleracea, S. aralocaspica, Daucus carota, Helianthus annuus, Nesolagus sinensis, Rhododendron simsii, and Nelumbo nucifera were selected for using OrthoFinder  (Emms and Kelly, 2015)  with default parameters to generate a matrix for phylogenetic analysis. Single-copy orthologs were identified from this dataset and used to construct a phylogenetic tree. Protein sequences were aligned using MAFFT  (Katoh and Standley, 2013). We further estimated the divergence times between species using the MCMCTree in the PAML package  (Yang, 2007). For the estimation of divergence time, we calibrated the model using the divergence time between N. nucifera and H. annuus (121.
6-134. 9 MYA), , and H. annuus and R. simsii (99-114 MYA) obtained from the TimeTree database (http://www. timetree. org/) and the crown age of 89. 8 MYA (~90 MYA) of either the Cornales or Ericales  (Li et al. , 2019). Gene families that underwent expansion or contraction were identified in the eight sequenced species using Computational Analysis of Gene Family Evolution  (Han et al. , 2013). Homologous pairs of D. caryophyllus proteins were identified using an all-to-all search in BLASTp with an e-value cutoff of 1eÀ5. MCScanX  (Wang et al. , 2012)  with default parameters to identify collinear blocks, each containing at least 15 collinear gene pairs. To look for polyploidy events, add_ka_and_ks_to_collinearity. pl of the downstream MCScanX script was used to calculate Ks between collinear genes in each pair of D.
caryophyllus_v1, D. caryophyllus r1_draft, B. vulgaris, S. oleracea, and L. sativa within each species. 4DTv values for these species were obtained using the PAML package. coloration of flowers begins, S5: buds before flowering and coloration, S6: open flowers, and S7: senescing flowers), as well as red margin tissue and yellow limb tissue, were collected for RNA-seq. We used HISAT2  (Kim et al. , 2015)  to align RNA-seq short reads from 27 different tissues to our chromosome-scale genome. Read counts of these samples were calculated using HTSeq, and differential gene expression analysis was conducted using the R package DESeq2 (|log2FC| >1 and padj <0. 05). Other samples of different floral organs (outer petals, middle petals, petaloid stamens, and stamens) were collected for qRT-PCR. We followed the qRT-PCR procedures and analyses described in a previous paper  (Zhang et al.
, 2018)  to quantify the gene expression among the different floral whorls. To identify relationships between DEGs, co-expression networks were constructed using the WGCNA package  (Langfelder and Horvath, 2008). The parameters used in the construction of WGCNA were weighted network, unsigned, hierarchal clustering tree, dynamic hybrid tree cut algorithm, power 8, and minModuleSize = 30. We then used a Cytoscape to display the network. Network statistics were calculated using NetworkAnalyzer in Cytoscape  (Shannon et al. , 2003). Expression heatmaps were generated using TBtools software  (Chen et al. , 2020)  with log and row scales and online heatmap software v0. 2. 2 (https://hiplot. com. cn/basic/heatmap) with default parameters.
Volatile compounds of 'Lorca' emitted from flowers were collected by solid-phase microextraction. These samples were added to a 15-mL headspace bottle, weighed, and balanced. After extraction, the extracted fiber was immediately inserted into the GC injection port for thermal analysis. Three independent replicates were performed for each stage for each sample. Analysis was performed using a GCMS-QP2010 system (Shimadzu, Japan). The ion source and interface temperatures were 200 and 250 °C, respectively. After obtaining the chromatograms and mass spectrograms of the floral compounds, these data were assessed by qualitative analysis using the NIST2011 library, and the identified compounds were checked against published plant volatiles in The Pherobase Database (www. pherobase. com).
Acknowledgements This work was supported by funding from the  National Natural Science Foundation of China  ( 32072607 ,  32002074 , and  31872135 ). This work was funded by the  Science, Technology and Innovation Commission of Shenzhen Municipality  (STIC:  RCYX20200714114538196 ). Finally, this work was also supported in part by the  Chinese Academy of Agricultural Sciences Elite Youth Program  to  Zhiqiang Wu.
Results and discussion Most bacteriocins of lactobacilli and lactic acid cocci are short peptides. Therefore, in a series of experiments the supernatants obtained after the growth of the potentially probiotic strain Lb. plantarum L4 were tested for the presence of substances with antimicrobial activity and protein nature in the supernatants. The inhibitory activity of the samples obtained after the enzymatic treatment of the cell-free supernatants against pathogenic bacteria was determined (Table  3 ). The experimental data presented in Table  3  unequivocally show that Lb. plantarum L4 demonstrated high antimicrobial activity against pathogenic bacteria and phytopathogenic fungi. Since the acidity as an inhibitory factor had been eliminated, this suggested that the observed inhibitory effect could be due to the synthesis of substances of protein nature, secreted in the medium and bound to the cell surface (Table  3 ).
The inactivation of the substances with antimicrobial action by proteolytic enzymes, while at the same time retaining their activity upon exposure to ᾳ-amylase and lipase, confirmed the protein nature of those substances and that the inhibition zones were not due only to the production of organic acids. Similar studies and observations have been cited by Todorov and Dicks  [10]  and Mezaini et al. [11]. The resistance of the substances produced by the potentially probiotic strain Lactobacillus plantarum L4 to different temperatures has also been examined. The temperature treatment was carried out at temperatures from 30°С to 121°С. The experimental data from these studies are reflected in Table  4. Experimental data show that the bacteriocins or bacteriocin-like substances formed by Lb. plantarum L4 were temperature resistant and although it changes below 70°C, these substances retain activity against pathogens up to 121°C (Table  4 ).
Acknowledgements: The research in the present study was funded by project  Programme "Healthy Foods for a Strong Bio-Economy and Quality of Life " approved by  DCM № 577/17. 08. 2018 , Bulgarian Ministry of Education and Science and Project "Strengthening the research excellence and innovation capacity of University of Food Technologies -Plovdiv , through the sustainable development of tailor-made food systems with programmable properties", part of the  European Scientific Networks National Programme  funded by the  Ministry of Education and Science of the Republic of Bulgaria  (agreement No  D01-288/07. 10. 2020 ).
extracted data was compiled into a table containing geometric properties of more than 50 0 0 0 analysed capsules. These data allow (i) to reproduce quickly the encapsulation experiments and be able to choose in a straight-forward manner the combination of parameters needed in order to generate capsules with desired properties; (ii) to create more general phase diagrams of the centrifugal encapsulation technology which can be widely used for prediction and/or parameter selection; (iii) to analyse more thoroughly the sensitivity of capsule properties to given stages of the encapsulation process. The research article on these data  [1]  was published in the journal Colloids and Surfaces A: Physicochemical and Engineering Aspect, with the title: Three-dimensional phase diagram for the centrifugal Calcium-alginate microcapsules production technology.
© Centrifugal capsule production experiments were carried out using different combinations of input parameters. After hardening in the gelation solution, the generated microcapsules were collected and placed in Petri dishes for microscope observation. A digital camera connected to an inverted microscope was used to collect photos of the capsules. The collected images were analysed using CellProfiler 4 image processing software, in order to detect the objects (capsules) and measure their geometric properties. Data.
Value of the Data • These data are useful for the centrifugal encapsulation technology because the full range of the four relevant control parameters is explored. To the best of our knowledge, such a complete dataset is not available in the literature. Moreover, a complete description of the device geometry and the full set of operating parameters are given for each experimental point, allowing to exploit our data and to reproduce our experiments. Additional information is given allowing to accurately retrieve three-dimensional information on the generated capsules based on analysis of two-dimensional images. • Researchers interested in microencapsulation of cells, bacteria, or any other active ingredients, can benefit from this data. Firstly, all information is given for rapidly building and operating low-cost centrifugal encapsulation devices.
Secondly, our parametric study allows to predict the output of the process and to choose the needed set of control parameters for an application. Moreover, image and data analysis scripts are provided allowing to quickly characterise the produced capsules. • These data can be used to guide the design and development of new centrifugal encapsulation platforms. It can also be used for the optimisation of existing centrifugal devices. Moreover, the vast amount of labelled capsule images in the dataset could be used for defining or training artificial intelligence algorithms for image analysis.
Data Description a summary of the images that were analysed by the software and the average of the properties of the capsules measured per image. File Properties_MX_RawCapsules. csv contains all the raw capsules detected by the corresponding script, including the metadata extracted from the image file name and the values of measured geometrical properties of each capsule. File Prop-erties_MX_FilteredCapsules. csv contains the same information as the previous one, but only the capsules that passed the filter, based on the value of the solidity, are preserved. If in a certain experiment number i a fibre was observed in the gelation bath, a few photos of this fibre were taken and placed inside the subfolder named fibre of the folder Exp_i. If in a certain experiment i an additional 3D analysis of the capsule shapes was performed, a subfolder called 3D_analysis was created inside folder Exp_i , where the raw images, the.
cppipe script, the analysed images and the image analysis tables are placed. In the main dataset, the table Qualitative_properties. csv contains supplementary properties of the capsules which were collected by visualisation of a few images per each experiment and labelled manually according to a certain code. The labels do not describe individual capsules, but rather average qualitative properties of the capsules formed in a given operating point. • Column Quality describes the quality (integrity) of the capsule surface, which was coded with the following 4 levels, 0: smooth capsule surface without visible damage; 1: surface with little damage or wrinkles; 2: damaged surface with the formation of a "skirt" or high surface irregularities; 3: totally broken or burst capsules. • Column Filament indicates the presence (value 1) or absence (value 0) of thin gelled filaments or cordlets attached to the capsule.
The image analysis files are not given, as these should only serve as examples of the 3D shape analysis method. The Python script gather_all_filtered_capsules. py (uses Python 3 and requires the packages Pandas and Numpy) was used to concatenate all the tables with properties of the filtered capsules from the subfolders (excluding the 3D image analysis tables) into a unique table Filtered_capsules_dataset. csv. Additionally, the units of all the geometric measurements having the size of a length, area or volume were converted from pixels (as exported by the image analysis software) into mm, using the conversion factors stored in table Micro-scope_pixel_mm_conversion. xlsx. The script then joins the tables of the experimental design and of qualitative properties to this table in order to create one dataset containing at the same time all the experimental data, the qualitative properties of the capsules and the extracted geometric measurements.
This table, named Complete_filtered_capsules_dataset. csv is the core result and comprises the most important data of this experimental campaign. The table contains one entry per each capsule detected, and provides at the same time all the information on how the capsule was produced and the characterisation (both quantitative and qualitative) of the capsule size and shape. The table contains information on more than 50 0 0 0 capsules that were produced in our experiments. The Python script mean_properties. py (uses Python 3 and requires the packages Pandas and Numpy) reads the previously created tables and creates a table with the summary of the obtained results, Capsule_properties_mean_and_std. csv. This table contains one entry per each ex- periment. Each entry contains the parameters characterising that operating point, the qualitative properties of the capsules produced and the mean and standard deviation of the geometric measurements of the capsules analysed in that operating point.
Finally, an example of data plotting script (under the form of a Jupyter Notebook, Data_plotting. ipynb ) is given in the main dataset. It extracts the data from the previously created tables and draws plots of capsule properties based on the input parameters. The following figures, representing the produced data, were plotted using this script. Fig. 1  represents a colour plots of capsule diameter 1  depending on two input parameters, the nozzle diameter and the rotation speed. Fig. 2  represents colour plots of 4 different capsule shape properties 2  depending on alginate concentration and collecting distance. The folder Data plots in the main dataset contains the figures created by this script.
The Petri dish is then covered with its transparent lid and placed under microscope for observation. The data for the analysis of capsule sizes and shapes is collected using a digital camera connected to an inverted microscope (Olympus IX50, Japan)  5  , using lens with a magnification varying from 2. 5x to 10x, chosen conveniently depending on the size of the beads. The focus of the lens is adjusted in order to capture clearly the contour of the capsules. Then, the image of the bottom plane of the Petri dish is swept manually using the microscope, in order to take photos of the capsules in the Petri dish, using the digital camera connected to the microscope. Around 15-20 photos are taken for each Petri dish with a low-zoom lens (typically 2. 5x), and 5-10 complementary photos are taken with a higher zoom (typically 4x).
The number of capsules captured in an image can vary from 5 to 50, depending on the size of the capsules and the lens chosen. The 2D images taken represent the raw data of our experiments. A previous calibration of the digital camera and microscope has been performed, using standard microscope calibration slides. For each lens used, a conversion factor has been obtained, allowing to convert the length from pixels to mm. In addition to the main experimental campaign, in order to investigate the three-dimensional shape of the capsules, we performed a complementary analysis, where single capsules, located inside the Petri dish with gelation solution, were turned manually using a needle and photos were taken from various sides. This analysis is important in order to avoid certain wrong hy- pothesis that can be made on the third dimension of objects when analysing them only from 2D images.
Design of experiments In this study, four main operating parameters are varied (see table  2  in the related research article  [1]  ): the alginate viscosity η alg , the inner diameter of the ejection needle D needle , the centrifuge rotating speed ω and the quantity of gelation solution in the centrifuge tube, which controls the fall height of the droplets h. A summary of the experimental design is given in table Experimental_design. csv in the dataset related to this article. Around 110 experiments (distinct sets of the 4 parameters) were realised in triplicate. Other 90 supplementary operating points were realised in unicate. For the 110 experiments performed in triplicate, the three samples were prepared using different tubes, supports and needles, and the solutions were handled using different pipettes, in order to avoid contamination.
The 3 samples were centrifuged in the same run, by placing the three tubes in different positions in the centrifuge (see Fig. 7  ). The tubes were numbered (1,2 or 3) and the tube number was recorded along with the experimental data and the results. The capsule images are marked according to the tube number, so that exact information about the origin of each capsule can be found if needed. For the experiments performed in unicate, only position 1 was used. In the experiments performed in triplicate, tubes 1 and 3 were placed in positions P1 and P2 ( Fig. 7  ) of a swing bucket, whereas tube 2 and the tare tube were placed in the positions P1 and P2 of the opposite swing bucket. The distance from the axis of the centrifuge tube to the y-z plane is marked in Fig. 7   For series of 110 experiments realised in triplicate, an analysis has been carried out to identify the degree of agreement between the results obtained with the different tubes 1,2 and 3 (see Fig.
Finally, we will discuss the importance and the potential of characterizing new and reassessing studied plant extracts for the development of ethnopharmacology-based innovative treatments for chronic pain. A historic standpoint: Lamiaceae, pulegone, and menthol Menthol and pulegone are monoterpene derivatives synthesized through the conserved terpenoid backbone biosynthesis pathway (Figure  1A ). They have been recently characterized and demonstrated to reduce the mechanical and thermal pain hypersensitivities in a rodent inflammatory pain model, thus reinforcing various previous results (de  Sousa et al. , 2007 Sousa et al. , , 2011)). We first wanted to explore natural products databases to establish a list of plants currently known to contain menthol or pulegone. Because there are many natural products repositories  (Sorokina and Steinbeck, 2020) , we chose to investigate the LOTUS database, as it is one of the latest intents to harmonize, curate, and validate structure-organism pairs, in an open access fashion  (Rutz et al.
, 2021). Dr. Duke's Phytochemical and Ethnobotanical Database (1992-2016) , hosted on the USDA website (https://data. nal. usda. gov/dataset/dr-dukes-phytochemical-and-ethnobotanicaldatabases), was also consulted for its richness in ethnobotanical mentions for each plant. We started by extracting all plant names mentioned to contain pulegone and/or menthol, and also their precursor piperitone and their associated derivatives: neomenthol, isomenthol, and neoisomenthol (Figure  1B ). To avoid any duplicates, their names were converted to their accurate scientific names and botanical families according to the Angiosperm Phylogeny Group IV (2016) and listed on "Plants of the World Online" (POWO, 2022 facilitated by the Royal Botanic Gardens; http://www. plantsoftheworldonline. org/---Retrieved 10 May 2022).
Results show that piperitone appears in the highest number of species inventoried in the databases (over 158 species listed), throughout most of the plant families registered, whereas the derivatives of pulegone: (neomenthol, neoisomenthol, and isomenthol) present the lowest occurrence in the catalog (24, 13, and 12 species, respectively) (Figure  1B ). The list of all plants recorded for the presence of menthol and pulegone forms a group of 85 and 110 species of plants, respectively (Supplementary Table  1 ). Among them, the Lamiaceae subgroup is best represented, with pulegone and menthol present in 84 and 56. 5% of the Lamiaceae, respectively (Figures  1C, D ). Because of their aromatic properties, plants within this family have been extensively used across the world and for all kinds of purposes: food, aromatics, cosmetics, and healing.
Funding This work was supported by the  Centre National de la Recherche Scientifique  contract  UPR3212 , the  Université de Strasbourg  contract  UPR3212 , the  Graduate School of Pain EURIDOL ,  ANR-17-EURE-0022 , and the  ANRT CIFRE  Grants No. 2018/1140  (to AC and HP) and no. 2016/047  (to HP).
, 2019) , it is important to evaluate the reliability of the method when used by non-experts in the case that technical or financial constraints prevent shipping the material to a single expert. In this study, we developed a picture quiz to evaluate the accuracy and reproducibility of the identification of marks left on the surface of artificial caterpillars. We expected that self-declared scientists would be more confident with their identification, and more accurate than non-scientists, and also that self-declared scientists with previous experience in predation mark identification would be also more confident and accurate than scientists without previous experience. We further used the data to identify which type of marks are more likely to be misidentified by observers; we predicted that beak marks left by birds would be more often correctly identified than more discrete marks left by arthropod mandibles.