dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The German samples of Humulus lupulus L. were grown and harvested by Locher-Hopfen (Missenhardt 2, 88069 Tettnang, Germany). The planted cultivars were cultivated in the experimental hop garden of the Ribella brewery (Patrimonio, 20123 Corse, France). We would like to thank Ludwig Locher (Locher-Hopfen) and Pierre-François Maestracci (Ribella) for their support and contribution.
To generate a Cre-VVD fusion, we designed sequence CreCVII (Supplementary Text S1) where the Cre sequence from GENBANK AAG34515. 1 was fused to the VVD-M135IM165I sequence from Zoltowski et al. 29  via four additional residues (GGSG). We ordered its synthesis from GeneCust, and we co-transformed it in yeast with pSH63 (previously digested by NdeI and SalI) for homologous recombination and plasmid rescue. This generated pGY286. We then noticed an unfortunate error in AAG34515. 1, which reads a threonine instead of an asparagine at position 327. We cured this mutation from pGY286 by site-directed mutagenesis using primers 1J47 and 1J48, which generated pGY339 which codes for Cre-4-VVD described in Supplementary Fig. S1. We constructed mutant C-terΔ14 of Cre by site-directed mutagenesis of pGY286 using primers 1J49 and 1J50 which simultaneously cured the N327T mutation and introduced an early stop codon.
The saturated culture was transferred to two 96-well polystyrene flat-bottom Falcon ® sterile plates (100 μl per well) and one plate was illuminated at the indicated intensities while the other plate was kept in the dark. After the indicated duration of illumination, cells from the two plates were transferred to a fresh medium allowing expression of GFP but not cell division (SD-W-H or SD-W-U-H, strain GY1761 being auxotroph for histidine) and these cultures were incubated at 30°C for 90 minutes. Cells were then either analyzed immediately by flow cytometry, or blocked in PBS + 1mM sodium azide and analyzed the following day. We acquired data for 10,000 events per sample using a FACSCalibur (BD Biosciences) or a MACSQuant VYB (Miltenyi Biotech) cytometer, after adjusting the concentration of cells in PBS.
We analyzed raw data files in the R statistical environment (www. r-project. org) using custom-made scripts based on the flowCore package  52  from bioconductor (www. bioconductor. org). We gated cells automatically by computing a perimeter of (FSC-H, SSC-H) values that contained 40% of events (using 2D-kernel density distributions). We plotted the gates of all samples belonging to a single experiment (usually corresponding to the same day). This occasionally highlighted samples for which the gates were clearly shifted (outliers), indicating cell populations of unexpected size, and we therefore discarded these rare samples for further analysis. A threshold of fluorescent intensity (GFP or mCherry) was set to distinguish ON and OFF cells ( i. e. expressing or not the reporter). To do this, we included in every experiment a negative control made of the reporter strain transformed with an empty vector, and we chose the 99.
9 th percentile of the corresponding 4,000 fluorescent values (gated cells) as the threshold. No statistical power analysis was done prior to the experiment, no masking of samples IDs was applied during the experiments, and no data randomization was used. Quantification of fluorescence levels from microscopy images. For Fig. 3g , we segmented individual cells on bright field images using the ImageJ Lasso plugin. Then, we measured on the fluorescence images the mean gray value of pixels in each segmented area, providing single-cell measures of fluorescence. For each image, the background fluorescence level was quantified from eight random regions outside of cells and with areas similar to single cells. This background level was subtracted from the fluorescence level of each cell. Quantification of carotenoids from yeast.
We strictly followed the procedure described in Verwaal et al. 34  , which consists of mechanical cell lysis using glass beads, addition of pyrogallol, KOH-based saponification, and extraction of carotenoids in hexane. Quantification was estimated by optical absorption at 449 nm using a Biowave spectrophotometer. No statistical power analysis was done prior to the experiment, no masking of samples IDs was applied during the experiments, and no data randomization was used. Human reporter cell line. We built a reporter construct for Cre-mediated recombination in human cells based on Addgene's plasmids 55779, containing a membrane-addressed mCherry sequence  53  (mCherry-Mem) and 51269, containing a zsGreen-based reporter of Cre recombination  54. Re-sequencing revealed that 51269 did not contain three terminator sequences but only one between the LoxP sites.
Calculation of potential mean force (PMF). We calculated the free-energy profile (reported in Figure  1f ) for the unbinding of the C-terminal α -helix in the tetrameric Cre-recombinase complex  26  (PDB Entry 1NZB) as follows. The software we used were: the CHARMM-GUI server  56  to generate initial input files; CHARMM version c39b1  57  to setup the structural models and subsequent umbrella sampling by molecular dynamics; WHAM, version 2. 0. 9 ( http://membrane. urmc. rochester. edu/content/wham/ ) to extract the PMF; and VMD, version 1. 9. 2  58  to visualize structures. To achieve sufficient sampling by molecular dynamics, we worked with a structurally reduced model system. We focused thereby only on the unbinding of the C-terminal α -helix of subunit A (residues 334:340) from subunit F.
The hydrogen bonds were constrained to their parameter values with SHAKE  60. The PMF was calculated for the distance between the center of mass of the α -helix (residues 334:340 of subunit A) and the center of mass of its environment (all residues that have at least one atom within 5 Å of this helix). Umbrella sampling  61  was performed with 13 independent molecular dynamics simulations where the system was restrained to different values of the reaction coordinate (equally spaced from 4 to 10 Å) using a harmonic biasing potential with a spring constant of 20 kcal mol -1 Å -1 (GEO/MMFP module of CHARMM). Note that this module uses a pre-factor of ½ for the harmonic potential (as in the case of the program WHAM). For each simulation the value of the reaction coordinate was saved at every time-step for 30 ns.
To estimate N Lox , we prepared mixtures of edited and non-edited genomic DNAs, at known ratios of 0%, 0. 5%, 1%, 5%, 10%, 50%, 70%, 90%, 100% and we applied (1P57,1P58) qPCR using these mixtures as templates. This provided us with a standard curve that we then used to convert Ct values of the samples of interest into N Lox values. To estimate N Total , we qPCR-amplified the HMLalpha control region from templates made of increasing concentrations of genomic DNA. We then used the corresponding standard curve to convert the Ct value of HMLalpha amplification obtained from the samples of interest into N Total values. No statistical power analysis was done prior to the experiment, no masking of samples IDs was applied during the experiments, and no data randomization was used. ACKNOWLEDGEMENTS method.
We thank  Fabien Duveau  for critical reading of the manuscript and for signal quantifications from microscopy images,  Grégory Batt  for fruitful discussions,  Maria Teresa Texeira  for strains,  Sandrine Mouradian ,  Véronique Barateau  and  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) for access to flow cytometers and technical assistance,  Christian Marchet  for advice on LED controllers, and developers of R, Bioconductor, VMD, QLC+ and Ubuntu for their software. This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme   FP7/2007-2013 Grant Agreement n°281359  and by  CNRS  under the " MITI 80 Prime " program grant READGEN. The authors declare the following competing interest: A patent application covering LiCre and its potential applications has been filed.
The average level of initial pesticide toxicity was set at 1 dead bee per 0. 75 mg of consumed contaminated pollen  (Tosi and Nieh, 2019). The initial density of wild bees (& e) for both farm-types was estimated according to the article of  Osborne et al. (2007). This study calculated the average number of wild bees' nests per natural habitat within a farmland. Hence, we estimated the average number of natural habitats within the farmland using data collected from the CRAMP and individual interviews and consequently, the potential number of wild bees' nests. We then extracted the initial density of wild bees for our farm-types following the study of Ellis (2016) on the average number of wild foragers per nest  (Kleczkowski et al. , 2017). Finally, the density of wild bees after cultivation period W (wild bees/S) becomes a parameter.
Advances in genomic sequencing have recently offered vast opportunities for biological exploration, unraveling the evolution and improving our understanding of Earth biodiversity. Due to distinct plant species characteristics in terms of genome size, ploidy and heterozygosity, transposable elements (TEs) are common characteristics of many genomes. TEs are ubiquitous and dispersed repetitive DNA sequences that frequently impact the evolution and composition of the genome, mainly due to their redundancy and rearrangements. For this study, we provided an atlas of TE data by employing an easy-to-use portal (APTE website). To our knowledge, this is the most extensive and standardized analysis of TEs in plant genomes. We evaluated 67 plant genomes assembled at chromosome scale, recovering a total of 49,802,023 TE records, representing a total of 47,992,091,043 (~47,62%) base pairs (bp) of the total genomic space.
Annotation of transposable elements We used similarity-based methods and de novo techniques to build a collection of putative transposable elements, based on the SPTEdb pipeline. 21 We refined, extended and increased steps in order to produce a novel annotation (Figure  1 ). Our reformulated steps (details in Supplementary Material 2, Extended data) guarantee a comprehensive knowledgebase of these TEs. RepeatScout was performed separately; the output was unified in a library to be labeled by PASTEClassifier  32  and later combined into a final annotation. To automate the pipeline, an in-house framework in Perl language was developed for each software output to be uniformized, described in steps 1 to 4. A main script in Bash starts the process of automatization using Perl scripts. All steps were supervised by researchers, carefully checked, and the output was manually verified at each step for each genome.
Web implementation APTE is hosted at the Universidade Tecnológica Federal do Paraná (Cornélio Procópio, PR, Brazil). It uses Debian 11 as operating system, Apache 2 as web server, PHP 5. 6 as web programming language. We also used Zend Framework 2, which implements model, view, controller (MVC), a methodology for web development that can be expanded for any future additional functionality. On the front-end, we used HyperText Markup Language 5 (HTML5), Cascading Style Sheet 3 (CSS3) and JavaScript to perform dynamic functions that provide user-friendly navigation. A built-in genome browser (JBrowse, version 1. 14. 1) is available to visualize and download the data as well.
Computational resources To run the pipeline described in Figure  1 , we used three platforms: (i) to runRepeatModeler,  25, 26  RepeatScout,  26, 27  RepeatMasker,  27  LTR_retriever,  28  MITE-Hunter 29 and HelitronScanner 30 ; (ii) to perform MGEScan-non-LTR  31  and PASTEClassifier  32  ; and (iii) to unify and filter outputs to the main annotation. The hardware utilized were (i) Xeon E7540 2. 00 GHz 256GB memory, Xeon E5-2620v3 2. 40 GHz 64GB memory, 2x Intel i7-3820 3. 60Ghz 32GB memory and Intel i7-3820 3. 60Ghz 64GB memory, (ii) Intel i7-3820 3. 60Ghz 64GB memory, and (iii) Intel Core 2 Duo 2. 4 GHz 8GB memory, a total of 30 physical cores and 456 GB of memory. In order to present a scale of time elapsed to measure, filter and standardize the results, we estimate that for the A. thaliana genome, the time needed to get the final annotation was ~18 hours, using all resources mentioned, including post-processing scripts (detailed on our website).
TE database comparison To compare the results of the identification performed and to ensure the reliability (details in Supplementary Material 1, Extended data) of our approach, we used SPTEdb 21 annotation data of the genome Populus trichocarpa (black cottonwood), which is explored in Table  1. The second comparison of TE annotations was performed for the Glycine max (soybean) genome, in which we used SoyTEdb  22  to compare the amount vs. type of TEs, shown in Table  1. A third comparison used data from GrTEdb  23  to explore the amount of TEs in Gossypium raimondii (cotton), available in Table  1.
We observe that for several other genomes, new types of TEs were identified and annotated; this ensures that our pipeline delivers not only the same TE identification, but also new ones, making the annotation process possible to use for any species. In this study, we contributed to expand the knowledge on TEs, by providing a large-scale, organized and standardized TE Atlas. We integrated all annotations to make it available to download in each genome separately from the Atlas of Plant Transposable Elements (APTE) website. An example how our pipeline works using the A. thaliana genome, software dependencies, and in-house scripts developed, which can be downloaded, used and changed freely, are available from https://github. com/alerpaschoal/apte_pipeline/. The full TE annotation is provided in the APTE database that is informative and straightforward.
It is a very good source of data for other researchers that might be interested in proceeding deepest analysis of a specific group of elements. The authors have identified a great number of repetitive sequences in each genome. My biggest concern is related to the sequences that fail to be classified. These "Unknown" sequences make up a huge proportion of all the repetitive sequences found. Even with the filtering of low complexity and simple repeat sequences, they could still be false positive. The authors could mention this possibility. Moreover, the difference in the amount of TE identification between the two databases shown in Table  1  is impressive. My doubt is whether there is a possibility that part of this difference is caused by the artificial "breaks" of TE copies into a greater number of fragments.
Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Genomics, Mobile elements, Repetitive DNA, Non-coding RNAs I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.
Underlying dataAll data underlying the Plant TE Atlas is available in the portal http://apte. cp. utfpr. edu. br/. Extended data Zenodo: Datasets from An Atlas of Plant Transposable Elements, https://doi. org/10. 5281/zenodo. 5672122. 33 This project contains the following extended data: -SuppMat_1. xlsx (the gen ome assembly reference access from Ensembl Plants species used) -SuppMat_2. docx (a brief transposable elements annotation steps used in this work) Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1. 0 Public domain dedication).
However, their analysis was restricted to data from a single tile drain, which may lead to representativity issues and prevent the extrapolation of their results to larger and/or contrasted catchments. The objective of the current study was therefore to analyze the tile drain functioning, based on a set of drained plots to increase the analysis representativity. In particular, the processes leading to the seasonal and flood event scale variations in water and suspended sediment dynamics will be analyzed. To this end, water and suspended sediment dynamics at the outlet of multiple tile drains were monitored with a high temporal resolution (10 minute time step) over two contrasted hydrological years.
Flood event separation Flood events were defined according to the methodology presented in  Grangeon et al. (2017). Flood events were identified based on the ratio between quick flow and base flow (calculation based on the methodology proposed by  Chapman, 1991). The correspondence of flood events occurring both in the tile drain network and in the main river was verified. Then, single storm events were defined as events cumulating more than 1 mm precipitation. Flood events were then associated with rainfall events, and when multiple storm events were recorded during a single flood event, they were grouped into a single event. This procedure resulted in a dataset of 36 coupled storm-flood events occurring in both the river and the tile drain network between December 2017 and June 2019. The following analysis is based on these 36 flood events, although some supplementary individual events were recorded only in the Bonnée River or in the tile drain network.
Data analysis For these 36 individual flood events, several characteristics were calculated including rainfall depth (mm), duration (h), mean and maximal intensity (mm⋅h -1 ), flood event volume (m 3 ), total, rising and falling limb durations (h), mean and maximal discharge (m 3 ⋅s -1 ), runoff coefficient (%), antecedent rainfall (rainfall depth cumulated over the previous 48 h, mm), mean and maximal concentration (mg⋅l -1 ), sediment load (kg). Specific peak discharge was calculated by dividing the peak discharge by the catchment or tile drain network area. Sediment loads were obtained through the integration of the product of the water discharge and the sediment concentration time series. Results were considered separately for winter (December to February), spring (March to May), summer (June to August) and autumn (September to November).
, 2020c). Fruit firmness was assessed in 24 intact fruits containing the pedicel, by determining the tension necessary to perforate the fruit skin. Tests were performed on a Shimadzu (model AG-IS) equipped with a 50-N load cell and a 1-mm diameter needle. Force-stroke plots were assembled in Trapezium 2. 0 Software and results were expressed in MPa, as described previously  (Martins et al. , 2020b).
1 software (Waters, Milford, MA, United States). Analyte separation was achieved by using a Waters Acquity HSS T3 C18 column (150 × 2. 1 mm, 1. 8 µm) with a flow rate of 0. 4 mL/min at 55 • C. Chromatographic separation and identification of analytes were achieved as optimized previously, using the same standards specified by  Martins et al. (2020a Martins et al. ( , 2021b)). UPLC-MS analyses were achieved using the selected ion monitoring (SIM) mode of the targeted molecular ions. SIM chromatograms were integrated using the subroutine QuanLynx 4. 1 for data mining. Peak integration was performed using the ApexTrack algorithm with a mass window of 0. 1 Da and relative retention time window of 1 min followed by Savitzky-Golay smoothing (iteration = 1 and width = 1). To evaluate the robustness of measurements and analytical variability, a pool of all samples was prepared to obtain a quality control sample and the samples were randomly injected.
FUNDING This work was supported by the " Contrato-Programa "  UIDB/04050/2020  funded by Portuguese national funds through the  FCT IP. This work was also supported by  FCT ,  CCDR-N (Norte Portugal Regional Coordination and Development Commission) ,  European Funds  ( FEDER/POCI/COMPETE2020 ) through the project  AgriFood XXI  ( NORTE-01-0145-FEDER-000041 ) and the research projects  BerryPlastid  ( PTDC/BIA-FBT/28165/2017  and  POCI-01-0145-FEDER-028165 ), and  MitiVineDrought  ( PTDC/BIA-FBT/30341/2017  and  POCI-01-0145-FEDER-030341 ). AT was supported by a postdoctoral researcher contract/position within the project " BerryPlastid ". The  Région-Centre Val de Loire (France)  supported this work under the grant agreement to Projects CEPATLAS and VINODRONE to AL. This work also benefited from the networking activities within the European  COST Action CA 17111 INTEGRAPE , the  CoLAB VINES & WINES , and the  CoLAB 4FOOD -Collaborative Laboratory for Innovation in the Food Industry.
fr/, Toulouse, France). Amplicons were purified and submitted to a second round of PCR, and the resulting PCR products were sequenced using an Illumina MiSeq run of 250 base-paired ends according to the manufacturer's instructions (Illumina Inc. , San Diego, CA, United States). Bioinformatics analyses were performed using the FROGS pipeline (Find Rapidly OTU with Galaxy Solution; http://frogs. toulouse. inra. fr). Resulting Operational Taxonomic Units (OTUs) that were not present in at least two samples or whose abundance was <5 × 10 -5 were removed. Remaining OTUs where then aligned to the SILVA123 16S database  (Quast et al. , 2013; Yilmaz et al. , 2014)  using BLAST. Relative abundances of the different families and genera were calculated and expressed as the percent of the total number of OTUs per sample.
Clostridium difficile Quantification Quantitative real-time PCR (qPCR) was performed to measure C. difficile in fecal DNA extracts as described by  Bandelj et al. (2013). Oligonucleotide primers CDIFF16S-F (5'TTGAGCGATTTACTTCGGTAAAGA3') and CDIFF16S-R (5'TGTACTGGCTCACCTTTGATATTCA3'), and CDIFF16S TaqMan probe (FAM-CCACGCGTTACTCACCCGTCCG; MGW, Eurofins Genomics, Germany) specific to C. difficile 16S rRNA gene (NCBI accession number AB548672; https://www. ncbi. nlm. nih. gov/nuccore/AB548672) were used for the qPCR assay  (Penders et al. , 2005). The qPCR mixture (final volume 25 µL) was composed of FastStart Universal Probe Master Mix 2X (12. 5 µL; Merck KGaA, Darmstadt, Germany), primers (900 nM), probe (200 nM), and DNA (1 µL). Twenty nanograms of bovine serum albumin (BSA) were added to the mixture to reduce inhibition.
Cytokine Profile Analysis A commercially available equine-specific multiplexed cytokine/chemokine magnetic bead kit (EMD Millipore, Merck, Darmstadt, Germany) was used to simultaneously test for IL-6, IL-10, and TNF-α in serum samples using a 96-well plate format and following the manufacturer's instructions. All samples were run without dilution. Plates were read using Bio-Plex Multiplex System instrumentation (Bio-Rad Laboratories, Hercules, CA, United States) using Luminex xMAP technology (Luminex Corporation, Austin, TX, United States). A minimum bead count of 50 for each cytokine/chemokine was acquired for data analysis. Data were analyzed using Bio-Plex Manager Software 6. 1 (Bio-Rad Laboratories, Hercules, CA, United States). Results are expressed in pg/mL of serum. Concentrations that were out of ranges were replaced by the detection limit values for IL-6 (14.
Statistical Analyses Statistical analysis was performed using the MIXED procedure in SAS software version 9. 3 (SAS Institute Inc. , Cary, NC, United States) with a model including the period, supplementation type, day, and the interaction between supplementation type and day as fixed effects. The model included "horse" as a random effect and "days" as repeated measures. The period was used as an intercept for each horse to avoid individual variations along the duration of the experimental trial. Thus, only the effects of supplementation, day, and their interaction will be discussed here. In case of significant day × supplementation interactions, means were compared using the pdiff option in the LSMeans statement. The significance threshold was set at P ≤ 0. 05, and trends were considered at P ≤ 0.