dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA Sequencing of Founder Newborns Primers were designed, using Primer3 software, in a manner so that they annealed ∼150 bp upstream and downstream from the expected TALEN-target site. PCR was carried out in a final volume of 60 µL using 200 ng of DNA and primers, which amplified a 333 pb fragment of Cep250 surrounding the target site in exon 5 (for primer sequences, Supplementary Table  S1 ), with standard GoTaq PCR reagents (Promega) on a Master Thermal Cycler (Eppendorf). The cycling conditions were as follows: initial denaturation at 94 °C for 2 min, followed by 35 cycles at 94 °C for 20 s, 58 °C for 30 s, 72 °C for 30 s. Sanger sequencing was performed using standard methods (Eurofins) to identify null mutations.
Semi-Quantitative Reverse Transcription-PCR (RT-PCR) Total RNA was extracted from the kidneys of wild-type and transgenic animals using the RNA NOW reagent (Ozyme). After DNase I treatment, the RNA (200 ng) was reverse transcribed into complementary DNA (cDNA) using the Superscript II kit (Invitrogen) with oligo (dT) according to the manufacturer's instructions. The cDNA was then amplified via PCR with primers for the Cep250 and Gapdh genes (Supplementary Table  S1 ). Primers were designed, using Primer3 software (http://bioinfo. ut. ee/primer3-0. 4. 0/primer3/), from separate exons to produce 100-300 bp amplicons. The Gapdh gene was used as a reference to normalize the relative expression of the target genes.
Antibodies were detected using DAB (DAB substrate reagent kit, Vector Laboratories) or the VIP Vector (VIP substrate reagent kit, Vector Laboratories). All counts were carried out blind and were done using Histolab analysis software (Microvision Instruments, Evry, France). For immunofluorescence staining, sections were blocked for 1 h in a gelatin-blocking solution, containing 0. 2% BSA, 0. 2% gelatin, and 0. 05% Tween in 1× PBS, and incubated with an appropriate primary antibody, diluted in the blocking solution, for 1. 5 h at 37 °C. After three washes with PBS containing 0. 05% Tween, the slides were incubated with secondary antibodies, diluted in the blocking solution, at 37 °C for 1 h. The slides were then incubated with DAPI and mounted in the ProLong Gold antifade mounting medium (Thermo Fisher).
Imaging was performed using a Leica DM5500 B epifluorescence microscope (Leica Microsystems) equipped with a CoolSNAP HQ2 camera (Photometrics) and Leica MMAF software (Metamorph) or a confocal microscope (Spinning disc). Images were processed using the ImageJ software. Pericentrin immunostaining was performed with dissociated testicular cells. After removal of tunica albuginea, testes were incubated in 1 mg/ml PBS-collagenase solution for 20 min at 32 °C with pipeting up and down regularly. Dissociated cells were washed twice in PBS. Cells were counted using Malassez chamber with Trypan staining and placed in Cytospin device to reach 25,000 cell per slide. Samples were then fixed with 4% PFA solution. Cells were permeabilized by a 5 min incubation in 0. 2% Triton X-100 solution and washed in PBS.
2% BSA, 0. 2% gelatin, and 0. 05% Tween in 1× PBS) for 1 h at room temperature and then with primary antibodies, dissolved in blocking solution, overnight at room temperature (Supplementary Table  S2  for the list of antibodies). Next, the slides were incubated with secondary antibodies at 37 °C for 1. 5 h, stained with DAPI, and mounted on the Prolongold medium. Imaging was performed using a Leica DM5500 B epifluorescence microscope (Leica Microsystems) equipped with a CoolSNAP HQ2camera (Photometrics) and Leica MMAF software (Metamorph). Images were processed using the ImageJ software.
CEP250 is Expressed in Male Germ Cells In order to assess Cep250 abundance in various tissues, we first analyzed the publicly available RNA-seq datasets for human adult tissues. The testis displayed the highest Cep250 expression compared to the somatic tissues (Figure  1A ). To refine Cep250 expression profile in the gonads, Cep250 expression was quantified via RT-qPCR at various stages in mouse fetal and postnatal gonads. Cep250 expression levels increased in postnatal testes from 5 dpp and onwards, indicating the possible roles of CEP250 in spermatogenesis and meiosis (Figure  1B ). Next, we investigated the localization of CEP250 protein in post-natal testis sections. Consistent with its known centrosomal role, CEP250 colocalized with γ-tubulin in the spermatogonia and spermatocytes (Figures  1C, D ; Supplementary Figure  S1 ).
ACKNOWLEDGMENTS We are most grateful to  Johann Laubier ,  Anne Vaiman ,  Mohammed Moudjou ,  Elodie Lesueur ,  Binnaz Yalcin ,  Céline Méhats ,  Alice Fontenelle , and  Lucie Glénisson  for technical help and helpful discussions. We thank the  IERP-INRAE  for providing care to the mice.
Food security Semantic resources Agrovoc Text Mining a b s t r a c t The main objective of the project LEAP4FNSSA (Long-term EU-AU Research and Innovation Partnership for Food and Nutrition Security and Sustainable Agriculture) is to provide a tool for European and African institutions to engage in a sustainable partnership platform for research and innovation on Food and Nutrition Security, and Sustainable Agriculture (FNSSA). The FNSSA roadmap facilitates the involvement of stakeholders for addressing and linking research to innovation dealing with food security issues. In this context, the LEAP4FNSSA project supports the driving of the roadmap. Research and innovation activities were captured in different data, i. e. LEAP4FNSSA database and heterogeneous textual data including project reports, websites, scientific publications, workshop reports and student theses.
The Knowledge Extractor Pipeline System (KEOPS) was implemented to support the processing and analysis of textual data associated with FNSSA activities. KEOPS is based on the LEAP4FNSSA lexicon presented in this data paper. The LEAP4FNSSA lexicon composed of 331 keywords associated with 12 concepts of the food security domain is the result of 3 steps of work and brainstorming. The lexicon enables the capturing of research and innovation topics dealing with food security and conducted by African and European partners. This data paper presents the obtained lexicon and a summary of the method to build it. ©.
Value of the Data • This dataset contributes to the available resources for Natural Language Processing (NLP) and data mining on specialized domains and more precisely in the field of food security. • This dataset is useful for computer scientists for enriching thesaurus and ontologies. • This dataset can be used for indexing data bases (for instance these keywords could be proposed as metadata). • This dataset can be used for analysing textual data dealing with agricultural sciences and social sciences. • This list of keywords can be used as part of a search strategy protocol for systematic review research in areas related to food security.
Data Description In order to analyse textual data dealing with food security we have to consider different topics related to this issue. The proposed lexicon takes into account the multifactorial aspect related to food security with 331 keywords associated with 12 concepts summarized in Table  1. Examples of the concepts "food security" and "water management" are given in Tables  2  and 3. Note that both examples represent only 2 out of 12 concepts. All these concepts refer to different aspects of food security and sustainable agriculture in Africa and Europe. The 12 concepts are given in the Dataverse repository: https://doi. org/10. 18167/DVN1/D1C53L.
Experimental Design, Materials and Methods The LEAP4FNSSA lexicon is the combination between 3 semantic resources, i. e. inputs in order to construct the final lexicon: • Pretoria vocabulary (list 1) : This first lexicon composed of 8 concepts has been obtained during a workshop organised in Pretoria in 2019 in the context of the LEAP4FNSSA project. The process and the lexicon obtained are described in  [1, 2]  • Agrovoc vocabulary (list 2) : Based on these 8 concepts (with one additional concept), Agrovoc terms associated with these concepts are manually extracted from the online 1 resource. Agrovoc is a multilingual thesaurus dedicated to the agricultural domain devel-oped by FAO (Food and Agriculture Organization)  [3]. This thesaurus is used for different applications, e. g. indexing, annotation, data linking, etc.
• Terms obtained by text-mining (list 3) : Terminology is extracted from the LEAP4FNSSA corpus using generic parameters of the BioTex tool  [4]. The LEAP4FNSSA corpus consists of documents and web pages relating to the FNSSA project database 2. BioTex uses both statistical and linguistic information to extract terminology from free texts. The process applied is described in  [5]. The initial terms (i. e. Pretoria vocabulary, Agrovoc vocabulary, terms obtained by text-mining) are given in the document 'LEAP4FNSSA_LEXICON_method_v2. pdf' available in the Dataverse repository: https://doi. org/10. 18167/DVN1/D1C53L. The LEAP4FNSSA lexicon is obtained with 3 iterative steps. In these different steps, 4 types of experts and skills were involved: research scientist in text mining  3  , IT engineer  4  , experts in database indexing  5  , experts in food security issues (i.
3. The last step is summarized below: • Starting point: the lexicon obtained at step 2. • Improvement of concepts: • Names of specific concepts have been changed. • Two new concepts are added: 'Food value chains and market' and 'Agricultural systems'. These concepts contain new terms and terms that come from other concepts. • Improvement of terms: • New keywords are added after a work conducted by the experts in charge of data indexing of the FNSSA project database. For instance, keywords extracted from the FNSSA project database and manually validated by the experts are added. • Some terms are swapped between different concepts. • Ambiguous terms are deleted (e. g. capacity, agriculture, etc. ) • The word 'crop' is deleted in the 2-word terms of the 'Crops' concept. • New keywords are integrated after a final checking by the experts in charge of data indexing.
These modifications to consolidate the LEAP4FNSSA lexicon (e. g. addition and/or deletion of concepts and/or terms) are detailed in the document ' LEAP4FNSSA _ LEXICON _ method _ v2. pdf '. Note that variations of terms could be automatically extracted with NLP approaches in dedicated corpora  [6, 7]. This will be integrated as future work to extend the current lexicon. The LEAP4FNSSA lexicon obtained is integrated into the KEOPS (Knowledge ExtractOr Pipeline System) tool that uses text mining approaches to highlight knowledge from heterogenous textual data  [5]. KEOPS is currently implemented on LEAP4FNSSA data in order to extract, visualise and analyse food security themes with maps, graphs, curves, and Venn diagrams  [8].
Description of data collection The dataset consists of (i) one table file with lexicon, (ii) a document describing the steps to obtain the final lexicon. Data source location The data are hosted on the CIRAD Dataverse. The data were built in the context of the LEAP4FNSSA project 1. Data accessibility Repository name: CIRAD Dataverse. Data identification number: 10. 18167/DVN1/D1C53L. Direct URL to data: https://www. doi. org/10. 18167/DVN1/D1C53L.
All calculations for EPR spectra analysis were performed with Matlab (script given in Supplementary Information). The Easyspin package  (Stoll and Schweiger, 2006)  was used for EPR spectrum simulation, by taking an axial g-matrix with principal values  ! =  " = 2. 055 =  # and  $ = 2. 350 =  ∥ for Cu 2+ in the Egyptian Blue pigment. Here the (x, y, z) molecular frame is such as the z-axis is the  & symmetry axis of the Cu 2+ coordination site with  &' symmetry  (Ford and Hitchman, 1979). This axis is also the crystal c-axis of the structure  (Pabst, 1959). A Voigt line shape was considered for the EPR transitions with an empirical dependence of the peak-to-peak width DB on the angle  between the applied field and the z-axis as ∆ = 0. 86 + √0. 37cos (  + 0. 12sin (  (in mT), for both Lorentzian and Gaussian components.
The correlation-based principal component analysis achieved for each species shows an overall good superposition of pollen grains measured in equatorial and polar views in the first principal plane, revealing that the P. persica pollen is morphometrically as homogeneous as that of P. subaequalis. Then, the significant difference, mainly driven by lumen density, has been highlighted between the two species. Ultimately, the cross-validation of the resulting two-species linear discriminants classifier shows that based upon this reference dataset, (sub)fossil pollen grain can now be confidently assigned to either of the two species with an 85. 8% correct-assignment rate. This opens new doors in the affiliation of fossil Parrotia pollen and suggests that previous pollen records need to be revised.
Origin and accessibility of samples Pollen from both extant Parrotia, freshly collected as well as herbarium material was analyzed. Flowers were collected from trees occurring in their natural habitats, Parrotia persica flowers from plants in the Hyrcanian forest of Iran, and P. subaequalis flowers from plants in the Yixing forest of eastern China. The herbarium material used for this study belongs to the following collections: 1) Herbarium of the Department of Paleontology of the University of Vienna (HDP-WU); 2) IMBE palynological database, CNRS, Aix-Marseille University (MEPRC); 3) ISEM reference palynological database, CNRS, University of Montpellier.
All analyzes were performed using PAST v. 4. 02  (Hammer et al. , 2001). As a preliminary step, these 6 quantitative descriptors measured for each of the 120 pollen grains included in the analyzed dataset were first transformed in order to optimize within-species multivariate normality (evaluated using the Doornik-Hansen test;  (Doornik and Hansen, 1994)  and homoscedasticity (i. e. , homogeneity of variances and co-variances, as evaluated using the Box's M test;  (Rencher and Christensen, 2012)  (Tables  1  and 2 ). The linear measurements (E, Lll, Wll, Lsl and Wsl, see caption Table  1 ) were log10-transformed, and the Nlumen (see caption Table  1 ) was square root-transformed, as customarily done for linear and count measurements, respectively  (Sokal and Rohlf, 1995). (Sefidi et al. , 2011; Zhang et al.
Climatic and biome niche analysis We compiled historical climate data  from 196 georeferenced occurrences of P. persica and seven of P. subaequalis using WorldClim vers. 2. 1 (https://www. worldclim. org/data/ worldclim21. html) at a resolution of 30 s (ca. 1km 2 ; Fick and Plate II. Parrotia subaequalis in its natural habitat in the Yixing forest, China. (1) Flowers, usually 1 cm in length. (2) Fruits, between 0. 7-1 cm in diameter. (3-4) Leaves, broad-obovate, 4-6. 5 cm long and 2-4. 5 cm wide. (5-6) Small tree or large shrub, 5-10 m tall. Scaler bar = 1 cm in 1-4; 1 m in 5, 6. Photos (1-3) from te open access website ebben. nl. Hijmans, 2017; see Supplementary File 2). The georeferenced data were filtered so that multiple occurrences with the same coordinates were treated as a single data point.
To characterize climatic and ecological niches of the two modern species, we created distribution maps for each species using the GBIF dataset (https://www. gbif. org/). These occurrences were plotted onto 5 arc minutes grid Köppen-Geiger climate maps  (1986 -2010 data; Kottek et al. , 2006; Rubel et al. , 2017)  to establish "Köppen signatures" for both species; and on major terrestrial biome maps  (Olson et al. , 2001)  to assess their biome preferences. For the Köppen-Geiger plots, the georeferenced datasets were filtered so that multiple occurrences in a single grid cell were only counted once (labeled 'unique grid cells' in the diagrams; Supplementary File 3). Likewise, for the biome plots, georeferenced data were filtered so that multiple occurrences within the same coordinates were treated as single data points (labeled 'unique localities' in the diagrams; Supplementary File 3).
Extant Parrotia biomes and climate niches Both extant Parrotia species are part of the "Temperate Broadleaf and Mixed Forests" biome  (Olson et al. , 2001; Supplementary File 3). Based on our georeferenced dataset, both species display relatively similar mean annual temperature (MAT) preferences and coldest month mean temperature (CMMT) tolerances (Supplementary Files 2 and 3). Significant differences were observed with precipitation. While P. subaequalis thrives under fully humid conditions (Cfa climate; Fig. 4 ), with 1052 (1287) 1585 mm mean annual precipitation (MAP) and 485 (549) 666 mm of precipitation during the three warmest month, P. persica tolerates 291 (750) 1195 mm MAP, with strong seasonality and a precipitation depression during the summer months. With only 22 (76) 135 mm of precipitation during the three warmest months, P.
25-30, Pollen in polar view, L. O. analysis: 25, surface of reticulate ornamentation; 26, lumina; 27, base of lumina; 28, foot of columellae; 29, colpi; 30, optical section. Scale bar = 10 μm. is higher in P. subaequalis compared to P. persica. Furthermore, sculpture elements along the colpus margins are differently arranged in the two species (compare Plate III, 5 and 6 to Plate IV, 5 and 6). Furthermore, statistical approaches confirm the significant difference between pollen of the two Parrotia species. Combining the available evidence, P. persica is best characterized by a dense and small lumina, while P. subaequalis is characterized by a less dense and larger lumina, as summarized in Table  1. Ultimately, the cross-validation of the resulting two-species linear discriminant classifier shows that based on this reference dataset, (sub)fossil pollen grains can now be confidently assigned to either one of the Parrotia species with an 85.
Acknowledgements We thank  Mrs. Xuefeng Yang  ( 杨雪峰 ) who collected and provided to us the flower of the species Parrotia subaequalis from China. We are also grateful to  Hossein Akhani  who provided photographs of Parrotia persica pollen from  Iran. Overall , we thank the respective teams from  China and Iran. Helpful discussion with Thomas Denk  is acknowledged. We thank reviewers  Prof. Gonzalo Jiménez-Moreno  and one anonymous reviewer for their work. This work was supported by a grant from  Carl Tryggers Stiftelse  (project no. CTS21 :  1585 ). The following are the supplementary data related to this article.
USER Predicted exclusion confidence for all points Figure  1 : High-level overview of the CONTUR ORACLE workflow. The standard CONTUR programme is used to determine CL s exclusions for points in the parameter space. This information is provided to the ORACLE module which trains a random forest classifier to make predictions of the CL s exclusions for arbitrary points. Those points with the highest entropy-a measure of lack of confidence of the classifier in its predictions-are then sampled by CONTUR and the process is repeated until pre-defined stopping conditions are met. The CL s exclusions for a grid of points in the parameter space are then returned to the user.
Benchmarking approach For each model, three or four parameters were varied, while the others were fixed or defined as functions of other parameters. The size of the grids for each model was 8 100, 7 260, 3 952 points for the DM, 2HDM+a and VLQ models respectively. For each point in the grid, event generation was performed at 7, 8 and 13 TeV, which tripled the number of event-generation jobs to be submitted to the HPC cluster. For each job, 30 000 simulated signal events were generated. Each job took on average between half an hour, for the 2HDM+a jobs, and nearly two hours, for the VLQ jobs. The total number of wall-time hours required to process these grids was therefore of the order of 17 600 hr, 12 000 hr and 17 200 hr for the DM, 2HDM+a and VLQ models respectively. Creating one-off data sets for benchmarking purposes is possible in some rare cases, but this approach does not scale.
Two other methods have been used to measure uncertainty in the benchmarking section: first, running several independent ORACLE instances in parallel, and then obtaining the average and standard deviation of the results, as can be seen in Figure  4. Second, the results of the Oracle were compared with the full-grid CONTUR results. These methods are only currently useful for benchmarking and not for analysing a novel BSM model, but they can easily be modified for this purpose. Boostrapping or cross-validation techniques can be used to perform the train/test split multiple times, and separate classifiers can be trained for each split. Their predictions can be averaged for selecting the next set of points to sample. This was manually done for the novel model in Section 4, but is not currently implemented in the software.
Sample Preparation and RNA Sequencing Plasmodium falciparum RNA was extracted from whole blood, preserved at -80°C in TRIzol LS reagent (Life Technologies), with a phenol-chloroform protocol, and purified using RNeasy minikit (QIAGEN). The RNA quality, purity, and concentration were assessed with a NanoDrop 2000c (Thermo Fisher Scientific) and Agilent RNA 6000 Pico Kit (Agilent Biotechnologies) (Supplemental Data 1). Samples with both RNA Integrity Number ≥6 and at least 100 ng of total RNA available were retained for sequencing. Libraries were prepared using the TruSeq Stranded mRNA protocol (Illumina) and sequenced after qPCR quantification on a NovaSeq 6000 (Illumina) system, using 2 × 100 cycles (paired-end reads, 100 nucleotides) and aiming to obtain approximately 20M clusters per sample (ICGex, next-generation sequencing [NGS] platform; Institut Curie).
Gene Expression Analysis Bioinformatical analysis was performed following a previously published script: https://github. com/gtonkinhill/falciparum_ transcriptome_manuscript  [5]. Raw reads were mapped on P falciparum (3D7 strain, PlasmoDB v49) and human (hg38, UCSC genome browser) reference genomes with Hisat2 v2. 2. Samples below a minimum cutoff of 4 million reads mapped to the parasite genome were excluded. Parasite stages proportions were evaluated, referring to published RNA-seq dataset  [20]  (Supplementary Method). Unwanted variations factors were estimated using control genes that are not expected to vary with disease severity (from a previous study  [21]  comparing pregnancy-associated and childhood malaria) with the Remove Unwanted Variation algorithm (R package ruv 0. 9. 7  [22] ), adjusting for systematic errors.
Differential expression (DE) analysis was performed including proportions of parasites at the ring-stage and unwanted variations factors as input of the Limma/Voom pipeline  [23]. A gene was considered as DE for P adjusted values ≤. 05 (Benjamini-Hochberg) and a fold-change ≥1. 5. The Gene Ontology (GO)  [24]  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway  [25]  enrichment analyses were performed by applying a hypergeometric test (P ≤. 05) on the GO and KEGG annotations downloaded from PlasmoDB (version 49). The protein-protein interaction (PPI) network was generated from the STRING online tool (version 11. 5)  [26]. Settings were set to high confidence interaction score (0. 7) and coexpression interactions source. Then, cluster analysis was performed in Cytoscape 3. 8. 2 [27]  with MCode application version 2.