dataesr/scientific-paragraphs-categorization huggingface dataset:
Concept and basic implementation The eVIVALDI model was developed in Python (version 2. 7. 3), using an object-oriented approach, with a focus on the flexibility and extensibility of mechanisms and parameters simulated. The complete ODD (Overview, Design concepts, and Details) protocol  [42]  of the developed model is available as supplementary text (Text S1, Table  S1  has the parameters modelled and their possible values), but below is a brief overview of the model. The source of the software can be obtained in the following link: https://gitlab. pasteur. fr/jsousa/eVIVALDI. The simulations can be run on a typical desktop computer. In a 3GHz 8-core Mac Pro, with 32GB of RAM, a replicate of a simulation (100 iterations), takes from ~5 to 30 minutes, depending on the parameters. Computations can also be performed in a cluster, allowing the parallel simulation of multiple parameters.
Random Forest Analysis The Random Forest Analysis is based on simulations performed with the model, covering 3000 random combinations of parameters, with 30 simulated repeats per combination. The output of this cohort of simulations is grouped and resumed in response variables, to which a column with 3000 rows of a random parameter is added (i. e. , a choice of a number between 1 and 3). This table is used as input of the randomForest package in R (version 4. 6. 12), where the randomForest function is run with the parameters ntrees set to 10000. The relative importance of each parameter (the percentage increase in minimum squared error, %IncMSE) is assessed using the importance function from the same package.
ACKNOWLEDGEMENTS We thank  Marie Touchon  and  José Pénadés  for comments, suggestions, and criticisms during the development and testing of the model. This work was funded by  European Research Council  grant [ EVOMOBILOME n°281605 ] to E. P. C. R. and by an  EU   FP7 PRESTIGE  grant [ PRESTIGE-2017-1-0012 ] from  Campus France  to J. A. M. S. H. I. is supported by the  Danish National Research Foundation's Centre of Excellence Bacterial Stress Response and Persistence  (DNRF12) and J. H. is supported by a  Young Elite Researcher grant  from the  Danish Research Council ,  Sapere Aude.
First, PfEMP1 are transmembrane proteins, and therefore present hydrophobic segments which decrease its solubility and its accessibility to proteases such as trypsin. Second, PfEMP1 are high molecular weight proteins (250-350 kDa), and finally, PfEMP1 are highly variable in sequences, yet database repository is usually simplified by eliminating redundancy. Thus, they do not reflect the natural sequence diversity which may occur in such a context. To date, PfEMP1 identification with mass-spectrometry remains limited. In fact, the identification of the semi-conserved variant VAR2CSA in the context of Pregnancy Associated Malaria has been performed  (33) , but the identification of PfEMP1 variant in a context of severe malaria (CM or SA) has not been reported yet. The association of PfEMP1 domains with clinical outcome (UM, CM and Pregnancy Associated Malaria) was performed, with relaxed analysis parameters to allow the identification of PfEMP1 domains  (34).
To identify PfEMP1 associated with P. falciparum clinical outcome in endemic settings, we used a "proteogenomic" strategy on field sample from children from Benin, West Africa, retrieved from May 2016 to August 2016. Specific PfEMP1 sequences from each isolate were reconstructed, using whole genome sequencing (WGS) data to enrich the protein database (Figure  1 ). We analyzed the whole proteome of samples from patients presenting either CM, SA or UM, and attribute PfEMP1 sequences within these samples. Corresponding samples were analyzed in RNAsequencing for PfEMP1 expression analysis, in relation to proteomic results. We managed to identify the PfEMP1 sequence associated with 4 CM samples, 9 SA and 9 UM samples using mass spectrometry (MS). For two samples, the relative expression levels from the PfEMP1 identified in MS was assessed using RT-qPCR using the corresponding mRNA.
Included Samples Var genes transcripts identification with RNAsequencing We then focused on a set of 120 proteins from PlasmoDB reference, corresponding either to PfEMP1, or to other proteins associated with knobs formation (Supplementary data Table  3 ). The presence of skeleton binding protein 1 (Pf3D7_0501300) and EMP1 (Pf3D7_0730900) was assessed in all samples except SA09. Focusing on PfEMP1, we observed that the conserved ATS coding region was the only site for PfEMP1 mRNA identification using reference sequences from PlasmoDB. We then performed a selective mapping of RNAsequencing reads using the sequences from var genes reconstruction, which allowed to identify the expressed var transcript for 3 samples (Supplementary Table  4 ). Protein identification Protein identification was performed using a home-made database (reference sequences from human and P.
Genomic and transcript data for specific protein identification Since we had access to preserved RNA for 2 samples in addition to RNAsequencing data (CM04 and SA06 at 3 additional sampling time each: H0 (diagnosis of malaria) and post diagnosis sample, H16, H24 for CM04 and H0, H8, H16, for SA06), we selectively designed primers that would specifically amplify the PfEMP1 transcripts from the mass spectrometry matching sequences (Figure  1 ). Our goal was to assess which ones of these proteins are expressed, and their corresponding relative expression level using the Tu measurement method. Using alignment tools, we managed to find discriminant primers between the 6 identified sequences for sample SA06 (g45, g949, g1030, g1025, g150 and g62) and the 3 sequences (g276, g299 and g522) for sample CM04.
Reference database allowed the identification of var transcripts; however, all mapped reads were in semi conserved ATS coding region. Protein identification was performed by matching mass-spectrometry data against a custom database containing the translated PfEMP1 sequences (Figure  1 ). As anticipated, most of the identified PfEMP1 (considering all PfEMP1 from all protein group) came from the newly added sequences to the database (10/96 were known sequences), confirming the validity of our approach. The technical improvement allowed us to identify more proteins than previously published studies  (34, 38) , with higher sequence coverage. We identified a set of 55 PfEMP1 associated in protein group. We investigated the structure of theses sequences and found that the two main domain organisations were NTS-DBLα-CIDRα-DBLβ and NTS-DBLα-CIDRα-DBLδ.
The high diversity of PfEMP1 protein sequences compared with the repository database prevented us from assigning a subdomain to every domain. This limitation did not allow us to specifically identify subtypes of domains, in relation to reference sequences, associated with P. falciparum infection severity. However, selective alignment from obtained peptides to reference sequences repository allowed to identify domain subtypes for 36% (40/110) of identified peptides. This difficulty to attribute a domain may be the consequence of either: the peptide being shared between two or more domains, or the peptide has never been previously identified thus absent from the database (Figure  2 ). Variant protein identification in mass spectrometry is challenging, since protein identification is the reflection of the quality of the database available.
Restrictive use of reference sequences from public repositories reduces the "chance" of protein identification, but we ride out this challenge with our home-custom sequence database obtained through whole genome sequencing of our samples. The variability of PfEMP1 primary sequence was overcome using protein structure modelling, which allowed us to classify the CIDRα identified within predicted binding target. We confirmed that PfEMP1 protein sequences which display ICAM-1 binding pattern present CIDRα domains with EPCR binding folding. This study highlights the perspective offered by proteogenomic approaches for VSA studies. Using a homemade database containing PfEMP1 sequences obtained from WGS of the corresponding samples, we managed to identify specific PfEMP1 pattern associated with each sample, where RNAsequencing alone could not discriminate the expressed PfEMP1 within a sample.
Sequencing technology used was an Illumina NexSeq500 (IPS2 POPS platform). RNA-seq libraries were performed by TruSeq Stranded mRNA protocol (Illumina®, California, U. S. A. ). The RNA-seq samples have been sequenced in paired-end (PE) with a sizing of 260 base pairs and a read length of 150 bases. 54 samples by lane of NextSeq500 using individual bar-coded adapters and giving approximately 5 million of PE reads by sample are generated. RNA-sequencing data bioinformatics treatment and analysis RNA-Seq preprocessing includes trimming library adapters and performing quality controls. The raw data (fastq) were trimmed with Trimmomatic  (45)  tool for Phred Quality Score Qscore >20, read length >30 bases, and ribosome sequences were removed with tool sortMeRNA  (46). Reads were mapped against the Plasmodium falciparum 3D7 reference genome (PlasmoDB v35) and against the reconstructed sequences from WGS, using STAR (version 2.
Mass spectrometry analysis Mass Spectrometry (MS) analysis were performed on a Dionex U3000 RSLC nano-LC-system coupled to an Orbitrap-fusion mass spectrometer (Thermo Fisher Scientific) as described  (48). Peptides were separated on a C18 reverse-phase resin (75-μm inner diameter and 15-cm length) with a 3-hr gradient. The mass spectrometer acquired data throughout the elution process and operated in a data-dependent scheme. Accession number The raw reads were available under the accession number listed in supplemental data 5. VarDOM server for var genes is available at the following address www. cbs. dtu. dk/services/VarDom/. PlasmoDB reference sequences are available on EupathDB portal dedicated to Plasmodium (http://plasmodb. org/plasmo/).
Database construction To get an accurate identification of proteins, a database was created as follows: Since our samples have been sequenced by the MalariaGen consortium (UK), whole genome sequences were available. Reconstructed var genes were kindly provided by Thomas Otto, Matt Berriman and Chris Newbold from the Welcome Trust Sanger Institute. We concatenated P. falciparum proteins sequences from PlasmoDB (v35) Uniprot and NCBI. We added our own PfEMP1 sequences, obtained after in silico translation from var genes reconstruction. Duplicate sequences were removed. FASTA sequence headers were uniformed to facilitate MaxQuant sequences analysis.
Raw data processing The mass spectrometry data were analyzed using Maxquant version 1. 5. 2. 8  (49). The database used was our homemade database (see above section) and the list of contaminant sequences from Maxquant. The precursor mass tolerance was set to 4. 5 ppm and the fragment mass tolerance to 0. 5 Da. Carbamidomethylation of cysteins was set as constant modification and acetylation of protein N-terminus and oxidation of methionine were set as variable modifications. Second peptide search was allowed, and minimal length of peptides was set at 7 amino acids. False discovery rate (FDR) was kept below 1% on both peptides and proteins. Label-free protein quantification (LFQ) was done using both unique and razor peptides. At least 2 such peptides were required for LFQ. The "match between runs" (MBR) option was allowed with a match time window of 1 min and an alignment time window of 30 min.
CC-BY 4. 0 International license available under a We considered a p value < 0. 05 as significant. Qualitative data were compared with Chi Squared test using contingency table. All analyses were performed using Prism v5 (Graphpad). Primer design We tried to specifically assign PfEMP1 expression within the samples. In addition to RNAsequencing data, we disposed of preserved RNA from 2 samples. Our strategy was as follows: first, we compared the sequences of the identified proteins using Multalin  (54). Second, we designed specific primers targeting the sequences using Primer3Plus web interface  (55). Finally, we assessed primers specificity using BLAST tools. (Primers are listed in Supplementary data 2). RT-qPCR and PCR RNA was preserved using Trizol reagents (Life Technology) and RNA extraction was performed as described  (43).
cDNA was obtained using Superscript II (Life Technology) retrotranscriptase following the manufacturer's instructions. qPCR was performed using Power SYBR Green PCR Master Mix (Applied Biosystems) on Rotorgen (Corbett technology) as described  (34). Melting stage was set up as default on the Rotorgen software. PCR specificity was assessed by the melting temperature (Tm). We considered that a sequence is unique for Tm included in the range Tm +/-0. 5°C. Transcript expression was quantified using the Transcript Unit (Tu) method, as described by Lavsten et al  (31). GAPDH and Seryl-tRNA synthetase transcripts were used as reference genes. For transcripts which displayed a Tm difference upper than 0. 5°C for a same primer set, we performed a conventional PCR using AmpliTaGold DNA polymerase (Applied Biosystem) with the following conditions: initial denaturation at 95°C for 10 min, followed by 52°C hybridization for 30s -68°C elongation for 15 s, repeated 35 times.
PCR products length was assessed using 2% agarose gel containing Syber Safe (Life Technology) at 1/10,000. Amplicons were purified using the QIAquick PCR Purification Kit (Qiagen). The purified fragments were sequenced with BigDye Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems) using g949, g150 (for patient SA06), and g299 primers (for patient CM04) (Supplementary data 1). The sequence reaction products were purified using the BigDye XTerminator® Purification Kit (Applied Biosystems), in. CC-BY 4. 0 International license available under a accordance with the manufacturer's instructions. The purified products were sequenced using an ABI Prism 3100 analyzer (Applied Biosystems), and the sequences were analyzed using DNA Baser v4 (Heracle BioSoft). Figure  1 : Proteogenomic approach on field samples for PfEMP1 identification.
Whole blood sample from patients are collected. DNA and RNA are extracted from parasite's ring forms. From the same collected tube, parasites are matured and the corresponding proteins from mature form (within one maturation cycle) are extracted and analyzed in LC-MS/MS. Whole genome sequencing data enrich the protein database used for protein identification with LC-MS/MS data. Selective mRNA. CC-BY 4. 0 International license available under a sequencing provides additional information's to elucidate proteins identification within the home-made database.
Acknowledgments The authors would like to thank patients who participate in the study, the clinicians and nurses who were involved in patient's inclusion. We especially acknowledge  Dr Nadine Fievet  help and counselling during the field study. We would like to thank  Matt Berriman ,  Chris Newbold  and  Thomas Otto  from the  Welcome Trust Sanger Institute  for the access to the unpublished PfEMP1 sequences. We also would like to thank  Emilie-Fleur Gautier  for her helpful advices in mass spectrometry, and  Evangeline Bennana  for her technical support in sample preparation. The authors thank  Romain Coppée  for his critical comments on the manuscript, and  Antoine Claessens  for his help in WGS. We also thank  Feder  for the opportunity to use Orbitrap Fusion mass spectrometer.
Funding This work was funded by  Merieux Research Grant  (awarded to  Dr Gwladys Bertin  for  CIVIC  project), by the  Laboratoire d'Excellence GR-Ex, Paris, France , reference  ANR-11-LABX-0051 , that is funded by the program Investissements d'avenir of the  French National Research Agency , reference  ANR-11-IDEX-0005-02  and by  NeuroCM  project, that is funded by  ANR-17-CE17-0001  The POPS platform benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). CK is awarded a PhD Scholarship from  French Minister of Research through MTCI Doctoral School  ( ED 563 ,  Paris Descartes University ). CC-BY 4. 0 International license available under a.
INTRODUCTION The SlideWiki project 1 is a European initiative, creating a platform that facilitates large-scale collaboration around educational content. Since its launch  [1] , the SlideWiki platform 2 has grown its user base to hundreds of educators and thousands of learners and has won the OpenCourseWare Consortium's Excellence Award. Several hundred comprehensive open learning materials are currently available on the SlideWiki platform in different languages. The SlideWiki platform allows the creation of educational content either by individual authors or collaboratively by groups of authors  [2]. Additionally, users can interact with the offered content in various ways, such as via commenting, answering quizzes, as well as by reusing and adapting the content according to their needs. Authors can either upload existing content to the platform in the form of a PowerPoint or OpenOffice presentation, or start creating a slide deck from scratch.
SlideWiki pilot at Vytautas Magnus University The main objectives of this pilot are to promote the usage of OERs and Creative Commons (CC) licensing in the teaching process among Vytautas Magnus University teachers, as well as involve students in the learning process, familiarise them with SlideWiki and encourage further usage of the platform. 15 teachers are participating in this pilot, selected from different faculties and different programmes, both undergraduate and postgraduate ones. All selected teachers have good computer literacy skills and are experienced in the use of the institutional Moodle Learning Management System (LMS) and its tools. Their teaching subjects vary between foreign languages, management, political and social sciences.
SlideWiki pilot at the University of Sousse This pilot involves 4 faculties at the University of Sousse with diverse learner profiles, including students with backgrounds in engineering, computer science, web design, medicine and management. 5 educators and around 400 students are involved in this pilot by producing and sharing open content via SlideWiki, following the 5 R's principles: • Retain: Students and educators have the ability to make their own copies of the content (e. g. , download, duplicate, store, and manage). • Reuse: Students and educators have the ability to use the content in a wide range of ways (e. g. , in a class, in a study group, on a website, in a video). • Revise: Students and educators have the ability to adapt, adjust, modify, or alter the content itself (e. g. , translate the content into another language).
During the 3 rd week of the course, trainees are asked to collaboratively create content in SlideWiki. In the last week, trainees are asked to discuss and express their opinions about their experiences with the SlideWiki platform and the content they have studied. 3. Analysis of the data: Sources of data collection are statistics collected via SlideWiki's Activity Feed and History tools, as well as the trainees' comments/answers. The comments/answers of trainees are analysed and grouped together forming characteristic answers in order to have a feedback with quantitative characteristics.
Anthropological settings The Kocabas ¸ skull was attributed to Homo erectus  (Kappelman et al. , 2008). It is composed of 3 fragments separated at the cranial sutures, which were not completely fused due to the young age of the specimen (Fig. 1 ). The three cranial fragments were scanned using the Philips helical scanner at the Pamukkale teaching hospital, in Denizli, on September 14th, 2009; slice thickness was 0. 80 mm, the space between slices was 0. 4 mm (field of view: 20 cm, matrix: 512 × 512, power: 175 mA, intensity: 120 kV). A first virtual reconstruction was carried out based on the CT data  (Vialet et al. , 2011 (Vialet et al. , , 2012)). This paper concerns the second virtual reconstruction with Geomagic Studio 12 software by A. V. and P. W. (technical platform ASIM, at the MNHN).
Acknowledgements The authors want to warmely thank  Pr. Yves Coppens  for his invitation in this special issue of  C. R. Palevol  dedicated to the " Hominins and tools. Expansions from Africa towards Eurasia ". Most of the contributions of this issue have benefited from the support of the  Agence Universitaire de la Francophonie  in the context of the session B52 of the  XVIIe Congrès de l'Union Internationale des Sciences Préhistoriques et Protohistoriques-UISPP  ( 2014 ,  Burgos, Spain). This research is an emanation of the " First Homo in Turkey" , a scientific project led by  A. Vialet  and supported by the  CNRS  ( PICS 2016 ( PICS -2018 )). We are grateful to  Louise Byrne  and  Phil Glauberman  for linguistic assistance. MCA is supported to the  GEB İP grant  ( The Outstanding Young Scientist Award ) given by the  Turkish Academy of Sciences  (TÜBA).
MRI Acquisition Multi-shell DWI data were acquired for NODDI analysis, and single-shell DWI data were acquired for standard DTI analysis at both sites using 3T MRI scanners (Tim Trio, Siemens Healthcare, Erlangen, Germany). A twice-refocused spin-echo echo-planar imaging sequence was used to minimize the eddy-current distortion for both acquisitions. The multi-shell protocol for NODDI consisted of b values of 2,000, 700, and 300s/mm 2 with 64, 32, and 8 nonlinear diffusion-encoding directions, respectively; 14 b = 0s/mm 2 images; voxel size = 2. 5 × 2. 5 × 2. 5mm 3 ; repetition time (TR) = 7,000 milliseconds; echo time (TE) = 90. 8 milliseconds; 55 slices; acquisition time = 15 minutes. The single-shell protocol for DTI consisted of a single b value of 1,000s/mm 2 and 42 nonlinear diffusion-encoding directions; 7 b = 0s/mm 2 images; voxel size = 2 × 2 × 2mm 3 ; TR = 13,100 milliseconds; TE = 88 milliseconds; 75 slices; acquisition time = 9.
MRI Data Analysis Both single-and multi-shell data were initially visually inspected for coverage, motion, and/or ghosting artifacts and to verify minimal eddy-current distortions. Following motion correction,  21  NODDI metrics including NDI and ODI were estimated from the multi-shell data using the NODDI MATLAB toolbox (http://nitrc. org/projects/noddi_toolbox), and DTI metrics including FA, MD, RD, and AD were estimated from the single-shell data using FSL (https://www. fmrib. ox. ac. uk/fsl). 22 ndividual NODDI and DTI metric maps were then spatially normalized to the group-specific template for subsequent TBSS and ROI analyses using DTI-TK (http://dti-tk. sf. net),  23  a stateof-the-art approach that has been successfully applied to studying white matter in other neurodegenerative diseases.
Acknowledgment This work was primarily supported by the  CHDI Foundation , with additional support from the  China Scholarship Council  (J. Z. ),  EPSRC  ( EP/L022680/1 ; H. Z. ),  Wellcome Trust  (G. R. ), and  UCL Leonard Wolfson Experimental Neurology Centre  (PR/ylr/18575; D. L. T. ). S. G. , R. I. S. , G. R. , H. Z. , and S. J. T. receive support from a  Wellcome Trust   Collaborative Award  ( 200181/Z/15/Z ). We thank the patients and controls for their participation; and  Drs G. Winston  and  X. -O. Lu  for discussions on the results.
We therefore use the peatland map obtained by  Yu et al. (2010). This global map at a resolution of 2 km recognized peatlands where a grid cell contains at least 5 % of peatlands. These data are obtained from inventories by countries, or from gleysols and histosols from the Harmonized World Soil Database V1. 1 when the inventory is missing  (Yu et al. , 2010). We constructed a new land cover map including peatlands as a combination of the original map of 13 PFTs and the global peatland map from  Yu et al. (2010) , with peatlands considered as a 14th PFT. The peatland map of  Yu et al. (2010)  is interpolated onto a 0. 5 • × 0. 5 • regular grid in order to obtain a fraction of peatland f Yu peat for each grid cell as shown in Fig. 1. The fraction of the new PFT f PFT 14 of peatlands then corresponds to f Yu peat , limited to the maximum fraction of grassland.