dataesr/scientific-paragraphs-categorization huggingface dataset:
To provide standard deviations on the fitting parameters a jackknife procedure was adopted as described in  Elf et al. (2007)  and  Loffreda et al. (2017). Briefly, we performed multiple fitting iterations, each of them after dropping 20% of the data for each of the dataset. Errors are provided as standard deviations of the obtained distribution of parameters following 200 individual fitting iterations. The diffusion coefficient of the slowest diffusing component D 1 = 0:07ðmm 2 =sÞ, compatible with the diffusion coefficients of chromatin bound proteins  (Chen et al. , 2014) , and F 1 therefore represent the fraction of molecules detected as immobilized to chromatin (as further validated by analyzing the MITF-Dbasic mutant). The MITF-Dbasic mutant also result in the increase of the diffusion coefficients of the unbound population, D 2 and D 3.
The average residence time is then calculated as: <t > = f 1 t 1 + ð1 À f 1 Þt 2 QUANTIFICATION AND STATISTICAL ANALYSIS Figure  3C. Binding data were analyzed using the GraphPad Prism software. Binding profiles were fitted using a simple model assuming a stoichiometry of one MITF dimer per double stranded DNA fragment. When K D values were higher than 500 nM, the maximum specific binding value (Bmax) was estimated according to the one obtained from other variants and maintained fixed for the fitting. K D values reported correspond to the means of three independent measurements and the ± error numbers represent the standard deviations. Figure  4A. p-values were calculated using a Two-Sample t-test, and the line in the box and whisker plots indicates the mean. The exact number of embryos injected and melanocytes count are shown in Figure  S3.
00 # Poisson p-value over local region = 1. 00e-04 and variable parameters (modeled according to each dataset as these are sequencing depth dependent) # FDR effective poisson threshold # FDR tag threshold Figure 5E. Boxplots show IQR with median value marked by line of the same color as the box and mean value marked by black line. Whiskers cover values between IQR and ± 1. 5 3 IQR. Any values outside 1. 5 3 IQR are plotted with circle. Figures 6C-6F. N cells = 20; 6; 15; 15; N displacements = 17802; 2684; 16422; 12999 for MITF WT, Dbasic, K243Q, K243R respectively. Error bars indicate SD.
ACKNOWLEDGMENTS The Piggybac vectors were provided by  Kazuhiro Murakami  ( RIKEN, Kobe, Japan ). This work was funded by the  Ludwig Institute for Cancer Research  (C. R. G. , R. F. , B. S. -B. ,  E. Suer , and X. L. ),  Cancer Research UK (CRUK)  grant number  C38302/A12981 , through a  CRUK Oxford Centre Prize DPhil Studentship  (HF), the  Medical Research Council  (R. S. , Z. Z. , P. F. ;  MR/N010051/1  and E. E. P. ;  MC_UU_00007/9 ),  L'Oreal-Melanoma Research Alliance 401181 (E. E. P. ) , the  Harry J. Lloyd Trust (R. S. ) , the  Wellcome Trust  (P. F. and A. S. ), the  Postdoc Fund of the University of Iceland  (A. S. ), the  Oxford Biomedical Research Centre  (R. L. ), the  Research Fund of Iceland  (E. S. ), a  European Research Consolidator Award  ( ZF-MEL-CHEMBIO 648489 ) (E. E.
P. ), the  CNRS, INSERM , the  Ligue Nationale Contre le Cancer , the  Institut National du Cancer (INCa) , the  ANR-10-LABX-0030-INRT French  state fund (I. D. ), and a Junior 1 salary award from the  Fonds de Recherche du Quebec-Sante (FRQ-S). This work was also funded by operating grants from the  Cancer Research Society  (J. -P. L. ), the  Fondazione Cariplo  (A. L. and D. M. ;  2014-1157 ), and the  Italian Cancer Research Association (AIRC ;  IG2018-21897 ; D. M. ). I. D. is an ''equipe labellisee'' of the  Ligue Nationale Contre le Cancer.
Experimental Models Cell Lines Human cell line information. 501mel Human melanoma cell line (female, RRID:CVCL_4633) A375M Human melanoma cell line (male, RRID:CVCL_B222) Phoenix-Ampho Human fetal cell line (female, RRID:CVCL_H716) All melanoma cell lines were authenticated by STR analysis using Eurofins Genomic service. All parental and derivative cell lines were verified mycoplasma free using Ludwig Cancer Research monthly mycoplasma check service. Culture/Growth condition Melanoma cell lines and their stable transfectants were grown in RPMI-1640 (GIBCO BRL, Invitrogen), supplemented with 10% fetal bovine serum (FBS, Biosera). Phoenix-ampho cells were grown in DMEM (Lonza), supplemented with 10% FBS. Cells were maintained in humidified incubator at 37 C with 10% CO 2. ) phoA supE44 thi-1 gyrA96 relA1 l -(Invitrogen #18265017) needle for mechanical disruption till no visible clump is seen follow by pre-clearing at 14,000 x g centrifugation for 10 mins.
Among the newly described CWPs families, the importance of the PAC (Proline-rich Arabinogalactan protein and Conserved Cysteines) domain containing-protein (PDP) family could be stressed because of their presence in many cell wall proteomes (see WallProtDB, www. polebio. lrsv. ups-tlse. fr/WallProtDB/, query with "Ole e1 allergen domain" as a keyword). The name of the PDP family was initially proposed by Baldwin et al. [9] , who described them as a sub-family of non-classical arabinogalactan proteins (AGPs) containing both an AGP domain and a C-terminal domain containing six Cysteines residues (named Cys 1 to Cys 6 herein). Later on, a domain partly describing the PAC domain has been proposed in the Pfam database (PF01190, http://pfam. xfam. org/). The firstly described member of this family was a protein from Nicotiana alata named AGPNa3  [10].
In this article, we aim at giving an evolutive overview of the PDPs throughout the green lineage, from Bryophytes to late divergent plants, such as monocots and dicots. We first define more precisely the PAC domain characteristics in order to retrieve PAC domain sequences from available genomic or RNA-seq databases using a tailor-made bioinformatic script. Since the conservation of the primary amino acid sequences of PAC domains was rather low, and since the presence of β -sheets seemed to be essential for domain folding, bona fide PAC domains were selected according to their secondary structure conservation, and protein alignment was done using a software taking into account secondary structures. Modeling of tertiary structures was done based on the available crystal structure of the Pla l 1 PAC domain.
About 50 putative PDPs were collected and manually checked for the presence of the six conserved Cys residues. From this first data mining step, it appeared that the level of conservation of the amino acid sequences of the PAC domains could be low. In particular, except between the two first conserved Cys residues (Cys 1 and Cys 2), the spacing between Cys residues could be variable. Thus, the usual homology-based mining was not sufficient, and an alternative strategy was necessary to obtain exhaustive results for each plant. The alignment of angiosperms PAC domains has allowed calculating the range of spacing between the conserved Cys residues. Then, a tailor-made script based on several points detailed in Table  1  has been set up to search for additional PDPs in the same genomes or in other genomics or transcriptomics databases.
However, the prediction of a signal peptide for protein secretion could not be made systematically for the proteins translated from transcriptomics data because the sequences could be incomplete. Furthermore, when genomic sequences were available, the presence of an intron between the sequences encoding, on the one hand, Cys 1 and Cys 2, and on the other hand, Cys 3 to Cys 6 was searched for to support the PAC domain identification. Using this script, sequences encoding PAC domains have been searched for in 78 plant species belonging to the green lineage from Bryophytes (Bryophyta, Marchantiophyta and Anthocerotophyta) to late divergent plants. Altogether, about 450 putative PAC domain sequences were collected (S1-S4). Three additional criteria have then been used to select bona fide PAC domain proteins.
The third criterion was the presence of associated predicted functional domains suggesting intracellular functions like aldehyde dehydrogenase domain (PF00171, Tetraphis pellucida HVBQ_2004216) or JmjC and JmjN domains of transcription factors (PF02373 and PF02375, Pallavicinia lyelli YFGP_2007785) (S3). In most of these latter cases, it was not possible to predict the sub-cellular localization of the proteins because they resulted from the translation of incomplete contigs obtained from RNA-seq data. The number of amino acids between two successive Cys residues is indicated between brackets. Using this script, sequences encoding PAC domains have been searched for in 78 plant species belonging to the green lineage from Bryophytes (Bryophyta, Marchantiophyta and Anthocerotophyta) to late divergent plants.
A Possible Origin for the PAC Domain We have performed an extensive search of PAC domain sequences in the available databases dedicated to ancestors of the green lineage using both the script described above and BLAST queries using several PAC domains in case the spacing between Cys residues would be slightly different. Mining was done in the following families: Stramenopiles (Synura petersenii), Cryptophyta (Chroomonas sp), Chlorophyta (Asteromonas gracilis, Chlamydomonas rheinardtii, Nephroselmis olivacea, Volvox carteri, Scenedesmus dimorphus, Scherffelia dubia), Streptophyta (Chara braunii, Coleochaete orbicularis, Klebsormidium flaccidum, Mesotaenium caldariorum, Penium margaritaceum) (S4). In many cases, the proteins were incomplete either at their N-termini and it was not possible to predict a signal peptide, or at their C-termini, and they could not be classified.
Three-Dimensional-Modeling of PAC Domain Proteins Three-dimensional-models were calculated for 41 bona fide and 9 putative PAC domains, based on the crystal structure of the P. lanceolata PAC domain  [24]. The sequence identities between the template and the PAC domains varied between 9. 6% and 30. 4% (median 15. 9%). A sequence identity of 30% is generally seen as a lower limit for reliable models predicted by homology modeling algorithms, but the assumption of disulfide bridges somewhat lowers this limit. However, the low sequence similarities were still an issue. In addition, in 6 out of the 50 PAC domains, the 3D-modeling software I-Tasser was not able to find conformations enabling the formation of the three disulfide bridges between the predefined Cys residues (S8). In all these cases, either the proteins were predicted to have α-helices, or they were missing the Glycine residue upstream Cys 1.
In addition, only PAC domains showing less than 85% of identity inside a given plant species were conserved. As a first step, the sequences were aligned according to their predicted secondary structure. Such a strategy was used in previous studies where the conservation of the primary sequences of the proteins was not sufficient to ensure relevant alignments  [33] [34] [35]. The PROMALS3D software was used, and the resulting alignment was introduced in the MEGA7 software to build up a maximum likelihood tree using 500 bootstraps. Due to the low level of conservation between amino acid sequences and especially between the PAC domain sequences of the older lineages, we have decided to build up two independent trees to avoid bias due to long-branch attraction: the first one (Tree I) including plants from Bryophytes to A.
Conserved Amino Acids Motifs Inside Clades A search for conserved amino acid motifs was done for the PAC domains of each clade of Tree II. The most significant results were found for clades A, B, D, E, G, H, and I (Figure  7 ). In each clade, the most conserved motifs were detected at the N-terminus of the PAC domain. This was consistent with the definition of the pollen Ole e 1 motif in the Pfam and Prosite databases (PF01190 and PS00925, respectively). However, the consensus defined for the PS00925 domain only exactly fitted with that of clade A PAC domains ([EQT]-G-x-V-Y-C-D-[TNP]-C-R). Furthermore, the most conserved PAC domains were found in the C clade (Figure  8 ). Their degree of conservation in the green lineage from Lycopodiales to Brassicales is impressive. Finally, the C-terminal W-W domain present in all the proteins belonging to clade D was also very well conserved from the Bryophytes to the Brassicales with common motifs mostly located in its N-terminus half (S10).
Conserved Amino Acids Motifs Inside Clades A search for conserved amino acid motifs was done for the PAC domains of each clade of Tree II. The most significant results were found for clades A, B, D, E, G, H, and I (Figure  7 ). In each clade, the most conserved motifs were detected at the N-terminus of the PAC domain. This was consistent with the definition of the pollen Ole e 1 motif in the Pfam and Prosite databases (PF01190 and PS00925, resp Clade G   The combination of sequence conservation with the accessibility of conserved residues on the protein surface shall hint to functional important sites while conserved residues located in the protein core are more likely important for maintaining the fold. Also, conserved residues in the loop regions may have a functional role, although they are less accessible in the static 3D-structural model as loops are often flexible and may move considerably.
The scores of the models are in the range of the modeling template structure (PDB code 4Z8W), indicating that none of the models should be largely wrong. The conservation of motifs in PAC domains suggests common biological activities. It is possible to infer that their interactions with cell wall polysaccharides or O-glycans assumed from in vitro studies have been conserved and that the distribution of PDPs in the different plant families reflects differences in cell wall polysaccharides. Regarding the W-W C-terminal domain of the clade D PAC domains, its role remains to be unraveled. It is encoded by a distinct exon and could originate from exon shuffling  [39].
Databases The sequences used in this study have been retrieved from different databases, such as Orchidstra 2. 0 ([40] http://orchidstra2. abrc. sinica. edu. tw/orchidstra2/orchid_blast. php), genome annotation Databases (  [41] , http://genome. microbedb. jp/blast/blast_search/klebsormidium/genes), Phytozome (  [42] , https://phytozome. jgi. doe. gov/pz/portal. html), OneKP (  [43] , https://db. cngb. org/onekp/) (see S1). When necessary, nucleotide sequences have been translated into amino acid sequences using EMBOSS transeq (  [44] , https://www. ebi. ac. uk/Tools/st/emboss_transeq/).
Comparisons and Alignment of PAC Domains The BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi) program has been used for sequence comparison. Similarities between PAC domain sequences have been calculated using either Blast2seq (https://blast. ncbi. nlm. nih. gov/Blast. cgi) or needle (http://www. bioinformatics. nl/cgi-bin/ emboss/needle). The sub-cellular localization of proteins has been predicted with TargetP-2. 0 (  [45] , http://www. cbs. dtu. dk/services/TargetP/) and the presence of β-sheets and/or α-helices using SABLE (  [46] , http://sable. cchmc. org/) and NetSurfP (  [47] , http://www. cbs. dtu. dk/services/NetSurfP/). The selected PAC domains starting at the Gly amino acid located three amino acids upstream of Cys 1 and ending at Cys 6 have been aligned using PROMALS3D (  [48] , http://prodata.
swmed. edu/ promals3d/promals3d. php) to take into account the prediction of α-sheets. The phylogeny has been calculated using MEGA7 (  [49] , https://www. megasoftware. net/) with the maximum likelihood option and 500 bootstraps. The presence of the PROSITE (PS00925,  [50] , https://prosite. expasy. org/) and Pfam (PF01190,  [51] , http://pfam. xfam. org/) domains have been checked in the retrieved sequences. Inside clades, conserved motifs have been identified using MEME (  [52] , http://meme-suite. org/tools/meme) or WebLogo3 (  [53] , http://weblogo. threeplusone. com/).
Three-Dimensional Modeling For a subset of PAC domains, models were generated utilizing MODELLER  [54]  and I-Tasser  [55]. Thereby, disulfide bridges were defined beforehand based on alignments with PDB entry 4Z8W corresponding to the P. lanceolata PAC domain  [24]. Subsequently, these models were scored with MAESTRO  [56] , DOPE  [57] , and ProSA 2003  [58]. Then the top-scoring models were relaxed with Rosetta  [59] , and finally, the relaxed models were scored with the same three methods. We consistently used PAC domains from A. trichopoda as representative models for each clade. The relative solvent accessibility of these models was calculated by an adaptation of the Geometry library algorithm  [60]. The secondary structure assignment was obtained by DSSP  [61, 62]. Both MODELLER and I-Tasser depend on template structures.
Funding: The authors are thankful to  Université Paul Sabatier-Toulouse III (France)  and  CNRS  for supporting their research work. HNG-K has been granted by the  Vietnamese Ministry of Education and Training  for his PhD work. This work was also supported by the  French Laboratory of Excellence  project entitled " TULIP " ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). JL is supported by the  Austrian Science Fund (FWF , grant  P30042 ).
In doing so we assign to every nucleotide a score representing its importance regarding the nucleosome positioning process. This genomic track is accessible at https://github. com/etirouthier/NucleosomeDensity/Results nucleosome/mutasome scerevisiae. bw and can be used freely by others, when designing genetically modified yeast, to anticipate the effect of induced mutations on nucleosome positioning. We also use this track to analyze the DNA motifs that present a high mutation score, corresponding to motifs that are important for nucleosome positioning.
2012 ) (Fig. 1C ). An important control is that the performance obtained by comparing the predicted density with the experimental density coming from the dataset used for training the model is not significantly higher than the performance obtained by comparing the predicted density with the experimental densities from datasets which were not used for training (i. e. correlation between rep1 pred, as defined on Fig. 1C , and rep1 is comparable to the correlation of rep1 pred with the three other experimental densities). The model thus filters a large part of the replicates variability, which indicates a good generalisability of the results. Training a network is a non deterministic process. We trained four networks on the same dataset and looked at the variability in predictions. We found that this variability can vary locally and is correlated with the experimental variability observed between replicates  (Supplemental Fig S1).
This suggests that this step can alter the nucleosome profile in a non-reproducible way. For the sake of comparison of our CNN based method with previously proposed CNN based method for predicting nucleosome positions from DNA sequences, we used two previously proposed networks (J  Zhang et al. 2018; Di Gangi et al. 2018)  to predict the nucleosome density over the entire Chromosome 16. We found a correlation between prediction and experiment of 0. 43 and O. 40 to be compared with 0. 68 obtained with our model trained and tested on Hughes  Hughes, Jin, et al. 2012)  dataset. The lower performance of previously published methods for this task is expected since both methodologies were designed as classifiers which discriminates between fragments of DNA containing a nucleosome and fragments of DNA devoid of nucleosomes, whereas our method is designed to predict directly the nucleosome density over a whole chromosome.
2000; Badis et al. 2008; Krietenstein et al. 2016 ), shown to be the core motifs preventing nucleosomes from binding in the NDR. While our study confirms the role of these core motifs, it also outlines that not all of these motifs in the genome are important for nucleosome positioning. We also show that other positions along the genome can play a role in this process. A general guideline for designing a synthetic yeast genome would be to preserve nucleotides that present a high mutation score. For a quantitative anticipation of effects of mutation, the mutation score track is available at https://github. com/etirouthier/NucleosomeDensity/Results nucleosome/mutasome scerevisiae. bw. Of course, the procedure used here in yeast for nucleosome positioning can be extended to other genomic tracks and other species.
Data accession and preprocessing We use the reference genome sacCer3 of Saccharomyces cerevisiae, available at: http://hgdownload. soe. ucsc. edu/goldenPath/sacCer3/bigZips/. MNase-Seq experimental results are available with accession numbers GSM552910 and GSE13622 (kaplan et al. (Kaplan et al. 2009) ). Data for klyuverii Lactis used in the present study has been obtained from Tsankov et al. (Tsankov et al. 2010 ) and is available through accession number GSE21960. The experimental averaged nucleosome density in the TSS regions was obtained from the study of  Hughes et al. (AL Hughes, Jin, et al. 2012 ) and is available using accession number GSM953761. To obtain the nucleosome density from single end reads, we take the beginning of each read and add one count for each bp in a region of 100 bp in the direction of the read.
Thus, AIS position can modulate the transmitted current very strongly, and indeed this strong modulation appears necessary given the three orders of magnitude of variability in input capacitance of various cell types (3500 pF in some motoneurons  Cormery et al. , 2005 , about 3 pF in cerebellar granule cells  Cathala et al. , 2003). In contrast, observed variations in voltage threshold appear rather modest, especially compared to variations in excitability due to changes in input resistance (again 3 orders of magnitude across cell types). An analysis of a large database of electrophysiological recordings reports a standard deviation in voltage threshold of just 6 mV  (Tripathy et al. , 2015) , and this includes considerable variations in methodological conditions (liquid junction potential, measurement method, solutions, etc).
Analysis of neuroanatomical data In Figure  3 , we extracted measurements of AIS diameter from electron microscopy studies, taken at the end of the AIS. Given the optical diffraction limit, it is necessary to consider electron rather than optical microscopy measurements, at least for thin axons. Soma diameter was measured with optical or electron microscopy. Four data points correspond to measurements of mean diameters, listed in Table  3. Soma and AIS diameters were taken from different studies (except cat olivary cells), but with matched cell type and species. We also digitized individual measurements in three studies: anterior horn cells (motoneurons) of human spinal cord, 44-75 years old  (Sasaki and Maruyama, 1992) ; ventral horn cells (motoneurons) cat spinal cord, P0-P16  (Conradi and Ronnevi, 1977) ; pyramidal, stellate and Martinotti cells of motor and somatosensory cortex of Rhesus monkeys, young adults  (Sloper and Powell, 1979).
Data availability Code to generate all figures is available on GitHub: https://github. com/romainbrette/AIS-geometryand-excitability-2019 (copy archived at https://github. com/elifesciences-publications/AIS-geometryand-excitability-2019). Electrophysiological data analyzed in Fig. 2  has been uploaded on Zenodo (http://doi. org/10. 5281/zenodo. 3539296), on behalf of Prof. Bean (Data from  Hu & Bean, 2018). Dig-.