dataesr/scientific-paragraphs-categorization huggingface dataset:
As a by-product, tRNA genes were also annotated via the tRNAscan-SE (v1. 3. 1)80 module of the Maker pipeline. The gene annotations produced by RATT, YGAP, and Maker together with the EST/protein alignment evidences generated by Maker were further leveraged by EVidenceModeler (EVM)81 to form an integrative annotation. Manual curation was carried out for selected cases (e. g. the CUP1 and ARR clusters) and pseudogenes were manually labeled when verified. The same pipeline was used for upgrading the protein-coding gene annotation of S. arboricolus, for which the originally annotated coding sequences (CDSs) and protein sequences was used for initial EST/protein alignment. In addition, for the 12 strains, we systematically annotated other genomic features encoded in their nuclear genomes, such as centromeres, Ty retrotransposable elements, and telomere-associated core X-and Y'-elements (Supplementary Note).
Orthology group identification For nuclear protein-coding genes, we used Proteinortho (v5. 15)83 to identify gene orthology across the 12 strains and six other sensu stricto outgroups: S. mikatae (strain IFO1815), S. kudriavzevii (strain IFO1802), S. kudriavzevii (strain ZP591), S. arboricolus (strain H6), S. eubayanus (strain FM1318) and S. bayanus var. uvarum (strain CBS7001). The orthology identification took into account both sequence homology and synteny conservation (the PoFF feature84 of Proteinortho). For each annotated strain, the systematic names of nondubious genes in the Saccharomyces Genome Database (SGD) (see URLs ) were mapped to our annotated genes based on the orthology groups identified above.
Conserved synteny block identification We used SynChro from the CHROnicle package (version: January 2015)91,92 to identify conserved synteny blocks. We prepared the input files for SynChro with custom Perl scripts to provide the genomic coordinates of all annotated features together with the genome assembly and proteome sequences. SynChro subsequently performed exhaustive pairwise comparisons to identify synteny blocks shared in the given strain pair.
Gene ontology analysis The CDSs of the S. cerevisiae non-dubious reference genes were BLAST against the NCBI non-redundant (nr) database using blastx (E-value = 1x10 -3 ) and further annotated by BLAST2GO (v. 3. 2)95,96 to generate gene ontology (GO) mapping for each gene. We performed Fisher's exact test97 to detect significantly enriched GO terms of our test gene set relative to the genome-wide background. False discovery rate (FDR) (cutoff: 0. 05)98 was used for multiple correction. The significantly enriched GO terms were further processed by the "Reduce to most specific terms" function implemented in BLAST2GO to only keep child terms.
Phenotyping the growth rates of yeast strains in copper-and arsenite-rich medium The homozygous diploid versions of the 12 strains were pre-cultured in Synthetic Complete (SC) medium overnight to saturation. To examine their conditional growth rates in copperand arsenite-rich environment, we mixed 350 µl conditional media (CuCl 2 (0. 38 mM) and arsenite (As[III], 3 mM) for the two environment respectively) with 10 µl saturated culture to the wells of Honeycomb plates. Oxygen permeable films were placed on top of the plates to enable a uniform oxygen distribution throughout the plate. The automatic screening was done with Bioscreen Analyser C (Thermic Labsystems Oy, Finland) at 30°C for 72 hours, measuring in 20 minute intervals using a wide-band filter at 420-580 nm103. Growth data pre-processing and phenotypic trait extraction were performed by PRECOG104.
Acknowledgements We thank  G. Drillon  for the help with using the program CHROnicle. We thank  O. Croce  and  R. Marangoni  for the help with maintaining the computing server and various bioinformatics tools. We thank  Liti lab  technician A. Llored technical help with yeast strains and DNA samples. This work was supported by  ATIP-Avenir (CNRS/ INSERM) ,  Fondation ARC pour la Recherche sur le Cancer  (grant  PJA20151203273 ),  Marie   Curie Career Integration Grants (grant   322035 ),  Agence Nationale de la Recherche  (grants:  ANR-16-CE12-0019 ,  ANR-13-BSV6-0006-01  and  ANR-11-LABX-0028-01 ),  Cancéropôle PACA  (AAP émergence 2015) and  DuPont Young Professor Award  to G. L. ,  Wellcome Trust  (grant  WT098051 ) to R. D. , and  Vetenskapsrådet  (The  Swedish Research Council , grant  325-2014-4605 ) to J.
W. J. -X. Y. is supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  (grant  n°PDF20150602803 ). J. L. is supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  (grant  n°PDF20140601375 ). J. H. is supported by the  Labex   SIGNALIFE program  from  Agence Nationale de la Recherche  (grant  ANR-11-LABX-0028-01 ).
All genome sequencing, assembly and annotation data that support the findings of this study have been deposited in public repositories. The PacBio sequencing reads for this project has been deposed in the European Nucleotide Archive (ENA) with the primary accession code "PRJEB7245" (http://www. ebi. ac. uk/ena/data/view/PRJEB7245). The Illumina sequencing reads for this project has been deposed in the Short Reads Archive (SRA) with the primary accession code "PRJNA340312" (https:// www. ncbi. nlm. nih. gov/bioproject/PRJNA340312). The genome assemblies and annotations generated by this study have been deposited at our dedicated website for this project:https://yjx1217. github. io/Yeast_PacBio_2016/data/. The genome assemblies have also been.
This interpretation is in line with the finding of a large gene expression overlap between the two samples. Somatic embryos have been widely reported to resemble both morphologically and physiologically zygotic embryos  [8, 33, 34]. In order to assess similarities between our cultures and developing zygotic embryos, we performed a PCA using our datasets together with publicly available sequencing data from 1 to 2 cell embryos, 8 cell embryos (octant) and 32 cell embryos (globular stage)  [35]. Whereas the first principal component separates samples by experiment, the second component accounts for more than 8% of the variance in the dataset and correlates well with embryo development. According to PC2, embryogenic cells are closely related to 8-cell embryos (Fig. 3a , green dots) and well differentiated from 2 or 32-cell embryos (Fig.
3a , yellow and blue dots respectively). In accordance to this result, we detected low or no transcripts for WUSCHEL (WUS), SHOOTMERISTEM-LESS (STM) and CLAVATA3 (CLV3), meristematic genes whose expression has been documented in embryogenic culture systems (Fig. 1b  and Additional file 4: Table  S3 )  [12, [36] [37] [38]. Indeed, during zygotic embryo development, markers of an organized shoot apical meristem (SAM) are visible only starting at the 16 cell stage, with WUS expression appearing in four sub-epidermal apical cells  [39] , later followed by STM activation in the apical domain of the early globular embryo  [40]  and last, CLV3 expression is detected at the heart stage between the emerging cotyledons  [41]. On the other hand, we did not detect WUSCHEL-related-HOMEOBOX 2 (WOX2) and WOX9, known to establish the apical and the basal domains of the early zygotic embryo  [42].
As shown above, the first principal component explained 75. 6% of the variance and differentiated well our dataset from the SAM dataset. However, when principal components 2 was considered (explaining 7. 4% of the variance), we observed a closer similarity of the embryonic cells to the pFIL expressing domain of the SAM rather than the stem-cell niche or the WUS expressing organizing center (Fig. 3b ). This is in line with the lack of transcript for the wellknown regulators of stem cell activity WUS, CLV3, CLV1 and CLV2  [39, 47]  (Fig. 1b ). Despite we did not detect FIL expression or other members of the YABBY family in embryogenic cells (Additional file 3: Table  S2  and Additional file 4: Table  S3 ), YABBY positive regulators KANADI 1 (KAN1) and KAN2  [48, 49]  are among the DEGsUP (Fig.
Black dots: non-hair cells (transverse sections 2 to 13). Purple squares: xylem pole (transverse sections 2 to 13). Purple dots: phloem (transverse sections 2 to 13). Orange diamonds: metaprotophloem (transverse sections 2 to 13). Yellow dot: quiescent center. Each dot represents an experimental replicate for embryogenic and callus cells, and a different transverse section along the root's longitudinal axis (according to  Brady et al. , 2007)  gene expression patterns reminiscent of root meristems, even if it is derived from aerial organs  [55]. We performed PCA using our samples and publicly available microarray expression profiles of a high-resolution set of developmental time points within a single Arabidopsis root  [56]. The first principal component explained 25. 57% of the variance and differentiated root tissues and our datasets, whereas the second (explaining the 11.
16% of the variance) suggested a closer similarity of our datasets to meristematic tissues of the root tip, such as the proximal lateral root cap (LRC) (Fig. 4 , blue triangles pointed by arrows) rather than other root tissues, or more distal parts of the LRC (Fig. 4 ). This result is in line with the observations previously made by Sugimoto et al. [55] , linking callus cell fate to meristematic root tissue fate. Moreover, this PCA is supported by expression of well-known markers for endodermis and LRC specification in our samples (Additional file 4: Table  S3 ). The NAC domain transcription factors FEZ, SOM-BRERO (SMB) and BEARSKIN1 (BRN1) are important factors in patterning the root tip by controlling cell division planes and root cap maturation  [57, 58]  and their transcripts are more abundant in callus cells rather than embryogenic cells, as they feature in our DEGsDOWN list (Additional file 3: Table  S2  and Additional file 4: Table  S3 ).
RNA deep sequencing and data processing RNA-seq libraries were prepared using the SMARTer Stranded RNA-Seq Kits (Takara). RNA-seq experiments were carried with an IG-CNS Illumina Hiseq2000. The RNA-seq samples have been sequenced in paired-end (PE) with a sizing of 260 bp and a read length of 100 bases. Four samples by lane of Hiseq2000 using individual bar-coded adapters and giving approximately 30 million of PE reads by sample were generated. To facilitate comparisons, each RNA-Seq sample followed the same pipeline from trimming to count of transcript abundance as follows. Read preprocessing criteria included trimming library adapters and performing quality control checks using FastQC. The raw data (fastq) were trimmed by fastx toolkit for Phred Quality Score > 20, read length > 30 bases, and the ribosome sequences were removed with tool sortMeRNA  [73].
The mapper Bowtie 2  [74]  was used to align reads against the Arabidopsis gene database TAIR10 (http://www. arabidopsis. org/) with one isoform per gene corresponding to the representative gene model. The abundance of each gene was calculated by a local script which parses SAM files and counts only reads that map unambiguously one gene, removing multi-hits. According to these rules, around 90% of PE reads were associated to a gene, 3 to 4% could not be mapped and 4 to 5% resulted in multi-hits and were discarded. For the differential analysis, genes which did not have at least 1 read after a count per million (CPM) normalization in at least one half of the samples were discarded. Library size was normalized using the method TMM and count distribution was modeled with a negative binomial generalized linear model where the harvest date was taken into account.
Dispersion was estimated by the edgeR method (Version 1. 12. 0) [75]  in the statistical software 'R' (Version 2. 15. 0, R Development Core Team). Expression differences were compared using likelihood ratio test and p values were adjusted by the Benjamini-Hochberg procedure to control FDR. A gene was declared differentially expressed if its adjusted p value was lower than 0. 05.
Acknowledgements We thank  R. B Deal  and  S. Henikoff  for sharing 35Spro:BirA transgenic plants and the ADF8:NTF construct. We thank  Dr. J. C. Fletcher  for helpful comments,  N. Provart  for advices on bioinformatics analyses,  D. Grain  for assistance with Arabidopsis handling,  W. Xu  for assistance with the INTACT method set up.
Funding E. F. was supported by an  EMBO  long term fellowship ( EMBO ALTF 243-2012 ). L. L. was funded by the  LabEx Saclay Plant Sciences-SPS  project  GESEM. The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). The funding agencies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials Sequence data from this article can be found in the Sequence Read Archive (SRA) data library (https://www. ncbi. nlm. nih. gov/sra) under accession number SRP126620 (Temporary Submission ID: SUB3340631; Release date: 2017-12-13). DEGs analysis is included in Additional file 3 Table  S2. Other datasets used in this study are publicly available (see text for original sources).
INTRODUCTION Atypical teratoid rhabdoid tumor (ATRT) is a rare and highly aggressive embryonal tumor of the central nervous system with a dismal prognosis  (Dufour et al. , 2012). Because the tumor is so rare and randomized controlled trials are so few, there is no consensus on optimal treatment for children with ATRT. Different multimodal strategies consisting of surgery, chemotherapy, and radiation therapy are currently being used in an attempt to improve outcomes  (Ginn and Gajjar, 2012). A recent review of the US Cancer Database from 2004 to 2012 shows that trimodality therapy seems to offers the best outcome  (Fischer-Valuck et al. , 2016). Another review of the literature points out high dose chemotherapy with stem cell rescue and radiotherapy as the two key components associated with improved outcome in children and adolescents with newly diagnosed ATRT  (Schrey et al.
that they need to be tested on a large scale of different kinases, before selectivity and specificity can be evaluated. Moreover, there is an increasing need for the analysis of off-target effects. Most of the inhibitors of CK2 known so far are indeed ATP-competitive inhibitors. Samer Haidar reported on the development of new inhibitors based on an indeno  [1,2-b] indole scaffold which was already known as a lead structure for the development of human CK2 inhibitors  [4]. By using a pharmacophore model, which was developed on the inhibition data obtained with CK2 and more than 50 indeno  [1,2-b] indoles, for mining the ZINC compound database, the natural compound bikaverin was identified and turned out to be a new potent CK2 inhibitor. The group of Emilio Itarte used the inhibitor CX-4945, which is orally available, either alone or in combination with other cytostatic drugs for the treatment of cancer  [5].
By systematic analyses of published literature and proteomics databases together with the computational assembly of networks of CK2, the Litchfield group found that by phosphorylating its substrates  [9] , CK2 can modulate other post-translational modifications and, vice versa, that post-translational modifications affect CK2 phosphorylation of substrates. Besides inhibitors, another approach was exploited for the study of CK2 functions, namely knock-down of expression of CK2 subunits. By this approach, the group of Hashemolhosseini found reduced activity of nicotinamide adenine dinucleotide dehydrogenase and succinate dehydrogenase in CK2β-deficient muscle fibres  [10]. This result indicates a role of CK2β in the regulation of the oxidative capacity of skeletal muscle fibres. Full or conditional CK2 knock-out mice were used for the elucidation of the role of CK2 in brain development, neuronal activity and behaviour, which was summarised by the group of Heike Rebholz  [11].
25 μm PT) for the identification of the hydrocarbons. We used the same oven temperature program for both GC-FID and GC-MS (from 70˚C to 150˚C at 30˚C/min and from 150˚C to 320˚C at 5˚C/min; 10 min at the 320˚C final temperature; carrier gas was helium at 1 bar and 50 ml/min flux; injector temperature was 250˚C). Results were registered with the Agilent ChemStation program for GC-FID and Agilent Mass Hunter Workstation program for GC-MS.
Identification and quantification of compounds Most of the compounds have been previously described in Bagnères et al. [42]  and Lorenzi et al. [39]  using GC-MS in EI and CI modes; determination was then completed in Uboni et al. [40]  and Bonelli et al. [41]. Verification of their presence was performed comparing the diagnostic ions and the M-15 of each peak by the GC-MS apparatus described above. For more details on structure and analysis of insect hydrocarbons see  [44]. For quantification, peak areas were integrated using the GC ChemStation software and corrected manually.
Then, we dropped the least non-significant interactions and terms and calculated the reduced models. Because most colonies were killed by predators during the summer, we could only sample few colonies in August (n = 12 fragments). In order to test whether the variation in the chemical traits was driven by the values in late August, we also ran all Models using a reduced dataset that excluded late August data. These analyses yielded approximately similar statistical results as those of the complete data set, which we report in the Results section. Preliminary analyses showed that nest side did not explain the variation in the traits describing nest signature, and, therefore, this factor was not included in the models presented in the paper. This result suggests that the signature was spatially homogeneous on the nest surface, as supported by the consistent results that we obtained by sampling nests once on the right side, and once on the left side.
Acknowledgements We thank the MIMA2 platform (http://www6. jouy. inra. fr/mima2) of the  INRA research centre of Jouy-en-Josas (France)  for access to their confocal and electronic microscopy facilities. Julien Deschamps  ( Micalis Institute ) is warmly acknowledged for CLSM image analysis. We would also like to thank  C eline Gautier , Japhete Mbouyou-boungou and  Victor Bandaly  for their technical assistance. We thank  Emma Pilling  (PhD) for English-language editing of the manuscript. This work was supported by the  Institut National de la Recherche Agronomique (INRA)  and by the  European Community 's  Seventh Framework Program  (FP7/2007-2013):  MetaHIT , grant agreement  HEALTH-F4-2007-201052. Benoit Couvigny  was the recipient of a fellowship from the  Ministere de la Recherche et de l'Education Nationale.
Overview of Spot-On Spot-On is a user-friendly web-interface that pedagogically guides the user through a series of quality-checks of uploaded datasets consisting of pooled single-molecule trajectories. It then performs kinetic model-based analysis that leverages the histogram of molecular displacements over time to infer the fraction and diffusion constant of each subpopulation (Figure  2 ). Spot-On does not directly analyze raw microscopy images, since a large number of localization and tracking algorithms exist that convert microscopy images into singlemolecule trajectories (for a comparison of particle tracking methods, see  (Chenouard et al. , 2014) ). To use Spot-On, a user uploads their SPT trajectory data in one of several formats (Figure  2 ). Spot-On then generates useful meta-data for assessing the quality of the experiment (e.
g. localization density, number of trajectories etc. ). Spot-On also allows a user to upload multiple datasets (e. g. different replicates) and merge them. Spot-On then calculates and displays histograms of displacements over multiple time delays. The next step is model fitting. Spot-On models the distribution of displacements for each subpopulation using Brownian motion under steady-state conditions without state transitions (full model description in Materials and Methods). Spot-On also accounts for localization errors (either user-defined or inferred from the SPT data). Crucially, Spot-On corrects for defocalization bias (Figure  1C ) by explicitly calculating the probability that molecules move out-of-focus as a function of time and their diffusion constant (Video 1). In fact, Spot-On uses the gradual loss of freely diffusing molecules over time as additional information to infer the diffusion constant and size of each subpopulation.
Validation of Spot-On using simulated SPT data and comparison to other methods However, this underestimate was due to particle confinement inside the nucleus: Spot-On correctly inferred the diffusion constant when the confinement was relaxed (Figure  3  -Figure  Supplement 4 ; 20 µm nuclear radius instead of 4 µm). This emphasizes that diffusion constants measured by SPT inside cells should be viewed as apparent diffusion constants. In contrast, the MSD i method failed under most conditions regardless of whether all trajectories were used (MSD i (all)) or a fitting filter applied (MSD i (R 2 >0. 8); Figure  3A-B ; Table  1 ). vbSPT performed almost as well as Spot-On for slow-diffusing proteins, but showed larger deviations for fast-diffusing proteins (Figure  3 -Figure Supplement 2-3). To illustrate how the methods could give such divergent results when run on the same SPT data, we considered two example simulations (Figure  3C-D ; more examples in Figure  3  -Figure Supplement 3).
We determined the coefficients a and b from Monte Carlo simulations. For a given diffusion constant, D, 50,000 molecules were randomly placed onedimensionally along the z-axis drawn from a uniform distribution from  MIN = -∆  2 to  MAX = ∆  2. Next, using a time-step ∆, one-dimensional Brownian diffusion was simulated along the z-axis using the Euler-Maruyama scheme. For time delays from 1∆ to 15∆, the fraction of molecules that were lost was calculated in the range of D=[1;12] μm 2 /s. ∆, ∆,  and  ∆, ∆,  were then estimated through least-squares fitting of  remaining ∆, ∆ corr ,  to the simulated fraction remaining. The process was repeated over a grid of plausible values of (∆, ∆, ) to derive a grid of 134,865 (a,b) parameter pairs. This pre-calculated library of (a,b) parameters enables Spot-On to perform model fitting on nearly any SPT dataset with minimal overhead.
Numerical implementation of models in Spot-On Spot-On calculates the empirical histogram of displacements based on a user-defined bin width. Spot-On allows the user to choose between PDF-and CDF-fitting of the kinetic model to the empirical displacement distributions; CDF-fitting is generally most accurate for smaller datasets and the two are similar for large datasets (Figure  3  -Figure  Supplement S9 ). The integral in  CORR ∆, ∆ corr was numerically evaluated using the midpoint method over 200 points and the terms of the series computed until the term falls below a threshold of 10 -10. Model fitting and parameter optimization was performed using a non-linear least squares algorithm (Levenberg-Marquardt). Random initial parameter guesses are drawn uniformly from the user-specified parameter range.
The excitation settings were as follows: 1 ms, 561 nm 100% AOTF, 633 nm 100% AOTF; 2 ms, 561 nm 43% AOTF, 633 nm 40% AOTF; 4 ms, 561 nm 28% AOTF, 633 nm 27% AOTF; 7 ms, 561 nm 20% AOTF, 633 nm 19% AOTF; constant illumination, 561 nm 17% AOTF, 633 nm 16% AOTF. spaSPT data was analyzed (localization and tracking) and converted into trajectories using a custom-written Matlab implementation of the MTT-algorithm  (Sergé et al. , 2008)  and the following settings: Localization error: 10 -6. 25 ; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max D (µm 2 /s): 20. The spaSPT trajectory data was then analyzed using the Matlab version of Spot-On (v1. 0; GitLab tag 1f9f782b) and the following parameters: dZ=0. 7 µm; GapsAllowed=1; TimePoints: 4 (50 Hz), 6 (74 Hz), 7 (100 Hz), 8 (134 Hz), 9 (167 and 200 Hz); JumpsToConsider=4; ModelFit=2; NumberOfStates=2; FitLocError=0; LocError=0.