dataesr/scientific-paragraphs-categorization huggingface dataset:
This resulted in  26, 908, 29, 253, 21, 327, 23, 144, 23, 232, 27, 385, 32, 338  genes for N. vectensis, A. pallida, A. fenestrafer, Discosoma sp. , A. digitifera, S. pistillata, and H. magnipapillata, respectively. After preparing the final gene and corresponding protein sets we ran OrthoMCL  (Li et al. 2003)  using an e-value cut-off of 10 À5 to create groups of orthologs and paralogs across all seven genomes that were subsequently assigned to the latest OrthoMCL-DB 4 (http://orthomcl. org/orthomcl/. ) (Chen et al. 2006 ) for further validation. Ortholog groups were annotated using the Pfam and BLAST database  (Punta et al. 2012) , and only groups with a single ortholog per species and a common Pfam domain, if available, were selected. This resulted in a set of 1,421 single-copy orthologs for subsequent phylogenetic analyses (supplementary table S1, Supplementary Material online).
4. 2 (http://tree. bio. ed. ac. uk/software/figtree). Presence/Absence Analysis Homologs, including orthologs and paralogs, were identified using OrthoMCL as described above. This analysis identified 21,718 orthology groups that were used to generate a binary presence/absence matrix (supplementary table  S4 , Supplementary Material online). Based on this matrix we calculated the distances between species using the binary (aka asymmetric binary) method in R and conducted a hierarchical cluster analysis using the average hclust method. Node supports were calculated as Approximately Unbiased P values (AU) and Bootstrap Probabilities (BP) using the R package Pvclust  (Suzuki and Shimodaira 2006)  and 1,000 bootstraps. To further provide a probabilistic analysis of the presence/absence information, we ran MrBayes using the binary restriction-site model and a discrete gamma distribution with four site rate categories (rates ¼ gamma) after applying an ascertainment bias correction by removing genes present in fewer than two species  (Pisani et al.
2015). In addition, we also reconstructed a phylogenetic tree using the number of paralogs identified for each species in the previously defined ortholog/paralog groups. Briefly, we calculated the distance based on the number of paralogs using the "canberra" method implemented in the R since the binary method used for the presence/absence data is not suitable for count data. The results were then clustered using the "average hclust" method and node supports were calculated using the R package Pvclust as described above.
Synteny-Based Phylogeny Synteny conservation analyses were performed using the programs SynChro  (Drillon et al. 2014)  and PhyChro from the CHROnicle package  (Drillon 2013). In a first step we identified synteny blocks with SynChro using multiple "Delta" (block stringency parameter) values ranging from 1 to 6. Subsequently, we analyzed the SynChro outputs using PhyChro in order to reconstruct phylogenetic relationships based on chromosomal rearrangement signals. Briefly, PhyChro looks for "incompatible" blocks adjacencies between two genomes (such as, for instance, the adjacency of the blocks A and B in a genome G1, and the adjacency of the blocks A and C in G2). Once these "incompatible" blocks adjacencies or "breakpoints" are identified between G1 and G2, PhyChro looks into the other genomes G3.
Trisomy 12 is observed in 15%-20% of CLL and while clinically distinct, little is known about the molecular pathways involved. To explore the role of trisomy 12 across cancer types, we tabulated its incidence in the Mitelman database  (56)  and found it strongly overrepresented in tumors with B cell lineage (Supplemental Figure  25 ). Altogether, these findings suggest that trisomy 12 drives B cell lymphoma by modulating PI3K, MEK/ERK, and mTOR pathways and amplifying BCR signaling. We conclude that in addition to the known biomarkers in CLL, there are a surprisingly large number of gene-drug associations, which in view of the disease's genetic heterogeneity implies a commensurate heterogeneity in responses to drugs. The example of trisomy 12 shows how an association of a genetic feature with a spectrum of drugs can elucidate molecular mechanisms.
More generally, at higher concentrations generic toxicity is expected to dominate over specific target effects. Moreover, to the extent that the data are intended as a model for what may happen in vivo, interest is on the effect of a drug at a con- eter (Life Technologies). Libraries for WES were prepared on the Sure-Select Automated Library Prep and Capture System (Agilent Technologies) according to the manufacturer's protocol (version E. 3). In brief, genomic DNA (1. 5-3 μg) from each sample was fragmented to a length distribution peak of 150 to 200 nt for the preparation of paired-end sequencing libraries. Enrichment for exomic sequence was performed using Agilent SureSelect V4+UTR in-solution capture reagents following vendor's protocol v2. 0. 1. Sequencing was carried out on HiSeq 2000 machines (Illumina) with 3 samples multiplexed per lane.
IGHV analysis was performed as described previously  (66). DNA copy number variants. DNA copy numbers were assessed using Illumina CytoSNP-12 and HumanOmni2. 5-8 microarrays (n = 169). DNA (200 ng) was processed according to the manufacturer's instructions. Arrays were read out using the iScan array scanner. Copy number variants were verified by using the exome sequencing data (n = 107). Fluorescence in situ hybridization (FISH) analysis was performed for del11q22. 3 (n = 162), del17p13 (n = 159), del13q14 (n = 155), trisomy 12 (n = 152), del6q21 (n = 132), and gain8q24 (n = 125). Information on structural variants from FISH, exome sequencing, and SNP arrays was combined into 1 table (n = 219). DNA methylation arrays. Genome-wide DNA methylation profiling was performed as described previously  (58).
A total of 196 CLL patients were assayed by Illumina Infinium HumanMethylation 450k or 850k. Data availability. European Genome-Phenome Archive (EGA) accession EGAS0000100174. The complete data and computational analysis code used in this study are available from www. bioconductor. org in the R package pace. Statistics. To quantify the response of a patient sample to a drug at a given concentration, we used viability relative to the control, i. e. , the CellTiter Glo luminescence readout of the respective well divided by the median of luminescence readouts of the 32 DMSO control wells on the same plate. Integrative data analysis of gene and RNA sequencing, CNV, methylation profiles and drug responses was performed using R version 3 and included univariate association tests, multivariate regression with and without lasso penalization, Cox regression, generalized linear models, principal component analysis and clustering.
The complete data analysis is described in further detail in the supplemental methods (Section 4), and a computerexecutable transcript of analyses is provided in the form of Rmarkdown files via http://pace. embl. de. Study approval. The study was approved by the Ethics Committee Heidelberg (University of Heidelberg, Germany; S-206/2011; S-356/2013). Patients who donated tumor material provided written informed consent prior to study. detailed list of compounds, see Supplemental Tables  1  and 2. Spebrutinib was obtained from Celgene, LGX818 from Novartis. ROS-targeting agents (MIS-43, SD07, SD51) were provided by A. Mokhir, Erlangen, Germany. Drug response assays. Drug response assays were performed with RPMI-1640 (Invitrogen) supplemented with penicillin/streptomycin (Invitrogen), L-glutamine (Invitrogen), and 10% pooled and heat-inactivated AB-type human serum (RPMI-HS, MP Biomedicals).
The work was supported by the  Helmholtz Virtual Institute "Understanding  and overcoming resistance to apoptosis and therapy in leukemia," the  Helmholtz initiative iMed on Personalized Medicine , the  European Union  ( FP7 projects Radiant ,  Systems Microscopy ,  Horizon 2020  project  SOUND ), the  Hairy Cell Leukemia Foundation , and  ERANET Transcan-2  (project  GCH-CLL-143 ), the  Swedish Cancer Society , the  Swedish Research Council , the  Lion's Cancer Research Foun-.
Plasmids For this study the open reading frame for Snakin-1 (StSN1, GenBank: EF206292. 1) was amplified by PCR employing potato Solanum tuberosum cDNA as template. The primers used for this amplification were as follow: Up: 5'GGATCCGGTTCAAATTTTTGTG3'and Low: 5' AATACAGGATCCTCAAGGGCATTTAGACTT GCC3' , the sequence of BamHI recognition site was added in both primers. The 210 bp amplified sequence was cloned into pGEMT (Promega, USA), sequenced for authentic amplification through commercial sequencing facilities and the sequence of interest (coding for the 63 aminoacid mature peptide) was released with BamHI. The resulting sequence were cloned in the baculovirus transfer vector pFastBac 1 (pFB1; Invitrogen), as a BamHI -BamHI fragment. Surprisingly, positive colonies were the little ones after growing 2 days at room temperature.
0 × 1. 20 WATER UV). The 488 nm line of the Argon laser and the 543 nm line of the HeNe laser were employed for GFP and mCherry excitation, respectively. Scanning was performed in sequential mode to minimize signal bleed-through and fluorescence emission was detected in 498-540 nm for GFP and 610-670 nm for mCherry. The microscope power settings, detectors gain and scanning speed were adjusted to optimize contrast and resolution for each individual image. Co-localizations were analyzed by calculating the Pearson's correlation coefficient with the co-localization module of the Leica LAS AF software. Cell lines and generation of recombinant baculoviruses using the BAC-to-BAC system Spodoptera frugiperda Sf9 cells (IPLBSF21-AE clonal isolate 9) were grown in Grace's insect cell medium supplemented with 10% fetal calf serum (FCS; Gibco-Invitrogen).
5) and then briefly in water before embedding the slides with DAPI DNA staining and PLA signal preserving mounting medium (DUO82040; Sigma-Aldrich). PLA signals were visualized by fluorescent microscopy using light-exciting FITC and acquired via a Coolsnap HQ Camera from a Leica DM6000 microscope using a 40× PL APO 1. 25 oil or 63× PL APO 1. 4 oil objective lens and Metamorph acquisition software. Fig. 1  F was acquired via a Zeiss CCD Axiocam Mrm monochrome from a Zeiss AxioImager Z1 microscope using a 25× Plan Neofluar 0. 8-NA Imm Korr objective lens and Zen software. Images were acquired at 20°C-23°C. Image mounting was done using Omero (University of Dundee and Open Microscopy Environment).
Quantification of PLA and IF signals All the individual signals of a nucleus were recorded for each nucleus using an ImageJ macro (signals in nuclei. ijm, see the supplemental ZIP file). Next, the data were exported to an Excel table in. csv file format. This file was subjected to a script in Galaxy (https ://usegalaxy. org) that adds up all values belonging to a single nucleus to obtain the total signal per nucleus. The workflow can be accessed by typing "intensity-per-nucleus" in the "published workflows" section of Galaxy, or directly uploading the command file ("Galaxy-Workflow-Intensityper-nucleus_IFs-quantification. ga," see Text S1 in the supplemental material). Final values were plotted using GraphPad Prism.
1% Tween 20 in darkness. DNA dye Hoechst was included in the final wash. Brief washing with water was performed before mounting with ProLong Antifade Reagent (P36930; Life Technologies). IF signals were detected using adequate fluorochromes according to antibodies listed in the Antibodies section and acquired via a Zeiss CCD Axiocam Mrm monochrome from a Zeiss AxioImager Z1 microscope with ApoTome technology (Zeiss; see next section for additional information) using a 40× Plan Apochromat 1. 3-NA oil or 63× Plan Apochromat 1. 4-NA oil objective lens and Zen software. Images were acquired at 20°C-23°C. Image mounting was done using OME RO (open microscopy environment).
Acknowledgments We thank  M. Delannoy  and  P. Bertrand  for technical help;  A. D'Andrea ,  T. Huang ,  F. Rosselli , and  Y-L. Lin  for reagents;  R. F. de Luco ,  M-H. Stern ,  R. Kiernan ,  A. Aguilera ,  C. Ribeyre , and  J. Basbous  for suggestions and critical reading; the  Montpellier Rio Imaging facility ;  J. Cau  and  R. Forey  for providing tools for IF quantification; and  P. Pasero  and  B. Pardo  for support. Fondation  ARC pour la Recherche sur le Cancer  ( CT060509 ),  Institut National du Cancer  ( 2015-1-PL BIO-03 ), and  MSD Avenir (GnoStiC)  supported this work. S. Ovejero  is the recipient of a fellowship from  Ligue Contre le Cancer. The authors declare no competing financial interests. Author contributions:  M. Moriel-Carretero ,  S. Ovejero , and M. Gérus-Durand: conceptualization, data curation, investigation, methodology, and writing.
4f ). The GISAXS data was processed using a software developed in-house (see SI for further details)  46. The software enables rapid analysis of a large number of GISAXS data sets. This yields quantitative information on the time-resolved evolution of lattice constants, lattice constant distributions and tilt angles of the nanoparticle superlattices with respect to the substrate. Moreover, by quantification of the coherent and incoherent intensities, it is possible to quantify the relative ratio of nanoparticles in ordered assemblies/superlattices (see Fig. 5a  for schematic illustration). Figure  5  displays a detailed analysis of the four major stages during drop casting of nanoparticle superlattices: dilute dispersion, concentrated dispersion, superlattice formation and growth, and superlattice rearrangement stage.
Conclusion In summary, the formation of highly ordered superlattices composed of oleic acid-coated iron oxide nanospheres could be analysed by in-situ GISAXS. A software developed in-house allows for the fitting of the peaks of large GISAXS data sets yielding superlattice tilt angles, lattice constants, and lattice constant distributions. It is possible to identify four different stages: dilute dispersion, concentrated dispersion, superlattice formation and growth, and superlattice rearrangement stage. We observed the decrease of all lattice constants over time, converging to a near-perfect fcc packing. We found that there is a strong correlation between the formation and growth of nanoparticle superlattices with the movement of the drying front across the dispersion film. The build up of the capillary pressure at the interface between the saturated and partially saturated regions promotes rapid formation of highly ordered superlattices.
SAXS and GISAXS. The in-situ SAXS and GISAXS measurements were carried out at the ID01 beamline at the European Synchrotron Radiation Facility (ESRF). All SAXS and GISAXS experiments were performed at 9. 8 keV photon energy. During the initial evaporation stage, transmission SAXS measurements (i. e. measurements through the droplet) were performed at several different heights for the slow evaporation rate experiments, and at a single position close to the substrate for the fast evaporation experiments (see SI for details). The GISAXS measurements were performed at an angle of θ = 0. 3°, with a beam size corresponding to a footprint of 1mm horizontal by 50 μm vertical. The different scattering geometries are shown in Fig. SI-1. GISAXS data was acquired with intervals of 7 seconds at a 1 second exposure time, limited by the read out of the area gas detector.
The quantitative analysis of the time-dependent scattering patterns was performed using a software developed in-house (see description in the SI)  46. In short, the software is able to fit automatically a large sequence of GISAXS data sets. The model of fit functions used is in very good agreement with the data measured (see Fig. SI-2 ). The software can be accessed for download in the supplement and run under the plot. py software. Available software. The software FITin-situGISAXS can be accessed for download at https://github. com/ science01/FITin-situGISAXS and run under the plot. py software https://sourceforge. net/projects/plotpy/.
Acknowledgements The in-situ GISAXS experiments were performed on beamline ID01 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. L. B. and E. W. acknowledge the  Swedish Research Council  for funding. E. W. acknowledges partial funding from the  FP-7 Nanomag project  ( FP7-NMP-604448 ). Frank Gossen  is acknowledged for assistance in the development and construction of the in-situ cell. We thank  Michael Agthe ,  Sabrina Disch  and  Raphael Herrmann  for the fruitful discussion and  Andras Kovacs  for taking the TEM image.
QUANTIFYING CHANGE FROM CORPUS DATA We worked on the French textual database Frantext  [22] , to our knowledge the only textual database allowing for a reliable study covering several centuries (see Material and Methods and Appendix C 2). We studied changes in frequency of use for 408 forms which have undergone one or several semantic expansions, on a time range going from 1321 up to nowadays. We choose forms so as to focus on semantic expansions leading to a functional meaning -such as discursive, prepositional, or procedural meanings. Semantic expansions whose outcome remains in the lexical realm (as the one undergone by sentence, whose meaning evolved from 'verdict, judgment' to 'meaningful string of words') have been left out. Functional meanings indeed present several advantages: They are often accompanied by a change of syntagmatic context, allowing to track the semantic expansion more accurately (e.
g. way in way too + adj. ); they are also less sensitive to sociocultural and historical influences; finally, they are less dependent on the specific content of a text, be it literary or academic. The profiles of frequency of use extracted from the database are illustrated on Fig. 1  for nine forms. We find that 295 cases (which makes up more than 70% of the total) display at least one sigmoidal increase of frequency in the course of their evolution, with a p-value significance of 0. 05 compared to a random growth. We provide a small selection of the observed frequency patterns (Fig. 2 ), whose associated logit transforms (Fig. 3 ) follows a linear behavior, indicative of the sigmoidal nature of the growth (see Material and Methods). We thus find a robust statistical validation of the sigmoidal pattern, confirming the general claim made in the literature.
2 ). How the latency time is extracted from data is explained in Materials & Methods. One should note that the latency times may be underestimated: If the average frequency is very low during the latency part, the word may not show up at all in the corpus, especially in decades for which the available texts are sparse. The pattern of frequency increase is thus better conceived of as a latency followed by a growth, as exemplified by de toute façon (Fig. 4 ) -best translated by anyway in English, since the present meanings of these two terms are very close, and remarkably, despite quite different origins, the two have followed parallel paths of change. To our knowledge, this latency feature has not been documented before, even though a number of specific cases of sporadic use of the novelty before the fast growth has been no-ticed.
The system, starting from x = 0, is then stuck at the smallest stable fixed point. The transmission time, i. e. the time required for the system to go from 0 to 1, becomes therefore infinite (Fig. 6B ). Above the threshold value γ c , only the fixed point x = 1 remains, so that the new variant eventually takes over the context for which it is competing. Our model thus describes how the strengthening of a cognitive link can trigger a semantic expansion process. Slightly above the transition, a stranglehold region appears where the speed almost vanishes. Accordingly, the time spent in this region diverges. The frequency of the new variant will stick to low values for a long time, in a way similar to the latent behavior evidenced by our dataset. This latency time in the process of change can thus be understood as a near-critical slowing down of the underlying dynamics.
are the same. From the model simulations, data is extracted and analyzed in two parallel ways. On one side, simulations provide surrogate data: We can mimic the corpus data analysis and count how many tokens of the new variant are produced in a given timespan (set equal to M ), to be compared with the total number of tokens produced in this timespan. We then extract 'empirical' latency and growth times (Fig. 7A ), applying the same procedure as for the corpus data. One the other side, for each run we track down the position of the walker, which is the frequency x(t) achieved by the new variant at time t. This allows to compute first passage times. We then alternatively compute analytical latency and growth times ('analytical' to distinguish them from the former 'empirical' times) as follows.
On Fig. 8  there are therefore no data for these growth times. The Inverse Gaussian fit is not perfect, and is not expected to be: The model only predicts the distribution to be of the same family as the Inverse Gaussian. Satisfyingly, among a set of usual distributions (exponential, Poisson, Gaussian, Maxwellian), the Inverse Gaussian proves to be the most adequate for both the growth and the latency (see Appendix A 3 for additional details). The main quantitative features extracted from the dataset are thus correctly mirrored by the behavior of our model. We confronted the model with the data on other quantities, such as the correlation between growth time and latency time, two quantities which our model predicts to be independent. There again, the model proves to match appropriately these quantitative aspects of semantic expansion processes (see Appendix A 4).
It does not involve some binary conversion of individuals towards the new variant; it is a spread within the individuals rather than a spread among them. On the other hand, the S-curve arises in the taking over a single context, and does not rely on a further diffusion over additional contexts to appear. Though the latter spread needs thus not be responsible for the S-shape, it may nonetheless influence the evolution in other ways (e. g. the total duration). The interplay between the specific features of an S-curve and the structure of the conceptual network remains to be investigated. We note, however, that our model may be given a different, purely sociolinguistic interpretation, as discussed in Appendix B 3. Nevertheless, several arguments argue against this interpretation. First, the semantic evolution involves very long timescales, up to several centuries  [41] ; second, societal diffusion, of a new technological device for instance, is associated to a specific scaling law between the steep and duration of the S-curve of -2/3  [43] , which is very different from the behavior of the forms in our dataset, where no scaling law is to be found (the two parameters are related by a trivial -1.
Moreover, as semantic change is seldom driven by external forces such as linguistic reforms, the exponential pattern is not to be expected in this case, and indeed we have not found it in our dataset. Finally, we argue that our results, though grounded on instances of semantic expansion in French, apply to semantic expansion in general. The time period covered is long enough (700 years) to exclude the possibility that our results be ascribable to a specific historical, sociological, or cultural context. The French language itself has evolved, so that Middle French and contemporary French could be considered as two different languages, yet our analysis apply to both indistinctly. Besides, the latency-growth pattern is to be found in other languages; for instance, although Google Ngram cannot be used here for a systematic quantitative study, specific queries for constructions such as way too, save for, no matter what, yield qualitative frequency profiles consistent with our claims.