dataesr/scientific-paragraphs-categorization huggingface dataset:
De novo transcriptome assembly and automatic annotation We used the Trinity program suite (with default parameters and read normalization) to assemble the six ABB libraries and the six ECB libraries into two separate assemblies  [37]. We then combined HiSeq data with 454 data previously obtained for ECB and ABB (details available from  [38] ). We used the EMBOSS splitter program to create overlapping subsequences (length 300 bp, overlap 200 bp) for each of the 454 transcripts of the HiSeq transcript sets. All 454 and HiSeq subsequences were then assembled with Newbler (  [39] , 454 Life Sciences Corporation, v2. 9), with a minimum overlap of 40 bp, a minimum overlap identity of 97% and the 'Het' (for heterozygote) options. Newbler has the option, compared to other assemblers, to reconstitute alternative transcripts of a same unigene (called isotigs and isogroup respectively in Newbler outputs).
ezlab. org/v2/datasets/arthropoda_odb9. tar. gz in January 2018). For functional and GO-term annotations, ECB-ref and ABB-ref genes were compared to NCBI nr (version 2015-04-25) and UniprotKb (  [41]  version june 2015), with blastx BLAST  [42]  and InterProScan  [43]. BLAST analysis revealed that some of the genes probably originated from organisms other than lepidopterans or insects (e. g. microorganisms, see results). We then rechecked the raw reads specifically for the presence of bacterial sequences because these latter had been filtered out before assembly of the ABB-ref and ECB-ref sequences. For that, raw reads were mapped on a subset of bacterial sequences extracted from the NCBI database and counted with the procedure detailed below for ECBref and ABB-ref.
Gene expression in ECB and ABB samples We mapped raw reads onto ABB-ref and ECB-ref with Bowtie2 (  [44] ; v2. 2. 4), using the default parameters and the "sensitive" option. The number of mapped reads per gene was determined with SAMtools (  [45] ; version 1. 2). Gene-specific variations (dispersion and logarithmic fold-changes in reads counts between experimental conditions) were estimated with the Bioconductor 'Edge R' package  ([46] ; version 3. 8. 6). A negative binomial distribution was fitted to count variations, and genes with less than 40 mapped reads in all conditions were excluded from the analysis. Reads counts were normalized for library size and for the total number of genes expressed in each sample, with the EdgeR function calcNormFactors, using the trimmed mean of M values (TMM) method  [46].
e. in plasticity that evolved between ECB and ABB and (D) genes displaying differential expression between environments (plants) and between moth species, but with opposite trends; these genes are involved in the species divergence related to host plant specialization. Let note that category C may also reflect similar stress responses in ECB and ABB to one of the two plants, and category D stress response induced by being reared on non-host plants. Blast2GO (v2. 5, database Sep2015,  [48] ) was used to implement Fisher's exact tests to assess the enrichment of these four categories of genes in particular GO terms. We identified under-and over-represented GO categories after a correction for multiple testing (FDR < 0. 05).
De novo assembly and annotation Search for homology in nucleotide and protein sequence databases yielded significant hits for 67% (ABBref ) and 67. 7% (ECB-ref ) of the genes, 54. 5% (ABB-ref ) and 54. 9% (ECB-ref ) of which had an associated GO term. Most of the annotated genes were homologous to lepidopteran genes or to other insect orders. However, we observed 'alien' genes from plants and microorganisms potentially associated to Lepidoptera. The observation of genes attributed to plant homologs was not surprising, given that the L4 were frozen immediately after sampling, without starvation or gut cleaning. Beside plant residues, we also detected viral, yeast and bacterial sequences. While the target of the sequencing was the expression of moth larvae genes, we did not filtered out these alien genes from the following analyses because of their potential importance in the defense response and more generally in the metabolism of ECB and ABB larvae (more details in the results section "Variation in expression of microorganisms' genes").
Funding This work received support from  ANR Adapt-Ome  ( ANR-13-BSV7-0012-01 ) to RS and DB and from the  INRA department 'Santé des Plantes et Environnement ', both permitting the sampling, the study design, the sequencing and the data analysis of RNA-seq data, and the manuscript writing. In addition,  University of Montpellier  has funded the PhD grant of MO.
Finite Element set-up and models Simulations have been done with LSDYNA software. The complete experimental set-up has been reproduced from the projectile up to anvil support in 2D-axisymetric configuration Figure  12  gives some views of the meshing used. This mesh is a full quadrangular element and a mesh size between 0. 01 (impact area) and 2. 0 mm (outside of impact area). Cowper-Symonds (C. S. )  [6] , Johnson-Cook (J. C. )  [7]  and Zerilli-Armstong (Z. A. )  [8]  strength models have been selected to reproduce specimen behavior in DPD tests. The C. S. includes strain hardening and strain rate effects, the J. C. adds thermal softening with an uncoupling relation and Z. A. includes all those aspects with strain rates and thermal softening coupled.
(2016). The main changes are (1) the inclusion of data to year 2016 (inclusive) and a projection for the global carbon budget for year 2017; (2) the use of two bookkeeping models to assess E LUC (instead of one); (3) the use of dynamic global vegetation models (DGVMs) to assess S LAND ; (4) the direct use of global ocean biogeochemistry models (GOBMs) to assess S OCEAN with no normalisation to observations; (5) the introduction of the budget imbalance B IM as the difference between the estimated emissions and sinks, thus removing the assumption in previous global carbon budgets that the main uncertainties are primarily on the land sink (S LAND ) and recognising uncertainties in the estimate of S OCEAN , particularly on decadal timescales; (6) the addition of a table presenting the major known sources of uncertainties; and (7) the expansion of the model descriptions.
Emissions estimates The estimates of global and national CO 2 emissions from fossil fuels, including gas flaring and cement production (E FF ), rely primarily on energy consumption data, specifically data on hydrocarbon fuels, collated and archived by several organisations  (Andres et al. , 2012). We use four main data sets for historical emissions (1751-2016): 1. Global and national emission estimates from CDIAC for the time period 1751-2014  (Boden et al. , 2017) , as it is the only data set that extends back to 1751 by country. 2. Official UNFCCC national inventory reports for 1990-2015 for the 42 Annex I countries in the UNFCCC (UN-FCCC, 2017), as we assess these to be the most accurate estimates because they are compiled by experts within countries which have access to detailed energy data, and they are periodically reviewed.
3. The BP Statistical Review of World Energy (BP, 2017), to project the emissions forward to 2016 to ensure the most recent estimates possible. 4. The US Geological Survey estimates of cement production (USGS, 2017), to estimate cement emissions. In the following we provide more details in each data set and additional modifications that are required to make the data set consistent and usable. CDIAC. The CDIAC estimates have been updated annually to include the most recent year (2014) and to include statistical revisions to recent historical data (UN, 2017). Fuel masses and volumes are converted to fuel energy content using country-level coefficients provided by the UN and then converted to CO 2 emissions using conversion factors that take into account the relationship between carbon content and energy (heat) content of the different fuel types (coal, oil, gas, gas flaring) and the combustion efficiency  (Marland and Rotty, 1984).
BP's data set explicitly covers about 70 countries (96 % of global emissions), and for the remaining countries we use growth rates from the subregion the country belongs to. For the most recent years, flaring is assumed constant from the most recent available year of data (2015 for countries that report to the UNFCCC, 2014 for the remainder). USGS. Estimates of emissions from cement production are based on USGS (USGS, 2017), applying the emission factors from CDIAC  (Marland and Rotty, 1984). The CDIAC cement emissions are known to be high and are likely to be revised downwards next year  (Andrew, 2018). Some fraction of the CaO and MgO in cement is returned to the carbonate form during cement weathering but this is omitted here  (Xi et al. , 2016). Country mappings. The published CDIAC data set includes 256 countries and regions.
, 2011b). The consumption-based emissions have the same global total but reflect the trade-driven movement of emissions across the Earth's surface in response to human activities. We estimate consumption-based emissions from 1990 to 2015 by enumerating the global supply chain using a global model of the economic relationships between economic sectors within and between every country  (Andrew and Peters, 2013; Peters et al. , 2011a). Our analysis is based on the economic and trade data from the Global Trade and Analysis Project (GTAP;  Narayanan et al. , 2015) , and we make detailed estimates for the years 1997  (GTAP version 5), 2001 (GTAP6), and 2004 , 2007 , and 2011 (GTAP9. 2), covering 57 sectors and 141 countries and regions. The detailed results are then extended into an annual time series from 1990 to the latest year of the gross domestic product (GDP) data (2015 in this budget), using GDP data by expenditure in current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregates Database; UN, 2016) and time series of trade data from GTAP (based on the methodology in  Peters et al.
Using these data, we estimate the change in emissions for the corresponding months in 2017 compared to 2016 assuming no change in the energy and carbon content of coal for 2017. We then use a central estimate for the growth rate of the whole year that is adjusted down somewhat relative to the first half of the year to account for a slowing trend in industrial growth observed since July and qualitative statements from the NEA saying that they expect oil and coal consumption to be relatively stable for the second half of the year. The main sources of uncertainty are from inconsistencies between available data sources, incomplete data on inventory changes, the carbon content of coal, and the assumptions for the behaviour for the rest of the year. These are discussed further in Sect. 3. 2. 1. For the USA, we use the forecast of the US Energy Information Administration (EIA) for emissions from fossil fuels  (EIA, 2017).
In contrast, the BLUE model includes sub-gridscale transitions at the grid level between all vegetation types as indicated by the harmonised land-use change data (LUH2) data set  (Hurtt et al. , 2018). Furthermore, H&N2017 assume conversion of natural grasslands to pasture, while BLUE allocates pasture proportionally on all natural vegetation that exists in a grid cell. This is one reason for generally higher emissions in BLUE. H&N2017 add carbon emissions from peat burning, based on the Global Fire Emissions Database (GFED4s;  van der Werf et al. , 2017) , and peat drainage, based on estimates by  Hooijer et al. (2010) , to the output of their bookkeeping model for the countries of Indonesia and Malaysia. Peat burning and emissions from the organic layers of drained peat soils, which are not captured by bookkeeping methods directly, need to be included to represent the substantially larger emissions and interannual variability due to synergies of land-use and climate variability in Southeast Asia, in particular during El-Niño events.
BLUE uses the harmonised land-use change data LUH2  (Hurtt et al. , 2018)  which describes land-use change, also based on the FAO data, but downscaled at a quarter-degree spatial resolution, considering sub-grid-scale transitions between primary forest, secondary forest, cropland, pasture, and rangeland. The new LUH2 data provide a new distinction between rangelands and pasture. This is implemented by assuming rangelands are treated either all as pastures or all as natural vegetation. These two assumptions are then averaged to provide the BLUE result that is closest to the expected real value. The estimate of H&N2017 was extended here by 1 year (to 2016) by adding the anomaly of total peat emissions (burning and drainage) from GFED4s over the previous decade  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  to the decadal average of the bookkeeping result.
4 %) in 2017 compared to 2016. This is based on estimated growth in coal (+3 %; the main fuel source in China), oil (+5. 0 %), and natural gas (+11. 7 %) consumption and a decline in cement production (-0. 5 %). The uncertainty range considers the spread between different data sources and variances of typical revisions of Chinese data over time. The uncertainty in the growth rate of coal consumption also reflects uncertainty in the evolution of energy density and carbon content of coal. For the USA, the EIA emissions projection for 2017 combined with cement data from USGS gives a decrease of -0. 4 % (range of -2. 7 to +1. 9 %) compared to 2016. For India, our projection for 2017 gives an increase of +2. 0 % (range of 0. 2 to +3. 8 %) over 2016. For the rest of the world (including EU28), the expected growth for 2017 is +1.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights of how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data included in the GCP (2017) release are given at the top of each page in the accompanying database and summarised in.
We thank Ed Dlugokencky, who provided the atmospheric CO 2 measurements used here; Camilla Stegen Landa, Christophe Bernard, and Steve Jones of the Bjerknes Climate Data Centre and the ICOS Ocean Thematic Centre data management at the University of Bergen, who helped with gathering information from the SOCAT community; and all those involved in collecting and providing oceanographic CO 2 measurements used here, in particular for the new ocean data for year 2016 (see Table  A4 ). This is NOAA-PMEL contribution number 4728. We thank the institutions and funding agencies responsible for the collection and quality control of the data included in SOCAT, as well as the support of the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS), and the Integrated Marine Biogeochemistry and Ecosystem Research (IMBER) programme.
Author initials Australia ,  Integrated Marine Observing System (IMOS) BT Australian National Environment Science Program (NESP) JGC, VH EC H2020 European Research Council (ERC)  (QUINCY; grant no. 647204 ). SZ EC  H2020 ERC Synergy grant  ( IMBALANCE-P ; grant no. ERC-2013-SyG-610028 )  DZ EC H2020  project  CRESCENDO  (grant no. 641816 ) PF, RS EC H2020-MSCA-IF-2015  ERC  (FIBER; grant no. 701329 )  BDS EC FP7  project  HELIX  (grant no. 603864 ) PF, RAB, SS  EU   FP7  project  LUC4C  (grant no. 603542 ) PF, MK,  SS French Institut National des Sciences de l'Univers (INSU)  and  Institut Paul Emile Victor (IPEV), NM Sorbonne Universités (UPMC ,  Univ Paris 06 )  German Federal Ministry for Education and Research (BMBF) GR, AK, SVH German Federal Ministry of Transport and Digital Infrastructure (BMVI) AK, SVH German Research Foundation's Emmy Noether Programme  (grant no.
Compound mutations were always confirmed by clonal Sanger sequencing. For ultra-deep sequencing, NPM-ALK kinase domain was amplified from parental and resistant cells using High Fidelity Taq Polymerase (Roche) and NPM-ALK KD primers (Supplementary Table  S1 ). Amplicons were purified from agarose gel and sent to GalSeq for sequencing at 10,000Â mean coverage. Fastq files were aligned onto the reference ALK transcript. Integrative Genomics Viewer (IGV; Broad Institute, Cambridge, MA) was used to visualize the data and annotate variants. Resistant cell-specific mutations were identified by filtering against parental cells' data. Whole-exome sequencing (WES) and RNA-sequencing were performed as described previously  (19). Briefly, genomic DNA was extracted from control and resistant cells using PureLink Genomic DNA Mini Kit (Invitrogen) and sent to GalSeq srl for sequencing at mean coverage 80Â.
Differential gene expression analysis was performed with DESeq2 tool  (22). Functional enrichment for GO biological processes was performed with the Gene Set Enrichment Analysis software  (23). Heatmaps were produced with GENE-E (Broad Institute, Cambridge, MA). Mutations were validated by Sanger sequencing, using the primers described in Supplementary Table  S2. The NGS data discussed in this publication have been deposited in NCBI's Sequence Read Archive and are accessible through accession number PRJNA491639.
Proliferation, apoptosis, and colony assays Cells (10,000/well) were incubated in the presence of the indicated compounds for 72 hours. Cell growth was assessed using the CellTiter 96 AQueous One Solution Cell Proliferation Assay System (Promega). Dose-response curves were generated using GraphPad Prism software. The IC 50 value was calculated as the concentration inhibiting 50% of vehicle-treated control response (absolute IC 50 ). For cells showing a bell-shaped dose-response curve, the IC 50 was calculated relative to the maximal response or peak (relative IC 50 ). The relative resistance (RR) index is defined as the fold shift of IC 50 value as compared with control  (24). Apoptosis was determined after 72 hours, using the eBioscience Annexin V Apoptosis Detection Kit FITC (Thermo Fisher Scientific) and analyzed on a FACSCalibur flow cytometer (BD Biosciences).
For quantification, >300 cells in at least 10 acquired fields were blindly analyzed per sample. Images were acquired using a LSM 710 inverted confocal microscope (Carl Zeiss) and analyzed using a specific macro with ImageJ software to measure the fraction of nuclear ALK mean intensity over the total signal. Statistical analysis was performed using a one-way ANOVA test for K299 and ex vivo-derived cell lines or an unpaired two-tailed t test for HEK-293T-transfected cells.
ALK-independent mechanisms of resistance Activation of PI3K/AKT and RAS/MAPK pathways in ALCL xenografts. To investigate alternative, ALK-independent mechanisms driving resistance in the xenografts that carried ALKi-sensitive mutants and/or showed a significant fraction of wild-type ALK sequence, a global next-generation sequencing (NGS) approach  was taken. Two cell lines harboring the L1196M mutation (BS1 and AS4) and one carrying an N1178H mutation (BD1) were analyzed by whole-exome and mRNA sequencing, using three control xenografts as a reference. Comparative exome data showed an average of 11 (range 7-15) mutations in resistant tumors (Fig. 3D ; Supplementary Table  S3 ). Only two mutated genes (other than ALK) were shared by at least two samples: ESYT3 L296M in BS1 and AS4 cells, carrying the same NPM/ALK mutation; thus, it might represent a passenger substitution in a preexisting L1196M clone.
After 24 hours pG1, PIK3IP1 levels increased 4-fold in AS4 cells (Fig. 4E ). Treatment with the PI3K inhibitor under these conditions led to near complete suppression of cell growth (Fig. 4F ) and concomitant induction of cell death (Fig. 4G  and H ), while pictilisib treatment in nonarrested cells had only modest effects. The second most enriched pathway in our dataset was RAS/MAPK; therefore, we investigated the activity of the MEK inhibitor trametinib in lorlatinib-resistant cells. Singleagent efficacy was comparable in parental and resistant cells (Fig. 4I ). However, trametinib induced a small but significant shift in sensitivity to lorlatinib when used in combination (Fig. 4J ), suggesting that MAPK pathway may partially contribute to resistance to ALK inhibition in these cells. A triple combination of lorlatinib, pictilisib, and trametinib further reduced cell growth, in an additive manner (Supplementary Fig.
Acknowledgments We thank  Pfizer  for providing crizotinib and lorlatinib. This work was supported by  AIRC  ( IG-14249  to  C. Gambacorti-Passerini ;  IG-17727  to  R. Piazza ; fellowship to  M. Ceccon ), by  EU H2020 Marie Skøodowska-Curie action  ( N. 675712 ) and by  Fondazione Umberto Veronesi  (fellowship 2015 to  N. Cordani ). Italian Association for Cancer Research  (grant no. IG-14249  to  C. Gambacorti-Passerini ; grant no. IG-17727  to  R. Piazza ; fellowship to  M. Ceccon );  European Union  (ITN-ETN grant no. 675712  to  C. Gambacorti-Passerini );  Fondazione Umberto Veronesi  (fellowship 2015 to  N. Cordani ). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.
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