dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge the support of the  Swedish Research Council , the  Knut and Alice Wallenberg Foundation , the  Swedish Foundation for Strategic Research , the  Triangle de la Physique  (Grant Agreement No. 2012-032TELISA ),  Laserlab-Europe/LEPP  (Grant Agreement No. 654148 ,  EC's Horizon 2020 ) and  EuCARD2/ANAC2  (Grant Agreement No. 312453 , EC's  7th Framework Programme ). The simulations were performed on resources provided by the  Swedish National Infrastructure  for Computing (SNIC) at Lunarc. SR acknowledges support from the  ERASMUS programme.
Patients The participants were recruited from the Observatoire des Infarctus de Côte d'Or (RICO) survey, a French regional survey of acute MI. Details on the survey have already been published  [10]. In the present study, patients admitted to an intensive care unit between 4th May 2012 and 23rd January 2014 for acute MI within 12 h after symptom onset were included. MI was defined by an increase in serum troponin Ic [Nupper limit of the hospital normal (ULN) range] associated with symptoms of ischemia and/or typical ECG signs  [11]. ST-segment elevation MI (STEMI) was defined as chest pain lasting for ≥ 20 min with typical ECG changes including ≥ 1 mV ST segment elevation in two or more limb leads or ≥ 2 mV in two or more contiguous precordial leads. We collected data on patients' age, sex, cardiovascular risk factors (history of hypertension or treated hypertension, known history of diabetes, treated hypercholesterolaemia, body mass index (kg/m 2 ), current smoking (within three months)) and chronic treatments.
Biological data Blood samples were drawn on admission (Median time from symptom onset to blood sampling: 16 (8-30) hours). C-reactive protein (CRP), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) concentrations were measured on a Dimension analyzer (Dade Behring, Newark, NE). The level of lowdensity lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula  [12]. Plasma glucose concentrations (enzymatic method (glucose oxidase)) and creatinine levels were measured on a Vitros 950 analyzer (Ortho Clinical Diagnostics, Rochester, NY) and creatinine clearance was calculated with the Cockcroft formula. Glycated hemoglobin A1c (HbA1c) was measured with ion exchange HPLC (Bio-Rad Laboratories, Richmond, CA).
Metabolic pathways are similar in M. persicae and A. pisum A global analysis of the metabolism enzymes of M. persicae was generated based on the annotated gene models (Additional File 3) and is available in the ArthropodaCyc metabolic database collection (http://arthropodacyc. cycadsys. org/) (Baa-Puyoulet et al. , in press). Metabolic reconstruction in A. pisum has highlighted the metabolic complementarity between the aphid and its obligate bacterial symbiont, Buchnera aphidicola, with the symbiont generating essential amino acids for the aphid  [25]. We compared the amino acid metabolism pathways identified in the two clones of M. persicae with those previously identified in A. pisum  [25, 26]. A. pisum and the two M. persicae gene sets share 170 enzymes belonging to known amino acid metabolism pathways.
Relative divergence times were then estimated based on this topology with RelTime  [29]  (Figure  1 ). With the exception of the placement of Pediculus humanus, all phylogenetic relationships received maximum support and are in agreement with a recently published large-scale phylogenomic study of insects  [30]. Annotation of the M. persicae genome reveals a gene count approximately half that of the specialist aphid A. pisum, and similar to that of other insect species (Figure  1 ), implying the massive increase in gene content observed in A. pisum  [26]  may not be a general feature of aphid species. Using our comparative dataset, we find that the larger gene count of A. pisum compared to M. persicae is explained by two features, an increase in lineage specific genes and widespread duplication of genes from conserved families (Figure  1 ).
05) compared to those reared on the At_dsGFP control plants (Figure  5B ) indicating that the cathepsin B genes contribute to M. persicae ability to colonise A. thaliana. To examine the impact of cathepsin B on the ability of M. persicae to adjust to host change, the cathB-RNAi aphids were transferred from At_dsCathB lines to nontransgenic A. thaliana and N. benthamiana plants and examined for survival and fecundity. In agreement with previous data  [38] , we found that the genes targeted by 1 8 RNAi remain down regulated at 2 days upon transfer from At_dsCathB lines to nontransgenic plants (Additional File 19: Figure  S9 ). Upon transfer to A. thaliana, the cathB-RNAi aphids had lower survival and reproduction rates than the dsGFPexposed (control) aphids (Figure  5C, E ). In contrast, no decline in survival and reproduction was seen of the cathB-RNAi aphids compared to the dsGFP-exposed aphids upon transfer to N.
Genome assembly and annotation Full details of genome assembly, annotation and quality control are given in Additional File 2. Briefly, the genomes of M. persicae clones G006 and clone O were independently assembled using a combination of short insert paired-end and matepair libraries (Additional File 1: Table  S1 ). Clone G006 was assembled with ALLPATHS-LG  [64]  and Clone O with ABySS  [65]  followed by scaffolding with SPPACE  [66]  and gapclosing with SOAP GapCloser  [67]. Repetitive elements were annotated in both genomes with the REPET package (v2. 0). We then predicted protein coding genes for each genome using the AUGUSTUS  [68]  and Maker  [69]  gene annotation pipelines using protein, cDNA and RNA-Seq alignments as evidence. A set of integrated gene models was derived from the AUGUSTUS and Maker gene predictions, along with the transcriptome and protein alignments, using EVidenceModeler  [70].
One hundred rapid bootstrap replicates were carried out followed by a thorough ML tree search. As the focus of the present study is not on estimating absolute dates of divergence we used RelTime  [29]  to estimate the relative divergence times for species using the RAxML topology to generate an ultrametric phylogeny to use in the comparative analysis. RelTime has been shown to give relative dates of divergence that are well correlated with absolute divergence times derived from the most advanced Bayesian dating methods and is computationally tractable with large genomic datasets  [29]. We estimated relative divergence times for species treating the supermatrix a single partition. RelTime was run with an LG model of protein evolution and the few clocks option (clocks merged on 2 std. errors).
When multiple transcripts of a gene were present in the proteome the sequence with the longest CDS was used. Paralogous gene pairs were retained if they aligned over at least 150 amino acids with a minimum of 30% identity  [76]. For each protein only the nearest paralog was retained (highest scoring BLASTP hit, excluding self hits) and reciprocal hits were removed to create a non-redundant set of paralog pairs. For each paralog pair a protein alignment was generated with muscle v. 3. 8. 31 [72]. These alignments were then used to guide codon alignments of the CDS of each paralog pair using PAL2NAL  [77]. From these codon alignments pairwise d N and d S values were calculated with paml v4. 4 using YN00  [78]. Paralog pairs with d S > 2 were excluded from our analysis as they likely suffer from saturation.
Acknowledgements We thank  Brain Fenton  for help with genotyping M. persicae clone O,  Linda M. Field  for being a co-investigator on the Capacity and  Capability Challenge  ( CCC-15 ) project that funded the first round of genome sequencing of M. persicae clone O. We are grateful to  Ian Bedford  and  Gavin Hatt  ( JIC Insectary ) for rearing and care of aphids and the  John Innes Horticultural Services  for growing the plants used in this study. Next-generation sequencing and library construction was delivered via the  BBSRC National Capability in Genomics  ( BB/J010375/1 ) at the  Earlham Institute (formerly The Genome Analysis Centre ), Norwich, by members of the  Platforms and Pipelines Group.
CpG position selection in the DRD4 gene for pyrosequence assay design A CGI motif was searched in the DRD4 genomic region (GenBank Accession no. DQ006802) using CpGFinder (Softberry, USA) with the base pair numbering set to 1 on the transcription start site. Within the DRD4 gene, four regions were chosen that contained high densities of CpG dinucleotides within a short range and had enough surrounding sequence variety for primer design (Fig. 1 ). The DRD4 5 0 upstream region was too dense with CpGs and primer design proved impossible. The PYROMARK ASSAY DESIGN SW 2. 0 (Qiagen) was used to design the pyrosequence assays, which contain two PCR primers of which one is labelled with Biotin and one sequence primer opposite of the Biotinylated primer. The selection lines are still highly variable, and SNPs or deletions/insertions could lead to out-of-frame sequencing and were avoided as much as possible when developing the assays.
The methylation percentage per CpG position was determined using the PYROMARK Q24 ADVANCE software (Qiagen). In short, pyrosequencing produces quantitative measures of DNA methylation based on a sequencing-by-synthesis method  (Tost & Gut 2007). The DNA methylation percentage is assessed by the ratio of real-time incorporated C and T nucleotides, through the conversion of released pyrophosphate into a light signal. In assays B, C and D, we included a control step to determine the completeness of bisulphite conversion (Table  S2 , Supporting information). We sequenced each individual multiple times for each assay. The PYROMARK Q24 ADVANCE software checked reliability of the results, and samples containing CpG sites marked as unreliable methylation results by the software were rerun. CpG sites that were again marked as unreliable after rerunning were excluded.
Acknowledgements We are grateful to  Torsten Pl€ osch (UMCG)  and  Sven van den Elsen  and  Hans Helder  ( WUR ) for allowing us to use of their pyrosequencers. Hakan Baykus  (Qiagen) assisted with pyroassay optimization. We thank  Marcel Visser  for input on draft versions of the manuscript. This work was financed by the  Netherlands Institute of Ecology (NIOO-KNAW ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
V. EXPERIMENTAL RESULTS In the experiments, a front-end compiler named GECOS  [35]  is used in our design flow to transform the source code to DFGs. After transformation, the subgraph enumeration step presented in  [5]  is carried out for enumerating all the subgraphs satisfying I/O constraints (maximum number of I/O) as custom instruction candidates. Then, the heuristic algorithms discussed in the previous section are applied to select the subgraphs. All the heuristic algorithms are implemented in Java and run on a Intel i3-3240 with (3. 4 GHz) with 3. 4 GB RAM. We compare the five standard heuristic algorithms in terms of search time and quality of the results when considering the same amount of solutions. Then, we compare the ACO algorithm and the PSO algorithm with the proposed adaptive local optimum search with the standard ACO algorithm and the standard PSO algorithm respectively.
Patient and Tumor Characteristics Data from a total of 144 patients tested for EGFR and KRAS mutations between January 2009 and December 2010 at MOTI were obtained. Among these patients, two were excluded because of mutations on both EGFR and KRAS genes. Out of the 142 remaining patients (48 females and 94 males), 140 (98. 59%) were Caucasian and two were Asian (1. 41%). Patient and disease characteristics are summarized in Table  1. With respect to metastatic status, 90 patients (63. 38%) had only one metastasis, 81 patients (57. 04%) had BM and 64. 2% of BM patients had synchronous BM. Sixteen tumor samples (11. 27%) were positive for EGFR mutations (seven different types of EGFR mutation were found: one in exon 18, four in exon 19 and two in exon 21) and 47 (33. 10%) tumor samples were positive for KRAS mutations (seven different types of KRAS mutations were found: five in codon 12 and two in codon 13).
Conversely, despite having a higher incidence of BM, patients with EGFR-mutant NSCLC have a better prognosis. The population studied was a homogeneous population of stage IV non-squamous NSCLC patients, representative of other European cohorts, with a very large majority of Caucasian patients. The frequency of KRAS-mutant (34%) and EGFR-mutant (12. 5%) tumors was indeed similar that observed in other Caucasian populations of advanced NSCLC patients  [20, 21]. Furthermore, we analyzed data not only from patients enrolled in clinical trials, but also from all patients diagnosed in routine practice. For this reason, the population studied here is more representative of NSCLC patient than other studies where stringent exclusion criteria were applied. Because of the high incidence (approximately 50% of advanced lung cancer patients  [3] ) and contribution to mortality  [4] , of BM, a better characterization of the molecular profile associated with BM incidence and prognosis in NSCLC patients was needed.
Patients and Data Collection Patients diagnosed at the Multidisciplinary Oncology and Therapeutic Innovations department (MOTI) of Aix Marseille University Hospital from January 2009 to December 2010 with metastatic non-squamous non-small cell lung cancer (ns-NSCLC) were included. In this study, inclusion criteria were the following: ns-NSCLC, stage IV at diagnosis, age above 18 years, and EGFR and KRAS mutations testing. Exclusion criteria were squamous cell carcinoma and/or no molecular testing. Patients with both EGFR and KRAS mutant tumors were also excluded this is an uncommon molecular profile that could not have been analyzed separately because of the small size (n = 2) of this population and would have induced statistical bias if they were analyzed twice with the other groups of patients with a single mutation.
High Resolution Melting (HRM) PCR amplification and HRM analysis from genomic DNA were carried out on LightCycler ® 480 (Roche Diagnostics, Meylan, France). Amplification consisted on 10 min at 95 • C, followed by 40 cycles of 10 s at 95 • C and 20 s at 65 and 50 • C for EGFR and KRAS, respectively, and 30 s at 72 • C for elongation. PCR was then carried out on LightCycler ® 480. Mutant cell lines, wild-type DNA (placenta), and PCR negative controls were included together with patient samples. Temperature ramping (from 60 to 95 • C, rising by 0. 02 • C/s) and fluorescence acquisition settings were recommended by the manufacturer. HRM curves were normalized for each sample, and compared with wild-type genomic DNA using LightCycler ® 480 gene-scanning software  [39].
Statistical Analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 20. 0 (IBM SPSS Inc. , Chicago, IL, United States of America (USA)). Continuous variables were expressed as the mean ± standard deviation (SD) or as median with range (min, max), and categorical variables were reported as count and percentages. Comparisons of mean values between two groups were performed using Student's t-test or Mann-Whitney U test. Comparisons of percentages were performed using Chi-Square test or Fisher's exact test, as appropriate. Independent-sample t-tests and one-way analysis of variance were used to compare continuous variables between more than two groups. Time-to-event endpoints were estimated by the Kaplan-Meier method and compared using the log-rank test. OS was defined as the time from diagnosis to death from any cause, censored at the date of last follow-up.
This is the case for the ORCHIDEE-STICS model (de  Noblet-Ducoudre et al. , 2004; Gervois et al. , 2004) , the LPJmL model  (Bondeau et al. , 2007) , the SIBcrop model  (Lokupitiya et al. , 2009) , the Jules-sucros model  (Van den Hoof et al. , 2011) , the ANTHRO-BGC model  (Ma et al. , 2011) , the CESM1 model  (Levis et al. , 2012)  or the CLM model  (Bilionis et al. , 2015). New model developments for croplands need to be evaluated especially against site data like eddy-flux measurements. In addition, agricultural information such as the crop variety used, the sowing date or the fertilization practices are poorly known at regional to global scales. Consequently, it is of interest to investigate the sensitivity of the modelled fluxes to these management practices in order to prioritize further actions aiming at increasing the quality of management datasets.
Based on sensitivity analyses, they concluded that any change in the intensity of each management practice considered in the study (fertilization, irrigation, crop calendar) changed the annual budget of NEE by more than 15%. Currently, ORCHIDEE-STICS has only been tested at one wheat site, regarding its performance at simulating the seasonal LAI and C flux variations. Consequently, there is a need for better evaluating ORCHIDEE-STICS against CO 2 flux data across many sites where wheat is cultivated and under contrasted climatic conditions, a necessary condition for more realistic future spatially explicit applications. The objectives of the study are: 1. To evaluate the capacity of the ORCHIDEE-STICS model to simulate the seasonal variations and annual budget of NEE and of its components (GPP and TER) over seven CarboEurope-IP wheat sites where carbon fluxes are monitored by eddycovariance technique.
These variety-specific parameterizations are summarized in Appendix A. 2. 1. 1. 2. ORCHIDEE component. The ORCHIDEE model  (Krinner et al. , 2005)  calculates CO 2 , H 2 O and heat fluxes, for diverse vegetation types (plant functional types, PFTs) at scales going from point to global gridded simulations. This model is mainly applied for carbon budgeting and carbon cycle applications. It contains a biophysical module calculating photosynthesis, water and energy balance calculations on a 30-min basis, and a carbon dynamics module dealing with phenology, growth, allocation, mortality and soil organic matter decomposition, on a daily basis. Carbon exported via leaching/imported from organic manure is not accounted for in ORCHIDEE. Carbon assimilation by photosynthesis is done per canopy layer, based on the formulas developed by  Farquhar et al.
5 C and 5. 0 hPa, respectively. If for a gap, no similar conditions were available, the averaging window was increased to AE14 days and similar conditions were defined only based on R g or simply the measurement time (see  Reichstein et al. , 2005 for details). An uncertainty of between 4% and 6% is expected due to gap-filling procedure  (Beziat et al. , 2009; Ammann et al. , 2009). The percentage of gap-filled half hourly data exceeds 50% on an annual basis at two sites (LAM and OEN) over the seven studied sites but more than 90% of the NEE data at any site were classified as "original value" (quality class 0) or "most reliable gap-filled" (quality class 1)  (Sus et al. , 2010). 2. 2. 3. 2. NEE partitioning into gross fluxes. The partitioning of NEE into GPP and TER fluxes was obtained from the CarboEurope-IP data portal.
LAI and AGB The model performance in simulating the seasonal dynamic of LAI varied across sites (Fig. 2 ). The modelled LAI matched well the amplitude and phase of the observations only at OEN (RMSE = 0. 6 m 2 m À2 and FMT = 0. 8) and to a lesser extent at KLI (RMSE = 1. 0 m 2 m À2 and FMT= 0. 6 ). However, one should note that there is no LAI data at OEN after the beginning of May. Note also that modelled LAI is not directly comparable to observations, ORCHIDEE-STICS representing only green LAI while total LAI is measured at site. At AUR, LAM and GRI, modelled crop emergence was delayed by a week to a month, which led to a FMT value of 0. 4 for these three sites. In addition, modelled maximal LAI over the growing season was overestimated at AUR and LAM by up to 4 m 2 m À2 (Fig. 2 ), which translated into the highest RMSE values ($2.
LUT exhibited an opposite behaviour to AUR, LAM and GRI, with an early bias of modelled GPP in spring. This GPP bias was not explained by an LAI bias of the same sign (compare Figs. 2  and 3 ). For three sites among the four at which there was a modelled GPP early-bias at the start or in the end of the growing season (AUR, LAM, LUT), this model/data mismatch during the early and late growing season period corresponds to the one-month critical period (area shaded in grey in Fig. 3 ). At the four other sites, we identified the one-month critical periods in April 2007 at the OEN and GEB sites (model underestimation of GPP), from mid May to mid June 2006 for KLI (model overestimation of GPP) and in May 2006 for GRI (model overestimation of GPP). All these critical periods occur in the middle of the growing season.
Overall, the dynamic of the latent heat flux over the growing season (see Fig. D1  in Appendix D) followed the one of the GPP, which shows the strong link between carbon and water fluxes via the stomatal conductance. In particular, the latent heat flux was strongly overestimated by the model at LAM in June 2007 as it was for GPP. Note, however the rather strong model/ data disagreement for the latent heat flux at OEN, where the model did not underperform at simulating GPP compared to other sites. Although all the one-month long critical periods of large modeldata misfits occurred during the growing season (between sowing and harvest), model/data differences were also significant outside of the growing season. This was notably the case after the harvest: in ORCHIDEE-STICS, there is no more assimilation (LAI equals zero) while in reality, re-growth (volunteer or not) induces GPP (see for instance AUR, GRI or OEN sites in Fig.
3 ) as observed by  Aubinet et al. (2009)  and  Beziat et al. (2009). In order to summarize the performances of different model simulations, the Taylor diagrams  (Taylor, 2001 ) that provide a graphic representation of the model/data correlation coefficient (R) and of the normalized Standard Deviation (NSTD) are shown in Fig. 4. The R-values of daily GPP were always higher than 0. 8, with 5 sites out of 7 exhibiting an R-value higher than 0. 9. These R-values were higher than those reported by  Li et al. (2011)  over maize sites (R-values range from 0. 56 to 0. 90). Observed GPP day-to-day amplitude was also well reproduced by the model, the distance between model and data NSTD generally not exceeding 0. 2 except for two sites (KLI and LAM, 0. 23 and 0. 56 respectively).
Overall, the mean modelled AR to GPP ratio averaged over the 7 sites equalled 0. 4. It is quite comparable with in-situ estimates that range from 0. 3 and 0. 6  (Amthor, 2000a; Cannell and Thornley, 2000; Thornley and Cannell, 2000; Aubinet et al. , 2008). Thus, some of the model/data mismatches for TER may be directly attributed to GPP mismatches. This was the case at LAM in June 2007 or at LUT from mid-May to mid-June 2007 (middle column of Fig. 3 ). The one-month critical periods associated to large model-data misfits of GPP and TER coincided exactly only at GRI and LAM, leading to the hypothesis that there are other TER specific mechanisms that explain the discrepancies with the data at other sites. For three sites, the onemonth critical period of high TER model-data misfit occurred once biomass was senescent before (OEN and LUT) or just after harvest (AUR).
3 ). At most of the sites, the critical period defining large model-data misfit for NEE was the same to those identified for either GPP or TER: At LUT, the critical periods were the same for NEE and GPP; at AUR, KLI, OEN and GEB, the critical periods were the same for NEE and TER; at LAM, the critical periods were the same for NEE, GPP and TER. It was only at GRI, that the critical period for NEE differs from those identified for GPP and TER. The values of the NSTD metrics for daily NEE were as good as those obtained for GPP, and R-values for NEE and TER are similar (see Fig. 4  and Table  C1  in Appendix C). Correlation coefficients (R) between simulated and modelled NEE are higher in the present study (mean R = 0. 75) than those (mean R = 0. 55) obtained for maize sites by  Li et al. (2011)  with the same model.