dataesr/scientific-paragraphs-categorization huggingface dataset:
Piecewise linear decision tree The Piecewise linear decision tree approach (Cubist) is based on the M5 algorithm  (Quinlan, 1992). It partitions the dataset into several subsets within which inputs (independent variables) are similar. In a given subset, the standard deviation of the target values is treated as a measure of error and is used as a node splitting criterion. Every potential split is evaluated by the reduction in standard deviation. After evaluating all possible splits, Cubist chooses the one split that maximizes the reduction in error. Then, pruning and smoothing processes are performed to get the final model. More details are given in  Quinlan (1992). In the final Cubist model, partitions are defined by a list of rules, which are arranged in a hierarchy. Each rule has the following form: if [condition] then [linear regression model] else [apply next rule].
Residual-based piecewise linear decision tree The Mean Square Error (RMSE), and Bias, were used to evaluate prediction accuracy. ME = 1 - ∑ (𝑧̂ -𝑧 ) ∑ (𝑧 -𝑧̅ ) (8) RMSE = 1 𝑛 (𝑧̂ -𝑧 ) (9) Bias = 1 𝑛 (𝑧 -𝑧 ) ( 10 ) where n is the size of the cross-validation dataset,  and  are measured and predicted values for the i-th observation in the cross-validation dataset, respectively, and ̅ is the mean of the observations in the cross-validation datset. A negative ME means that the model performs worse than using the average of the observations as a prediction.
The effect of national SOC maps on model averaging The IGCS map was generated using the entire IGCS dataset (about 30,000 soil profiles), which is very large and hence is an example of a case study in a datarich country (1 profile per 18 km 2 ). To assess the usefulness of model averaging in data-poor situations, we applied model averaging to a case in which the national SOC map (IGCS) was generated from a much smaller number of soil profiles. To do so, we generated IGCS SOC maps by randomly selecting 10,000, 5,000, 1,000, 800, 600, 400, and 200 soil profiles from the whole IGCS dataset. To filter out random sampling effects, we repeated this procedure 100 times for each sample size and reported the average results. These IGCS SOC maps with LUCAS and SoilGrids were finally merged only with the best model averaging approach and using the minimum necessary number of calibration sites as previously estimated.
Summary of IGCS, RMQS, and LUCAS datasets Table  1  summarises SOC statistics of the IGCS, RMQS, and LUCAS (located in France) datasets. About 80% (24,596) of IGCS soil profiles were located in arable soils, and 20% (5,785) were located in forest and permanent grassland soils. In the IGCS soil database, grassland and forest soils (mean SOC of 24. 88 g kg -1 ) had higher SOC values than arable soils (mean SOC of 16. 66 g kg -1 ). Nearly half (985) of the RMQS sampling sites were located in permanent grasslands or forest soils, and the remaining half (1011) were under arable soils. In the RMQS dataset, the mean SOC was 18. 19 g kg -1 for arable soils and 35. 51 g kg -1 for permanent grassland and forest soils. LUCAS observations had a mean SOC of 26. 20 g kg -1 for permanent grassland and arable soils.
Evaluation of SOC maps from IGCS, LUCAS, and SoilGrids datasets The IGCS SOC map has the lowest RMSE (18. 86 g kg -1 ) and highest ME (0. 25) among the three SOC maps (Fig. 5 ). The negative Bias (-6. 17 g kg -1 ) indicates that SOC is underestimated in the IGCS SOC map. When the performance of the IGCS SOC map for arable and forest/grassland soils was separately evaluated, arable soils (ME of 0. 19 and RMSE of 10. 02 g kg -1 ) were found to have higher accuracy than forest/grassland soils (ME of 0. 09 and RMSE of 24. 85 g kg -1 ). SOC maps of LUCAS and SoilGrids have a much higher RMSE of 30. 62 and 32. 75 g kg -1 , and a negative ME of -1. 18 and -1. 27, respectively. Positive Bias of LUCAS (6. 73 g kg -1 ) and SoilGrids (21. 81 g kg -1 ) showed that these two maps overestimated SOC. The overestimation was larger in SoilGrids than in the LUCAS SOC map.
Performance evaluation of SOC maps from IGCS, LUCAS, and SoilGrids The IGCS SOC map had the best performance indicators among the three source SOC products. However, it showed a slight overall underestimation and a clear tendency to underestimate large SOC values. This may be because the calibration data for generating the IGCS SOC map are dominated by cultivated soils (80% of IGCS dataset), which typically have low SOC values because of management practices (Table  1 ). As natural soils occupy 45% of the total area of mainland France  (Chen et al. , 2018) , high SOC values are under-represented in the dataset for producing the IGCS SOC map. It consequently resulted in underestimating the effect of some controlling factors driving high SOC values (e. g. , forest or grassland land uses, high elevations).
Although the effects of land use and elevation are still clearly visible (Fig. 2a ), the spatial patterns of the resulting map are too smooth, as was already described by  Mulder et al. (2016a; 2016b). In the French GlobalSoilMap product,  Mulder et al. (2016a)  produced national SOC maps at the first three depth intervals (0-5, 5-15, and 15-30 cm) using both IGCS and RMQS data. The ME evaluated using 10-fold cross-validation ranged from 0. 26 to 0. 36 for the first three depth intervals. This shows that including RMQS data into national SOC modelling improves model performance. Nevertheless, SOC was still slightly underestimated because the IGCS dataset is almost 15 times larger than the RMQS dataset and IGCS data generally have low SOC content (Table  1 ). The predictive performance of the LUCAS map and SoilGrids map was much worse than that of the IGCS map, as illustrated in Fig.
Potential and limitations of model averaging approaches Our results demonstrate the ability of model averaging approaches to improve national SOC maps (Fig. 5  and Fig. 6 ). The improvement strongly depends on the calibration sample size used for model averaging. It is encouraging that 200 spatially stratified samples (1 sample per 2,500 km 2 ) were enough for producing a sufficiently accurate national SOC map (ME of 0. 37 for BC-VW approach) when applying model averaging in France. Note also that the performance of this SOC map is comparable to that of the GlobalSoilMap SOC map using IGCS and RMQS datasets  (Mulder et al. , 2016a). We note that we did not map the uncertainty of SOC predictions when applying model averaging. Prediction uncertainty should be considered in future studies because it is crucial for assessing model quality and robustness.
Moreover, the added value of model averaging is larger than that of only increasing the number of profiles used for producing the IGCS SOC map. For example, using 200 samples for model averaging calibration results in an ME increase of 0. 12, whereas the ME only increases by 0. 07 when the number of profiles used for producing the IGCS SOC map increases from 200 to 10,000. This indicates that adding a relatively small regular grid of soil samples to merge several maps might be more efficient than expanding the database with a large number of soil samples for which the sample locations are not controlled. In many countries, soil mapping activities are frequently guided by local needs and interests. This explains why national soil datasets are often clustered and why adding more legacy data may sometimes lead to increasing sources of bias (e.
g. ,  Poggio et al. , 2019). Overall, our study advocates merging predictions in both data-rich and data-poor situations and demonstrates that the added value of merging is relatively higher in data-poor situations. However, notably, the performance of BC-VW drops substantially when excluding the IGCS SOC map and when it only uses LUCAS and SoilGrids for model averaging. This indicates the importance of a national SOC map in model averaging, even if this SOC map is produced with a small dataset (i. e. 200 samples).
Conclusion We tested the ability of five model averaging approaches for improving existing SOC maps by merging national, continental, and global SOC products. All five model averaging approaches could improve the national SOC map when more than 100 soil samples were used for calibration of the model averaging approaches. The BC-VW approach performed better than the other four approaches. Model averaging approaches using a rather small calibration dataset (i. e. 200 observations uniformly spread over mainland France) for calibration proved to be efficient. The national SOC map was very important and drove performance when merging all SOC maps, however SoilGrids and LUCAS SOC maps had added value by capturing relevant patterns additional to the national SOC map. By reducing the number of national soil samples in France for producing the national SOC map, we found that merging maps using model averaging is also applicable to data-poor situations and might thus be attractive to data-poor countries, provided sufficient soil data are available for calibration of the model averaging approach.
Summary statistics of SOC content (g kg -1 ) in topsoil (0-20 cm) for IGCS, 778 RMQS and LUCAS datasets. 779 , dataset size ; Min. , minimum; Q1, first quantile; Q3, third quantile; Max. , maximum; Sk. , skewness; 780 SD, standard deviation. * F, forest; G, permanent grasslands; A, arable.
Acknowledgements Soil data collection was supported by the  French Scientific Group of Interest  on soils: the  GIS Sol , involving the  French Ministry of Ecology , the  French Ministry of Agriculture , the  French Environment and Energy Management Agency (ADEME) , the  French Institute for Research and Development (IRD) , the  French National Geographic and Forest Inventory Institute (IGN)  and the  French National Institute for Agronomic Research (INRA). This work was partly funded by the  Environment & Agronomy Department of INRA , in the framework of its calls for innovative research (grant no. 6282 ), and parly funded by the project  Coordination of International Research Cooperation on soil CArbon Sequestration in Agriculture (CIRCASA ) (grant no. 774378 ) under  H2020-EU. 3. 2. 1. 1.
Database and etiologies A prospective and registered multicenter database (ETIS Registry, NCT03776877) was used to export the data of the AIS for each patient after a 3-month follow-up. Clinical data, such as age, sex, baseline and 24-hour National Institutes of Health Stroke Scale (NIHSS) score were collected for each patient. Other variables such as the occlusion site, the IV tPA administration, the MT technique, the final modified Thrombolysis in Cerebral Infarction score (mTICI) and the parenchymal hemorrhage after 24-hours were also collected. An extensive research for the etiology of the AIS was performed. The presence of atrial fibrillation (AF), dissection and atheroma was verified using normal ECG or ECG Holter when required, carotid Doppler ultrasound, CT angiography/MR angiography, and DSA.
First, a univariate analysis was performed, and only factors with a P value. 25 were selected for the multivariate analysis. All statistical tests were 2-sided, and the significance level was set at p = 0. 05. We calculated the diagnostic values for the SVS (sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], accuracy rate) to predict a Red/Red-Black type thrombus. Statistical analysis was performed by using R statistical and computing software, Version 3. 3. 1 (http://www. r-project. org/) using the caret (https://cran. r-project. org/ web/ packages/caret/index. html) package.
Methods GPS data. We use 59 GPS daily time series from the final Geodesy Advancing Geosciences and EarthScope combined solution  28  available from the UNAVCO data center and added 3 stations (ELIZ, WOST, GLDR) from the Pacific Northwest Geodetic Array of the Central Washington University, north of Vancouver island where coverage is sparse. The location of the 62 used stations is shown in Supplementary Fig. 1. We remove outliers and seasonal signals over the whole time series. We detrend the time series, so that the velocity during the weeks before and after the 2013 event is zero. This procedure ensures that the transient displacement recorded at the GPS sites reflects positive reverse slip at the plate interface. Regional common mode motion evaluated by stacking time series for 10 sites with complete data and good repeatability located 100-300 km away for the slip area was found to be negligible.
Acknowledgements We built our fault geometry based on the slab2. 0model available online at the following address: https://www. sciencebase. gov/catalog/item/5aa1b00ee4b0b1c392e86467/. We thank  A. Wech ,  K. Creager , and PNSN for letting their tremor catalog available (https:// tremor. pnsn. org). We downloaded the GPS time series from the UNAVCO website (https://www. unavco. org). We used the Generic Mapping Tools GMT5 (https://gmt. soest. hawaii. edu/). We thank  Jean-Paul Ampuero  and  Martin Vallée  for helpful discussion. This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)   ANR-15-IDEX-01  and by the  ANR research grants E-POST , REMAKE, and  S5.
Furthermore, similarities between RNA degradosome foci and eukaryotic mRNP granules suggest a conserved requirement for compartmentalization of these structures, a feature that is most likely central in global post-transcriptional regulation and response to stresses both in prokaryotic and eukaryotic organisms. chosen based on phylogenetic diversity as previously described  [101] , using the PubSEED database  [102]. Details are provided in supplemental Table  S1  and the results are also available in the 'RNAse_2019_Minimal' subsystem on the public PubSEED server (http://pubseed. theseed. org/SubsysEditor. cgi). -Some RNA degradosomes seem to be compartmentalized at the membrane, which could affect their activity. -Some bacterial RNA degradosomes show similarities with eukaryotic mRNP granules at the level of their properties and their physicochemical properties.
Figures were generated with images from Servier Medical Art (https://smart. servier. com/), licensed under the Creative Commons Attribution 3. 0 Unported License (http://creativecommons. org/license/by/3. 0/). We thank the "Fondation pour la Recherche Médicale" for the support to the Institut Pasteur laboratory (grant DBF20161136767 to HDR). The degradosome studies of the Institut Pasteur laboratory are supported by the Pasteur-Weizmann Consortium of "The Roles of Noncoding RNAs in Regulation of Microbial Life Styles and Virulence". The work of V dC-L was partially funded by the National Institutes of Health (R01 GM70614).
Figures Figures were generated with images from Servier Medical Art (https://smart. servier. com/), licensed under the Creative Commons Attribution 3. 0 Unported License (http://creativecommons. org/license/by/3. 0/). We thank the "Fondation pour la Recherche Médicale" for the support to the Institut Pasteur laboratory (grant DBF20161136767 to HDR). The degradosome studies of the Institut Pasteur laboratory are supported by the Pasteur-Weizmann Consortium of "The Roles of Noncoding RNAs in Regulation of Microbial Life Styles and Virulence". The work of V dC-L was partially funded by the National Institutes of Health (R01 GM70614).
We do not find tRNA genes to be individually regulated in cells proliferating at different rates, in agreement with a few reports  [29, 39] , but not with an initial study comparing the proliferation/ differentiation state of cells  [24]. Profiling of tRNA expression being very challenging  [49, 53] , it remains possible that more accurate methods can reveal some differences in the expression of individual tRNAs between cellular states. However, the coverage of tRNAs by reads in our sequencing dataset, though not uniform over the tRNA length, was relatively similar for tRNAs decoding codons with A/U or G/C at the third nucleotide position (Additional file 1: Figure  S7B ), making it unlikely that a sequence-dependent bias in the sample preparation has masked true changes in individual tRNA abundance between conditions.
Libraries were quality-checked on the Fragment Analyzer (Advanced Analytical, Ames, IA, USA) using the Standard Sensitivity NGS Fragment Analysis Kit (Advanced Analytical, Cat# DNF-473). The samples were pooled to equal molarity. Each pool was quantified by PicoGreen Fluorometric measurement to be adjusted to 1. 8 pM and used for clustering on the NextSeq 500 instrument (Illumina). The samples were sequenced using the NextSeq 500 High Output Kit 75-cycles (Illumina, Cat# FC-404-1005). Primary data analysis was performed with the Illumina RTA version 2. 4. 11 and base calling software version bcl2fastq-2. 20. 0. 422.
Differential expression analysis of ribosome protected fragments Ribosome protected fragment (RPF) reads were subjected to 3′ adapter trimming (Additional file 7: Table  S6 ) and quality control (reads shorter than 20 nucleotides or for which over 10% of the nucleotides had a PHRED quality score < 20 were discarded). Filtered reads were then mapped to the mouse transcriptome based on genome assembly mm10 and transcript annotations from RefSeq (November 2017) with segemehl v0. 1. 7-411 [55]  allowing a minimum mapping accuracy of 90%. Transcript counts were calculated based on uniquely mapped reads to the coding sequence and used for differential expression analysis with DESeq2  [56]. Three biological replicates were obtained for each serum condition (1% or 10% FCS). Library sizes (before quality control) ranged from 60 to 80 million reads, and mapping rates varied between 83 and 90% across replicates.
LC-MS/MS data acquisition For each sample, aliquots of 0. 5 μg of peptides were analyzed by LC-MS. A chromatic separation was carried out using an EASY nano-LC 1200 system (Thermo Fisher Scientific), equipped with a heated RP-HPLC column (75 μm × 37 cm) packed in-house with 1. 9 μm C18 resin (Reprosil-AQ Pur, Dr. Maisch). Peptides were separated using a stepwise gradient ranging from 95% solvent A (0. 15% formic acid, 2% acetonitrile) and 5% solvent B (80% acetonitrile, 20% water, 0. 15% formic acid) to 50% solvent B over 90 min at a flow rate of 200 nl/min (5-12% B by 5 min, 12-35% B by 65 min and 35-50% B by 90 min). Mass spectrometry analysis was performed on Orbitrap Fusion Lumos mass spectrometer equipped with a nanoelectrospray ion source (both Thermo Fisher Scientific). Each MS1 scan was followed by highcollision dissociation (HCD) of the 20 most abundant precursor ions with a dynamic exclusion for 30 s.
Label-free quantification The acquired raw files were imported into the Progenesis QI software (v2. 0, Nonlinear Dynamics Limited), which was used to extract peptide precursor ion intensities across all samples applying the default parameters. The generated mgf files were searched using the MASCOT algorithm (Matrix Science, version 2. 4. 1); the mgf files were searched against a database containing normal and reverse sequences of UniProt entries of Mus musculus (March 07, 2019) and commonly observed contaminants (in total 34, 794 sequences. The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues, unless followed by proline), 3 missed cleavages were allowed, carbamidomethylation (C) was set as fixed modification, oxidation (M) and N-terminal acetylation were set as variable modifications, and mass tolerance of 10 ppm (precursor) and 0.
6 Da (fragments). The database search results were imported into Progenesis QI software, and the list with quantified peptides was exported. The quantitative data were further processed and statistically analyzed using the SafeQuant software tool  [59]. In brief, the false discovery rate (FDR) of peptide and protein identification was set to 1%. For quantification, the analysis included global data normalization by equalizing the total peak areas across all LC-MS runs and summation of peak areas per protein. The summarized protein expression values were used for statistical analysis using Bayes moderated t-statistics. Finally, the calculated P values were corrected for multiple testing using the Benjamini-Hochberg method.
For quantification, band intensities of eIF2α and p-eIF2α were normalized by the corresponding loading control (tubulin). Then, for both eIF2α and p-eIF2α, normalized band intensities were divided by the average normalized band intensity across the different conditions. Finally, the ratio of p-eIF2α to eIF2α was calculated by dividing the normalized intensities calculated in the previous step. The band intensities were quantified using the ImageJ software  [60].
1% SDS buffer for overnight at 42 °C. For detection, the membrane was exposed to phosphorimager screen for 8 h, and the image was captured on a phosphorimager. The ratio of charged to total tRNA was quantified by dividing the intensity of the band corresponding to charged tRNA by the sum of intensities for the bands of charged and uncharged tRNA. The band intensities were quantified using the ImageJ software  [60].
Calculation of codon usage The frequency of each codon in every protein-coding mouse transcript, based on genome assembly mm10 and transcript annotations from RefSeq (November 2017), was calculated using codonR vFeb2005 (http://people. cryst. bbk. ac. uk/~fdosr01/tAI/). Then, for each transcript, the relative usage of different synonymous codons was calculated by dividing the frequency of usage of each codon by the total frequency of codons encoding the same amino acid. Determination of tissue-specific transcripts mRNA-seq data for different mouse tissues was retrieved from  [67]  (GSE29184). Kallisto v0. 43. 0 (https://pachterlab. github. io/kallisto/) [68]  (parameters: --single -l 200 and -s 20) was used to map the reads to a reference transcriptome based on genome assembly mm10 and transcript annotations from RefSeq (November 2017).
Gene set enrichment analysis The tool GSEA v2. 2. 3 (http://software. broadinstitute. org/ gsea/index. jsp) [70]  was used to calculate the enrichment of gene sets derived from the KEGG pathway database, Gene Ontology: Biological Processes, and the Hallmark collection. P values were estimated by comparing the empirical enrichment score (ES) of a gene set relative to a null distribution of ESs derived from permuting the gene set 1000 times and then adjusted for multiple hypotheses testing  [70].
Acknowledgements We thank  Beatrice Dimitriades  for the help with the cell culture and western blot and  Ana Correia ,  Anastasiya Börsch , and the members of the  Zavolan group  for their input on the project. We thank  Martin Spiess  for the generous gift of the NIH-3T3 cell line and  Yaron Shav-Tal  at  Bar-Ilan University, Israel , for the FUCCI plasmids. We also thank  Verena Jäggin  and  Telma Lopes  from the  FACS  facility at  D-BSSE ,  ETH , for the assistance with cell sorting experiments;  Janine Bögli  from the  FACS facility at Biozentrum, University of Basel , for the help with cell sorting and flow cytometry experiments;  Philippe Demougin  from the  Genomics Facility Basel  for the genomic library preparation; and the sciCORE team for their maintenance of the  HPC  facility at the  University of Basel.
Availability of data and materials Sequencing data from this study have been submitted to NCBI under the accession number PRJNA472989  [71]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [72]  partner repository with the dataset identifier PXD016034  [73]. mRNAs expression data for 15 different mouse tissues were retrieved from  [67]. The source code to replicate the analysis presented in this study is available from Zenodo at https://doi. org/10. 5281/zenodo. 3612157 [74].