dataesr/scientific-paragraphs-categorization huggingface dataset:
B. (dataset 1) and A. W. (dataset 2) using a standardized protocol. For dataset 1, each participant was first instructed to report 12 personal episodic events (3 events within the next 6 months, 3 events during the following school year, 3 events within the last 6 months and 3 events during the previous school year) from 3 different settings: 4 events related to family life, 4 events related to school life and 4 events related to their spare time activities). For dataset 2, each participant was instructed to report 5 personal episodic events that happened one year ago, and 5 personal episodic events that could happen one year from now. For further details on the cue selection procedure, please refer to the original publications  22, 23. Verbal cues referring to each event were then presented to each participant, and they were given 3 min to freely elaborate on the respective past or future event.
For each participant, a sum score for internal and external episodic details was computed as the sum of the details across these categories. Temporal discounting. Temporal discounting refers to the reward devaluation that typically occurs with increasing delay. All subjects completed a simple short and adaptive discounting task  28  that involved repeated choices between a smaller-sooner reward of 20€ available now and larger-but-later rewards available only after some delay (2, 7, 14, 30, 90, 180 days). The procedure was adaptive such that the reward amount of the larger-later reward was increased following two successive choices of the smaller-sooner reward, and decreased following two successive choices of the larger-later reward. Choice data were then fitted with a standard hyperbolic discounting function of the form = + × SV A k D /(1 ).
Text feature extraction. Computation of text features proceeded in a purely data-driven manner. That is, we did not have a priori assumptions about which low-level text features might be informative regarding AI details sum scores. Rather, we simply applied two complementary methods to extract a large number of low-level linguistic features from the narratives. Since we used regression approaches suitable for collinear data (see below), high correlations between some of the variables are not problematic per se. First, we used custom in-lab Matlab © procedures to cross-reference words with publicly available linguistic databases ('manual feature extraction'). Second, we used commercially available text analysis software ('Linguistic Inquiry and Word Count' , LIWC)  31  to obtain additional text features.
The two approaches are described in detail in the following. Manual text feature extraction. Transcripts of narratives were first pre-processed to extract potentially relevant text features. The data from each subject were read into Matlab © and separated according to the two experimental conditions (EFT, AM). Then, individual words and sentences were extracted from the narratives. Words were then further analyzed using Webservices provided by the "Projekt Deutscher Wortschatz" (http://wortschatz. uni-leipzig. de), which is part of the Leipzig Corpora Collection. Each word was converted to base form and classified (noun/adjective/verb/other) using the "baseform" webservice (http://wortschatz. uni-leipzig. de/axis/ servlet/ServiceOverviewServlet). For an input of e. g. freundlichste (nicest), this service returns both the baseform freundlich (nice) and the classification (adjective).
This allowed us to calculate the proportion of nouns, verbs and adjectives separately for each condition. Next, we cross-referenced words (both pre-and post-baseform conversion) with the Berlin Affective Word List -Reloaded (BAWL-R) (Vo et al. , 2009). The BAWL-R is a database that contains ≈3000 German words (≈2100 nouns, ≈500 verbs, ≈290 adjectives). It provides normative ratings for the dimensions valence (-3 [very negative] through 0 [neutral] to +3 [very positive]), imageability (1 [hardly imageable] through 7 [highly imageable]), and emotional arousal (1 [low arousal] through 5 [high arousal]) as well as a measure of word frequency (frequency/million). Figure  1  illustrates that baseform conversion increased the number of words that could be successfully cross-referenced with the BAWL-R database by around 50% in both experimental conditions.
Linguistic Inquiry and Word Count (LIWC) features. We also applied a commercially available text analysis software that is frequently used in psychological research, the Linguistic Inquiry and Word Count (LIWC). LIWC is a dictionary-based method that counts words falling into one of 64 content categories, and normalizes these counts by the total length of the texts. These categories include specific word classes (e. g. filler words, numbers, pronouns, articles) but also semantic content categories (words related to e. g. leisure, home, school, job, sports, TV) as well as cognitive-emotional categories (e. g. positive emotion, negative emotion, anxiety, affect). See results section for a complete list of LIWC content categories used in the present study. We used a validated German LIWC dictionary  31  and separately analyzed EFT and AM data for each participant.
Principal component regression (PCR). PCR consists of first performing a principal component analysis (PCA) on the data matrix X. In the next step some target vector y is regressed onto a subset of n of these components, with n being typically determined via cross validation. PCA is a completely data-driven approach that extracts the main axes of variation from a multi-dimensional data set. Often, a relatively small subset of these principal directions accounts for the majority of variability in the data, and PCA is thus an effective technique for dimensionality reduction. The resulting component scores, which are linear combinations of the original variables, are orthogonal, and PCR thus solves the problem of predictor co-linearity. PCR was performed by first computing a PCA using the Matlab function pca.
Partial least squares regression (PLS). PCR uses PCA to construct new orthogonal predictor variables, but it is completely data driven -components are constructed without regard to the target data y that one wishes to eventually predict. Thus it is possible that information predictive of y ends up in later components that are excluded from the PCR. Thus, when, the ultimate goal is prediction, PCR may not be the optimal choice. In contrast to PCA, in PLS, components are constructed based on both X and y, that is, components are constructed such as to jointly maximize the amount of variance that is explained in X and the correlation of the resulting components with y  32. We performed PLS using the Matlab function plsregress, which also implements cross-validation.
Results Autobiographical memory interview data. Figure  2a-c  shows histograms of the distributions of ratings for internal, semantic and external details. Overall, internal details ratings (t 85 = 5. 0057, p < 0. 001) and external details ratings (t 85 = 2. 236, p = 0. 028) where higher for AM than EFT, whereas semantic details ratings were higher for EFT than AM (t 85 = 2. 5189, p = 0. 014). All ratings were significantly correlated between conditions (Fig. 2d-f ). Text feature data. We used two types of extracted text features (see methods section for details). First, text features were manually extracted using custom Matlab © code, in combination with research-dedicated word databases (see Methods section for details and Table  1  for an overview of manually computed features). Second, we used commercially available software (Linguistic Inquiry and Word Count, see methods section) to derive dictionary-based word counts for 64 content categories.
For simplicity, the following analyses used only the manual text features, and not the LIWC features. Given the similar results for the different regression approaches in the previous sections, we focussed on 2-component PLS models. Also, we used AM data for predicting AM ratings and EFT data to predict EFT ratings. Thus, for EFT and AM each, a separate 2-component PLS model trained on the respective feature sets and was used for prediction. We then varied the amount of test data used for prediction. Note that this resembles a situation where one has a model trained on a large data set, but would like to more efficiently predict AM/EFT in a subsequent shorter test session. We first computed all manual features (see Table  1 ) separately for each cue (see section "Autobiographical Interview" in the methods section).
g. the manual feature extraction methods employed here) may yield better prediction accuracy for AI detail scores than either feature class alone. Future studies on quantitative text analysis might benefit from complementing dictionary-based methods such as the LIWC with additional text features such as those examined in the present study (e. g. proportions of different word classes; imageability, valence and arousal scores, etc. ). Finally, by systematically varying the size of the test data sets, we could show that increasing the test data size beyond n = 3 event cues per participant and condition may not further improve prediction accuracy. This might be of interest for future studies employing the methods described here for a semi-automatic analysis of novel AI data sets. There may be an upper limit for our feature-based prediction approach that is reached with considerably smaller test data sizes than typically used in studies employing the AI.
Whole-Transcriptome Analysis Whole-genome expression analysis was done on SurePrint G3 Human GeneExpression v1 8x60K arrays (Agilent Technologies, Les Ulis, France) following the manufacturer's standard protocol. Microarray analyses and signal normalization were done with GeneSpring software  (11. 5. 1). The P values were obtained with Student t test and adjusted with Benjamini-Hochberg correction. The cutoff levels were adjusted P value <. 05 and an absolute fold change >2. Gene expression data files are available under the Gene Expression Omnibus accession number GSE84796.
A decrease of the laser power did not modify the nature of the detected species but induced a significant decrease in their abundance. The resulting ions from (8 laser shots) were stored in a RF-only hexapole before their transfer to the FT-ICR cell. The ion guide was tuned to optimize the efficient transfer of ions in the m/z 150-500 range into the FT-ICR MS cell. A 1. 049 s transient length was acquired with 4096 K data points. Typically, the average mass measurement accuracy was better than 500 ppb, and the mass resolution was between 500 000 and 2 000 000. To increase the signal to noise ratio, each mass spectrum reported in following sections was the sum of 45 individual mass spectra.
Data post-acquisition treatment The 𝑚 𝑧 = 𝐴 𝑓 + 𝐵 𝑓 2 For the non-assigned signals or the assigned signals with an error greater than 1 ppm a manual assignment was conducted by using Omega 8 Elemental Composition software (Varian-IonSpec Inc. ) with the previously reported search criteria. Post-treatment was applied to process data to achieve some graphical representations that were necessary in order to compare the results obtained in the different analytical conditions. More specifically, van Krevelen diagrams were used to illustrate the different components of the bio-oil.
ACKNOWLEDGMENTS The authors, more especially  Jasmine Hertzog , would like to thank the  European COST Action 1306 "Lignoval"  for its financial support, as it enabled a new collaboration between the  LCP-A2MC ,  Université de Lorraine  and the  UK Biochar Research Center, Edinburgh University. The analyses with the 9. 4 T FT-ICR MS where performed at the  LCP-A2MC  whereas the measurements carried out with the 12 T FT-ICR MS were done at the  Scottish Instrumentation and Resource Centre for Advanced Mass Spectrometry (SIRCAMS) at the University of Edinburgh. The  TGIR program FR 3624, infrastructure program  of  CNRS , is acknowledged for further financial support. Authors also thank  Dr Faye Cruickshank  for her valuable editing of the manuscript.
Multivitamin-supplement use and smoking Data regarding the self-reported use of multivitaminsupplements (defined as supplements that contained $3 vitamins) were obtained from questionnaires and were coded as current or no-current use for 12 cohorts and as ever or never for 4 (United States) cohorts (Supplemental Table  1 ). In the United States, circulating vitamin concentrations were similar between subjects who reported current use of multivitamin supplements and those who reported ever use of multivitamin supplements (data not shown). Similar vitamin concentrations were also found for those reporting no-current use compared with those reporting never use (data not shown). Therefore, we combined subjects who reported current and ever use into multivitaminsupplement users and those who reported no-current or never use into nonusers.
8% in the Nordic region (data available for only 205 participants), 6. 9% in Asians, and 13. 3% in Australians. All samples from the United States were collected from 1985 to 2008 except for a small number of samples (n = 15) from the Campaign Against Cancer and Stroke and the Campaign Against Cancer and Heart Disease, which were collected in 1974. For the US-based Women's Health Initiative cohort, information on the year of blood sampling for each participant was not available because of confidentiality concerns. Samples were collected from Nordic cohorts during 1985-2010, from Asian cohorts during 1986-2006, and from the Australian cohort during 1990-1994 (Figure  1 ). Supplemental Table  2  shows the crude geometric mean (5th and 95th percentile) biomarker concentrations for the total population and each geographic region, whereas Supplemental Tables  3  and 4  show these data for individual cohorts.
Despite the reported increases in use of vitamin supplements over the past decades  (56, 57) , we found that the year of blood sample collection was not related to vitamin status (data not shown). This result might be related to the considerable heterogeneity of the participating cohorts or the distribution of participants over the time span during which blood samples were collected. Notably, US multivitamin-supplement users showed distributions in the upper concentration range for all vitamins except for g-tocopherol, which was lower. A study of middle-aged Americans showed that supplement users, on average, had vitamin intakes that were considerably higher than the estimated average requirements for several vitamins  (58). It has also been reported that, in some US subgroups, supplement use has provided vitamins that are already consumed in adequate amounts  (9, 59).
Limitations of the study also exist. Because the consortium was formed to investigate lung cancer, it was oversampled with regard to the number of smokers. However, all models were adjusted for smoking. The included Asian cohorts consisted of Chinese subjects only. Thus, the current data cannot be expected to be representative of the Asian region. The Australian cohort was rather small. Information on multivitamin-supplement use was available for relatively few participants from the Nordic and Asian regions. Information on the use of single vitamin supplements was available for very few participants and, thus, was not included in this work. Most participants were middle aged or older, and the reported data may not be representative of other age groups. Finally, information on fasting status, which influences the circulating concentrations of some biomarkers  (67) , was not available for all cohorts and, therefore, was not included in the analyses.
Calculation of the Proton Binding Capacity of Viral Proteins Proton binding and the total charge of the proteins was calculated as follows. First, the pKa values of all titrable residues in the proteins were determined with electrostatic energy calculations using the software Karlsberg+  [37]. The calculations are based on the crystal structures with PDB IDs 1hgg, 1hgd, 2bat and 2q06. All non-protein molecules were removed from the structures and therefore not considered in the calculations. Except for hydrogen atoms, all atomic coordinates were kept as found in the crystal structure in the Karlsberg+ calculations. If a titratable residue was missing in the structure, it was assumed that its pKa value equals the model pKa of this residue. The model pKa is the experimental pKa value of a residue in aqueous solution.
ACKNOWLEDGMENTS This research was supported by a  Marie Curie  grant (DH), by the  Deutsche Forschungsgemeinschaft  ( HE 3763/15-1 ) (AH) and the  Bundesministerium fur Bildung und Forschung  (eBio: ViroSign) (CS and AH). TL is partially funded by a fellowship from the  Fondation pour la Recherche Medicale  and a grant from the  Philippe Foundation.
Plant Inoculation and Disease Assessment Plants were inoculated with spore suspensions as described in  Berruyer et al. (2003). For RNAseq experiments, we used GUY11 isolate as it is a reference for many M. oryzae experimental studies. For gene expression studies, we also used a mock treatment corresponding to the solution into which spores are re-suspended (i. e. , 0. 5% gelatin solution). Five to seven days after inoculation, the symptoms were scanned and the number of susceptible lesions were counted using ImageJ software (http:// rsbweb. nih. gov/ij/) as described in  Ballini et al. (2013).
RNA-Seq Data Analysis Total RNA was isolated with TRIzol (Invitrogen) from each sample according to manufacturer's instructions. It was treated with RNase-free DNase according to manufacturers's instructions (Promega) to remove residual DNA. For RNA-seq, total RNA samples were sent to BGI Tech (Shenzhen, China) for sequencing. The libraries were sequenced as 101-bp paired-end reads using Illumina Hiseq2500 according to the manufacturer's instructions. Clean reads have been mapped using SOAP2 on reference genomes: Kasalath for rice and 70-15 for Magnaporthe. Annotated rice transcripts from Kasalath genome were downloaded from the TASUKE webplatform  (Kumagai et al. , 2013; Sakai et al. , 2014). M. oryzae annotation datasets were downloaded from the Magnaporthe comparative Sequencing Project at the Broad Institute (assembly release 8;  Chiapello et al.
FastQ files, reads counts and RPKM per gene are available on GEO database (GSE83219). Differential expression between all repetitions was performed on DEB website  (Yao and Yu, 2011)  for each of the four conditions 0N mock inoculated, 0N Guy11 inoculated, 1N mock inoculated and 1N Guy11 inoculated. All the data were normalized together. For differentially expressed gene analysis we used DESeq with a minimum FDR of 5%. We considered as differentially expressed genes (DEGs) all genes that showed a significant difference in at least one of the four conditions. In order to conduct enrichment analysis we transferred when possible annotations from Nipponbare to Kasalath. We also used the Archipelago database referencing defense-related genes defense regulators  (Vergne et al. , 2008). Nitrogen pathway was annotated manually based on function annotation.
Gene expressed after glutamine treatment were annotated based on GEO microarray GSE56770  (Kan et al. , 2015). ABA responsive genes were collected based on literature  (Gómez-Porras et al. , 2007). Enrichment analysis in the different functions was tested against whole genome annotation with a Chi square test corrected by Bonferroni. GSEA was performed against whole genome on the rice array database (http://www. ricearray. org/). For Differentially expressed gene analysis we used two different statistical analysis methods, namely DESeq and EdgeR with a minimum FDR of 5%. We considered as differentially expressed genes (DEGs) all genes that were significant in at least one of the statistical test. In order to perform enrichment analysis we did some complementary annotations. Gene coding for Carbohydrate-Active enZYmes (CAZY) were annotated based on CAZY.
org database  (Lombard et al. , 2014). Pathogenicity genes were annotated based on literature. Small secreted proteins were annotated based on three criteria: (1) the presence of a signal peptide longer than 15 amino acids at the beginning of the protein, (2) the absence of a transmembrane (TM) domain or a TM domain in the first 10 aa and (3) a protein size smaller than 250 aa. Signal peptide was detected using SignalP program  (Petersen et al. , 2011). Transmembrane domains was detected using TMHMM program  (Krogh et al. , 2001). Enrichment analyses was performed on Fungidb database  (Stajich et al. , 2012)  and for the selected functions the frequency was tested against the whole genome annotation with a Chi square test corrected by Bonferroni.
Transcriptomic Changes during NIS In order to investigate the transcriptional regulation during NIS in Kasalath inoculated with Guy11, we decided to conduct a dual RNA-seq experiment. In this way we were therefore able to follow both rice and M. oryzae gene expression before (only for rice) and 2 days after inoculation. The data are available on GEO database (GSE83219). At 0 dpi RNA-seq analysis on rice genes allowed the detection of 243 differentially expressed genes (DEG): 65 are more expressed in 1N and, 178 are less expressed in 1N (Supplementary Table  2 ). The 65 highly expressed genes are enriched in defense-responsive genes present in the rice defense database  (Vergne et al. , 2008). Thus, this analysis shows that there is no massive modification of rice transcription 1 day after nitrogen supply in our condition, although some defense regulators may be induced.
Using our statistical analysis on the four conditions separately, we could identify a total of 2,665 DEGs in six groups (Figure  3 ). We compared all group of genes (list of genes in Supplementary Table  3 ) showing similar expression pattern with available known groups of genes involved in disease resistance  (Vergne et al. , 2008 and updated versions)  or in nitrogen/glutamine response (GEO database; GSE56770) and enrichment values are given in Supplementary Table  4. The first group represents 637 nitrogen-specific DEGs that are genes differentially expressed between 0N and 1N conditions independently of infection: 222 are induced and 415 are repressed by nitrogen (Figure  3A ). No detectable enrichment in gene related to nitrogen pathway was observed in this experiment, which suggests that transcriptional changes following fertilization may have happened earlier than 2 dpi to increase the observed nitrogen content (Supplementary Figure  2 ).
Glutamate Metabolism Is Only Slightly Modified during NIS Glutamate metabolism is not only crucial for amino acid metabolism but it has been reported as a key player in defense  (Seifi et al. , 2013). In their review Seifi et al. have reported that glutamate metabolism functions in opposite ways: fueling plant defense or diverted by the pathogen to facilitate infection. They identified three major pathways that may lead to resistance or susceptibility: Nitrogen remobilization away from infected tissues, TCA cycle replenishment and cell death. We used this review to identify the genes associated with these three defense strategies that were identified in our RNA-seq data set using annotation (Figure  4 ) and looked if these pathways were affected in infected plants under high nitrogen. We identified 18 genes implicated in nitrogen assimilation in our dataset: 15 are not differentially expressed in none of the four considered conditions at 2dpi.