dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The systems submitted to the EmpiriST2015 shared task have improved the state-of-the-art for tokenization and PoS tagging of CMC and Web corpora. The best submitted tokenizer achieved an F 1 -score of 99. 54% (vs. 98. 47% baseline) for the CMC data set and an F 1 -score of 99. 77% (vs. 99. 42% baseline) for the Web corpora data set. For PoS tagging, the results are still far from optimal. Nevertheless, the improvement against baseline systems is striking especially for the CMC subset: The best submitted tagger achieved an accuracy of 87. 33% evaluated against STTS IBK (vs. 77. 89% baseline), and an accuracy of 90. 28% against STTS 1. 0 (vs. 81. 51% baseline). For the Web corpora subset, where the baseline systems already peform much better than on gen- Further evaluation of the results in future work should include a close examination and discussion of the performance of the tagger models with respect to the tag set extensions defined in STTS IBK, as well as their performance on different genres and text sources.
This will be the topic of a round table organized at the 3rd NLP4CMC workshop at KONVENS 2016. 17 he results of the shared task can be considered a promising step towards better NLP tools for German CMC data, especially since all participants (except for UdS) have made their systems available to the community as open-source software. However, the adaptation of NLP tools to the linguistic peculiarities of CMC discourseespecially for PoS tagging-is still a challenging task. The resources developed for EmpiriST 2015 (gold standard and annotation guidelines) will remain available on the task Web site under a Creative Commons licence. 18 We hope that they will stimulate further advances in adapting NLP technologies to CMC discourse as well as in improving the annotation quality of German Web corpora.
Acknowledgments We thank the  German Society for Computational Linguistics and Language Technology (GSCL)  for financial support,  Stefanie Dipper  and  Marcel Bollmann  ( Ruhr-Universität Bochum ) for providing the CorA tool for PoS annotations, our student annotators in Berlin, Darmstadt, Dortmund and Erlangen for their careful work and the members of the  DFG network Empirikom  for fruitful discussions in the design stage of the task.
Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github. com/menta78/tsEva/ (Mentaschi et al. , 2016).
Data and validation To assess the generality of the approach, the TS methodology has been validated on time series of different variables, from different sources and with different statistical properties. The analysis of annual and monthly maxima has been carried out on time series of significant wave height at two locations: the first located in the Atlantic Ocean, west of Ireland (-10. 533 • E, 55. 366 • N), and the second close to Cape Horn (60. 237 • E, -57. 397 • N). The data have been obtained by means of wave simulations performed with the spectral model Wavewatch III ®  (Tolman, 2014)  forced by the wind data projections of the RCP8. 5 scenario  (van Vuuren et al. , 2011)  of the CMIP5 model GFDL-ESM2M  (Dunne et al. , 2012)  on a time horizon spanning from 1970 to 2100. This data set is referred to from now on as GWWIII.
Here, the TS methodology is used in order to examine its applicability to climate change studies. The annual and monthly analyses have been repeated on a series of water level residuals offshore of the Hebrides Islands (Scotland, -7. 9 • E, 57. 3 • N) obtained from a 35-year hindcast of storm surges at European scale  (Vousdoukas et al. , 2016a, b)  forced by the ERA-Interim reanalysis data  (Dee et al. , 2011). This data set is further referred to as JRCSURGES. For the annual maxima of the considered series, we further compare the TS methodology with the SS technique as implemented by  Alfieri et al. (2015)  and  Vousdoukas et al. (2016a). For this purpose, we extracted time series from projections of streamflow in the Rhine and Po rivers covering a time horizon from 1970 to 2100  (Alfieri et al.
, 2015) , from now on referred to as JRCRIVER. Also, the two series of significant wave height of west Ireland and Cape Horn extracted from the GWWIII data set have been used in this comparison. Finally, we compare the TS methodology and the EM for monthly maxima using time series of significant wave height extracted from a 35-year wave hindcast database  (Mentaschi et al. , 2015)  near the locations of La Spezia and Ortona. The analysis of this data set, further referred to as WWIII_MED, focuses on a comparison between seasonal cycles modeled by the two approaches.
Waves: annual extremes The validation of the TS methodology was performed first on the time series of significant wave height of west Ireland and Cape Horn from the GWWIII data set. We verified first the non-seasonal transformation given by Eq. (  2 ) and the timedependent GEV and GPD given by Eqs. (  7 )-(  9 ) and (  19 )-(  21 ), respectively. By ignoring the seasonality, this formulation is suitable for finding extremes and peaks on an annual basis. For technical reasons the two series do not have data in two time intervals, from 2005 to 2010 and from 2092 to 2095. The impact of the missing data on the analysis is small, however, especially if we choose a time window W large enough for the estimation of the trend and standard deviation using Eqs. (  31 ) and (33). In particular, for this analysis we chose a time window of 20 years, which is long enough to ensure the accuracy of the results and short enough to include the multi-decadal variability of a 130-year time series.
Residual water levels To verify the performance of the TS methodology on a series from a different source, of a different size, and with different statistical characteristics, we tested it on a series of water level residuals extracted from the JRCSURGES data set for an off-shore location of the Hebrides Islands, Scot- Since the time horizon of this series is shorter than that of the GWWIII projections, a time window of 6 years was adopted for the computation of the trend to better identify its inter-annual variability. The results of the TS analysis of the yearly maxima are shown in Fig. 6. The series displays also a strong seasonal behavior with annual maxima usually occurring during the local winter (for brevity, the seasonal analysis is not illustrated). An interesting aspect is that the estimated standard deviation S y (t) presents a strong correlation (ρ = 0.
79) with the annual means of the North Atlantic Oscillation (NAO) index. This is illustrated in Fig. 7 , where the scatter plot of S y (t) vs. the annual means of the NAO index (Fig. 7a ) and the two time series (Fig. 7b ) are represented. As a consequence, the estimated annual maxima are also correlated with the NAO index. A comparison was carried out between the TS methodology and the SS technique, consisting of a stationary analysis on quasi-stationary slices of data. This analysis was carried out on river discharge projections for the Po and the Rhine extracted from the JRCRIVER data set and on the projections of significant wave height extracted from the GWWIII data set for the locations of west Ireland and Cape Horn. The TS methodology was applied with a time window of 30 years to estimate a non-stationary GPD of annual maxima.
In particular, the maximum value tested for N µ and N ψ is 3, while the maximum considered value of N ε is 2. In general, this model can be extended to incorporate long-term trends, but the two series examined in this test display flat trends. Hence, Eqs. (  62 )-(  64 ) are adequate to model them. In the comparison, the EM and the seasonal TS methodology (GEV only) were applied to the same series of significant wave heights relative to the WWIII_MED data set described in Sect. 2. 3. For the transformed-stationary approach, a 10-year time window was used for the computation of the long-term trend. The results of the two methodologies are similar, with a roughly flat trend and strong seasonal pattern. The comparison of the seasonal cycles estimated by the two techniques is represented in Fig.
The detection of non-stationarity can be performed by means of easily implemented and fast low-pass filters, which do not require as input any parametric function for the variability. This makes the methodology well suited for massive applications where the simultaneous evaluation of several time series is required. An implementation of the TS methodology has been developed in an open-source MATLAB toolbox (tsEva), which is available at https://github. com/menta78/tsEva/.
Acknowledgements. The authors would like to thank  Simone Russo  of the  JRC  and  Francesco Fedele  of the  GIT  for the precious suggestions, and  Niall McCormick  of the JRC for the careful review of this manuscript. This work was co-funded by the  JRC  exploratory research project Coastalrisk and by the  European Union Seventh Framework Programme   FP7/2007-2013  under grant agreement no. 603864  ( HELIX: High-End cLimate Impacts  and eXtremes; http://www. helixclimate. eu). Edited by: S. Archfield Reviewed by: two anonymous referees.
Summary of phenotypic data Forty-eight growing pigs of two lines divergently selected on RFI, a measure of feed efficiency, were fed diets formulated at isocaloric and isoproteic bases but differing in energy source and nutrients (lipids and fibers vs. starch). Full description of performance of experimental pigs (n = 12 per line and per diet) after 58 days of dietary treatment can be found in an associated paper  [21]  and are briefly summarized here. Importantly, there was no interaction between diet and line on performance and body composition. Irrespective of RFI line, pigs fed the high-fat high-fiber (HF) diet ate 12 % less (p < 0. 001) than pigs fed the low-fat low-fiber starch-based (LF) diet during the trial and they developed less body fat, as illustrated by 1. 4-fold lower proportions of ATs at the perirenal (%PRAT) and dorsal subcutaneous (%SCAT) locations.
Diet markedly affected adipose tissue molecular profiles Microarray analyses were separately performed in PRAT and in SCAT from the 48 experimental pigs. After normalization, the signal intensity was found to be above background noise for 38,142 spots in PRAT and 38,987 spots in SCAT, respectively. The linear model to analyze the log2-transformed signals revealed no significant interaction (p > 0. 01) between diet and line on the expression levels of the molecular probes in the two ATs, so that diet and line effects could be inferred independently. The global effects of diet and of line on the number of differentially-expressed probes (DEP) were more pronounced in PRAT than in SCAT (Fig. 1 ). In the two ATs, diet had also a more pronounced effect on the transcriptome than did the line. Thus, only data related to dietary effects are further considered in this manuscript.
2 % of the variation and did not clearly separate the data according to diet or adiposity phenotypic traits. As illustrated (Fig. 2 ), the two first comprised variables synthesizing the molecular information of the originallydistinct data tables (dim_1_PRAT and dim_1_SCAT, respectively) were projected together along Dim1 in the MFA plot. This means that large similarities between Pigs were fed a high-fat high-fiber diet (HF) or a low-fat high-starch diet (LF). Duration of the feeding trial was 58. 5 ± 0. 5 days for all pigs. Two divergent pig lines that have been selected over eight generations for residual feed intake (RFI), a measure of feed efficiency, were used. Pigs selected for low RFI were the most efficient in the conversion of food to weight gain. There was no significant interaction (p > 0.
10) between diet and line on performance and adiposity traits. At the end of the trial, weights of perirenal and dorsal subcutaneous adipose tissues (AT) were used as surrogates of body adiposity Microarray data obtained in subcutaneous (SCAT) or in perirenal (PRAT) adipose tissues were separately analyzed for the main effects of diet (HF: high-fat high-fiber vs. LF: low-fat high-starch), line (low RFI: residual feed intake below the average; high RFI: residual feed intake above the average), and the interaction between diet and line. Molecular probes were declared as differentially-expressed between diets or between lines according to cutoffs for fold-change between conditions > |1. 1| and p < 0. 01 (Benjamin-Hochberg adjusted p-value < 0. 08). Venn diagrams illustrate the number of differentiallyexpressed probes in each experimental group for SCAT and for PRAT.
Altogether, different genes were thus listed in more than one of these pathways, suggesting that these various biological processes were at least in part, inter-connected. (See figure on previous page. ) Fig 2  Multi-way datasets analysis: consensus in microarray data relative to dietary effect across two adipose tissues. The first two synthetic variables obtained for the perirenal (Dim_1_PRAT) and subcutaneous adipose tissue (Dim_1_SCAT) transcriptomes were projected in the correlation circle of the multiple factor analysis (MFA), an integrated statistical method used to reveal communalities across separate datasets. Large similarities across adipose tissues can be deduced from molecular variables contributing to the first dimension (Dim1) of MFA (Fig. 1a ). Relative weights of perirenal fat (%PRAT) and subcutaneous fat (%SCAT) were superimposed on the plot, showing strong correlation between the molecular probes contributing to Dim1 and adiposity variations.
Particularities of perirenal adipose tissue response to diet To provide another representation of the transcriptional changes in ATs response to diet, the Weighed Gene Correlation Network Analysis (WGCNA) was used to capture strong relationships between transcripts in modules of interconnected genes  [23]. In each module, the eigengene (the weighed mean of the transcripts providing the best univariate summary of the within-module variability) was calculated to relate transcriptional changes to external phenotypic traits and to deduce the biological meaning of the module. Four distinct network modules were thus obtained (Table  3 ). The first two big modules represented 48 % and 20 % of the DEP, respectively. The first module (turquoise) included a higher number of DEP from PRAT than from SCAT, and the second one (blue) had almost an equal number of DEP from the two adipose tissues.
Eigengenes in the turquoise and blue modules were highly correlated to the first comprised dimension Dim1 in MFA (data not shown), suggesting that these modules included not much more supplementary biological meaning than that deduced from Dim1. Two smaller network modules represented 11 % and 7 % of the DEP data set, respectively. The brown module corresponded mainly to a co-expression network in DEP from SCAT; however, most of these DEP were also found in PRAT including gene transcripts related to protein catabolic process, protein transport, pyruvate metabolism and cell respiration. The pattern of expression in the yellow module was mainly assigned to PRAT; only 4 % of the DEP in this module were also listed as differentially-expressed in SCAT in response to diet. In addition, the eigengene of this yellow module was not highly correlated with %PRAT (r = -0.
The higher is the connectivity value, the stronger is the evidence that the gene is part of the module within each AT were submitted to a dedicated algorithm (http://keyregulatorfinder. genouest. org/) which matches the lists of DEP to a causality graph of reactions and regulations built from Transpath® database information, and ranks upstream candidates in accordance to their ability to regulate significant numbers of the DEP. The obtained candidates were listed in Additional file 5: Table  S5  for PRAT and in Additional file 6: Table  S6  for SCAT. A biological post-prioritization in these candidates was manually made by adding GOBP terms. Only a summary of the most relevant candidates was given in Table  6 , but more background information on the biological processes in which the candidates may play a role is provided in Additional file 5: TableS5 and Additional file 6: Table  S6.
Discussion Irrespective of species, there are still a limited number of studies which have deciphered the effects of changing The lists of differentially-expressed genes in porcine perirenal (PRAT) or subcutaneous (SCAT) adipose tissues in response to diet were automatically confronted to the academic information on regulations and reactions in mammalian cell signaling. Genes that were able to regulate a significant number of the differentially-expressed genes were proposed as upstream regulatory candidates. The biological processes in which these genes could be involved were indicated as referenced in the gene ontology (GO) Entrez database. The complete lists of candidates are given in Additional file 5: Table  S5  for PRAT and Additional file 6: Table  S6  for SCAT dietary energy source rather than dietary energy level on gene expression levels in AT  [7, 24, 25].
Animals and diets Data from a total of 48 pigs (pure Large White breed) were considered in the present study. These pigs originated from two lines divergently selected over eight generations for residual feed intake (RFI) as a measure of feed efficiency  [56]. The RFI index was typically calculated as the difference between actual and predicted feed intakes, so that low RFI value signified better efficiency in the conversion of food to weight gain. In this experiment, barrows originated from the selection herd (INRA GeneSI, Le Magneraud, France) were transferred after weaning (pig age: 28 days) at INRA Pegase (St-Gilles, France). From 74 d ± 0. 3 d of age onwards, pigs were randomized by line in two dietary groups (n = 12 per diet and per line): a high-fat high-fiber diet (HF) or a low-fat high-starch diet (LF).
Samples were then purified with an RNeasy Mini kit (Qiagen, Hilden, Germany). Microarray hybridizations were carried out in Agilent's hybridization chambers containing 600 ng of Cy3-labeled cRNA sample per hybridization. Hybridization reactions were performed at 65 °C for 17 h using Agilent's Gene Expression Hybridization Kit. After washing, microarrays were scanned at 3 μm/pixel resolution using the Agilent DNA Microarray Scanner, and images were analyzed with Agilent Feature Extraction Software (Version 10. 7. 3. 1, protocol GE1_107_Sep09). Microarray analyses were performed using the R software version 2. 10. 0  [57]. Raw spots intensities were first submitted to quality filtration based on 4 criteria: intensity, uniformity, saturation and outliers detection. Intensities of filtered spots were transformed into log2(Cy3), and data were normalized by median centering, i.
e. subtracting the median value across all probes from all raw values for each sample in order to obtain experimentally consolidated gene expression values. Microarray selected data have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO subseries accession number GSE70836 (http:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE70836) for SCAT and GSE70837 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE70837) for PRAT.
Statistical analyses In each AT, the data were first analyzed by a two-way analysis of variance (ANOVA) with diet (HF vs. LF), line (low vs. high RFI) and the interaction between diet and line as the main effects. A raw p-value < 0. 01 and foldchange between conditions > |1. 1| were considered as cutoffs  [58]  in a heuristic way to retain differentiallyexpressed probes (DEP). Data were also submitted to the Benjamini-Hochberg (BH) multiplicity correction of the p-values for a control of the FDR at level 0. 08  [59] , proving stringent control of statistical errors. Within each AT, the lists of DEP, one for diet and one for line, were thus edited. Venn diagrams representing the number of DEP in the two ATs as affected by diet or by line were produced. Second, to derive a common framework that might be more convenient to highlight communalities across the two ATs, the Multiple Factor Analysis (MFA) was used as a data integration statistical method  [12]  under the FactoMiner package in R software.
Briefly, two principal component analyses (PCA) were first performed from the DEP dataset obtained in each adipose tissue (one for PRAT, one for SCAT). Each variable identified by the probe name was weighed by the first eigenvalue in PCA. Then, the analysis consisted in realizing a meta-PCA by using these weighed variables as new entries, so that each tissue influence was equally represented. The first two MFA components (Dim1; Dim2) were linked to the three first axes of the two separate PCA (Dim_i_PRAT and Dim_i_SCAT with i = 1 to 3) by calculating the correlations between MFA and PCA components. This allows building a MFA diagnostic plot to study the relationship between the observations, the variables and the tables, and which facilitates the description of communalities. Adiposity phenotypic traits (%_SCAT and %_PRAT, respectively) were superimposed on the plot to relate variations in transcriptomic profiles to changes in body fat.
Calculating the correlations between each DEP and MFA dimensions (Dim_1; Dim_2) allows deciphering the DEP that were mainly responsible for communalities in molecular responses between the two ATs; the threshold r > |0. 70| (p < 0. 001) was retained to list these main contributors. Third, global gene co-expression networks were constructed from the dataset joining the DEP of the two ATs, with the main goal to find modules of genes with similar transcriptional profiles  [23]. The WGCNA package in R software, which has been described as one of the methods that performed the best for describing the correlation patterns among genes, was used. As recommended  [60] , a power (s, β = 6) was retained to strengthen the separation between low and high correlation in gene expression levels within the modules.
Functional pathways analysis The most important DEP (r > |0. 70|) participating in the comprised MFA dimensions or in the WGCNA modules were considered for functional analyses. The correspondence between DEP and its official gene symbol was first made, when applicable. The gene ontology (GO) terms for biological processes were then automatically searched, using the Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resource database (http://david. abcc. ncifcrf. gov/). The GOBP terms_FAT were selected to filter the broadest terms without overshadowing the more specific ones. The results were downloaded using the "Functional annotation clustering" option of the DAVID tool. The enrichment score (measured by the geometric mean of the EASE score of all enriched annotations terms) and the modified Fisher exact p-value, were obtained.