dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The author thanks  Gaël Clément  ( MNHN ) for allowing access to the specimen of study,  Alexandre Bannikov  ( PIN ) for his useful information on the lampridiform fossil record and for suggesting the redescription of this specimen, as well as  Giorgio Carnevale  ( Università degli Studi di Torino ) for insightful anatomical discussion. Sandra Raredon  (USNM) kindly sent radiographs of extant lophotids for comparison, while  Lilian Cazes ,  Philippe Loubry (MNHN)  and  AV Mazin (PIN)  provided high-quality photographs of the fossil specimens. Finally, the editor  Jérémy Anquetin  and referees  Alexandre Bannikov  and  Dave Johnson  are thanked for their invaluable reviews and comments.
(30). In this method, probes are assigned to Entrez IDs as an unique gene identifier. The p-values were calculated using an Intensity-Based Moderated T-statistic (IBMT)  (31). The q-value was calculated as measure of significance for false discovery rate  (32). To identify the pathways most significantly altered in the livers of the GG group compared to the CTRL group, Ingenuity Pathway Analysis (Ingenuity Systems, Redwood City, USA) was performed. Input criteria were a relative fold change equal to or above 1. 3 and a q-value equal to or below 0. 01. Array data have been submitted to the Gene Expression Omnibus under accession number GSE76087. A publicly available dataset (GSE34730) was downloaded from Gene Expression Omnibus and further processed as described above to obtain individual gene expression data.
Raw sequence data quality was assessed using FastQC, version: 0. 11. 2 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads quality was checked with Sickle, version: 1. 33 (https://github. com/najoshi/sickle) and low quality reads were removed. For visualising the taxonomic composition of the fecal microbiota and further beta diversity analysis, QIIME, version: 1. 9. 0 was used  (35). In brief, closed reference OTU picking with 97% sequence similarity against GreenGenes 13. 8 reference database was done. Jackknifed beta-diversity of unweighted UniFrac distances with 10 jackknife replicates was measured at rarefaction depth of 22000 reads/sample. For statistical significance, biological relevance and visualisation we used linear discriminant analysis (LDA) effect size (LEfSe) method (https://bitbucket.
Dietary guar gum alters colonic microbial composition To evaluate the potential impact of alterations in the gut microbial community on the development of NAFLD, we fed mice the highly fermentable dietary fiber guar gum (GG) on a background of HFCFD for 18 weeks. Mice fed the poorly fermentable dietary fiber resistant starch (RS) and mice that were not fed any dietary fiber (CTRL) served as negative controls. To assess the effect of GG on colonic microbial composition and the relative abundance of specific gut microbiota taxa, 16s rRNA gene sequencing was performed on faecal samples collected at the end of the dietary intervention. Clustering of 16s rRNA gene sequences by unweighted UniFrac distances per mouse revealed a sharp clustering of microbiome sequence data of the GG mice, indicating that the colonic microbial community was distinct between CTRL and GG mice.
Intriguingly, the higher expression of Cd11c and the lower expression of Cd206 and the anti-inflammatory cytokine Il-10 suggested that the macrophages present in the liver of the mice fed GG had a more pro-inflammatory phenotype (Figure  4G ). To further elucidate the inflammatory status of the liver of the GG mice, whole genome expression profiling was performed. It was found that 3,722 genes were significantly altered upon GG feeding as compared to CTRL (p<0. 01)(Supplemental Figure  1A ). Numerous immune-related pathways, including IL-8 signaling (p=1. 05x10 -  ) and complement system activation (p=7. 76x10 -7 ), were altered in the livers of the GG mice, as determined by Ingenuity pathway analysis (Supplemental Figure  1B ). Together, these data demonstrate that although feeding GG suppressed diet-induced obesity, it stimulated specific inflammatory cells and pathways in liver, whereas RS had no effect.
Importantly, provision of taurocholic acid markedly increased plasma alanine aminotransferase activity (Figure  8D ), upregulated the expression of inflammatory genes (Figure  8E ), and stimulated leukocyte infiltration (Figure  8F ). Moreover, taurocholic acid promoted hepatic fibrosis as revealed by elevated expression of the fibrosis markers collagen type I alpha 1 and Timp1 (Figure  8G ), and enhanced collagen deposition (Figure  8H ). Analysis of a publicly available transcriptomics dataset (GSE34730) of livers of mice treated with cholic acid or deoxycholic acid for 2 weeks confirmed the marked stimulatory effect of oral administration of bile acids on genes implicated in hepatic inflammation and fibrosis. Specifically, Ingenuity pathway analysis showed the significant induction of numerous pathways related to immune cells and cytokines signaling, including IL-8 signaling and acute phase response signaling, as well as hepatic fibrosis/hepatic stellate activation.
Acknowledgements We thank  Dr. A. H. Mulder  ( Rijnstate Hospital, the Netherlands ) for excellent advice with histopathological analysis and  Martijn Koehorst  for the bile acid measurements. This research was supported by The  Netherlands Cardiovascular Research Committee IN-CONTROL grant  ( CVON 2012-03 ) and the  Rembrandt Institute for Cardiovascular Research 2012. TMA and TMAO analyses were supported by grants from the  National Institutes of Health  and the  Office of Dietary Supplements  ( HL103866 ,  HL122283 , and  AA024333 ).
Data accessibility. Primary data used on each publication referred to in this manuscript are hosted and managed locally by the respective laboratories where the work presented at this workshop was carried out and may be accessed upon request to studies' Principal Investigators at the respective institutions. All evidence discussed and presented in this manuscript has been presented/submitted previously elsewhere and can be accessed via Web of Knowledge database. The presentations/materials discussed at this workshop can be accessed at the following links: Making do with less (data): Compressed Sensing in MRI: http://hdl. handle. net/1842/18762; Optimizing calibration kernels and sampling pattern for ESPIRiT-based compressed sensing implementation in 3D MRI: http:// hdl. handle. net/1842/18759; Multi-frame Super Resolution based on Sparse Coding: http://hdl.
handle. net/1842/ 18761; Magnetic Resonance Microimaging of a Swine Infarcted Heart: Performing Cardiac Virtual Histologies: http://hdl. handle. net/1842/18763; Proactive Evolutionary Algorithms for Dynamic Optimization at the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. gg5td [12] ; Considerations in applying compressed sensing to in vivo phosphorus MR spectroscopic imaging of human brain at 3T: http://link. springer. com/article/10.
Funding. The workshop was sponsored by the  Royal Society  through the  Newton Mobility Grant   NI150340  to E. O. -I. and M. C. V. H. M. C. V. H. is funded by  Row Fogo Charitable Trust ; R. O. R. is funded by the  Ministry of Education, Research, Culture and Sports of Valencia (Spain)  under the programme VALi+d 2015; E. O. -I. is funded by  Bogazici University , and the research presented at the workshop was supported by  TUBITAK Career Development Grant   112E036 ,  EU Marie Curie IRG  Grant  FP7-PEOPLE-RG-2009 256528 , Tubitak  1001 Research Grant   115S219 , and  Bogazici University BAP  Grant  10844SUP ; I. M. is funded by core funds from the  University of Edinburgh , including the  Scottish Funding Council ; A. J. V. B. is funded by the  Marie Sklodowska Curie scholarship  which is part of the  European Union 's  H2020 Framework Programme  ( H2020-MSCA-ITN-2014 ) under the grant agreement number  642685  MacSeNet; and V.
We present a new water percolation routine added to the 1D snowpack model Crocus as an alternative to the empirical bucket routine. This routine is based on Richards equation, and describes flow in an unsaturated porous medium governed by capillary suction and hydraulic conductivity of snow layers. We tested the Richards routine on two data sets, one recorded from an automatic weather station over the winter of 2013-2014 at Filefjell, Norway, and a simple synthetic data set. Model results using the Richards routine generally lead to thinner and denser simulated crust layers compared to the bucket routine. Wet snow layers often reach the transition between the pendular and funicular regimes, with 17% of snow layers obtaining saturation of >10% and 4. 3% of layers had saturation of >15% for the synthetic data set.
, 2016). The Richards equation when applied to snow describes water percolation through a porous ice matrix considering the water retention curve and the hydraulic conductivity of snow layers. A one-dimensional Richards equation solver was recently added to the detailed snow model SNOWPACK  (Wever et al. , 2014 (Wever et al. , , 2015)). We added a similar physical water transport routine to the snowpack model Crocus. This paper will discuss the parameterization, sensitivity of the new routine and compare difference in water percolation with the bucket routine for two data sets.
The bucket approach To describe water percolation in snow, Crocus has been using an empirical based routine, the so-called bucket routine that has been calibrated using long time series of lysimeter data  (Morin et al. , 2012)  and drainage experiments on the irreducible water content  (Coleou and Lesaffre, 1998). The bucket routine uses a holding capacity, defined by a percentage of the snow layers pore space. A snow layers "bucket" is filled up with water when water is introduced via rain or melt. Once the bucket is full, overflow occurs to fill up the subsequent snow layer's bucket, restricting water motion to downward direction. Crocus bucket routine uses 5% of a snow layers pores spaces volume as the holding capacity or "bucket size"  (Vionnet et al. , 2012). For Crocus, the holding capacity is proportional to the density of the snow layer and is independent of snow grain type or surrounding environment.
Implementation of Richards routine in Crocus Richards routine describes motion of water through an unsaturated porous matrix considering capillary driven and gravity flow. Recent developments in parameterizing snows conductivity and snowpack water retention allow for the implementation of Richards equation in layered snow pack models. In contrast to the bucket model, the Richards routine allows for upward motion of water if capillary pressure conditions are suitable. A Richards solver has recently been implemented in the SNOWPACK model  (Wever et al. , 2014). A comparison between the bucket percolation and Richards percolation in the SNOWPACK model showed that Richards routine performed better than the bucket routine for a sub-day time scale when compared to lysimeter data  (Wever et al. , 2014 (Wever et al.
Filefjell Simulation Simulation results for the Filefjell data set are presented in Fig. wetting occurs during the April melt. The wetting front reaches the bottom of the snowpack at approximately the same time independent of which routine was used (Fig. 5  A and C). The three rain/melt events that occur early season (23/24 Oct. , 15 Nov. and 26 Nov. ) pass water to the soil layers for both percolation routines. The two melt/rain events that occur after the cold period in January (7 Mar. and 6/7 Apr. ) wet the snowpack's surface before it is in an isothermal state. These two events are more pronounce and reach deeper snow layers in the simulation using Richards routine. The event on Mar 7 formed a layer with density ~300 kg m -3 (at 0. 7 m to 0. 9 m) when run with Richards routine, which is missing from simulations using the bucket routine (see Fig.
Synthetic data set's simulation The synthetic data set's results are presented in The percentage of pore space that is filled by water is the measure used to calculate "bucket size" which makes it an intuitive way to view water content for the bucket routine. The results of the bucket simulation show a uniform wetting front that features a stepped pattern (Fig. 8 A ). The stepped pattern arises from the diurnal cycle of water input at the surface. With the Richards routine, water percolates during the high and low phase of the diurnal radiation cycle, which results in a faster water front progression and a lack of the stepped pattern (Fig. 9 A ). There is also a big variance in the percentage of pore space filled with water throughout the simulation. Effects of the diurnal cycle are visible in the Richards routine's results, in the form of percolation fronts that move down the snow layers.
Liquid water content magnitude The bucket routine has a LWC upper limit of 5% pore space. There are two wetness states that are frequented in the bucket routine, the dry state and wet state at holding capacity. A snow layer spends little time over the course of a snow season in the transition between dry and at holding capacity, which can be seen in Fig (  7 C ). Routines such as the compaction and grain metamorphisms were developed using the bucket routine and rely on a nearly binary water content configuration. Using Richards routine, we obtain much higher LWCs, which constantly vary between time steps (Fig. 7  C & 9 A). Richards routine is capable of wetting snow layers to the transition between pendular and funicular regimes as defined, by  Denoth (1980)  where 17% of the simulated data sets snow layers that entered the percolation routine had > 10% of their pores filled with water.
The fiducial cross sections are corrected for detector effects and measured differentially as a function of six kinematic observables. The measured cross section integrated within the fiducial volume is 16. 8 ± 0. 8 pb. The data are compared to fixed-order QCD calculations at next-to-leading-order and next-to-next-to-leading-order accuracy as well as next-to-leadingorder computations including resummation of initial-state gluon radiation at next-to-next-toleading logarithm or matched to a parton shower, with relative uncertainties varying from 5% to 20%. c 2017 CERN for the benefit of the ATLAS Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4. 0 license.
Data sample and event selection The data used in this analysis were recorded using a diphoton trigger with E T thresholds of 35 GeV and 25 GeV for the E T -ordered leading and subleading photon candidates, respectively. In the highlevel trigger, the shapes of the energy depositions in the EM calorimeter are required to match those expected for electromagnetic showers initiated by photons. The signal efficiency of the trigger  [28] , estimated using data events recorded with alternative trigger requirements, is (99. 4 ± 0. 2)% for events fulfilling the final event selection. Only events acquired in stable beam conditions and passing detector and data-quality requirements are considered. Furthermore, in order to reduce non-collision backgrounds, events are required to have a reconstructed primary vertex with at least three associated tracks with a transverse momentum above 400 MeV and consistent with the average beam spot position.
1%. The contribution from the Higgs-boson production and subsequent decay into two photons to the measured cross sections is expected to be at the few-percent level around 120 < m γγ < 130 GeV and negligible elsewhere. Although this process is neglected in the event generation, no correction is applied given its small magnitude. The cross section in each bin is computed from the corrected number of diphoton events in the bin divided by the product of the integrated luminosity  [29]  and the trigger efficiency. Tabulated values of all measured cross sections are available in the Durham HEP database  [54]. The measured fiducial cross section is: σ fid. tot = 16. 8 ± 0. 1 (stat) ± 0. 7 (syst) ± 0. 3 (lumi) pb = 16. 8 ± 0. 8 pb. ( 1 ) The uncertainties represent the statistical (stat), the systematic (syst) and the luminosity (lumi) uncertainties.
Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, ERDF, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.
Being these texts short, without standard spelling and with specific conventions (e. g. , hashtags, emoticons), they represent an open challenge for standard argument mining approaches  (Snajder, 2017). The nature and peculiarity of social media data rise also the need of defining new tasks in the argument mining domain  (Addawood and Bashir, 2016; Llewellyn et al. , 2014). In this paper, we tackle the first standard task in argument mining, addressing the research question: how to mine arguments from Twitter? Going a step further, we address also the following subquestions that arise in the context of social media: i) how to distinguish factual arguments from opinions? ii) how to automatically detect the source of factual arguments? To answer these questions, we extend and annotate a dataset of tweets extracted from the streams about the Grexit and the Brexit news.
Mining arguments on Twitter In this section, we describe the approaches we have developed to address the following tasks: i) Argument detection, ii) Factual vs opinion classification, and iii) Source identification, on social media data. Our experimental setting -whose goal is to investigate the tasks' feasibility on such peculiar data -considers a dataset of tweets related to the political debates on whether or not Great Britain and Greece had to leave the European Union (i. e. #Brexit and #Grexit threads in Twitter).
Experimental setting Dataset. 3 The only available resource of annotated tweets for argument mining is DART  (Bosc et al. , 2016a). From the highly heterogeneous topics contained in such resource (i. e. the letter to Iran written by 47 U. S. senators; the referendum for or against Greece leaving the EU; the release of Apple iWatch; the airing of the 4th episode of the 5th season of the TV series Game of Thrones), and considering the fact that tweets discussing a political topic generally have a more developed argumentative structure than tweets commenting on a product release, we decided to select for our experiments the subset of the DART dataset on the thread #Grexit (987 tweets). Then, following the same methodology described in  (Bosc et al. , 2016a) , we have extended such dataset collecting 900 tweets from the thread on #Brexit.
From the original thread, we filtered away retweets, accounts with a bot probability >0. 5  (Davis et al. , 2016) , and almost identical tweets (Jaccard distance, empirically evaluated threshold). Given that tweets in DART are already annotated for task 1 (argument/non-argument, see Section 2. 2), two annotators carried out the same task on the newly extracted data. Moreover, the same annotators annotated both datasets (Grexit/Brexit) for the other two tasks of our experiments, i. e. i) given the argument tweets, annotation of tweets as either containing factual information or opinions (see Section 2. 3), and ii) given factual argument tweets, annotate their source when explicitly cited (see Section 2. 4). Tables 1, 2 and 3 contain statistical information on the datasets. 3 Annotated data are available upon request to the authors.
Inter annotator agreement (IAA)  (Carletta, 1996)  between the two annotators has been calculated for the three annotation tasks, resulting in κ=0. 767 on the first task (calculated on 100 tweets), κ=0. 727 on the second task (on 80 tweets), and Dice=0. 84  (Dice, 1945)   4  on the third task (on the whole dataset). More specifically, to compute IAA, we sampled the data applying the same strategy: for the first task, we randomly selected 10% of the tweets of the Grexit dataset (our training set); for task 2, again we randomly selected 10% of the tweets annotated as argument in the previous annotation step; for task 3, given the small size of the dataset, both annotators annotated the whole corpus. • Twitter-specific (T): punctuation, emoticons; • Syntactic/Semantic (S): we have two versions of dependency relations as features, one being the original form, the other generalizing a word to its POS tag in turn.
Task 1: Argument detection The task consists in classifying a tweet as being an argument or not. We consider as arguments all those text snippets providing a portion of a standard argument structure, i. e. , opinions under the form of claims, facts mirroring the data in the Toulmin model of argument  (Toulmin, 2003) , or persuasive claims, following the definition of argument tweet provided in  (Bosc et al. , 2016a,b)  Most of the miss-classified tweets are either ironical, e. g. : If #Greece had a euro for every time someone mentioned #Grexit and #Greferendum they would probably have enough for a bailout. #GreekCrisis that was wrongly classified as argument, or contain reported speech, e. g. : Jeremy Warner: Unintentionally, the Greeks have done themselves a favour. Soon, they will be out of the euro http://t.
co/YmqXi36lGj #Grexit that was wrongly classified as non argument. Our results are comparable to those reported in  (Bosc et al. , 2016b)  (they trained a supervised classifier on the tweets of all topics in the DART dataset but the iWatch, used as test set). Better performances obtained in our setting are most likely due to a better feature selection, and to the fact that in our case the topics in the training and test sets are more homogeneous.
All the other argument tweets are considered as "opinion" (see example (c) below). (a) 72% of people who identified as "English" supported #Brexit (while no majority among those identifying as "British") https://t. co/MuUXqncUBe (b) #Hollande urges #UK to start #Brexit talks as soon as possible. https://t. co/ d12TV8JqYD. (c) Trump is going to sell us back to England. #Brexit #RNCinCLE Our dataset contains 230 factual argument tweets and 516 opinion argument tweets for Grexit (training set), and 138 factual argument tweets and 575 opinion argument tweets for Brexit (test set). To address the task of factual vs opinion arguments classification, we apply the supervised classification algorithms described in Section 2. 1. Tweets from Grexit dataset are used as training set, and those from Brexit dataset as test set.
Task 3: Source identification Since factual arguments (as defined above) are generally reported by news agencies and individuals, the third task we address -and that can be of a value in the context of social media -is the recognition of the information source that disseminates the news reported in a tweet (when explicitly mentioned). For instance, in: The Guardian: Greek crisis: European leaders scramble for response to referendum no vote. http://t. co/cUNiyLGfg3 the source of information is The Guardian newspaper. Such annotation is useful to rank factual tweet-arguments depending on the reliability or expertise of their source in news summarization or fact-checking applications, for example. Our dataset contains 79 factual argument tweets where the source is explicitly cited for Grexit (training set), and 40 factual argument tweets where the source is explicitly cited for Brexit (test set).