dataesr/scientific-paragraphs-categorization huggingface dataset:
The network was constructed using a software script developed in R using the package igraph 0. 7. 1 (ref. 57) and interrogated using statistical tools to describe its properties. The degree distribution of sponges and OTUs was analysed to assess the heterogeneity of the network in terms of node connectivity. Degree distributions depict the statistical probability distribution of finding nodes with a certain degree (number of other nodes it is connected to). A variant of the degree distribution was employed: the cumulative degree distribution, which has the same probability distribution, but shows the probability of finding nodes with that degree or less. These probability distributions (one for the OTUs and another for the sponges) were obtained using the cumsum function in R (ref. 58). In addition, we fitted truncated power law and exponential functions to the OTUs and sponges cumulative degree distributions, respectively.
We use Markov chain Monte Carlo simulation methods in R using the runjags package  60  to sample from the joint posterior distribution of the model parameters. We ran 10 independent chains with dispersed initial values for 5e6 iterations, discarding the first 2e6 samples of each chain as burn-in and thinned the remainder to every 50th sample. We evaluated convergence of model parameters by visually inspecting trace and density plots. In addition, to ensure good mixing of a i,j we calculated the number of jumps g i,j did between its two states (0 and 1). Finally, to build the representative networks, we analysed the interaction and sign structure of the posterior distribution for the interaction coefficient a i,j. a i,j is a full probability distribution, hence it contains the probability of OTU j having a non-zero per capita effect on the growth of OTU i (interaction strength), and vice versa.
Using all information in a i,j , we constructed a representative network for each host species as a mean of visualizing the most 'credible' network structure. This was done by mapping the posterior average number of links onto a i,j , and in doing so, extracting the links with the highest probability of non-zero interactions. This was done by custom-written R scripts. As a way of validating the structure of each representative network, we compared the connectance of each representative network with the posterior average connectance for a i,j for each host species. The representative networks was plotted using the igraph package  57  in R. Identification of sponge-specific and sponge/coral-specific clusters. A representative sequence from each OTU was taxonomically assigned using a BLAST  61  search against a curated ARB-SILVA database containing 178 previously identified sponge-specific clusters (SC) and 32 sponge/coral-specific clusters (SCC)  14.
For each BLAST search, the 10 best hits were aligned to determine sequence similarities. The most similar OTU sequence to the respective reference sequence within the database was then assigned (or otherwise) to an SC or SCC based on the application of a 75% similarity threshold (that is, a sequence read was only assigned to a cluster if it was more similar to the members of that cluster than to sequences outside the cluster and its similarity to the most similar sequence within that cluster was above 75%). In cases where the assignment of the most similar sequences was inconsistent, a majority rule was applied, and the OTU sequence was only assigned to an SC or SCC if at least 60% of the reference sequences were affiliated with this cluster. Phylogenetic analysis of host species and correlation with symbiont communities.
Our phylogenetic analysis considered 61 sponge species for which at least one of three gene sequences (small subunit of nuclear ribosomal RNA [18S], large subunit of nuclear ribosomal RNA [28S] or mitochondrial cytochrome oxidase subunit 1 [cox1]) could be obtained from GenBank. For 39 of the 61 species (64%), sponge gene sequences were also obtained from at least one identical specimen collected for the current study. The biologists who collected specimens were very familiar with sponge taxonomy and identification, and we intentionally focused our study on easily recognized and common taxa to avoid misidentification. Some genetic markers used for sponge taxonomy are easier to sequence than others and here we used all available data for the most frequently used markers for sponge molecular systematics.
For each gene, sequences were aligned using the default options of MAFFT  7. 017 (ref. 62). Each alignment was analysed using the Gblocks Server  63  to eliminate non-conserved regions; the resulting three alignments were then concatenated using the Geneious software (version 6. 1. 8, Biomatters Limited). The phylogeny was constructed with MrBayes version 3. 2. 1 (ref. 64), using the computational resources provided by CIPRES  65. Within MrBayes, five partitions (18S, 28S and the three codon positions of cox1) were specified and separate general time reversible models of evolution for each partition were estimated, incorporating a gamma distribution of substitution rates among sites and a proportion of invariant sites (GTR þ I þ G) as suggested by ref. 66. The Homoscleromorpha Pseudocorticium jarrei and Plakortis halichondrioides (the only non-Demospongiae sponges of the taxon set) were constrained as an outgroup and the independent gamma rates relaxed-clock model with a birth-death process was implemented.
The phylogenetic analysis included three parallel runs of 10 million generations, each utilizing four Markov chains and sampling every 100 generations. At the end of the runs, we assessed convergence by the average s. d. of split frequencies, which was 0. 03, and the potential scale reduction factors of all parameters, which ranged from 1. 00 to 1. 01. Following a burn-in of 25%, the trees sampled by each of the three runs were summarized into a consensus tree. The diversity of OTUs associated with each host species was evaluated by calculating OTU richness, the Shannon index of diversity, and the inverse Simpson index of diversity. All indices demonstrated clear differences among sponge species (Po0. 001). Beta-diversity analysis was conducted by calculating the Bray-Curtis distance among specimens, and testing for host species differences in this distance using the function adonis in the R package vegan  53.
D. sp. D. ava D. eth D. fra E. ala E. fer E. for Geodia G. bar H. pan H. pha H. tub Hippo H. alt H. ere I. bas Iotro I. bir I. fel I. oro I. str I. var Lisso L. col M. lae M. lax M. lin  Myxil N. erec Parat P. fici P. ven P. fict Phyll P. int Plako P. hal P. jar R. odo S. fas S. vag S. are S. mao Styli S. car S. mas S. div T. ign Tethya Xesto X. boc X. mut X. 1055 X. tes Water Sedim Acknowledgements T. T. and N. S. W. were funded through  Australian Research Council Future Fellowships   FT140100197  and  FT120100480 , respectively. S. L. L. and P. M. E. were funded by the  Spanish Government  project  MARSYMBIOMICS CTM2013-43287-P. R. C. was funded by the  Portuguese Foundation for Science and Technology  through the  Investigator Grant   IF/01076/2014  and the project  UID/Multi/04326/2013.
C. G. E. and R. W. T. were supported by grants from the  US National Science Foundation  ( DEB-0829986  and  DEB-1208340 ). A. C. -F. was supported by the  UNESCO L'Oreal Fellowship for Young Women in Science. U. H. and L. M. S. received funding from the  EU- FP7 Program  ( KBBE. 2012. 3. 2-01 ;grant no. 311932 ; SeaBioTech). J. M. M. was supported by the  French Laboratory of Excellence Project  ' TULIP'  ( ANR-10-LABX-41 ;  ANR-11-IDEX-002-02 ) and by a  Region Midi-Pyrenees Project  ( CNRS 121090 ). We thank  Professor Russell Hill  and members of his laboratory for providing samples and hosting a community data analysis workshop as part of the 2nd  International Symposium for Sponge Microbiology. We also thank  Emmanuelle Botte  and  Christine Gernert  for DNA extractions and the  Earth Microbiome Project  for sequencing and sample preparation.
The information units selected in this research are characters and POS tags. With the character n-grams the goal is to extract as many stylistic elements as possible: characters frequency, use of suffixes (gender, number, tense, diminutives, superlatives, etc. ), use of punctuation, use of emoticons, etc  (Stamatatos, 2006; Stamatatos, 2009). POS n-grams provide information about the structure of the text: frequency of grammatical elements, diversity of grammatical structures and the interaction between grammatical elements. The POS tags were obtained using the Freeling 5  POS tagger, which follows the POS tags proposed by EAGLES 6. To fully control the standardization process and make it independent of a detector of names, we preferred to perform a specific normalization for both datasets instead of using the Freeling functionalities  (Padró and Stanilovsky, 2012).
RESULTS In order to evaluate the performance of the model in all the datasets, a group of classical measures was implemented. Accuracy (A), Precision (P), Recall (R) and F-score (F)  (Manning and Schütze, 1999)  were used to evaluate gender and age prediction. In relation to the personality traits prediction in the PAN-CLEF2015 corpus, the Root Mean Squared Error (RMSE) measure was used.
All ages lie in stratigraphic order (within 2s uncertainty) and have typical uncertainties of 1-3%. The age models for both records were first calculated using a Bayesian-Monte Carlo approach  12  and then refined using the intra-site correlation age modelling programme (iscam)  13  written in MATLAB (see the Methods for details). The age models give an average growth rate of B125-135 mm per year for both stalagmites. The d 18 O and d 13 C records are based on 1,157 measurements performed on calcite powders micromilled along the central growth axes of LR06-B1 and LR06-B3. The Mg/Ca and Sr/Ca ratios in stalagmite LR06-B1, previously presented in Griffiths et al. 14  , were analysed on H 3 PO 4 residues following calcite dissolution for stable isotope analysis. The strong degree of replication between the two stable isotope profiles (Fig.
07% for d 18 O and d 13 C. All results are expressed as the deviation in per mil (%) between the sample and Vienna Peedee Belemnite standard using the delta notation. Trace element analysis. The Mg/Ca and Sr/Ca ratios in stalagmite LR06-B1 were analysed on H 3 PO 4 residues after calcite dissolution for stable isotope analysis. The samples were analysed for Mg, Sr and Ca on a Varian Liberty 4,000 inductively coupled plasma atomic emission spectrometer. Molar concentrations of the elements were calculated from inductively coupled plasma atomic emission spectrometer intensities using four internal working standards of known concentration and a blank, with concentrations expressed as ratios to Ca. Acid blank corrections were applied to both sample and standard data before conversion of the data to ratios.
The relative standard deviation of replicate standards of Ca, Mg and Sr was 1. 5%, 1. 6% and 2. 4%, respectively. These data were previously published in Griffiths et al. 14, 22, 30  PC analysis. The speleothem hydroclimate record was constructed using the d 18 O and d 13 C for stalagmites LR06-B1 and LR06-B3, and trace elements (Mg/Ca, Sr/Ca) for LR06-B1. Due to the average resolution among all records being B5 years, we linearly interpolated each proxy record to a common 5-year temporal resolution before analysis. To extract the dominant climate signal embedded within the stable isotope and trace element records, we performed an unrotated PC analysis on the six profiles, where the leading PC (explains 50% of the variance) represents the dominant hydrologic signal common to all individual time series.
Because other factors besides rainfall can affect the d 18 O (for example, cave temperature, changes in ratio of summer versus winter moisture delivery) and d 13 C (for example, above-cave vegetation, soil productivity, cave ventilation) and also the trace elements (for example, cave temperature, speleothem growth rate), this method has proven useful in isolating the dominant hydrologic signal from other potential influences  30. However, the d 18 O time series load weakly on PC1, and load much more strongly on PC2. This suggests that factors in addition to local rainfall amount likely influenced the speleothem d 18 O. Confidence intervals (95%) for the PCs were calculated using bias-corrected and accelerated nonparametric bootstrap technique performed in MATLAB. General circulation modelling.
Acknowledgements We thank the  Indonesian Institute of Sciences (LIPI)  for operational support and are particularly grateful to  Neil Anderson ,  Garry Smith ,  Bambang Suwargadi  and  Linda Ayliffe , who provided expertise in cave conservation and technical assistance. Heather Scott-Gagan  provided technical assistance at  ANU. The work in Indonesia was carried out under LIPI research permit number  04057/SU/KS/2006. Financial support for the research was provided by  Australian Research Council Discovery  grants  DP0663274  and  DP1095673  to M. K. G. , R. N. D. , J. -X. Z. , J. C. H. and W. S. H. This research was also supported by a  2010-2012 NOAA/UCAR Climate  and  Global Change Postdoctoral Fellowship  to M. L. G. and an internal summer 2013 research grant through  William Paterson University.
However, due to computational cost, the global models have to use a limited number of modes or bins to describe the aerosol distribution. In addition, the validation of simulations is often restricted to datasets well documented over the globe, i. e. surface PM concentrations (without speciation) and Aerosol Optical Depth (AOD) but with a low spatial resolution. CTMs at the regional scale simulate the same processes but usually with a more accurate descriptions for the processes involved in the aerosols formation and evolution. At the regional scale, air quality models tend to underestimate PM and the main discrepancies are often attributed to a lack of emissions or difficulties to reproduce stable meteorological conditions during PM episodes  (Bessagnet et al. (2015) ;  Solazzo et al. (2012) ).
The EMEP network surface concentrations For the comparisons between observed and modeled concentrations, the background stations measurements performed during campaigns or in a routine way are used. These measurements are available on the EBAS database (http://ebas. nilu. no/) and are used here on a mean daily basis. Only the background stations are used due to the coarse horizontal resolution of the model. Depending on each station, several pollutants are measured: O 3 , NO 2 , SO 2 , PM 2. 5 and PM 10. For some stations, the inorganic species sulfate, nitrate and ammonium are used. All the stations are listed in Table  1  and located in Figure  1.
Emissions fluxes calculations The anthropogenic emissions are estimated using the same methodology as the one described in  Menut et al. (2012)  but using the global emission database EDGAR-HTAP annual totals as input data. The EDGAR-HTAP project compiled a global emission dataset with annual inventories for CH 4 , NMVOC, CO,SO 2 , NO x , NH 3 ,PM 10 , PM 2. 5 , EC and OC, at the national or regional scale that are likely to be acceptable for policy makers in each region of the world. This compilation of different official inventories from EMEP, UNFCCC, EPA for USA, GAINS for China and REAS was first gap-filled with global emission data (Janssens-Maenhout et al. (  2012 )). The version 2 of this emission inventory was available for the year 2010. The 'fine' part of H 2 SO 4 corresponds to 1% of the SO x anthropogenic emissions and thus to primary sulfuric acid.
These emissions were already used in this region and for this period in  Menut et al. (2015a). The biogenic emissions are calculated using the MEGAN emissions scheme  (Guenther et al. , 2006)  which provides emission fluxes of nitrogen monoxide, isoprene and monoterpenes. The mineral dust emissions are calculated using new soil and surface databases described in  Menut et al. (2013b)  and with a spatial extension of potentially emitting areas in Europe as described in  Briant et al. (2014). The dust production model used is the one of  Alfaro and Gomes (2001). The sea salt emissions are calculated following the  Monahan (1986)  parameterization: dF dr = 1. 373 u 3. 41 10 r -3 1 + 0. 057 r 1. 05 × 10 1. 19 exp(-B 2 ) with B = 0. 38 -log(r) 0. 65 (1) F is the flux of sea salt particle number in unit "particles m -2 s -1 µm -1 ", r the particle radius in µm and u 10 is the wind speed at 10 m (in m s -1 ).
Acknowledgements. INERIS is funded by the  French Ministry in charge of Ecology. The EBAS database has largely been funded by the  CLRTAP-EMEP  programme, AMAP and by  NILU  internal resources. Specific developments have been possible due to projects like EU-SAAR (EBAS web interphase),  EBAS-Online  (upgrading of database platform) and HTAP (import and export routines to build a secondary repository for in support of www. htap. org. A large number of specific projects have supported development of data and metadata reporting schemes in dialog with data providers (CREATE, ACTRIS and others). For a complete list of programmes and projects for which EBAS serves as a database, please consult the information box in the Framework filter of the web interface.
In NSCLC models, OTX015 was equally active in both EML4-ALK positive and negative cell lines, whereas in SCLC models the presence of functional RB1 protein, which controls cell progression at G1, may be related to the final biological outcome of OTX015. Gene expression profiling in NSCLC and SCLC cell lines showed that OTX015 affects important genes and pathways with a very high overlapping between both sensitive and resistant cell lines. These data support the rationale for the OTX015 Phase Ib (NCT02259114) in solid tumors, where NSCLC patients with rearranged ALK gene or KRAS-positive mutations are currently being treated.
OTX015 has shown potent in vitro and in vivo antitumor activity against a range of cell lines derived from hematologic malignancies  [14, 16]  and solid tumors including neuroblastoma, glioblastoma, breast and prostate cancers  [15, [17] [18] [19] [20]. In some models, OTX015 biological activity was shown to modulate transcriptional pathways characteristic of MYC functioning, however overt effects on other key transcriptional pathways such as AP-1 and NF-kB were also observed  [14]. An inhibitory effect on MYCN function has been reported in translational models of neuroblastoma  [15]. OTX015 is the first BET inhibitor to have moved into the clinic, with three phase Ib clinical trials initiated in hematologic malignancies (NCT01713582)  [21, 22] , selected solid tumors (NCT02259114) and glioblastoma multiforme (NCT02296476).
05) (Figure  1A ) after 72 h-treatment. No modulation of cell cycle phases was observed at any time point for the OTX015-resistant cell line A549. Similar results were obtained with JQ1 (data not shown). The effect of 500 nM OTX015 on mRNA levels of MYC and MYCN were evaluated in the four OTX015-sensitive and one resistant cell lines. As shown in Figure  1B , OTX015 treatment resulted in a rapid and sustained downregulation of MYC in HOP92 cells. MYCN was downregulated in HOP62 and H3122 after 4 and 24 h, while in the resistant cell line, A549 after 24 h only. On the other hand, it was upregulated in HOP92 after 4 h but downregulated at 24 h (Figure  1B ). At the protein level, MYC was downregulated after 24 h in the MYC amplified cell line HOP92, which was maintained up to 72 h (Figure  1C ).
In addition, OTX015 presented good oral bioavailability and induced tumor regression in EML4-ALK(+) xenografts. It inhibited MYC expression however this was not related to its sensitivity in cancer cell lines. In addition, we provide a rationale for investigating OTX015 as a single agent and combined with crizotinib in NSCLC-ALK(+) patients. These findings were the scientific support for the OTX015 dose escalation study in solid tumors including NSCLC patients (NCT02259114).
Gene expression profiling (GEP) RNA samples isolated using Trizol extraction from 2 × 10 6 cells treated with 500 nM OTX015 or 0. 1% DMSO were processed using the Human HT-12 v4 Expression BeadChip (Illumina), as previously described  [14]. Data processing and statistical analysis were performed using R/Bioconductor. Transcript mapping was based on HG19 using manufacturer supplied annotation. Data were quantile normalized and differential expression analysis was performed using limma  [53]. Functional annotation was performed using gene set enrichment analysis (GSEA) method using the GSEA gene sets  [54] , the Signature Database  [55] , and previously reported gene sets  [14]. Differential expression of gene sets was calculated using the gene set variation analysis (GSVA) method  [56]. The GEP data have been deposited in NCBI's Gene Expression Omnibus  [57]  and are accessible through GEO Series accession number GSE72961 (https://www.
Chou-Talalay combination studies For combination studies, 20 × 10 3 cells/well were seeded in a 96-well plate and treated 48 h later with increasing concentrations of OTX015 alone or combined with crizotinib at a range of concentrations. GI 20 , GI 40 , GI 60 and GI 80 drug concentrations were evaluated. To assess drug-drug interactions, dose-response data were evaluated with the CalcuSyn program (Biosoft) which applies median effect methodology to estimate a combination index (CI), CI values < 0. 9 indicate synergism, 0. 9 to 1. 1 indicate additive effects, and > 1. 1 indicate antagonism  [58].
Data preparation Information on sequenced genomes was downloaded from the GOLD database (www. genomesonline. org) (version of 07 March 2013). Gene and protein sequences were downloaded from NCBI via iMOMi  [13]  except for those of Megamonas rupellensis DSM 1994 and Caloramator australicus KCTC 5601 which were downloaded from Integrated Microbial Genomes data warehouse (www. img. jgi. doe. gov).
Phylogenetic analysis The 16S rRNA gene sequences used in this study were retrieved in fasta format from GenBank (NCBI-GenBank Flat File Release 195. 0). Phylogenetic analysis was conducted using the Sea-View 4. 4. 1 platform  [14]. MUSCLE  [15]  was used for multiple sequence alignment with default parameters and blocks of evolutionary conserved sites were selected by Gblocks  [16]. The tree was computed using phyML 3. 0  [16]  based on the Maximum Likelihood method and visualized using iTOL v3. 0  [17]. The 16S rRNA gene sequence of Methanobrevibacter smithii ATCC 35061 was used as an outgroup for the analysis.
Closely related gut and non-gut bacterial species constitute distinct functional groups Bacterial diversity in the gut is largely restricted to two major phyla, Firmicutes and Bacteroidetes. In this study, we chose to focus on gut bacteria from the Firmicutes phylum and compare the functions encoded in their genomes with those encoded in closely related bacteria from other environments. Information on sequenced genomes was downloaded from the GOLD database (www. genomesonline. org) and filtered to select publicly available complete genomes from the Firmicutes phylum. Among these, "gut species" were identified by comparison with the gut metagenome data presented in  [1] , choosing species that in the latter study were marked "frequent" (i. e. highly abundant in the gut) and/or "common" (i. e.