dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Part of this work was funded by  ESA projects Multi-TASTE  and VALID, by the  EU  under  FP6  project  GEOmon  ( FP6-2005-Global-4-036677 ), and by  ESA's CCI Ozone project. D. Hubert ,  A. Keppens  and  T. Verhoelst  acknowledge national funding from the  Belgian Science Policy Office (BELSPO)  and  ProDEx projects SECPEA  and  A3C. K. Stebel  acknowledges funding from the  ESA/ProDEx projects Ex Val  ( C90190 ,  CN1-4 ,  2005CN1-4, -2011 )). J. A. E. van  Gijsel  and  D. Swart  acknowledge support from the  Dutch Ministry of Infrastructure  and Environment. Work performed at the  Jet Propulsion Laboratory  was done under contract with the  National Aeronautics and Space Administration. We are also grateful to  C. De Clercq ,  D. Pieroux  and  S. Vandenbussche  for their valuable input.
The ozonesonde and lidar data used in this publication were obtained as part of  WMO's Global Atmosphere Watch (GAW)  and two of its main contributors, namely, the  Network for the Detection of Atmospheric Composition Change (NDACC)  and the  Southern Hemisphere ADditional OZonesondes programme (SHADOZ). The authors acknowledge the meticulous and sustained work of the PIs and staff at ozonesonde and lidar stations to acquire and maintain long-term ozone data records of high quality. The data records are publicly available via the NDACC Data Host Facility (http://www. ndacc. org), the SHADOZ archive (http://croc. gsfc. nasa. gov/shadoz)and the  World Ozone  and  Ultraviolet Data Centre  (http://www. woudc. org). NDACC and SHADOZ are supported by meteorological services and space agencies from many countries, with archives funded by  NASA  and  NOAA.
We acknowledge the work by  F. Posny , as PI of the ozonesonde observations at Réunion Island. The authors also thank the satellite science and processing teams and the contributing space agencies. Measurements from the  SAGE  and HALOE missions are provided and maintained through support from  NASA's Earth Science Division. The Atmospheric Chemistry Experiment (ACE) , also known as SCISAT, is a Canadian-led mission mainly supported by the  Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIA-MACHY)  is a joint contribution of Germany, The Netherlands and  Belgium  to  ESA 's environmental satellite Envisat and is funded by the  German (DLR)  and  Dutch (NSO)  space agencies with Belgian.
Our analysis is based on the operational SCIAMACHY SGP v5. 02 data set. The v2. 9 data record by the IUP Bremen Level-2 processor drifts by -5 % decade -1 between 30 and 35 km (Lambert et al. , 2014)  Atmos. Meas. Tech. , 9, 2497-2534, 2016   www. atmos-meas-tech. net/9/2497/2016/.
In the work that follows, we use the symbol K Trans to indicate experimental or simulated measurements and PS to indicate the permeability-surface area product used to generate simulated data. The Patlak model can be seen as a special case of the modified Tofts model, which ignores backflux from the EES into the blood plasma compartment. Consequently, it only allows for the estimation of the two parameters K Trans and v p : C t ðtÞ ¼ v p C p t ð Þ þ K Trans ∫ t 0 C p τ ð Þ dτ: ð4Þ If it is further assumed that there is no transfer of contrast to the EES, the one-parameter steady-state model  (Sourbron and Buckley, 2013 ) is obtained: C t ðtÞ ¼ v p C p t ð Þ: ð5Þ Model fitting was performed using in-house software programmed in MATLAB (MathWorks, Natick, MA, USA) by non-linear minimisation of the sum of squared residuals.
It is therefore important to optimise study MRI protocols for measurement of low-level permeability and to assess the magnitude and consistency of drift in future studies by performing non-contrast experiments in volunteers as done here or in a subset of patients  (Armitage et al. , 2011; Cramer and Larsson, 2014)  and simulations to predict the likely effect on study findings. These considerations are particularly critical for multi-centre studies. Supplementary data to this article can be found online at http://dx. doi. org/10. 1016/j. neuroimage. 2015. 10. 018.
Acknowledgements This work was funded by  Wellcome Trust  (SDM, recruitment and MRI scanning costs; grant  088134/Z/09/A ),  Row Fogo Charitable Trust  (MCVH, AKH),  Age UK (SMM) ,  NHS Lothian Research and Development Office (MJT) ,  Scottish Funding Council  and the  Chief Scientist Office of Scotland  for funding the  Scottish Imaging Network: A Platform for Scientific Excellence  ("SINAPSE"; JMW, radiography staff). We thank  K. Shuler  for providing expert administrative support during data collection, analyses and manuscript preparation.
2012). Previous studies on the vegetation of Khakassian steppes were mainly devoted to plant community classification  (Kuminova et al. 1976; Makunina 2006; Ermakov 2012) , conservation assessment of communities with Red Data Book species  (Polyakova 2013 ) and large-scale mapping  (Ermakov et al. 2014). One study addressed diversity-environment relationships of steppe, forest and tundra communities across along bioclimatic and edaphic gradient in a region adjacent to our study area  (Chytry ´et al. 2007). However this study was restricted to vascular plants and had a fixed plot size of 100 m 2. We sampled vascular plant, bryophyte and lichen composition in nested plots of seven different sizes (0. 0001-100 m 2 ) along topographic, edaphic and bioclimatic gradients, using the well-established multi-scale sampling approach of the EDGG Research Expeditions (see Dengler 2009b for a description of the methodology), to answer the following questions:(i) How rich in plant species are the natural steppes of Southern Siberia compared to natural and semi-natural grasslands in other regions of the Palaearctic? (ii) What are the main environmental drivers of the diversity patterns in these steppes? (iii) What are the diversity-environment relationships and do they vary between spatial scales and among different taxonomic groups?.
The median and the range of soil depth (the difference of maximum and minimum measurement within plot) were used in the analyses. Soil sample was taken to measure pH (in water), content of humus, CaCO 3 and soil skeleton content (%) of a mixed sample from five randomly placed sub-samples from the upper 10 cm layer. We are aware that the microhabitat conditions in plots of sizes other than 10 m 2 could differ from the measured values, but we nevertheless use those values to model richness patterns within all sampled spatial scales. We assume that within-plot variation of measured factors is smaller than between-plot variation. Moreover, measuring all microhabitat variables within all spatial grains would not have been feasible. The plot data are stored in and available from the Database Species-Area Relationships in Palaearctic Grasslands (EU-00-003;  Dengler et al.
65 K per 100 m of elevation. Precipitation was computed from precipitation-altitude charts compiled for each of the bioclimatic sectors of the Altai-Sayan region, defined on the basis of their precipitation regime  (Polikarpov et al. 1986 ). Evapotranspiration was calculated by the climatic model on the basis of temperature and air humidity during the growing season. We decided against using the WorldClim precipitation data  (Hijmans et al. 2005 ; http://www. worldclim. org/), because they appeared unrealistic for our study area when compared to local sources.
14. ; Barton ´2015). This approach calculates AICc values for the full model and all partial models derived by any possible combination of 1 to all variables of the full model. The relative importance of each variable is then calculated by summing the Akaike weights over all models that contain the specific variable  (Johnson and Omland 2004). Importance values range from 0 to 1, with values close to 1 indicating that a variable is included in all statistically plausible models. Analyses of richness-environment relationships were finally carried out for data collected within plots of the area 10 m 2 (n = 126) for overall species richness and separately for different taxonomic groups as vascular plants, non-vascular plants (bryophytes and lichens together) and bryophytes and lichens separately.
Moreover, to identify differences in important predictors between spatial scales, nested-plot series (data collected from spatial scales from 0. 0001 m 2 to 100 m 2 , n = 77 per scale but 39 for 100 m 2 ) were analysed for total species richness, vascular and non-vascular plants. The assumption of equidispersion was evaluated using function dispersion test R-package AER version 1. 2-4 tests  (Kleiber and Zeileis 2008). As significant overdispersion was found particularly for overall species richness, all analyses were repeated using negative binomial generalized linear models. Results did not differ qualitatively from those of the GLMs (Online Resource 1). No significant spatial autocorrelation was detected in the residuals of any model when tested using function moran. test in R-package spdep version 0.
Species richness at different spatial scales In the whole dataset, we identified 498 taxa: 352 vascular plants (71 %), 75 bryophytes (15 %), 69 lichens (14 %) and two macroscopic taxa of terricolous ''algae''. Among vascular plants, the most frequent graminoid species were Stipa krylovii, Helictotrichon schellianum and Carex humilis, and the most frequent forbs were Thalictrum foetidum, Aster alpinus, Bupleurum scorzonerifolium, Galium verum, Hedysarum gmelinii and Schizonepeta multifida (Online Resource 3). Regarding non-vascular plants, the most frequent species were Cladonia pyxidata, Peltulaeuploca, Barbula unguiculata, Bryum caespiticium and Weissia condensa. Mean total species richness varied between 2. 3 species at the smallest spatial scale (0. 0001 m 2 ) and 73. 4 species at 100 m 2 scale (Table  2 ).
Richness-environment relationships In our dataset, the environmental variable most markedly controlling species richness of vascular plants (and, less strongly, of non-vascular plants, especially lichens) was heat load index, with a negative correlation. This is very similar to the findings by  Kuzemko et al. (2016, in this issue)  for the Podolian steppes in Ukraine and by  Turtureanu et al. (2014)  for Transylvanian semi-natural grasslands. For Podolia, this finding was explained in that south-facing, steep slopes in a summer-warm climate give rise to a habitat with a marked drought stress that prevents colonization by many species of the regional species pool  (Kuzemko et al. 2016 , in this issue). In the extreme continental climate of Khakassia, the causal mechanism for the negative correlation between richness and heat load could also take place in spring, when solar radiation on south-facing slopes is already high but temperatures are still low and the soil is still frozen, leading to strong physiological drought (see  Jarvis and Linder 2000).
2007) , which can act at different spatial scales (for example through cryogenic processes). It is interesting to note that disturbance (grazing, mowing) was found in similar studies as an important predictor variable  (Lo ¨bel et al. 2006; Turtureanu et al. 2014; Kuzemko et al. 2016 , in this issue) positively influencing species richness at different spatial scales. For instance, in Transylvania it had a strong positive effect across all plot sizes between 0. 1 and 100 m 2 , but in the Khakassian dataset it was important for total species richness only at 0. 1 m 2. At the 10-m 2 scale its influence was rather weak, but still positive, for all taxonomic groups except lichens. The influence of grazing on grassland species richness strongly depends on its intensity  (Dupreand Diekmann 2001).
Acknowledgments The study was jointly planned by J. D. (methodology) with N. E. and M. A. P. (site selection and field work). All authors except M. J. S. were involved in the field sampling, while M. J. S. , I. D. , R. J. and J. D. carried out the statistical analyses and G. F. , Ł. K. and I. D. created the maps. The text was drafted by M. A. P. , I. D. , G. F. , Ł. K. and J. D. with all authors contributing to its revision. We are grateful to the  administration of the ''Khakassky'' nature reserve  for allowing this research and supporting it logistically. We thank  Dieter Frank  who contributed much to the field sampling of plants and to  Svetlana Lebedeva  for the excellent food supply under field camp conditions in Podzaploty. We thank  Olga Zyryanova  for determination of critical lichens,  Mikhail S.
Pancreatic cancer mouse models Pancreatitis-induced tumorigenesis Acute pancreatitis was induced at 6 to 8 wk of age in Ptf1a +/Cre ;Kras +/LSL-G12D (Kras) mice by administration of eight hourly intraperitoneal injections of 100 µg of caerulein per kilogram of body weight (Sigma-Aldrich) over two consecutive days as described previously  (Morris et al. 2010; Mazur et al. 2014). Mice were treated as indicated with 30 mg/kg MAPKAPK2/3 inhibitor PF-3644022 intraperitoneally twice daily or 10% vehicle (2-hydroxypropyl)-β-cyclodextrin (Sigma-Aldrich). Pancreatic lesions were analyzed 7 d after the last injection. In parallel, animals were analyzed at day 0 to confirm the caerulein-induced damage. None of the treatments affected the initial pancreatic injury (data not shown). Spontaneous model of PanIN development PanIN progression was analyzed in Ptf1a +/Cre ;Kras +/LSL-G12D (Kras) and Ptf1a +/Cre ;Kras +/LSL-G12D ; Smyd2 loxP/loxP (Kras;Smyd2) mice aged for 6 mo.
3014), Ki67 (1:1000; BD Bioscience, no. 550609), MUC5AC (1:500; NeoMarkers, no. 145P1), pERK1/2 (1:500; Cell Signaling, no. 4370), pH3 (1:1000; Millipore, no. 06-570), and SMYD2 (1:200; Sigma-Aldrich, no. HPA029023). Sections were developed with DAB and counterstained with hematoxylin. Pictures were taken using a Zeiss microscope equipped with Axiovision software. Analysis of the tumor area and immunohistochemical analysis was done using ImageJ software by measuring pixel units. Human pancreatic and lung adenocarcinoma samples were obtained from deidentified surgical specimens and confirmed by a certified pathologist (E. B. Kaznowska).
Serum analysis Cytokine concentrations in serum were measured using the mouse inflammatory cytokine and chemokine Multi-Analyte ELISArray kit (Qiagen) according to the manufacturer's specifications. Data are presented as relative to the wild-type and treatment controls. Sera were collected from the blood samples of individual mice at the end point of the experiment under terminal anesthesia following a protocol for cardiac puncture. Serum samples were separated from blood within 1 h following blood collection by centrifugation at 500g for 10 min.
ProtoArray, methylation assays, and MS analysis In vitro methylation assays were performed using 1-2 µg of recombinant protein substrates incubated with 1 µg of recombinant methyltransferases and 0. 1 mM SAM (Sigma), 0. 1 mM S-adenosyl-l-methionine-d3 tetra (p-toluenesulfonate) salt (deuterated SAM, CDN isotope) or 2 µCi of 3 H-AdoMet (American Radiolabeled Chemicals) in buffer containing 50 mM Tris-HCl (pH 8. 0), 10% glycerol, 20 mM KCl, 5 mM MgCl 2 , and 1 mM PMSF overnight at 30°C. The reaction mixture was resolved by SDS-PAGE followed by autoradiography, Coomassie stain (Pierce), or MS analysis. Similar conditions were used for methylation assays on ProtoArray (Invitrogen), except that 50 µg of recombinant SMYD2 was incubated on the arrays with 25 µCi of 3 H-AdoMet (American Radiolabelled Chemicals) overnight at 30°C and analyzed by autoradiography and GenePix software (Molecular Devices).
Peptides were desalted using C18 stage tips (Thermo Scientific). Peptides were separated by high-performance liquid chromatography (HPLC) using an Ekspert NanoLC 420 (AB Sciex) and analyzed with an Orbitrap Elite mass spectrometer (Thermo Scientific). Data were analyzed using MaxQuant version 1. 3. 0. 5 with a 1% false discovery rate (FDR) for proteins and peptides and allowing methionine oxidation, acetylation of protein N termini, and monomethylation, dimethylation, and trimethylation of lysine as variable modifications. Candidate methylation sites were verified by manual inspection.
Meta-analysis of public PDAC data sets We downloaded raw data for eight publically available PDAC gene expression studies from the NCBI Gene Expression Omnibuse and European Bioinformatics Institute ArrayExpress. After reannotating the probes, each data set was separately normalized using GC robust multiarray average (GCRMA). We applied two meta-analysis approaches to the normalized data  (Khatri et al. 2013). Briefly, the first approach combined the effect sizes from each data set into a meta-effect size to estimate the amount of chang-es in expression across all data sets. For each gene in each data set, an effect size was computed using Hedges' adjusted g. If multiple probes mapped to a gene, the effect size for each gene was summarized using the fixed effect inverse variance model. We combined study-specific effect sizes to obtain the pooled effect size and its standard error using the random effects inverse variance technique.
Statistics Kaplan-Meier survival curves were calculated using the survival time for each mouse from all littermate groups. The log-rank test was used to test for significant differences between the groups. For image quantification and gene expression analysis, statistical significance was assayed by Student's t-test with Prism GraphPad software (twotailed unpaired and paired t-test depending on the experiment; variance was first systematically examined using an F-test). In the figures, P < 0. 05 is indicated by an asterisk, P < 0. 01 is indicated by two asterisks, P < 0. 001 is indicated by three asterisks, and not significant is indicated by ns. Data are represented as mean ± standard error of the mean (SEM).
Acknowledgments This work was supported in part by grants from the  National Institutes of Health  ( RO1 CA172560  to O. G. and J. S. ) and the  Lustgarten Foundation  (O. G. , P. K. , and J. S. ),  National Institutes of Health   K99/R00 Pathway  to  Independence Awards  to P. K. M. ( K99CA19781601 ) and S. M. C. ( K99CA190803 ), the  Tobacco- Related Disease Research Program  (P. K. M. ), a  Dean's Fellowship  from  Stanford University  (P. K. M. ), and the  Lucile Packard Foundation for Children's Health at Stanford  (P. K. M. and J. S. ). N. R. is the recipient of an  ERS-EU RESPIRE2 Marie Skłodowska-Curie Postdoctoral Research Fellowship  ( MCF 3776-2013 ). The research leading to these results has received funding from the  People Programme of the European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement 600368.
Design of specific primers The 16S rRNA gene was chosen for the design of the primer, based on reference nucleotide sequences of 16S rDNA from GenBank plus the sequences obtained in the laboratory from Potamopyrgus samples of different origins. Sequences of this gene (either individual 16S DNA sequences or complete mitochondrial genomes), available for Potamopyrgus and other mollusk species were downloaded and aligned with the ClustalW application included in BioEdit  [43]. Polymorphisms were analyzed with the DNASP software  [44]. The different haplotypes were visualized employing the BioEdit Sequence Alignment Editor software  [45]. The universal primers designed by Palumbi et al. [46]  amplifying a 16S rDNA region of approximately 600 nucleotides were used for species barcoding. A region within these amplicons conserved in the genus Potamopyrgus but different in the rest of mollusk species was searched.
Marker validation The new primer was first tested in silico by an alignment with the BLAST tool in the NCBI database  [48]. Adult individuals of brackish and freshwater mollusks (five per species) were collected for testing in vitro possible cross-species amplification of the new primer (Table  1 ). PCR amplification with the universal primers of Palumbi et al. [46]  was done. The new set of specific primers was assayed on DNA extracted from eleven mollusk species described in Table  1. The sensitivity of the specific primers was determined in vitro with serial dilutions of Potamopyrgus antipodarum DNA from a known concentration (43μg/ml). PCR amplification and visualization of the PCR product in a 2% agarose gel were performed for each concentration. DNA concentration was measured with a spectrophotometer (SimpliNano™ GEHealthcare).
Phylogenetic analysis Potamopyrgus individuals from River Nora and from different locations in New Zealand (as representatives of native populations), were collected and taxonomically classified de visu (Table  2 ). Three different sequences were obtained from these samples: COI gene  [47] , 16S rRNA gene  [46]  and partial 16S rDNA amplified with the specific primers set. Additional sequences assigned to Potamopyrgus species were downloaded from GenBank. For each gene, the sequences were aligned with the ClustalW application included in BioEdit  [43]. The alignment was converted to MEGA file and a phylogenetic neighbor-joining tree was built using MEGA 4. 0  [49] , with 10000 bootstrapping and the evolutionary distances were computed using the Tamura-Nei method  [50].
Specific primers The new specific primer designed in silico within the 16S rDNA sequence was: Reverse primer: 16SPA-R (5'-TCAAAGATTTTGGATCATAGCT-3'). Using the 16SAr described by Palumbi et al. [46] : 16SAr (5'-CGCCTGTTTATCAAAAACA T-3') as a forward primer and the new 16SPA-R as a reverse primer, a region of 380 nucleotides within the 16S rRNA gene was amplified. The region is located between sites 5350 and 5730 of the Potamopyrgus antipodarum mitochondrion complete genome with GenBank accession number GQ996421. 1.
In the other two control sites, Llanes beach and River Nalón; no amplification was obtained with these primers as expected since Potamopyrgus mollusks are not present there. The positive bands observed in agarose gel for River Nora water samples were purified, sequenced and the sequences unequivocally identified as Potamopyrgus antipodarum, Gen-Bank accession numbers KU933000-KU933002. The PCR products gave clear chromatograms directly readable, without any trace of nucleotide mixture in any site. In the agarose gel it can be seen that the band for Lugones is bigger and brighter than for San Claudio location (Fig  3A ), concordantly with different population densities. The method can be considered quite sensitive because PCR product was detectable in agarose gel even for San Claudio sample where the observed density was only 6 individuals/m 2 (Fig  3A ).
Phylogenetic inferences From the individuals analyzed in this study a total of 26 haplotypes were found: 11 (two from Asturias P. antipodarum individuals), 8 (two from Asturias individuals) and 7 (also two from Asturias) for COI gene, long, and short 16S rDNA fragments respectively. The haplotypes obtained in this study are available in NCBI GenBank database with the accession numbers KU932989-KU932999 (COI gene), KU933003-KU933010 (16S rDNA large fragment). The shorter 16S rDNA amplicon obtained from taxon-specific primers corresponds to the sequence comprised between site 01 and site 325 on KU933003-KU933010. The tree reconstructed from the COI gene (Fig  4A ) and 16S rDNA (Fig  4B ) haplotypes obtained in our mud snail samples with universal primers separated consistently the samples from San Claudio (downstream) from those collected mid-and upstream.