dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS Partial financial support has been received from the  Spanish Ministry of Economy (MINECO)  and  FEDER (EU)  under the project  ESOTECOS  ( FIS2015-63628-C2-2-R ), and from the  EU Commission  through project  INSIGHT. The work of M-HS-O was supported in part by a post-doctoral fellowship of  MINECO  at  Universidad Complutense de Madrid  ( FPDI 2013/17001 ). BG thanks the Moore and  Sloan Foundations  for support as part of the Moore-Sloan Data Science Environment at  New York University.
34 48. 5 Positive Total 0. 62 268. 6 Positive Spanish 0. 62 267. 3 Positive English 0. 32 159. 2 Positive Madrid French Italian 0. 37 151. 5 0. 26 146. 3 Positive Positive Portuguese 0. 44 204. 9 Positive Arabic 0. 07 41. 5 Random East-Slavic 0. 06 37. 7 Random detected in three cities for which the data from cen- sus offices was available. These cities are Barcelona, London and Madrid, and for the comparison we use data from the so-called Continuous Register Statis- tics in Spain and the Census Office in the UK. In the Spanish case, the information is collected when people residing in a certain area must inform the municipal authorities for tax purposes and to obtain social ser- vices such as health care. The smallest spatial units for this dataset are census tracts, so Twitter data must be translated into the same geographical units (see the Appendix for further details).
More specifically, the new political leadership in the biggest and most century is believed to be "The Asian Century", the policymakers of this region are convinced to accelerate the regional coordination amongst them to emerge as a countervailing power to the west. dominant of the South Asian countries i. e India which came to power in 2014 wants to push for greater cooperation with its neighbours 3 The rest of the paper is organized as follows: Section 2 deals with the trends and developments in the regional integration in the South Asian region. Section 3 gives an overview of the review of literature. Section 4 presents the data while Section 5 covers the methodology related to Price based and Quantity based approach to evaluate equity market integration amongst the SAARC members. Section 6 discusses the fundamental determinants of the equity market interdependence of this region while Section 7 provides summary and policy recommendations.
to help in further opening of the SAARC economies for the betterment of the lives of the people and reduce the regional poverty. Hence, this study intends to evaluate the state of financial integration of the South Asian countries focussing on the equity market segments using quantity based and price based approaches by applying contemporary econometric tools and techniques. Thus it aims to empirically evaluate the dynamic co-movements between the equity indices of the member countries of the SAARC region. We further study the trends of the fundamental determinants which drive the process of financial integration. This paper also suggests policy roadmap from the Indian perspective to further deepen the financial development and integration in the region. The present study employs Copula Garch models on equity market price data and makes an important contribution to the existing literature by showing that the South Asian countries exhibit varying degrees of integration with the Asian benchmark.
, 2004 and Threshold DCC-GARCH of Pesaran et al. , 2007)  to study the dynamic process of integration 5 The paper fills important research gaps not covered by previous literature on equity market integration for the SAARC region in the following ways: a) this is the first study to assess financial integration in six out of eight SAARC countries for the data spanning 6. Recently, copula based models have been used to study the asymmetric nature of dependence between financial market returns  (Patton, 2006; Rodriguez, 2007). The copula-GARCH (C-GARCH) model is a multidimensional GARCH process that uses a copula function to model the interlinkages. The application of the C-GARCH model has recently attracted increased academic attention  (Peng and Ng, 2011; Yang and Hamori, 2013; Basher et al.
Section 4: Data We begin our analysis on the daily stock market benchmark index closing prices retrieved from Bloomberg for SAARC member countries for the period 6 January 2004 to 31 st March 2015. The starting date has been selected keeping in mind the date when the SAFTA agreement (6 January 2004) was reached which was a landmark event in the process of regional cooperation in South Asia. Table  2  The benchmark indices which were selected for USA is included as a proxy for global factor as in prior research  (Baele et al. , 2004; Bartram, et al. , 2007 , Gupta et al. , 2015). We take the natural logarithm of the daily closing values. Daily returns are then computed as the first difference of the log-transformed series. For the quantity based measures, we construct the annual portfolio equity inflows for each SAARC member countries from the SAARC region as well as ASEAN+6 region 6 for the period 2004 to 2014.
Similarly we construct portfolio outflows from each SAARC member country to the SAARC and the AEAN+6 regions. The portfolio flow data was sourced from IMF-CPIS (International Monetary Fund-Coordinated Portfolio Investment Survey) database. The quantity based measures have been constructed for the same 6 countries (Bangladesh, India, Maldives, Nepal, Pakistan and Srilanka) that were a part of the empirical analysis performed using the price based measures as no data was available for Afghanistan and Bhutan. Finally, for the fundamental determinants of financial integration, a comprehensive set of factors was constructed (Refer table 1) belonging to 3 broad set of categories.
Section 5: Evaluating Stock Market Integration in SAARC Region i. e Macroeconomic, Trade and Market based category. The data for these factors was sourced for the SAARC member countries subject to their data availability from 2004 to 2015. These variables were predominantly of annual frequency except for volatility of forex rate, volatility of domestic market index return, domestic growth opportunities and local stock market conditions which were of daily frequency. The daily data was averaged over the year and it was not annualized as even a common scaling factor of 250 trading days would not have led to a change in its relative position across the sample.
Section 6: Fundamental Determinants of Financial Integration After estimating the time-varying dependence measure for the sample SAARC countries, in this section we investigate the fundamental determinants of stock market integration. We consider the exhaustive list of potential determinants of financial integration that have been covered by prior studies which were summarized in Table  1. These fundamental determinants are primarily divided into Macroeconomic, Trade and Market based categories. These determinants (variables) potentially play an important role in explaining the intertemporal behaviour of equity market integration measured by the time varying copula parameter provided by the best fit copula model selected on goodness of fit test. But, since our equity market dependences are close to 0 between SAARC member countries, hence we are unable to perform the panel data analysis (as absence of financial integration implies no dependent variable to be explained by explanatory variables) to confirm the role of significant variables driving integration in this region.
In this paper, we examine the extent of financial integration amongst the equity market segments of the SAARC member countries. Daily stock market index closing price data was used for the 6 of the 8 SAARC member countries for the period of 6 January 2004 to 31 st March 2015 as data for Afghanistan and Bhutan was not available. Our empirical analysis is based on Copula Garch approach to capture the dynamic process of financial integration by investigating the return co-movement of the sample country's equity index with each other. The results obtained from both static and time varying copula models show that the dependences between the equity markets of SAARC member countries are close to 0 indicating very little level of financial integration in the region. This can be attributed to the low regional trade intensity in the SAARC region which is infact the lowest amongst all the major regional economic blocs of the world.
Delivery of sediment by icebergs was strengthened between 8. 3 and 7. 5 cal. ka BP following the collapse of the buttress of glacial ice in Kennedy Channel that triggered the acceleration of GIS and IIS fluxes toward Nares Strait. The destabilisation in glacial ice eventually led to the rapid retreat of the GIS in eastern Kane Basin at 8. 1 cal. ka BP as evidenced by a noticeable change in sediment source in our core. The gradual decrease of carbonate inputs to Kane Basin between 8. 1 and 4. 1 cal. ka BP reflects the late deglaciation of Washington Land. The shoaling of Kane Basin can be observed in our record by the increased winnowing of lighter particles as the glacio-isostatic rebound brought the seabed closer to subsurface currents. Our dataset suggests reduced iceberg delivery from 7.
XRF core-scanning High-resolution (0. 5 cm) X-Ray Fluorescence (XRF) scanning was conducted along the archive using an AVAATECH XRF core-scanner. The semi-quantitative elemental composition of the sediment was measured throughout the whole archive with the exception of two units which contain large clasts. Measurements were acquired with generator settings of 10, 30 and 50 kV in order to detect elements in the range of Al to Ba. Raw XRF data for each element were normalized to the sum of XRF counts of all elements except Rh and Ag whose counts are biased during data acquisition  (Bahr et al. , 2014)  in order to correct for changes induced by down-core variations in grain size and/or water content  (Tjallingii et al. , 2007). In this study, we use the elemental signature as an indication of the sediment sources for detrital material.
Chonology and age model The chronology is based on a set of twenty radiocarbon ages obtained from mixed benthic foraminifera samples and unidentified mollusc shells. Only one mollusc fragment was dated (at 301. 5 cm) and yielded an age of >43 ka and is thus clearly remobilised (Table  1 ). The core top is dated at -5 years BP  (1955 AD)   2006 ) dataset of pre-bomb radiocarbon dated molluscs and taking into account the general oceanographic circulation in the CAA. However, unlike in other passages of the CAA which present shallow sills at their southern extremities, younger Atlantic water from Baffin Bay enters Nares Straitor at least Kane Basinfrom the south  (Bailey, 1957; Muench, 1971; Münchow et al. , 2007). We thus choose to correct 14 C ages in this study with the average R of the three pre-bomb collected mollusc shells in Nares Strait, i.
A contrario, when establishing the age model of sediment cores, the age vs. depth relationship reveals any outliers that can be identified as being either (1) remobilised by ice-rafting, slumping or bioturbation, or (2) potentially affected by the Portlandia effect. Hence, we deem the Portlandia effect to be of minor concern in the establishment of the age model in this study despite the possible inclusion of deposit-feeders in our radiocarbon dataset. The 14 C ages were calibrated with the Marine13 curve  (Reimer et al. , 2013 ) using Calib7. 1 (Stuiver et al. , 2018)  with a marine reservoir age correction of 640 years (ΔR=240). We computed an age/depth model for core AMD14-Kane2b based on radiocarbon-dated material using CLAM 2. 2  (Blaauw, 2010) , and assuming that a 20 cm long clast-rich deposit (300-320 cm) was deposited near instantaneously at the scale of our chronology.
Acknowledgments E. Georgiadis ' studentship is funded by both  the Initiative d'Excellence (IdEx) programme of the University of Bordeaux  and the  Natural Science and Engineering Research Council of Canada (NSERC). We would like to thank  Sofia Ribeiro ,  Audrey Limoges  and  Karen Luise Knudsen  for constructive conversations on the history of North Water Polynya and  Benoit Lecavalier  for having shared with us the much appreciated Agassiz ice core temperature record. This work is supported by the  Fondation Total , the  French Agence Nationale de la Recherche  ( GreenEdge  project), the  Network of Centres of Excellence ArcticNet  and the  European Research Council (StG IceProxy ). Finally, we wish to thank the  CCGS Amundsen captain , officers and crew for their support during the 2014 ArcticNet cruise.
9 This together with two information circulation network setups and 50 Monte Carlo iterations gives us the total of 756,000 runs of the economy which constitute the data for the analysis. Ultimately our aim is to relate the parameters of the model to the sales performance of the product. However, these relationships may be different across two WoM network settings. In order to see whether there are actual differences in outcomes across two scenarios recall that we have 7560 pairs of settings with 50 observations each. In other words, every parameter setting in dynamic WoM network scenario has its exact counterpart with static WoM network scenario, each of these alters having 50 Monte Carlo iterations. Therefore, we conduct a simple test of mean difference across the two corresponding sets of observations.
Therefore, there are no stationarity problems involved in our case. As we want to see the relation between the model parameters and equilibrium sales, which is a bounded variable (expressed in shares it is bounded between zero and one), we have to run the generalized linear regressions relating all model parameters to sales. However, as we are dealing with the complex model (mostly due to the actual social network being explicitly modeled), it is unclear, a priori, which of the generalized linear shapes could describe the data the best. Therefore, we perform a model evaluation exercise using out-of-sample prediction power as a discriminant across the alternatives. We proceed as follows. We take our 7560 scenarios and split them randomly in two parts: 80% of scenarios will be used to estimate the econometric model and the remaining 20% will be used to evaluate the out-of-sample fit of the model.
As the split between the two groups is random we are running the risk of generating a spurious relationship if we do this just once. Therefore, we bootstrap the process 100 timesfor each of the models evaluated we perform split-estimation-fitting procedure 100 times and average the results. After carrying out this exercise separately for static and dynamic WoM networks, we have sales calculated from the actual simulated data and predicted sales calculated from the estimated econometric model. We use the average mean squared error (AMSE) as the discriminant across the econometric models. We contrast seven most popular generalized linear functions -Linear, Logit, Probit, Log, Log complement, Log-log and Negative binomial. Table  1  presents the results. As one can see three of the models (Logit, Probit and Log-log) significantly outperform the other four alternatives in case of the both types of WoM networks.
Results Our data comprises 7560 distinct scenarios. Some of these scenarios are somewhat similar in terms of the environment. Others are quite different from one another. Our main concern is the impact of advertising on sales. There are two channels through which advertising affects sales. The first is the direct channel: advertising raises people's prepurchase valuations of the product, and therefore more people pass the threshold of minimum quality requirement and are eligible to purchase the product. This channel is always positive. The second channel, 8 An algorithm for generating a regular lattice is straight forward. We use the algorithm by  Erdos and Renyi (1959)  to generate random architectures, algorithm by  Watts and Strogatz (1998)  for small world and algorithm by  Barabasi and Albert (1999)  for scale free topologies.
However, we see no visible difference in two panels of Fig. 6  in the Appendix B. 15 Notice that this results highlights the importance of the integrity of highly connected individuals in social networks to counter-act the diffusion of "fake news". These "stars" seem to have the power to counteract the generic effect of decreasing shortest path length that electronic social media have. However, this hinges on their motivation to contribute to finding the truth which (implicitly) exists in our model, but might not exist in the field. 16 This is confirmed by the regression analysis too: if we run the any of the regressions presented in the paper on data combining static and dynamic WoM network setups and include a dummy variable for one of the scenarios, say static WoM, the coefficient of this dummy is always significant.
Acknowledgements This work has been supported by the  French government , through the  UCA JEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01  (SubGrant -DigiCom). I wish to thank  Alessio Delre ,  David Gill ,  Nobi Hanaki ,  Marco Janssen ,  Yogesh Joshi ,  Oliver Kirchkamp ,  PJ Lamberson ,  Lionel Nesta ,  Bill Rand ,  Bulat Sanditov  and other participants of various meetings in Aix-en-Provence, Jena, Maastricht,  New York ,  Santa Fe  and  Washington DC , as well as anonymous referees for their comments on previous versions of the paper.
All enrolled subjects underwent a baseline visit during which clinical data, and urine and sputum samples were collected. An optional broncoscopy visit was carried out only in specialist centres. Clinical and omic data of severe asthma cohorts were downloaded on June 2016 from U-BIOPRED database (tranSMART system)  [15]. We obtained urine 8-iso-PGF 2α data from 411 severe asthma subjects, and IS, BB, BBr transcriptomincs data from 84, 53, 67 severe asthma subjects, respectively. There is a good overlap, in term of patient coverage between BB, BBr and urine, and between sputum and urine. In contrast, there is little overlap between patients who had IS and BB/BBr. The study protocol was approved by the Ethics Review Board of the Academic Medical Centre of the University of Amsterdam (The Netherlands) and subsequently by the Ethics Boards of all other clinical centres in the study (see S1 File.
Microarray assessment RNA from RNAlater-preserved IS cell pellet, BB, and BBr samples were extracted using Qiagen miRNeasy kit and amplified with Nugen ovation pico WTA kit (NuGen Technologies; San Carlos, CA). The cDNA was analysed using the Affymetrix HG-U133+PM microarray platform (Affymetrix, Santa Clara, CA). The primary raw data images (DAT files) were processed into numerical CEL files. CEL files were normalized, assessed for quality control to exclude technical outliers, and re-normalized using the robust multi-array (RMA) method. Batch effects from RNA processing sets were observed for the sputum and BBr datasets, with the batch effect adjusted in the data matrices using linear modeling of batch (as a random factor) and cohort in the ComBat R programme. For the sputum dataset, 3 subjects had duplicate samples, of which the mean of the log2 intensities, after RNA processing set batch adjustment, were used in the final analysis dataset.
Usually, probes are selected to represent genes and are designed to match particular mRNA transcripts, often based on deposited NCBI sequences. However, those sequences might be incorrect, partially inaccurate or incomplete due to different problems. Moreover a gene can have multiple splice variants  [17]. Thus, multiple probe sets assigned to a common gene were studied separately in order to keep an unbiased reporting of all relevant probe sets. Public repository for microarray data are published in GEO website: GSE76262 (IS data); GSE76225 (BB data); GSE76226 (BBr data).
001). Gastro-esophageal reflux disease (GERD) was increased in SAs/ex compared to SAn in the urine-8-iso-PGF 2α subset (p = 0. 006), and this trend was also observed in the other subsets (p> 0. 05). GERD was also increased in CSA for the urine-8-iso-PGF 2α subset (p = 0. 009) compared to ESA and NSA. FEV 1 /FVC ratio was decreased in CSA than ESA and NSA with significant p-value in the urine-8-iso-PGF 2α subset (p = 0. 033). FeNO levels were significantly lower in SAs/ex than SAn for those subjects for whom BB and BBr transcriptomic data was available, but this was not seen across all subsets. However, when compared SA smoking subgroups we observed that FeNO levels were significantly lower in CSA than ESA and NSA for all subject subsets. Regular OCS use was increased in ESA than CSA and NSA with significant p value in the urine-8-iso-PGF 2α (p = 0.
Discussion We used data from the U-BIOPRED severe asthma cohorts to assess the effect of cigarette smoke on oxidative stress markers in severe asthma subjects with a significat smoking history. We found an increased systemic oxidative stress in SAs/ex, and in particular among severe asthma current smokers, who exhibited the highest concentration of urinary 8-iso-PGF 2α. In sputum, NOX2 mRNA expression was increased in SAs/ex compared to SAn, while NOS2 mRNA expression was decresead in bronchial brushing of SAs/ex. Moreover, levels of FeNO were decresed in severe asthma current smokers, and was correlated with NOS2 mRNA expression in bronchial brushing. Tobacco smoke is one of the major environmental sources of oxidative stress and can lead to greater lipid peroxidation. It has been shown that 8-iso-PGF 2α levels are elevated with asthma severity  [11]  and further enhanced during acute exacerbations  [18]  and allergen challenge in asthmatics  [11].
Acknowledgments On behalf of The  U-BIOPRED Study Group  with input from the  U-BIOPRED Patient Input Platform  and patient representatives from the  Ethics Board and Safety Management Board. The  U-BIOPRED consortium  wishes to acknowledge the help and expertise of the following individuals and groups without whom, the study would not have been possible. Full list and definition of U-Biopred supplementary author is provided in  S5 Table U-BIOPRED Consortium Information.
U-BIOPRED  is supported through an  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115010 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  (FP7/2007-2013) and EFPIA We would also like to acknowledge help from the  IMI  funded eTRIKS project ( EU Grant  Code No. 115446  to R. E. ). Funding information for this article has been deposited with FundRef. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acclarogen Ltd. ,  Janssen Research & Development, LLC. ,  GSK ,  Astra Zeneca R&D  and  Areteva R&D  provided support in the form of salaries for authors ATB, MJL, ARS, consultancies. B. Dahlen declares consultancies for TEVA.
Results and Discussion The most relevant results of our study are presented in this section. Additional data are included in the Results section of S1 Supplemental Materials 1 that also comprises Figs. S-1 through S-29, and in S2 Supplemental Materials 2 with Figures S-30 through S-45 and Tables S-1 through S-5. Taken together, the values given in our tables indicate the ranges to be expected for statistical descriptors in experimental distribution curves.