dataesr/scientific-paragraphs-categorization huggingface dataset:
Emissions This section briefly describes the databases used for both primary particulate matter emissions and the emission of gaseous precursors. Anthropogenic emissions are the only emissions that can be controlled; their inventories are updated constantly to take into account precise emissions in scenario studies. In this paper, the EDGAR-HTAP_V2 inventory for the reference year 2010 is used. It uses HTAP (Hemispheric Transport of Air Pollution) annual total masses that include nationally reported emissions combined with regional scientific inventories, complemented with EDGARv4. 3 data (http://edgar. jrc. ec. europa. eu/htap_v2/index. php?SECURE=123). The inventory provides global annual emissions for CH 4 , NMVOC, CO, SO 2 , NO x , NH 3 , PM 10 , PM 2. 5 , BC and OC, with a resolution of 0.
Comparison with aerosol optical depth (AOD) measurements To evaluate modeled AOD, we compare it to two data sets: MODIS satellite data and AERONET data at specific stations. For each statistic, we collocate in time and space the simulated AOD with the corresponding data set. Figure  7  illustrates the time series of observed AOD and corresponding simulated values for each sub-region, and Table 4, the associated statistics. The modeled AOD is close to the observations in all regions except for NEU-We, with a correlation coefficient ranging from 0. 56 (NEU-Ea) to 0. 77 (MED-Ce) and a MFE of 34. 3 % for comparisons to AERONET and 27. 6 % for comparisons to MODIS. The simulated AOD is underestimated in the eastern and southern parts of the domain, with a MFB of -10 % in MED-Ea for AERONET, -31.
4 % for MODIS. In MED-Ea, the evaluation with MODIS data is more robust as there is more information than AERONET data in this sub-region (Fig. 1 ). This underestimation is also seen in the time series of Fig. 7  and coherent with the underestimation of PM 10 noticed in the previous section. The variability of observations is greater than in the model, and than in other regions. The fine fraction of AOD from MODIS (not shown) is low, indicating that the total AOD in this region is due to coarse particles. The model is thus missing some coarse particle sources in this area. Highest values are seen in MED-We, and the model manages to reproduce these values at the right timing (highest correlation coefficient) even if it moderately underestimates them (MFB of -27. 4 % with respect to MODIS). Particularly, the peak at the beginning of July is underestimated by CHIMERE.
For PM 2. 5 , only 23. 5 % of exceedances are correctly simulated, with the majority in NEU-We. Figure  14  shows the contribution of each source when an exceedance is detected only by CHIMERE, and not in the observations. The peak of 20-21 June is present in the model simulations at almost every stations so that the number of simulated exceedances is a lot larger than in the observational data set. The model-only exceedances associated with this peak are attributed to mineral dust in the three regions (mixed with anthropogenic and biogenic contributions). The model also overestimates the number of stations in daily exceedances at the end of July (37 vs. 6 for observations in MED-We, 82 vs. 14 for NEU-We for PM 10 ), with contributions from mineral dust, anthropogenic sources, and fires (stronger in MED-We and NEU-Ce).
Acknowledgements. The authors acknowledge financial support from  EIT -Climate-KIC  as well as from the  Centre National d'Etudes Spatiales (CNES)  and  DGA  (in the framework of the  APPAD  project). The authors are grateful to the  EEA  for maintaining and providing the AirBase database for surface concentrations of several pollutants over Europe. We thank for the availability of the ChArMEx data  Wani Tamas  for the Venaco station and  José Nicolas  for the Ersa station. All investigators and staff who maintain and provide the AERONET data are also thanked. We finally wish to thank the  National Aeronautics and Space Agency (NASA)  for the availability of the MODIS data. Edited by:  F. Dulac.
The seasonal variability of the SCS surface temperature is larger (>6 °C) in the north and relatively weaker (<4 °C) in the equatorial region  (Qu 2001; Chen et al. 2003 ). In the spatial distribution of annual mean SST, the isotherms mainly have a southwest to northeast orientation  (Chu et al. 1997; Qu 2001). In general, the SCS SST is warmer (>28 °C) during mid spring to early fall (April-October) and cooler (<28 °C) from late fall to early spring (November-March) seasons. Abstract Spatial distribution of the South China Sea (SCS) surface temperature shows strong cold anomalies over the Sunda Shelf during the boreal winter season. The band of low sea surface temperature (SST) region located south/southeast of Vietnam is called as the winter cold tongue (CT) in the SCS. Using observational and re-analysis datasets a comprehensive investigation of the dynamical and thermodynamical processes associated with the evolution of SCS CT is performed in this study.
Data The in situ observations of ocean temperature and surface heat fluxes over the SCS are relatively sparse compared with adjacent eastern Indian and western Pacific Oceans. In this context, the re-analysis datasets provide immense opportunity to understand the thermodynamics and dynamics of oceans where observations are limited. Here we have used satellite observations as well as the atmospheric and ocean re-analysis datasets from different sources to understand the evolution and dynamics of the SCS CT. Datasets such as the National Oceanographic and Atmospheric Administration (NOAA) daily SST observation, geostrophic currents obtained from Ssalto/Ducas multimission altimeter data products, European Centre for Medium-Range Weather Forecasts Re-Analysis ocean surface wind fields, oceanic parameters from Simple Ocean Data Assimilation and National Oceanography Centre surface heat flux are used in the analysis.
A brief discussion on different datasets used in the study is provided in this section. For the observational analysis of SST, the NOAA daily Optimum Interpolation Sea Surface Temperature (AVHRR OISSTv2) is employed. The data is available in 0. 25° × 0. 25° horizontal resolution. The data product is constructed by combining observations from different platforms, including the ships, buoys and Advanced Very High Resolution Radiometer (AVHRR) infrared satellite data, on a regular global grid  (Reynolds et al. 2007 ). As satellite observations, the product uses Pathfinder AVHRR data from September 1981 to December 2005 and operational AVHRR data from 2006 onwards. The dataset is freely available at the website http://iridl. ldeo. columbia. edu/ SOURCES/. NOAA/. NCDC/. OISST/. version2/. AVHRR.
The wind-driven ocean transports are computed using the zonal and meridional wind fields from the European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-interim) dataset. The ERA-Interim is a global atmospheric re-analysis started in 1979 and updated in real time. The data assimilation procedure includes a 4-dimensional variational analysis with a 12 h analysis window. The spatial resolution of the data set is approximately 80 km  (Dee et al. 2011). The ocean temperature, salinity and current fields derived from Simple Ocean Data Assimilation (SODA) version 2. 2. 4 is employed in the heat budget analysis of the SCS CT. SODA v2. 2. 4 is a data assimilation product spanning over a period from 1871 to 2008. The oceanic variables used in the assimilation are simulated using the general circulation model based on Parallel Ocean Program numerics with an average horizontal resolution of 0.
25° × 0. 4° and 40 vertical layers. Assimilation of temperature and salinity observations from hydrographic profiles, ocean station data, moored measurements and a variety of instruments (e. g. , MBT, XBT, CTD) are performed. The SST and sea level observations from satellites are also included in the assimilation system  (Carton and Giese 2008). The ocean variables remapped to a uniform horizontal grid with 0. 5° × 0. 5° horizontal resolution and 40 vertical levels is available to the users. SODA data from 1950 to 2008 are used in the present study. The ocean surface heat flux components for the heat budget analysis are obtained from the National Oceanography Centre (NOC) flux dataset v2. 0  (Berry and Kent 2009, 2011). NOC flux data provides latent heat flux, sensible heat flux, net longwave and shortwave radiation on a 1° monthly mean grid for the global ice-free ocean.
Evolution of the SCS cold tongue The evolution of the winter cold tongue is investigated using the daily SST climatology computed from OIS-STv2 data for the 1982-2012 period. The spatial distribution of daily SST climatology over the southern SCS during the months from November to March is shown in Fig. 2. The CT region is distinguished as the region of water where the temperature is less than 28 °C in the figure (indicated by the contours). During early November, SST over most of the southern and central SCS is greater than 28 °C. The CT originates with the onset of northeast monsoon in November along the east coast of Vietnam. Gradually, the CT region extends continuously southwestwards and covers most of the Sunda Shelf, except the Gulf of Thailand, by mid-December. Subsequently a rapid drop in SST is noted till early February.
In order to have an understanding of the upper ocean circulation, the climatological geostrophic current velocity vectors are overlaid on the SST distribution (Fig. 6a-d ). The monthly climatology of geostrophic velocity is computed from weekly geostrophic velocity (from 1993 to 2013) obtained from Ssalto/Ducas multimission altimeter products. Detailed information on the Ssalto/Ducas data products is available in http://www. aviso. oceanobs. com/en/data/productinformation/information-about-mono-and-multi-missionprocessing/ssaltoduacs-multimission-altimeter-products. html. As it flows south, the geostrophic circulation shows strong southward currents along the coast of Vietnam with increased velocity. At around 5°N, South off Vietnam, the current turns towards left and forms a cyclonic circulation pattern (Fig.
Mixed layer heat budget of SCS cold tongue The oceanic parameters from SODA and sea surface heat flux from NOC flux are used to evaluate the mixed layer heat budget of the SCS CT. The ocean advection and diffusion terms are computed based on the ocean currents, vertical motion and temperature provided by the SODA re-analysis as discussed in the Sect. 3. 2. The contribution of surface heat flux is estimated based on the mixed layer depth computed from SODA and air-sea heat fluxes obtained from NOC flux data. SODA data has been used for the upper ocean heat budget analysis of various regions in earlier studies (e. g. ,  Shenoi et al. 2005; Madhusoodanan and Thompson 2011). The mixed layer heat budget terms averaged over the SCS CT (105°E-110°E and 3°N-10°N) is shown in Fig. 7. The climatological mixed layer temperature tendency (∂T t ), net surface heat flux (Q flx ), the sum of vertical advection and vertical diffusion at the base of the mixed layer (Q vert-process ), horizontal advection (Q hadv ) and the sum of Q flx , Q vert-process and Q hadv (Q total ) are plotted in the figure.
Here, positive values indicate heat gained by mixed layer and negative values denote the loss of heat. MLD averaged over the same region is also shown in the figure. The mixed layer temperature tendency and MLD shows a clear semi-annual distribution under the influence of northeast and southwest monsoons. The primary and secondary maxima in ∂T t occur in March-April and Septem- ber respectively while the primary and secondary minima are observed during December and July, respectively. The MLD over the CT has seasonal maxima in January and August/September with the minima seen during May and October/November. Though the heat budget is not exactly closed, the figure suggests that ∂T t and Q total are varying almost in same phase and the two curves closely match. The usage of datasets from different sources may be one possible reason for this discrepancy.
Climatological evolution of the SCS CT and the oceanic processes responsible for SST/mixed layer temperature changes have been investigated in the present study using a set of observational as well as ocean-atmosphere re-analysis datasets. The seasonally varying monsoons have a strong influence in shaping the basin scale circulation and temperature distributions in the SCS  (Wyrtki 1961; Qu et al. 2005). The wind-driven ocean transports are closely associated with the SST evolution over the CT. The north-south Sverdrup transport demonstrates strong southward transport during the northeast monsoon period while it leads the SST cooling by almost half a month. The southward Sverdrup transport aids the SST cooling by bringing relatively cold water from the northern regions. The zonal and meridional Ekman transports exhibit relatively weak westward and northward transports to the CT region during this period.
Acknowledgments This research was supported by the  National Research Foundation Singapore  through the  Singapore MIT Alliance for Research and Technology's Centre for Environmental Sensing and Modeling interdisciplinary research program. The authors thank the reviewers and the editor for their constructive comments to improve the manuscript. We acknowledge  NCDC-NOAA, ECMWF ,  UMD ,  NOC  and Aviso for the AVHRR OISSTv2, ERA-interim, SODA,  NOC  flux and Ssalto/Ducas geostrophic current field data sets respectively.
In addition, the starBase database Figure  1  miR-371a-5p regulates expression of BAG3. To test whether the predicted miRNAs could bind BAG3 target, both Ago2/pull-down assay and pMir dual-luciferase assay were performed. mRNA from HEK293 cells was IP with the anti-AGO2 antibody and BAG3 mRNA was quantized by quantitative RT-PCR (a). Data were normalized on the pri-miRNA 15a/16-1 expression of the RNA, not IP (INPUT). In the lower part of a there is also a schematic representation of the BAG3 gene. BAG3 mRNA and BAG3 coding sequence (black arrows), hsa-miR-371-5p targeted sequence (red bar) and fragments used for the qRT-PCR in the RIP analysis are shown. Cos-7 (b) and HCMa cells (c) were co-transfected with the pre-miR-371a-5p (0. 1 nmol/l) (miR-371a-5p) or a SCR control (scr) and a firefly luciferase pMir reporter plasmid containing downstream the luciferase gene, the wt (pmir WT) or mutated (g2252c) (pmir mut) BAG3 3′-UTR sequence.
Luciferase activity differences among samples were assayed 24 h after transfection and Renilla luciferase was co-transfected as a control. Data represent the mean of six experimental replicates; P-value versus SCR and the mutated sequence are shown. HCMa cells were transfected with pre-miR-371a-5p (2 nmol/l) (pre-miR-371a-5p) or SCR sequences (SCR) as a negative control, and BAG3 protein (d) and mRNA levels (e) were evaluated after 24 h. (d) Densitometric analysis of five blots, normalized to the corresponding GAPDH band intensities, is shown in d (lower panel). BAG3 mRNA levels, evaluated by real-time RT-PCR, were compared in three independent experiments using the relative ct method: the amount of target, normalized to the amount of endogenous control (GAPDH), relative to the control sample, is given by 2 -ΔΔCt.
Significance calculated by t-test: ***Po0. 0001, **Po0. 005 and *Po0. 01; NS, not significant (http://starbase. sysu. edu. cn/index. php) that harbors the interaction map from Argonaute CLIP-seq data  31, 32  also demonstrated the specific binding of the miR-371a-5p to the BAG3 3′-UTR. To further experimentally validate whether miR-371a-5p directly binds to the 3′-UTR of BAG3 and evaluate whether this binding is affected by the g2252c nucleotide change, we performed dual-luciferase reporter assays using pMIRreporters with either the wild-type (wt) or polymorphic putative miRNA target sites cloned downstream of the firefly luciferase gene (luc2). In this experiment, firefly luciferase was used as the primary reporter to monitor gene regulation and Renilla luciferase (hRluc-neo) acted as a control reporter for signal normalization.
(d) miR-371a-5p levels were detected by real-time RT-PCR: transcript quantities were compared using the relative Ct method, where the amount of the target, normalized to the amount of endogenous control (RNU6) and relative to the control sample is given by 2 -ΔΔCt. To silence the miRNA activity and generate a transient miRNA-371a-5p knockout model, cardiomyocytes were transfected with the anti-miR-371-5p (anti-miR) or SCR sequences as a negative control (10 nmol/l) and 24 h later stimulated with epi (500 μmol/l) for 2 h or 15 min. Cells were subsequently analysed by WB, to evaluate BAG3 protein levels (e), and by real-time RT-PCR, to evaluate miR-371a-5p levels (f), as described above. Data are representative of three independent experiments with similar results and WB densitometric analysis of experimental replicates, normalized to the corresponding GAPDH band intensities, are shown in the lower part of e.
PCR products (20-100 ng) were sequenced using BigDYE v3. 1 (1 μl; Applied Biosystems, Foster City, CA, USA), SBDD buffer (1. 9 μl; Applied Biosystems), Fw or Rw primer (5 pmol) and water to reach the final volume of 10 μl, by following the supplier instructions for cycling conditions. The reaction volumes were purified by precipitation with ethanol and NaAc (50 mmol/l), and shipped to the sequencing facility. Bioinformatic analysis. Sequencing data were analyzed and aligned using ClustalW software (available at www. clustal. org, UCD Dublin, Ireland). We assessed miRNA-binding potential to the BAG3 3′-UTR at position 2252 (±10 bp) using open computational software tools developed by the Segal Lab of Computational Biology (http://genie. weizmann. ac. il/), miRBase, miRWalk and TargetScan. Sequence conservation analysis among species was performed using the mVISTA tool.
Cells were incubated for the established times in the absence and in the presence of different concentrations of epi. The number of viable cells was quantified by MTT ([3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide]) assay. Absorption at 550 nm for each well was assessed using a microplate reader (LabSystems, Vienna, VA, USA). Apoptosis evaluation. Epi-treated and control cells were collected and then incubated with a propidium iodide (PI) solution (0. 1% sodium citrate, 0. 1% Triton X-100 and 50 mg/ml of PI) for 30 min at 4 °C. Apoptosis was quantified as the proportion of cells with hypodiploid DNA (sub G0-G1 peak) using flow cytometry (FACScan, Becton Dickinson, Franklin Lakes, NJ, USA). Data were normalized as percentage relative to positive control (doxorubicin). To analyze Caspase-3 activity, cells (2 × 10 4 ) were lysed in a buffer containing HEPES 50 mM, DTT 1 mM, EDTA 0.
Data were analyzed by using Microplate Manager 4. 0 software (Bio-Rad, Hercules, CA, USA). mRNA/miRNA isolation and real-time RT-PCR. Total RNA isolation (mRNA/miRNA) was performed by using the Ambion mirVana PARIS Kit (Ambion). RNA (1 μg) was reverse transcribed by using miScriptII Reverse Transcription Kit (Qiagen) and the HiFlex Buffer. Primers were designed to detect transcripts for hBAG3 (NM_004281. 3) and hGAPDH (NM_001256799. 1). Real-time PCR was performed on Light Cycler480 (Roche) using LightCycler480 SYBR Green Master Mix. For real-time PCR detection of miRNAs, miScript Primer Assays for miR-371a-5p and RNU6, and the miScript SYBR Green PCR Kit (Qiagen) were used. Transcript quantities were compared using the relative Ct method, where the amount of target normalized to the amount of endogenous control (GAPDH or RNU6) and relative to the control sample is given by 2  (-ΔΔCt).
LeicaQ9 Confocal software (Leica Microsystems, Wetzlar, Germany), was used for data analysis. Statistical analysis. The results are presented as mean ± S. D. Statistical analyses were performed using Microsoft Excel (Microsoft, Redmond, WA, USA) and GraphPad software (GraphPad, San Diego, CA, USA). Student's t-test was used to compare two experimental groups and differences were considered significant when Po0. 05. Allele frequencies were estimated by direct counting. The significance of the distribution of alleles between the patients and the controls was tested by the χ 2 -method with Fisher's exact probability test (P-value test). The odds ratio of the risk of stress cardiomyopathy was calculated by the 2 × 2 contingency table.
To silence the miRNA activity and generate a transient miRNA-371a-5p knockout model, cardiomyocytes were transfected with the anti-miR-371-5p (anti-miR) or SCR sequences as a negative control (10 nmol/l) and 24 h later stimulated with epi (500 μmol/l) for 2 h or 15 min. Cells were subsequently analysed by WB, to evaluate BAG3 protein levels (e), and by real-time RT-PCR, to evaluate miR-371a-5p levels (f), as described above. Data are representative of three independent experiments with similar results and WB densitometric analysis of experimental replicates, normalized to the corresponding GAPDH band intensities, are shown in the lower part of e. Significance versus the SCR or the control samples were calculated by t-test: ***Po0. 0005, **Po0. 005 and *Po0. 01; NS, not significant.
Acknowledgements. We are grateful to  Edwin Cuppen  from  The Hubrecht Institute , for his suggestions, and to  Professor Francesco Silvestris  from  Bari University. MdA was supported by the  European Molecular Biology Organization   EMBO2012  award  ASTF325-2012. LJDW acknowledges support from the  Netherlands CardioVascular Research Initiative : the  Dutch Heart Foundation ,  Dutch Federation of University Medical Centers , the  Netherlands Organization for Health Research and Development (ZonMW)  and the  Royal Netherlands Academy of Sciences. LJDW was further supported by grant  311549  from the  European Research Council (ERC).
Related Work In order to find empirical works on Bitcoin adoption and usage, we conducted a thorough literature search following the frameworks of vom Brocke et al. , Levy and Ellis, and Webster and Watson  [14] [15] [16]. Search terms were 'Bitcoin', 'crypto AND currency[ies]', and 'cryptocurrency[-ies]'. As suggested by Chen et al. [17] , we searched the databases Web of Science, EBSCO Business Source Complete, IEEE Xplore, ScienceDirect, and the ACM Digital Library. To be as exhaustive as possible, the important IS conferences ICIS, ECIS, HICSS, PACIS, and AMCIS were also explored with the identical keywords. As the 'mother paper' of Bitcoin by Satoshi Nakamoto (most likely a group pseudonym) was just released in 2008  [18] , we did not limit the search timeframe. In fact, it was not until 2011 that research papers about cryptocurrencies were published in journals and from conferences.
Perceived Ease of Use Consumers found mobile wallets and Bitcoin in general difficult to use, therefore requiring some training, although one interviewee regarded Bitcoin as easy and selfexplanatory. As buying, selling, or sending Bitcoin is very similar to using a Google Wallet, it is possible that more consumers will come across technical difficulties when using Bitcoin for the first time. They agreed that especially the younger generation should manage to handle Bitcoin and related software/apps, however. These findings are similar to those of Spenkelink  [42]. Risks were not mentioned, but instead consumers focused on convenience, especially at offline stores (POS). One merchant complained about the implementation process at POS and stressed that it's not a technical, but rather a societal challenge to switch to Bitcoin.