dataesr/scientific-paragraphs-categorization huggingface dataset:
Another two global rankings of city visitor attractiveness worth mentioning in that context are the ones presented by Euromonitor 3  and MasterCard 4. Although one should not really expect them to be consistent with our ranking, as those rankings are built on diverse (and sometimes heterogeneous) sources of data trying to include all the visits and not necessary only tourists who are willing to take photographs as we do it in our study, we will compare them against our ranking. One could often expect one city to attract more people, but another one, attracting less, being more picturesque, and motivating those fewer people attracted for taking more photos, which would result in a higher total photographic activity. However, all 10 of our top cities are included in Euromonitor's top destinations list.
Worth mentioning is that this is already not the case for a newer version Euromonitor's ranking from 2015  5  for example Washington falls out of the top 100 world destinations according to their recent estimate. This serves as a good example of how dynamic the world is, while it is not too surprising that our ranking built based on the data before 2010 happens to be more consistent with the older version of Euromonitor's report. Morover we found that our top 3 cities -New York, London and Paris -are also the top 3 (the order is different however) in MasterCard's ranking, while in total 7 of out top 10 cities (besides Berlin, Washington and Chicago) are mentioned among the "Global Top 20 Destination Cities by International Overnight Visitor Spend" in 2014. Further, we focus this study on the global attractiveness of these 5 US cities and 5 European Union cities (EU), and add together the remaining information as the rest of Europe, the rest of the US and the rest of world.
In many places across the world people are almost never using geotagging location services (e. g. , Flickr) and are not sharing their photographs publicly because of different reasons: e. g. , technical, cultural or even political ones. Among the top 10 ranked cities this activity varies in a magnitude of 20 times between people living in San Francisco (the most active population) and Rome (the least active). In order to use the O/D flows of geotagged photography as a proxy for actual human mobility between cities across the globe, the appropriate normalization for the above heterogeneity is required. One way of doing it is by normalizing the O/D flows shown in Figure  2  by the number of photographs taken per 1000 of residents of the origin location reported in Table  1. However, this would require further assumptions about the homogeneity of the dataset representativeness for different modes of people travel behavior that would be a questionable assumption given the dataset sparsity and heterogeneity.
g. , Flickr). Over the last decade big data analyses have being increasingly utilized in urban planning (e. g. , analysis of cell phone records for the transportation planning). However, information from geotagged photographs was not very often analyzed although they can provide us with an additional layer of information useful for the urbanism in general. Namely, taken photographs indicate places in cities important enough for people to visit them and to decide to leave their digital trails there. By analyzing the global dataset of geotagged photographs we identified 10 most photographed cities, which happened to be distributed evenly between the US and EU. Focusing on the top 10 selected destinations, we studied spatial patterns of visitor attraction versus behavior of the residential users together with analyzing people mobility between those 10 cities and other places all around the world.
For all cities with the exception of Berlin, activity of the tourists and especially foreign tourists appeared to be more concentrated on the major attractions. To conclude, by showing differences between people visiting the US and EU cities our study revealed interesting patterns in human activity. The results of our study are useful for understanding of what has to be enhanced in cities and where it can be appropriate to increase services targeting different categories of users. In past those questions were traditionally answered by analyzing different available datasets such as hotel information or survey data. However, collecting or getting the access to such datasets usually requires significant efforts and/or expenses, while geotagged photography is publicly available while providing a unique global perspective on addressing many research questions at both global and local scale.
Acknowledgements The authors wish to thank  MIT SMART Program ,  Accenture ,  Air Liquide ,  BBVA , The  Coca Cola Company, Emirates Integrated Telecommunications Company , The ENEL foundation, Ericsson, Expo 2015, Ferrovial, Liberty Mutual, The Regional Municipality of  Wood Buffalo ,  Volkswagen Electronics Research Lab, UBER  and all the members of the  MIT Senseable City Lab Consortium  for supporting the research. We further thank  MIT  research support committee via the  NEC and Bushbaum research funds , as well as the research project " Managing Trust and Coordinating Interactions in Smart Networks of People, Machines and Organizations ", funded by the  Croatian Science Foundation. Finally, we would like to thank  Eric Fisher  for providing the dataset for this research,  Alexander Belyi  for some help with the visualizations and also  Jameson L Toole ,  Yingxiang Yang ,  Lauren Alexander  for useful recommendations at the early stage of this work.
ACKNOWLEDGEMENTS The work in Groningen was partially supported by the  European Research Council ,  ERC Starting Grant  (No. 306983 ) ' Hybrid solution processable materials for opto-electronic devices'  ( ERC-HySPOD ) and by the  Foundation for Fundamental Research on Matter (FOM) , which is part of the  Netherlands Organization for Scientific Research (NWO) , under the framework of the  FOM Focus Group 'Next Generation Organic Photovoltaics'. J Even 's work is supported by the  Fondation d'entreprises banque Populaire de l'Ouest  under Grant  PEROPHOT 2015. The technical support of  A Kamp  and  J Kuiper  is highly appreciated. We thank  TTM Palstra  for insightful discussions.
ACKNOWLEDGMENTS We thank the staff of our transgenic facility and cell imaging facilities;  Mylene Brousse  and  Sandrine Lecardeur  for technical assistance;  Tasuku Honjo ,  Bernard Malissen ,  Marie-Christine Birling , and  James Di Santo  for respectively providing AID À/À , LAT  Y136F  , ubiquitous creERT2, and  RAG2  À/À gC À/À mice;  Patrick Smith  for anti-FcgRIV antibody; and  Jeanne Cook-Moreau  for critical reading of the manuscript. This work was supported by  Ligue Nationale Contre le Cancer, Association pour la Recherche sur le Cancer  (grant  SL220100601332 ),  Institut Universitaire de France , and  Conseil Regional du Limousin. B. L. and S. D. PhD fellowships were from  CNRS, Region Limousin and Association pour la Recherche sur le Cancer.
Quantitative PCRs were run in triplicate. Data analysis was performed using Roche LightCycler 480 Software (version 1. 5). PCR Array : Reverse transcription (RT) of RNA samples used on the PCR array was performed using RT 2 First Strand Kit, as per manufacturer's instructions (Qiagen). Real-time PCR reactions were performed using RT  2  Profi ler PCR array (Qiagen) in a 384-well optical plates. Real-time reactions were conducted on an ABI 7900HT (Applied Biosystems). Results were PCR reactions performed in duplicate for each target gene. The gene expression was analyzed by RT 2 Profi ler PCR array Data Analysis version 5. 0 (Qiagen) in the form of cycle threshold (Ct) values. Cell Transfection : hESC-derived HLCs were transfected for two consecutive days (day 13 and day 14) postreplating using the Silencer Select siRNAs (Life Technologies).
siRNAs were used at a fi nal concentration of 80 × 10 -6 M diluted in Opti-MEM I containing Lipofectamine 2000 (Life Technologies). The siRNA are listed in Table  S2  (Supporting Information). Cells were collected at day 15 postreplating for further analysis. Each knockdwon was perfromed in triplicate and scrambled RNA controls were used throughout. Cytochrome P450 Functional Assay : CYP3A and CYP1A2 activity were measured using the pGlo kit from Promega and the assays were carried out as per the manufacturer's instructions (http://www. promega. com/ tbs/tb325/tb325. pdf). CYP activities were expressed as relative light units (RLU), per mg -1 of protein per mL-1 of tissue culture medium. Measurement of Secreted Albumin : hESC-derived HLCs were cultured in HepatoZYME-SFM. The supernatants were collected for analysis by ELISA (Alpha Diagnostic International) and the data was expressed as micrograms (µg) or nanograms (ng) of albumin or AFP per mL -1 , per 24 hours, per mg -1 of protein.
Acknowledgements K. C. , H. R. , S. J. K. , S. J. F. , M. B. , and D. C. H. were supported by the  UK Regenerative medicine platform  ( MR/K026666/1  and  MR/L022974/1 ). B. L. -V. and D. S. were supported by  MRC  Ph. D. studentships. S. J. K. and N. B. were supported by the  MRC  ( G0801057 ) and the  BBSRC/TSB  ( BB/ J021636/1 ). Author contributions: B. L. -V. : Experimental design; collection and/or assembly of data, data analysis and interpretation; paper writing and proofreading. K. C. : Collection and/or assembly of data; data analysis and interpretation. D. S. : Collection and/or assembly of data; data analysis and interpretation. H. R. : Collection and/or assembly of data; data analysis and interpretation. N. B. : Derivation, characterization, and provision of GMP cell lines. S. J.
Avipoxviruses are divided into three clades: canarypox-like viruses, fowlpox-like viruses, and psittacinepox-like viruses. Several molecular clock and demographic models available in the BEAST package were compared on three avipoxvirus genes (P4b, cnpv186 and DNA polymerase genes), which enabled to determine that avipoxviruses evolved at a rate of 2-8 Â 10 À5 substitution/site/year, in the range of poxviruses previously reported evolution rates. In addition, the date of mean time of divergence of avipoxviruses from a common ancestor was extrapolated to be about 10,000-30,000 years ago, at the same period as modern poxvirus species. Our findings will facilitate epidemiological investigations on avipoxviruses' spread, origin and circulation.
Virus data sets All available DNA sequences of APV fpv094/cnpv121 (locus P4b, hereafter ''P4b''), fpv140/cnpv186 (hereafter ''cnpv186'') and fpv167/cnpv240 (hereafter ''DNA polymerase'') genes were retrieved from GenBank/EMBL databases. The sampling date (at least year, month and day when available) was collected for each sequence. In case no information was available, the 1st of July of the year of sequence publication was then considered as sample collection date. Only the oldest sequence was kept when identical sequences were available. Table  1  summarizes the details of included APV sequences. For each gene, DNA sequences were translated to amino acid (aa) sequences using BioEdit 7. 2. 5  (Hall, 1999) , the aa sequences were aligned with ClustalX 2. 1  (Larkin et al. , 2007) , and the original BioEdit format aa sequences were aligned with the ClustalX aa aligned sequences before they were reverse-translated in an aligned nucleotide file using BioEdit.
The sequence of the APV pigeon/CAPM V-101/CZE/1965 P4b gene (accession number: GU982736) was removed due to many deletions resulting in non-functional short proteins; the P4b sequences of APV isolates from Hawaii EL1. 1H1900, APAP16. 2H03, and HAAMB. 07H02 (accession numbers EF568402 to EF568404) were also removed because they were too short (116 bp). In order to exclude recombinant sequences (as done in  Firth et al. , 2010)  which can bias the calculation, possible recombinant sequences were searched with the Recombinant Identification Program (RIP) (www. hiv. lanl. gov). None were identified. Overall, we analyzed 81 P4b gene sequences (partial open reading frame, ORF: 426 nucleotides) from specimens collected between 1865 and 2014; 31 cnpv186 genes (complete ORF: 933 to 1020 nucleotides) from specimens collected between 1965 and 2014; and 47 DNA polymerase genes (partial ORF: 555 nucleotides) from specimens collected between 1980 and 2014 (Table  1 ).
Evolutionary analyses Phylogenies taking sampling time into account were estimated using the Bayesian Markov chain Monte Carlo (MCMC) inference methods available in the BEAST package  (v. 1. 4. 8, (Drummond and Rambaut, 2007) ). The analyses presented were run using the Tamura and Nei 1993 model (TN93) with gamma-distributed rate: the closest substitution model available in BEAST to the best fitting nucleotide substitution model estimated by means of hierarchical likelihood ratio approach using Mega v 6. 06  (Tamura et al. , 2013) ; and the second best nucleotide substitution model as per MrModeltest  (Nylander, 2004). We used empirical base frequencies and three partitions into codon positions. Different combinations of demographic models and clock models were compared as suggested  (Drummond and Rambaut, 2007) : constant size or extended Bayesian skyline plot models, strict or relaxed (lognormal or exponential) clocks.
At least 200 million MCMC iterations were run for each gene and each demographic model/clock model combination. Subsampling was performed every 10,000 generations to decrease autocorrelation between model parameter samples. The estimation of parameters and divergence time was carried out using Tracer (http://beast. bio. ed. ac. uk/Tracer). A correct mixing of MCMC was verified by effective sampling size (ESS) calculations available in Tracer. The best fitting model was also assessed using ''Model Comparison'' available in Tracer and choosing the lowest Akaike's information criterion (AICM) value. The dN/dS ratios (x) were calculated using the single-likelihood ancestor (SLAC) method available in the HyPhy package available on the http://www. datamonkey. org/ server (Kosakovsky  Pond, 2005; Kosakovsky Pond and Frost, 2005).
Discussion The present study aimed at assessing the substitution rate of APV and at estimating their TMRCA. We compared several clock and demographic models available in the BEAST package and found that APV evolved at a rate of 2-8 Â 10 À5 substitution/site/year. The date of mean time of divergence of APV from a common ancestor was extrapolated to be about 10,000-30,000 years ago. We observed a threefold difference between substitution rates and TMRCA of P4b on the one hand and cnpv186 and DNA polymerase on the other hand (Table  2 ). DNA polymerase encodes a polymerase protein, cnpv186 an immunodominant protein, and P4b a core protein  (Tulman et al. , 2004). The slower evolution rate of P4b is therefore somewhat surprising: while viable DNA polymerase and cnpv186 mutants may be more difficult to generate, one could expect more flexibility on a core protein.
Fig. 1. Phylogenetic analyzing all available avipoxvirus sequences of the (A) P4b, (B) cnpv186, and (C) DNA polymerase genes. Numbers at main tree nodes represent the age of the nodes in years with their [95% HPD]. ''Accession number_estimated collection date'' are indicated on the tree and can be linked to full strains names on Table  1. Subclades as described in  (Le Loc'h et al. , 2014)  are indicated on (A). very small fragment of avipoxvirus genomes (260-306 kbp). Cnpv186, P4b and DNA polymerase genes were selected on the basis of available sequence data but might have a substitution rate different from the rest of the avipoxvirus genome. The molecular clock analyses of avipoxviruses will gain in precision when more sequence data (and more full genomes) become available. To run the analyses with as much data as possible, we extrapolated sampling years for some sequences.
Several research teams have in the past warned evolutionary biologists studying DNA viruses about the biases associated with the use of heterochronous phylogenetic modeling designed for faster evolving RNA pathogens  (Firth et al. , 2010). In addition, it was shown that molecular evolution is artificially accelerated on short timescales  (Ho et al. , 2007). Indeed our datasets contain very recent sequences only (from 1865 to now) compared to the TMCRA of APV. Hence our results should be considered with caution. But Babkin and Shchelkunov estimated that Poxviridae evolve at a rate of 0. 9-1. 2 Â 10 -6 substitutions/site/year. They calculated the ancestors of poxviruses, orthopoxviruses, and modern poxvirus species dated approximately 500,000, 300,000, and 14,000 years, respectively  (Babkin and Shchelkunov, 2006).
considering APV collected the same year in the same location such as Chlamydotis undulata/MA/2009/LK021680, C. undulata/MA/2009/LN795880, and C. undulata/MA/2009/ LN795881, all three isolated from houbara bustards in Morocco in 2009, the cnpv186 phylogeny and molecular clock analysis (Fig. 1B) enabled to conclude to clear independent introductions of viruses and rule out the hypothesis of virus spread and evolution within the site. C. undulata/MA/2009/LN795881 interestingly clustered in sub-clade B2 (sequence identical to C. undulata/ MA/2011/LK021679 described in (Le Loc'h et al. , 2014)) whereas C. undulata/MA/2009/LN795880 (sequence identical to C. undulata/MA/2011/LK021670 described in (Le Loc'h et al. , 2014)) and C. undulata/MA/2009/LK021680 clustered in a distinct sub-clade (B1). The two sub-clades showed a divergence 3,455 (95% HPD: 222-27,041) years ago which is not compatible with an on-site evolution of the virus.
Acknowledgments G. L. L. is supported by the  Emirate Centre for the Conservation of the Houbara (ECCH)  and the  International Fund for Houbara Conservation (IFHC). We are grateful to  H. H. Sheikh Mohammed Bin Rashid Al Maktum  funder of the ECCH, to  H. H. Sheikh Mohammed  bin  Zayed Al Nahyan, Crown Prince of Abu Dhabi and Chairman of IFHC , and  H. E. Mohammed Al Bowardi ,  Deputy Chairman  of  IFHC , for their support. SB and MFD are supported by the  French ministries of Agriculture, Research and Education. The authors thank  Habib Salami  and  Vijaykrishna Dhanasekaran  for their help with using the BEAST package.
Glaciers and their changes through time are increasingly obtained from a wide range of satellite sensors. Due to the often remote location of glaciers in inaccessible and highmountain terrain, satellite observations frequently provide the only available measurements. Furthermore, satellite data provide observations of glacier characteristics that are difficult to monitor using ground-based measurements, thus complementing the latter. In the Glaciers_cci project of the European Space Agency (ESA), three of these characteristics are investigated in detail: glacier area, elevation change and surface velocity. We use (a) data from optical sensors to derive glacier outlines, (b) digital elevation models from at least two points in time and (c) repeat altimetry for determining elevation changes, and (d) data from repeat optical and microwave sensors for calculating surface velocity.
The wide range of available sensors (e. g. imaging sensors and altimeters working in both the optical and microwave region of the electromagnetic spectrum) and archives from ongoing and historic missions combined with already existing geospatial information like digital elevation models (DEMs) or former glacier outlines as available from GLIMS (Global Land Ice Measurements from Space), allows measurement of a wide range of glaciologically relevant parameters  (Kargel et al. , 2005; Malenovsky et al. , 2012; IGOS, 2007). The Glaciers_cci project focuses on three of these parameters: glacier area, elevation changes (from DEM differencing and repeat altimetry), and surface velocity fields (from optical and microwave sensors). Numerous algorithms are available for product retrieval from each of the input data sets and sensor combinations.
They differ in complexity (from simple arithmetics such as division or subtraction of raw data to rather complex calculations and processing lines) and in the required operator interaction (e. g. from manual control and editing to almost fully automatic processing), but a pre-, main-and post-processing stage is common to all of them. In general, only the main processing stage is automated while the other stages require operator interaction. The consistency of the manual corrections applied in the post-processing stage are critical when products are derived in a globally collaborative effort such as for GLIMS  (Kargel et al. , 2005; Raup et al. , 2007). Accordingly, a major objective of the Glaciers_cci project is to find the most suitable algorithms for data processing and an improved error characterisation of the generated products.
g. Bayr et al. , 1994; Rott, 1994; Paul, 2002)  and various supervised (Maximum-Likelihood) and unsupervised (ISODATA clustering) algorithms (e. g. Aniya et al. , 1996; Sidjak and Wheate, 1999). All methods utilise the very low spectral reflectance of ice and snow in the shortwave infrared (SWIR) versus the high reflectance in the visible spectrum (VIS) to identify glaciers (e. g. Dozier, 1989). Severeal methods have been compared in regard to their performance (e. g. computation time, accuracy) in a relative sense (e. g. Albert, 2002; Paul and KŠŠb, 2005) , as well as against higher-resolution datasets (serving as a reference) to determine their absolute accuracy (e. g. Andreassen et al. , 2008; Paul et al. , 2003). However, this is a challenging task as also highresolution data might not provide a reference with higher accuracy  (Paul et al.
Practical guidelines (e. g. Raup and Khalsa, 2007; Racoviteanu et al. , 2009)  and general advice (e. g. Paul et al. , 2002 and 2009)  have been prepared for the analysts contributing to GLIMS allowing a more consistent data processing. The generated outlines are stored as shapefiles (a vector format) along with their metainformation in the GLIMS glacier database (www. glims. org) for free access by the community  (Raup et al. , 2007). One globally complete dataset of glacier outlines has recently been made available as the Randolph Glacier Inventory (RGI) via the GLIMS website  (Arendt et al. , 2012).
e. with seasonal snow outside of glaciers) for glacier mapping. The workload to correct these regions after an automated mapping of clean ice can be huge as a decision on thousands of small polygons has to be made. A size-filter is an option to remove many of the smallest snow patches, but if they are as large as glaciers (e. g. >0. 05 km 2 ), manual editing might have to be applied. As the workload can be very high for this editing and the quality of the result still unsatisfactory, it is recommended to use only the best available scenes (in regard to snow conditions) for glacier mapping (e. g. Racoviteanu et al. , 2009). The availability of full resolution preview images from the glovis. usgs. gov website for Landsat data allows a fast analysis of the snow and cloud conditions, facilitating the selection of suitable images before downloading and processing the original data.
Test site, methods and results The round robin for deriving glacier elevation changes from DEM differencing tested coregistration algorithms. The experiment covered the Southern Alps of New Zealand using two ASTER  DEMs (2002 and 2006)  and the SRTM DEM  (2000) , with optional auxiliary ICESat data. A common glacier mask  (Gjermundsen et al. , 2011)  was provided together with the elevation datasets. Participants were asked to co-register the three DEMs to each other, and if possible, also to ICESat, return their co-registration parameters (Δx, Δy, Δz) and an elevation change grid. Six contributions implemented in various software products or combinations thereof (IDL, Matlab, ArcGIS, ENVI, PCI Geomatica, GMT tools, GDAL) were received. Three automatic algorithms were tested: (A) a robust surface matching technique solving for linear translation, rotation and scale  (Gruen and Akca, 2005; Miller et al.