dataesr/scientific-paragraphs-categorization huggingface dataset:
079] [-0. 106 ; -0. 026] [0. 015 ; 0. 041] [0. 006 ; 0. 027] [-0. 096 ;-0. 066] urbanization process, we wanted to evaluate whether the ge-netic structure of this synanthropic species may be reflecting both the heterogeneity of the current cityscape and the histori-cal dynamics of urbanization. We used various approaches, in-cluding population genetics model-based clustering, model-free smoothing of genetic differentiation as well as random forests that allow investigating non-linear relationships and taking into account multicollinearity between predictor variables (i. e. condi-tional permutation importance) and a Bayesian conditional au-toregressive model, which allows accounting for spatial autocor-relation. We also performed a simulation study and an analysis of subsampled datasets to assess whether the observed genetic structure could be biased due to the suboptimal spatial cover-age of the study area (i.
In fact, GO was reported to enter both the phagosomes and cytoplasm in RAW 264. 7 macro phages  [71]  and in primary human and murine macrophages. [72] As such, our data suggest that in human DCs, hBN passively translocates through the cell membrane, while both GO and MoS 2 both passively translocate and are phagocytosed into the cells. The amount of material that was taken up by DCs after 24 h was also quantified using inductively coupled plasma atomic emission spectroscopy (ICP-AES). More MoS 2 (332 ng mg -1 ) was found to enter DCs compared to hBN (134 ng mg -1 ). No molybdenum or boron was found in untreated control. The relatively higher material association of MoS 2 was expected, having been previously reported by our group in macrophages. [73] t was not possible to perform the same analysis with GO as carbon and oxygen are the most abundant elements of the cellular composition.
For ICP-AES measurements, emission wavelengths of B and Mo were detected at 208. 889 nm and at 204. 598 nm, respectively. Mo was measured with a calibration by standard addition because a matrix effect was observed on spiked samples. Statistical Analysis: Experiments were conducted at least three times and the data were processed by GraphPad Prism 7. Results were expressed as mean ± standard deviations (SD). One-way ANOVA followed by Bonferroni's test was performed to determine the statistical differences among samples versus control untreated cells (*, p ≤ 0. 05; **, p ≤ 0. 01, ***, p ≤ 0. 001).
Acknowledgements The authors gratefully acknowledge the financial support from the  EU Graphene Flagship  project (no. 881603 ). This work was partly supported by the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ), and through the program " Investissements d'Avenir"  ( ANR-11-EQPX-022 ). The authors wish to acknowledge the  Centre National de la Recherche Scientifique (CNRS) , the  International Center for Frontier Research in Chemistry (icFRC)  and the " Plateforme Imagerie In Vitro de l'ITI Neurostra ",  CNRS UAR 3156 ,  University of Strasbourg (Strasbourg, France ). The authors wish to thank  Tengfei Wang  for the zeta potential recording,  Lucas Jacquemin  for the TGA analyses, and  Pascale Ronot  from the  "Plateforme Analytique des Inorganiques ,  IPHC UMR7178 " for ICP-AES measurements.
Then, the continuum becomes redder, resembling that of a K-type star with a forest of metal absorption lines during the plateau phase. Finally, late-time spectra show a very red continuum (T BB ≈ 2050 K) with molecular features (e. g. , TiO) resembling those of M-type stars. Spectropolarimetric analysis indicates that AT 2021biy has local dust properties similar to those of V838 Mon in the Milky Way Galaxy. Inspection of archival Hubble Space Telescope data taken on 2003 August 3 reveals a ∼20 M progenitor candidate with log (L/L ) = 5. 0 dex and T eff = 5900 K at solar metallicity. The above luminosity and colour match those of a luminous yellow supergiant. Most likely, this source is a close binary, with a 17-24 M primary component.
Facilities and data reduction Follow-up images in the Johnson-Cousins U BV, Sloan ugriz, and NIR JHK filters were obtained using a number of facilities available to our collaboration. Their setups are summarised as follows: The Las Cumbres Observatory (LCO;  Brown et al. 2013)   Optical raw images were first corrected for bias, overscan, and flat-field, following standard steps in the Iraf 5 environment  (Tody 1986 (Tody , 1993)). If multiple exposures were observed in one night, they were median combined to increase the S/N. The photometric measurements were performed using a Pythonbased pipeline, SNOoPY 6 , which consists of several packages for photometry, such as Sextractor 7  (Bertin & Arnouts 1996) , Daophot 8  (Stetson 1987) , and Hotpants 9  (Becker 2015). Given that AT 2021biy was strongly contaminated by the host-galaxy background, the template-subtraction technique was applied in our measurements.
These templates were obtained through public archives, such as the Sloan Digital Sky Survey (SDSS; u image taken in 2004;  Adelman-McCarthy et al. 2006)  and the Panoramic Survey Telescope and Rapid Response Sys-4 These photometric data come from the Global Supernova Project. Specifically, the instrumental magnitudes were measured through the PSF-fitting technique. A PSF model was built by fitting the profiles of bright, isolated stars in the field of the transient. Then, the fitted source was removed from the original images, and the residuals at the object's location were used to evaluate the quality of the fits. If the target source was not detected, a magnitude limit was estimated. We used zero points (ZPs) and colour terms (CTs) of individual instruments to calibrate the instrumental magnitudes, which were determined through observations of standard photometric fields during photometric nights.
Specifically, Johnson-Cousinsfilter images were calibrated via the Landolt (1992) catalogue, while Sloan-filter data were retrieved from the SDSS DR 13 catalogue  (Albareti et al. 2017). In order to improve the calibration accuracy, a local sequence of stars in the transient's field was used to correct the instrumental ZPs during nonphotometric nights. Their catalogued Sloan magnitudes were directly taken from SDSS, while the Johnson-Cousins magnitudes of the reference stars were derived from the Sloan magnitudes after applying the conversion relations of  Chonis & Gaskell (2008). Photometric errors were estimated through artificial-star simulations, in which several fake stars with known magnitudes (similar to that of the transient) were placed near the position of AT 2021biy. The magnitudes of the simulated stars were also measured with the PSF procedure.
Multiband light curves of AT 2021biy Although AT 2021biy was discovered by ATLAS on MJD = 59243. 56 (∼7 days before maximum brightness), we searched for earlier survey data from the forced-photometry servers of ATLAS  (Smith et al. 2020 ) and Pan-STARRS  (Chambers et al. 2016; Magnier et al. 2020). This allows us to constrain the prediscovery evolution of AT 2021biy. ATLAS provides magnitudes in the cyan and orange filters, while Pan-STARRS gives iz (very close to Sloan iz filters) and y-band data. In this paper, the above magnitudes are maintained in their original photometric systems. The ATLAS and Pan-STARRS magnitudes are made available at the CDS. ATLAS monitored the site of AT 2021biy on MJD = 57364. 61 (∼1886 days prior to maximum brightness), providing a first upper limit of o > 19.
2011). Soon after the discovery, we started a high-cadence followup campaign lasting over 400 days. In addition, the subsequent outburst of AT 2021biy was well monitored by ATLAS and PS1. The multiband light curves of AT 2021biy, including preoutburst data extending up to ∼1 yr before the LRN discovery, are shown in Fig. 2. Similar to other LRNe, AT 2021biy rapidly rises to the light-curve peak in all bands in ∼7 days. We performed a fifth-order polynomial fit to the early-time r-band light curve to derive the time of r maximum on MJD = 59251. 0 ± 1. 0 (r max = 16. 28 ± 0. 03 mag), which will be used throughout this paper. The peak is followed by a rapid decline (with a rate of 11 V1309 Sco exhibits a slow rise in luminosity a few months prior to its outburst, consistent with the photometric evolution of the system after the common envelope ejection.
Spectroscopic evolution We collected 31 optical spectra of AT 2021biy, covering about 13 months and all crucial phases of its evolution. They exhibit the typical evolution of LRNe (e. g. ,  Pastorello et al. 2019a Pastorello et al. ,b, 2021a,b),b). We identify four distinct phases: Phase i -at early times, around the blue peak; Phase ii -an intermediate phase during the plateau; Phase iii -at late times, during the fast postplateau decline; and Phase iv -during the late-time light-curve hump. 12 https://github. com/ishivvers/TheKastShiv Phase I. At early epochs (until +29. 5 days after the lightcurve maximum), spectra of AT 2021biy show a blue continuum with superimposed prominent Balmer emission lines. Several Fe ii emission lines are also observed, along with Ca ii H&K in absorption and the barely visible Ca ii NIR triplet (see the top panel of Fig.
46±0. 50 Mpc; E(B -V) = 0. 271±0. 096 mag), and converting to standard Johnson-Cousins filters, this corresponds to M V = -7. 93 ± 0. 17 mag and V -I = 0. 52 ± 0. 01 mag. In order to determine the luminosity of this source, we use the YBC database  (Chen et al. 2019). We find that the colour and magnitude of the progenitor candidate match an ATLAS9  (Castelli & Kurucz 2003)  model at solar metallicity with log (L/L ) = 5. 0 dex and T eff = 5900 K. Such a star would be a luminous yellow supergiant, and consistent with a zero-age main-sequence (ZAMS) mass of ∼20 M. We also conducted a more systematic analysis of the progenitor candidate using the Hoki code  (Stevance et al. 2020)  to explore a grid of BPASS models  (Eldridge et al. 2017; Stanway & Eldridge 2018 ). The BPASS model grid contains both single-star evolutionary tracks and binary models, making it especially suitable for analysing the progenitors of LRNe.
We remark, however, that BPASS does not resolve the common envelope phase in the models on a dynamical timescale. We searched the grid of solar-metallicity BPASS models for any  binary models that matched the measured progenitor magnitude in F606W to within 0. 17 mag, and match the F606W -F814W colour of the progenitor to within 0. 05 mag. We note that the tolerance in colour is larger than our photometric uncertainty in order to account for possible model systematics as well as the possibility that two consecutive model points may change by more than 0. 01 mag owing to sparse grid resolution. We plot the matching models in Fig. 13. A large number of models match our photometry, and these are also consistent with the luminosity inferred from the YBC database. We note that detailed MESA (Modules for Experiments in Stellar Astrophysics;  Paxton et al.
Discussion and concluding remarks AT 2021biy is one of the best-observed LRNe, with extensive optical and NIR datasets during the major outburst phase. Although AT 2021biy shares a number of common properties with other LRNe, it shows an unprecedentedly long plateau, and a pronounced hump in the late-time (post-plateau) light curve. The field of AT 2021biy was imaged for ∼7. 6 yr prior to the outburst by the ATLAS and Pan-STARRS surveys. However, a weak source is observed at the LRN location only from about -230 days to -200 days before the outburst onset. The subsequent observational gap lasts for about 165 days. For this reason, the timing of the onset of the common-envelope phase is not well constrained. AT 2021biy shows a very fast initial rise lasting ∼7 days, reaching the first blue peak at a luminosity of 1.
Nevertheless, the amount of FDI projects carried out by profit-maximising firms reflects the relative attractiveness of their targets, providing a useful and traceable indicator of city competitiveness (see, e. g. ,  Guimaraes et al. (2004) ). In a sense, by focusing on FDI, we look at the tip of the (urban) iceberg. In addition, as we make clear later, FDI is primarily an urban phenomenon. According to our database, over the period  [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012] [2013] [2014] , between 68% and 90% of greenfield FDI were located in urban areas. It is therefore of primary interest to look at the determinants of FDI location decisions at the city level. Regarding metro systems, they are only one of the many dimensions of urban transport infrastructure.
From a broader perspective, our paper deepens our understanding of what it means, for a city, to be competitive at the international level. This is a key issue given that attracting FDI is often seen as a desirable policy to accelerate economic development  (Javorcik, 2015). The rest of the paper is organised as follows. Section 2 details the construction of our database along with a number of stylised facts. Our empirical strategy is discussed in Section 3. Our results are presented in Section 4. Section 5 provides concluding remarks.
Construction of a new city-level database To be able to carry out the analysis conducted in this paper, we have built an original database of urban areas worldwide. As we make clear below, our database contains a wide range of information stemming from different sources. In addition, we faced several challenges. First, we had to identify urban areas and to rely on satellite pictures to fix their limits. Second, FDI projects had to be geolocated at the city level. Third, no database existed on metro infrastructures. We collected this information by hand. The construction of the various aspects of this new database is described in detail below. We also provide some descriptive statistics to 3 see e. g. Baum-Snow (2007) ,  Duranton and Turner (2011) ,  Duranton and Turner (2012). 4 see e. g. Baum-Snow and Kahn (2000) ,  Baum-Snow et al.
The identification of cities Scholars working on cities often face the issue of 'choosing' the limits of the city when it comes to harmonising and merging data from different sources. First of all, the definition of cities can encompass very different geographic areas in different countries. Second, there is not a common and standard list of city's identifiers in the world. Finally, in many cases, the urban area of a city can be a much more relevant unit for economic analysis than administrative borders. In order to overcome that issue, we decided to make use of geolocated data, coming from satellite observations, whose availability and precision are increasing over time. The Lincoln Institute provides a geolocalised database of 3646 urban areas identified thanks to an evaluation of human-built environment for a grid scale of 500 m for the entire planet.
To do so, the Lincoln Institute uses Modis 9 500, which is, according to  Potere et al. (2009) , one of the most accurate databases to represent the urbanised area. In our empirical analysis, we mainly rely on this definition of urban areas albeit with two modifications. First, given that in some cases the continuity in urbanisation is quite spread over space, some urban areas are actually encompassing two cities. We therefore split some of these areas in two when they correspond to two different cities, as attested by the existence of two distinct transport networks. We end up with 3, 729 urban areas. Second, in our empirical analysis, we consider a FDI project to be located in one of these urban areas as long as it is located within the urban area or within a buffer of 5 km around that urban area.
The location of FDI projects at the city-level Our measure of FDI comes from the fDiMarkets database, published by the Financial Times, which gathers economic information regarding the worldwide location of 145,000 FDI projects over the period 2003-2014. 11 For each project, fDiMarkets provides information, among other details, on the date and location of the FDI. 12 These investments are considered as projects because that they tend to be registered in the dataset as soon as they are announced rather than when they actually occur. However, this is not an issue for two reasons. First, if a project happened to be finally cancelled for whatever reason, what matters is the fact the company would have chosen this location in 'normal' times. Second, the providers of this database tried to correct for these 'ghost' projects.
Given its numerous advantages, the fDiMarkets database has been used, among other papers, by  Defever (2006) , or  Desbordes and Wei (2017)  to study location determinants at the country level. Similar data from the Invest in France Agency has also been exploited by  Py and Hatem (2010)  to study the location decisions of multinational firms in European countries. To attribute each project to an urban area, we created a table of correspondence between the names of the urban areas in our database and the names of the cities in the fDiMarkets database. Table  1  presents the number of projects of FDI we managed to locate by type of FDI (greenfield, extension, co-location) in an urban area, outside our identified cities, and those for which the information was either missing in the database or that we could not locate.
In the empirical analysis, we focus on greenfield FDI, which is a cross-border investment in a new physical project, not directly related to previous (i. e. expansion) or other (i. e. co-location) projects. We thus look at the FDI projects which are the most likely to react to contemporaneous changes in city attractiveness. As shown in Table  1 , about 68% of the FDI from the fDiMarkets database were geolocated in an urban area while about 10% were geolocated outside urban areas. Information on location was missing for about 22% of the projects. In other words, FDI projects are mainly located in urban areas, with between 68% and 90% of greenfield FDI project located in cities. The broad aim of this paper is to understand what makes a foreign investor chooses a city rather than another. We therefore dropped those FDI projects that we could not locate or which were located outside one of 'our' urban areas.
13 Our final sample consists in slightly more than 95, 744 FDI projects located in 2000 urban areas. About 47% of the urban areas in our database did not receive a single FDI over the period 2003-2014. We produce a worldwide map of the distribution of urban FDI along with a table presenting the main investing and receiving countries over the period 2003-2014 in the online appendix A. 2. Countries which receive the most FDI are among the largest economies. Fig. 2    9 MODIS stands for Moderate Resolution Imaging Spectroradiometer. 10 Our results are robust to the use of 1 and 10 km buffers. 11 <hyperlink refid¼"http://www. fdimarkets. com/ ">https://www. fdimarkets. com/. 12 The database also provides estimations of the amount of each FDI project and the number of job created. We prefer to use the number of FDI because these values have often been estimated by fDiMarkets.
Metro systems in the world A comprehensive database gathering information on all metros was not available. We therefore built our own worldwide metro dataset. There are online social communities passionate about metros. The most famous ones are Metrobits 14  and UrbanRail. Net. 15 In addition, a list of metro systems can also be found on Wikipedia. As a starting point, we collected information from these databases. We then used various other sources to complete and cross-check our data. Based on the existing literature, we decided to consider an urban transport system as a metro if it respects the three following characteristics: (i) no sharing of dedicated infrastructure with other transport modes; (ii) high service frequency; (iii) train stations relatively close to one another. 16 his work enabled us to identify 186 cities with a metro in operation in 2014 and 36 with a metro 'under construction', but not yet in operation.
Control variables We control for market size, political power, infrastructure. These characteristics have often been found to determine FDI and can be correlated with the presence of a metro network  (Blonigen and Piger, 2014; Nielsen et al. , 2017). The market size is assessed by the use of both, the log of gross value added per capita in 2000 and the log of total population in 2000. Data come from  Kummu et al. (2018). These variables measure market size, market potential and, to a certain extent, city productivity. We include a dummy variable which takes the value of one if a city is the administrative capital of the country. We also take into account the availability of transport infrastructure connecting the city with the rest of the world and/or facilitating the movement of goods and people between cities: airport, port, railways.
The port and airport variables are dummy variables which take the value of one if there is, respectively, a port or an airport of medium or large size within a distance of 50 km. All other variables are expressed in number of km. The length of railways are measured with a buffer of 10 km around the borders of urban areas and normalised by the urban area. Data on airports, roads and railways come from the 1997. Global VMap0 dataset created by the U. S. National Geospatial Agency. 22 Data on ports come from the World Port Index. 23 astly, we include continental dummy variables for the following continents: Africa, Asia, Europe, North America, Oceania, South America. In that way, we can account for continent-specific determinants.
Conclusion In this paper, using an original city-level database, we have shown that expanding metro infrastructure improves the attractiveness of cities in the form of greater greenfield foreign direct investment (FDI) received over the period 2005-2014. A natural explanation is that a metro system improves accessibility and commuting speed. Our study has focused on foreign direct investment but we naturally do not claim that metros ought to be built for the sole purpose of attracting foreign investors. The influence of metro systems on the location choice of multinational firms should rather be interpreted as revealing whether such an investment in public transport infrastructure contributes to improving a city's attractiveness, which may benefit all firms and individuals, be they foreign or native.
on Investment for their useful comments. Lastly, we would like to thank the  Soci'et e du Grand Paris  and the LIEPP center of excellence for providing financial support to this research. This research was indeed supported by the  French National Agency (ANR)  as part of the  "Investissements d'Avenir" program LIEPP  ( ANR-11-LABX-0091 ,  ANR-11-IDEX-0005-02 ) and the  Universit e de Paris IdEx  ( ANR-18-IDEX-0001 ). ☆☆ The views expressed in this paper reflect the opinions of the authors and do not necessarily express the views of the  Banque de France.
The total amount adsorbed can be calculated from the equation: Q T = 1/ m ∑ j=1 x j Q 0 j (14) where Q 0 j is the adsorbed amount of pure compound, j, at the hypothetical pressure, P 0 j , that is: Q 0 j = f P 0 j In our case, the functional f in the equation above takes the form of the Langmuir, Sips, Toth, and UNILAN equations. The amount adsorbed contributed by compound 'j' is therefore given by: Q j = Q T x j (15) Equation (  12 ) was solved numerically using the Newton-Raphson method for solving algebraic equations. In order to evaluate numerically the integral in Equation (  13 ), the quadratic method was chosen. The programming environment MatLab was used to benefit from the relevant file functions and software communication links corresponding to these numerical solutions.