dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1992)  to a 2105-2080 Ma period, defined by U-Pb and Pb-Pb crystallisation ages of zircon, monazite and titanite  (Hirdes et al. , 1996; Kouamelan et al. , 1996; Oberthür et al. , 1998; Feybesse et al. , 2006; De Kock et al. , 2011). Reworking of the Archean nucleus between 2080 and 2030 Ma  (Kouamelan et al. , 1996 (Kouamelan et al. , , 1997) )  is supported by geochronological data obtained by different radiometric methods, including U-Pb zircon and monazite EPMA dating. However, due to the limited number of age data from only a few outcrops throughout the West African Craton, it remains unclear whether the metamorphic overprint was continuous during ~70 Ma or occurred during several discrete events. Thus, the available age data are of little help to discriminate between the polyand monocyclic orogenic models.
P-T conditions and P-T paths The metamorphic conditions and P-T paths were constrained by using P-T pseudosections  (De Capitani & Brown, 1987; Powell et al. , 1998; Zeh, 2001; Connolly & Petrini, 2002; Johnson et al. , 2008; Millonig et al. , 2010)  and geothermobarometry with a multi-equilibria method. Mineral compositions were measured using a Cameca SX50 Microprobe at the GET lab in Toulouse, France. P-T pseudosections were constructed using the Perplex software  (Connolly, 2005). Pseudosections used to constrain the prograde evolution of migmatite samples were constructed by using an average composition of subsolidus paragneiss (Table  2 , Appendix S2). Garnet porphyroblast fractionation was taken into account when its mode was found to be significant (> 1vol%). Methodological details relevant for individual samples are described in Appendix S2, along with the chosen solution models.
Coupled metamorphic and structural data support the view that extensional detachments developed in a thickened, partially molten orogenic crust and contributed to the exhumation of high-grade rocks. Evidence for this crustal mechanical behaviour has not been reported from the Paleoproterozoic West African Craton so far, and evidence for extensional detachments are lacking in Precambrian accretionary orogens in general. The coexistence of metamorphic terranes recording diverse geothermal gradients implies the existence of regional-scale heterogeneities in the thermal evolution of maturing crusts. Similar to modern orogens, the source of such thermo-mechanical variations may lie in the large-scale architecture of the Eburnean orogen  (Hyndman et al. , 2005; Moresi et al. , 2014) , which remains an active research topic.
ACKNOWLEDGEMENTS We gratefully acknowledge  AMIRA International  and the industry sponsors, including AusAid and the  ARC   Linkage  Project  LP110100667 , for their support of the  WAXI  project ( P934A ). We acknowledge the facilities, and scientific and technical assistance of the  Australian Microscopy & Microanalysis Research Facility at the Centre for Microscopy, Characterization & Analysis of UWA , a facility funded by the  University, State and Commonwealth Governments. We thank  Erwann Lebrun  and the staff of the  John De Laeter Centre for Isotope Research , hosted at  Curtin University of Technology. Dominique Chardon  is warmly thanked for sharing advice and constructive discussions, which contributed to greatly improve the quality of the manuscript. Allen Kennedy  is thanked for providing expertise concerning SHRIMP dating.
We thank  Philippe de Parseval  and  Sophie Gouy  for their assistance for micro-analysis acquisition. We recognise the logistical support and datasets provided by the  Geological Survey Department of Ghana , as well as the chauffeurs from the  IRD in Ouagadougou  ( Salifou Yougbaré ,  Boukary Ouedraogo  and  Matthieu Kaboré ) and from the  GSD of Ghana (Kwasi Duah ).
For scoliotic patients, corrected height was computed by adjusting trunk loss using Bjure formula: LogY = 0. 011X -0. 177, where Y is the loss of trunk height (cm) caused by the spinal deformity, and X is the Cobb angle of the primary curve. Weight was measured on a digital scale in the fasting state to the nearest 0. 1 kg in normal indoor clothing and barefoot. BMI was calculated by dividing weight (kg) by height squared (m 2 ) and corrected BMI (cBMI) by dividing weight (kg) by corrected height squared (m 2 ). BMI and cBMI were then expressed in Z-score according to the reference data for the French population  (21). Overnight fasting blood was obtained for ghrelin level in both AIS patients and controls. Serum samples were obtained after centrifugation and stored at -80 °C until assay. At the end of the study, total ghrelin was measured using a commercial radioimmunoassay (Phoenix Pharmaceuticals, Belmont, CA) with a detection limit at 2 pg/ml.
The intra and interassay coefficients of variation were 7. 2 and 9. 9 %, respectively. Data were analyzed using STATA SE v11. 0. Data reported as means ± SD. P < 0. 05 was considered significant. We used Student's t test or Wilcoxon rank sum test to compare the two samples. Univariate and multiple regression analyses were performed to determine predictors of hormonal secretion. Square root transformation was performed to approximate a normal distribution for the ghrelin data. Pearson or Spearman's rank correlations between circulating ghrelin levels and growth-related parameters (age, weight, height, BMI, corrected height and corrected BMI) were estimated and compared in AIS and controls.
Measurements of Cytoplasmic and Nuclear Cyclin D1 Relative cytoplasmatic and nuclear Cyclin D1 protein levels were estimated in confocal images of brain sections stained for Cyclin D1 and the nuclei labelled with Hoechst. First, images were converted to binary images by applying a threshold level on them using the Image-J software. Cytoplasmatic Cyclin D1 was assigned to the Cyclin D1 signal that did not overlap with the Hoechst signal and was obtained by subtracting the Hoechst binary image to the Cyclin D1 binary image. Nuclear Cyclin D1 corresponded to the Cyclin D1 signal that overlapped with the Hoechst signal and was obtained by subtracting the cytoplasmatic Cyclin D1 binary image to the total Cyclin D1 binary image (see Supplementary Fig. 2C  and D ). Labelling intensities of total, nuclear and cytoplasmatic Cyclin D1 were obtained in each image using the integrated density option of the Image-J software.
, 1989; Takahashi et al. , 1995)  to estimate the growth fraction, GF (fraction of cells that are proliferating and that correspond to the maximum labelling index); the cell cycle duration, Tc; and the Sphase duration, Ts. Tc and Ts were calculated taking into account the Tc-Ts value, which is the cumulative labelling time required to achieve the maximum labelling index, and the Ts/Tc value was given by the intersection of the extrapolation of the linear regression line to the Y axis. To generate the best fitted-regression lines we applied the Linear Regression Model of the Prism software (version 5; GraphPad software) to the averaged labelling indexes of the time points before reaching the GF. The slopes of the best-fitted lines in wild-type and TgDyrk1a samples were analysed for statistical significance with the same software.
RNA Extraction and Real-time qPCR Total RNA from the telencephalon of E10. 5 and E11. 5 embryos were extracted using the RNeasy Mini kit (Qiagen) according to manufacturer's instructions and the eluted RNA treated with DNAse (Ambion) for 30 min at 37 °C. cDNAs were synthesized from 1 μg of total RNA using Superscript II retrotranscriptase (Life Technologies) and random hexamers (Life Technologies). Real-time qPCR was carried out with the Lightcycler 480 platform (Roche) using SYBR Green I Master Kit (Roche). Peptidyl-prolyl isomerase A (Ppia) was used as reference gene for data normalization. A complete list of the primers used can be found in Supplementary data.
, 2013; GEO GSE51606) , indicating that DYRK1A is likely to be the only DYRK kinase involved in the regulation of Cyclin D1 turnover in cortical RG progenitors. During late corticogenesis, by E16. 5, Cyclin D1 protein levels in TgDyrk1a RG progenitors were normal despite the fact that the levels of DYRK1A in the dorsal telencephalon were still augmented around 50%. Our Cyclin D1 expression data indicates that the levels of Cyclin D1 in wild-type cortical RG progenitors progressively decrease during neurogenesis, paralleling the increased duration of G1 phase  (Takahashi et al. , 1995). Thus, it is possible that nuclear Cyclin D1 during late neurogenesis is not regulating progression through G1 and, therefore, their levels are not subject to cell cycle regulation. We show here that IPs express DYRK1A and that at late corticogenesis the pool size of this type of progenitor is significantly diminished in TgDyrk1a embryos.
Acknowledgements This work was supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  [Grant numbers  SAF-2010-17004 ,  SAF2013-46676-P  and  CSIC-201020I003 ], the  Jérôme Lejeune Foundation , and the  Generalitat de Catalunya  [Grant number  2009SGR1464 ]. S. N. was supported by a  FI fellowship  from the  Generalitat de Catalunya  [Fellowship number  2011B1-OG242 ] and by a  FPU fellowship  from the  MINECO  [Fellowship number  AP2012-3064 ]. M. J. B. is supported by the program  JAE DOC-CSIC/European Social Fund  and JA by a  FPI fellowship  from the  MINECO  [Fellowship number  BES2011-047472 ]. S. J. C and D. O. were supported by a strategic grant from the  Biotechnology and Biological Sciences Research Council (BBSRC). A. L. A. was supported by a  BBSRC CASE  PhD studentship with AstraZeneca and P.
Both techniques rely on the high-resolution quality of images in order to achieve small-scale measurements (better than 20 cm for our case). Structure from motion and cross-correlation processing of airborne images are robust tools for measuring displacements of geological objects  [De Michele and Briole, 2007; Roer et al. , 2008; Heid and Kääb, 2012]. This work aims to (1) use these techniques on a data set of multitemporal airborne images to detect and quantify, for the first time, the evolution of Piton de la Fournaise's summit structure and deformation over the last 65 years and (2) evaluate the potential of these techniques to retrieve past deformation histories for active volcanoes displaying large deformation (metric to decametric). We validate the technique by comparing our most recent results with the deformation database recorded by OVPF.
Method SfM is an automated technique that can create a 3-D model out of digital images of a target object. In particular, SfM uses the differences between images from multiple-viewing points to rebuild the imaged structure in 3-D. First, we applied SfM to process images from seven aerial photography campaigns of the summit area. These were those of the French Institut Géographique National (IGN), the private society "Acquisition Information Géo-Localisée"  (AIGL), and OVPF in 1950 , 1966 , 1981 , 1997 , 2003 , 2008 , and 2015  (Table  1 ). Processing was carried out using Agisoft Photoscan Professional software, which has proven excellent for SfM reconstruction [e. g. ,  Bolognesi et al. , 2015]. This allowed us to produce a multitemporal set of orthoimages and digital elevation models (DEMs), georeferenced to UTM coordinates.
Each campaign was referenced using the available geodetic ground control data: namely, static natural objects  for 1950, 1966, and 2003, and stereo preparation ground control points for 1981, 1997, 2008, and 2015. Next, we cross correlated each consecutive pair of orthoimages using ENVI's "Correlation Image Analysis Software"  [Roer et al. , 2008; Heid and Kääb, 2012]. This allowed us to minimize the decorrelated areas (new lava flows, crater collapses, landslides, etc. ) in the output maps. Displacement values in the decorrelated areas were then interpolated from nearby values. The final outputs were multitemporal maps of deformation since 1950, with error estimates for the photogrammetric reconstruction of the model and the coregistration of the image locations for cross correlation. These errors were minimized by constraining the camera parameters in  2.
11 ± 0. 24 Mm 3 /yr), during which the inflation was slower than the average for the total 1950-1997 period. The temporal evolution of the planimetric displacements across four areas representative of the four cardinal points around the summit has been plotted and compared with the corresponding GNSS data from the OVPF permanent network, which is available for the period 2008-2015. Comparison of our results with measurements of the permanent GNSS stations SNEG, DERG, DSRG, and BORG (Figure  4a ) shows errors on displacement values of 7. 8%, 8. 4%, 6. 5%, and 52%, respectively. At the BORG station, the large error can be explained by its very small displacement. The station is also located very close to the February 2015 vents, leading to a significant overestimation of the motion by cross correlation and thus a high error.
Acknowledgments Airborne photographs have been provided by  IGN  ( 1950IGN  ( , 1966IGN ( , 1981IGN ( , and  2003 )), AIGL (2008), and OVPF (1981, 1997, and 2015), and corresponding data are available at each of these institutions. Constructive reviews were provided by  M. Battaglia  and  Y. Aoki. The authors warmly thank  Andrew Harris  who greatly improved the manuscript. This is IPGP contribution 3656. The Editor thanks  Maurizio Battaglia  and  Yosuke Aoki  for their assistance in evaluating this paper.
The calculations of RQA parameters were done by using the Matlab toolbox developed by Marwan  [29]. The number of the sampled points was 5000 (as in Fig. 5 ). break into smaller pieces. Rather then long slugs we observe air slugs and air bubbles of different sizes. Obviously, they are moving in the water environment with different speeds. For the condition q ≈ 0. 065 (Fig. 5a ) we observe the spectacular effect of structure self-organizing which corresponds to formation of series of short slugs with repeatable sizes (see Fig. 2 ). Interestingly, the parameters: determinism -DET and laminarity -LAM are very low but they are slightly increased around q ≈ 0. 065. The above tendency is confirmed also by the topological structure of the RPs measured by clustering (Fig. 6k ) transitivity -T RAN (Fig.
However, their study did not address the measurement of urbanised areas per se and instead concentrated on identifying factors that influence population density. In their study,  Arribas-Bel et al. (2011)  used spatial data, derived from the European Corine Land Cover database to consider the issue of urban sprawl from a multidimensional viewpoint. Using six dimensions to define the concept (i. e. connectivity, decentralisation, density, scattering, availability of open space and land-use mix), they developed various indices of sprawl that were then calculated for a sample of 209 European cities for the year 2000. Even though their study offered a new methodological approach using rich data sets to measure urban sprawl, it did not explicitly address the determinants of the phenomenon. The main objective of our paper is to explore the relationship between urban sprawl and a set of variables that urban economic theory and the empirical literature suggest may be correlated with the phenomenon.
Data Based on the theoretical and empirical literature, we seek to explain differences in urban sprawl in Europe across space and time. The approach taken here is mainly based on the monocentric city framework and conceptually the empirical model is given by: Sprawl index = f (income, population, agricultural land value, transportation costs, other socio À economic, climatic and geographic variables) ð1Þ This study considers two indices of sprawl that reflect the spatial scale of cities and urban morphology. By considering these indices, we examine the extent to which the determinants of urban expansion are linked to the fragmentation of urban areas. As independent variables, both indices will be estimated using the same explanatory variables. In this section we specify the sources and extent of the data used in this study before discussing the methodological features of sprawl measurements and the choice of explanatory variables.
Data on urbanisation and sprawl measures We focus on a sample of European cities obtained by combining various existing data sources. Our starting point was the complete set of 320 cities used in the Urban Audit (UA) database. 2 Here, all cities are defined at three scales: the Core city, which encompasses the administrative boundaries of the city; the Large Urban Zone (LUZ), which is an approximation of the functional urban region centred around the Core city; and the Sub-City District, which is a subdivision of the LUZ  (Eurostat, 2004). We concentrate on LUZs, because sprawl is observed around the fringes of cities from where it spreads out across the whole urban region. Therefore, the boundaries of each LUZ define the spatial units upon which this study is based. UA provides rather limited information on land use, with poor coverage for many cities.
As an alternative to this data set, we use data on Urban Morphological Zones (UMZ), compiled by the EEA, which contains spatial information for three years  (1990, 2000 and 2006). 3 Derived from Corine Land Cover, UMZ data covers the whole EU-27 at a 200 m resolution for those urban areas considered to contribute to urban tissue and function  (Gue´rois et al. , 2012). Geospatial data on land use for each city is obtained by superimposing the LUZ boundaries onto the UMZ spatial data, using a Geographical Information System (GIS). To illustrate this process and the nature of the spatial data, Figure  1  provides maps documenting the changes in the urban (artificial) area for four selected cities: Kielce and Radom (Poland), Eindhoven (Netherlands) and Murcia (Spain), over the three snapshot years for which the UMZ data were available.
While the external boundary (shown in grey) of each LUZ remains stationary over time, the fragments of urban land (i. e. the artificial area) within the boundaries (represented by the black patches) vary in both size and quantity. The cities in Figure  1  were chosen to illustrate different urban dynamics. For Kielce and Radom, both the number of fragments and the total artificial area increased significantly in a relatively short period of time  (between 2000 and 2006). In Eindhoven the total artificial area increased between 1990 and 2006, even though the number of fragments decreased. Finally, during the same period, Murcia experienced major urban development (as evidenced by the overall increase in artificial area), but the number of fragments remained relatively constant. While the UA database covers 320 cities, the publicly available UMZ data does not include information for the UK, Cyprus and Finland for 1990 and for Greece and Cyprus for 2006.
Taking this into account, the sample used in this study comprises the 237 LUZs where, for 1990, 2000 and 2006, complete information is available on artificial area and the number of urban fragments. Figure  2  shows the extent of the sample and the individual cities are identified by four colours, depending on their supra-national region group. 4  Two indices of urban sprawl are constructed. The first index aims to measure the spatial scale of each city by considering the total artificial area in square kilometres (ArtifArea) as a proxy for all urbanised land in each LUZ. These areas were obtained directly from the spatial UMZ data according to Corine Land Cover nomenclature. This simple measure reflects the evolution of urban land cover in a given area without any prejudgment on internal composition or urban morphology, i.
Data on explanatory variables The UA database provides a wide range of variables, including those commonly used in the monocentric city model (e. g. urban area, population, revenue, transport). However, data are missing for some cities, which makes the use of some variables unfeasible. Therefore, the UA data are supplemented by data obtained from the European Observation Network, Territorial Development and Cohesion (ESPON). 5  When combined, these data sources provide a set of explanatory variables covering a broad sample of European cities. The ESPON database provides comprehensive data for each LUZ on Gross Domestic Product (GDP) adjusted for Purchasing Power Standards 6 and total population (POP). 7 GDP per capita (GDPcap) is used as a proxy for income. All of these variables are defined for the three reference years  (1990, 2000 and 2006)  for 282 cities across Europe.
However, no direct measures of transport costs or agricultural land rents exist for the whole of Europe over the relevant time periods. Similarly, there are no European data sets relating to agricultural land markets or transport costs at the city level. Based on the empirical studies cited earlier, proxies were identified that provide adequate measurements of these variables. First, to account for agricultural land rent, the ratio of agricultural added value to the area of agricultural land (Agriprox) was calculated. Data on agricultural added value were available from ESPON, and the agricultural land area for each LUZ was calculated. The rationale for including this proxy is that this ratio could explain different levels of agricultural productivity. Normally, higher agricultural productivity should be capitalised into land rent.
Similarly, highway density (Highway) data from the Eurostat regional data set was used as a proxy for transport costs. The assumption here, is that investments in highways make travelling faster and more convenient, which reduces the time and the costs of commuting. Following  Burchfield et al. (2006)  and  Deng et al. (2008) , a set of climatic and environmental data was collected from the UA database. The former includes the number of days of rain per year (Rain) and the average temperature of the warmest months of the year (Temperature). The latter includes the annual average concentration of NO 2 (NO2) as a good indicator of air pollution. A terrain variable, median city centre altitude above sea level (MedAlt), is also included. This variable is a partial indicator for the ruggedness of the terrain in the LUZ, as this may have an impact on the potential for urban growth.
In addition to the economic and geographical variables of interest, other social and cultural variables are considered. First, data on recorded crime (Crime) from the UA is used to account for the security situation in the central city. As mentioned previously,  Patacchini and Zenou (2009)  found that higher crime rates increase sprawl. Second, the number of cinema seats (CineSeats) is included as a proxy for the cultural attractiveness of the central city. A vibrant central city would be expected to discourage decentralisation, thus reducing sprawl and resulting in more compact urban areas. Despite some of these variables having missing data for certain cities, they were used to estimate the differentiating factors between different LUZs in the sample. Table  1  provides a statistical summary of the panel data used in this study.
Estimation strategy Panel data analysis is adopted to deal with observations from multiple cities over three periods. Given the variables discussed above, the estimating equation of sprawl indices is given by: log(SI it ) = a i + b log POP it ð Þ + mlog(GDPcap it ) + g i D i + e it ð3Þ where i and t stand for cities and time periods, respectively. The dependant variable SI it represents urban sprawl indices (ArtifArea or Scatt). 8 There are two time-varying regressors, Population (POP) and GDP per capita (GDPcap). D i is a vector of timeinvariant variables. a i is specified as random or fixed effects. e it is the error term. 9 Equation (3) may be estimated using ordinary least squares (OLS) regression, pooling observations across cities and over time. However, OLS does not take the panel nature of the data into account and could therefore result in invalid inferences being made from the data  (Baltagi, 2005).