dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Using the framework of the monocentric city model, this paper has empirically investigated the determinants that influence urban sprawl across a large set of European cities. The phenomenon of sprawl was examined both as an increase in the spatial scale of urban areas and as a process of fragmentation, where urban area is shown to be characterised by a number of discrete parcels of urban settlement scattered around the central city. For each city in our sample, data on these two dimensions of urban sprawl were accurately measured using GIS software. Based on the urban economic literature on urban sprawl, a set of potential explanatory variables was drawn up and appropriate data collected from a range of existing sources (e. g. Eurostat, Urban Audit, ESPON). Where data on potential explanatory variables were not available, a suitable proxy variable was constructed.
Data were obtained for these variables over three reference years  (1990, 2000 and 2006). The use of panel data allows unobservable individual heterogeneity to be controlled but also means that a simple OLS estimator is unlikely to be suitable, as this would not account for such unobservable heterogeneity across cities. Several different estimators were considered and statistical tests were performed to determine the ability of each to account for the specific structure of the panel data for the two aspects of sprawl measured by the study. The Hausman-Taylor estimator was used in the case where sprawl is measured in terms of changes to the urban (artificial) area, but where the dependent variable is an index of fragmentation (i. e. scattering) a randomeffects estimator was adopted. Our results are robust and when urban sprawl is approximated by the spatial scale, i.
: 1989-1993, 1994-1998, 1999-2002, 2003-2006  6. Purchasing Power Standards (PPS) reflect price ratios between countries and are expressed in a single currency. They thus eliminate from GDP both differences in currency expression and in price levels between countries. 7. Total population includes all individuals who reside within the LUZ. 8. As there is a strong correlation between ArtifArea and density, similar results are obtained for both dependent variables, except for the sign of some coefficients. 9. Our panel data is unbalanced because of randomly missing observations. We used ANOVA to estimate the error component from the unbalanced random effect term. Unsurprisingly, as our model is linear, the error term has no significant effect on the system. 10. The Lagrange-Multiplier test (LM test) is used to test for the existence of individual heterogeneity, i.
e. testing whether or not the pooled OLS is an appropriate model  (Breusch and Pagan, 1980). The OLS hypothesis is unsurprisingly rejected for all configurations. The results of the LM test are reported at the bottom of Tables  4  and 5. 11. For more details see  Hausman and Taylor (1981) ,  Wooldridge (2002)  and  Baltagi et al. (2003). 12. All estimates presented in this paper are obtained using the Plm package of R. For details see  Croissant and Millo (2008). Fixed effects estimates are not reported in the paper, but are available on request from the authors. 13. Estimated parameters for ArtifArea correspond to those obtained in Table  4  column 3. Estimated parameters for Scatt correspond to those reported in Table  5  column 3. Considering estimated parameters without year dummies does not change the conclusions drawn from the decomposition analysis.
support from the European Union under the Marie Curie Intra-European program (FP7-PEOPLE-2010-IEF -273191 -LUPUS) is gratefully acknowledged. Notes 1. Patacchini and Zenou (2009) used data provided by the Urban Audit database. 2. The UA database arises from a project coordinated by Eurostat that aims to provide a wide range of indicators of socio-economic and environmental issues. These indicators are measured across four periods.
Financial support from the European Union under the Marie Curie Intra-European program (FP7-PEOPLE-2010-IEF -273191 -LUPUS) is gratefully acknowledged. Notes 1. Patacchini and Zenou (2009) used data provided by the Urban Audit database. 2. The UA database arises from a project coordinated by Eurostat that aims to provide a wide range of indicators of socio-economic and environmental issues. These indicators are measured across four periods.
By discovering new phages effective against pathogenic bacteria, it is possible to develop alternative treatment methods to antibiotics. A few studies exist regarding the use of phages for curing dental infections with various pathogens such as Enterococcus faecalis  [15] , Fusobacterium nucleatum  [16]  and P. aeruginosa  [17]. To our knowledge, no study has been dedicated to the use of phages to control S. mutans growth and biofilm formation. Relatively little is known about S. mutans phages. To date only two S. mutans phage genomes, M102 and M102AD, are available in public databases  [18, 19]. Two other S. mutans phages, e10 and f1, have previously been isolated and tested for their host range and morphology, without being sequenced  [20]. The aim of the current study was to isolate and characterize new S.
Acknowledgments This publication has emanated from research conducted with the financial support of  Science Foundation Ireland (SFI)  under Grant Number  SFI/12/RC/2273. The authors thank all the volunteers involved in this study. The authors would like to thank  Dr Jennifer Mahony ,  Dr Eoghan Casey  and  Brian McDonnell  for their help and advice. The authors also thank  Marie-Noelle McSweeney  and  Juliette Bougnon  for their help.
Quantifying wing morphogenesis What are the cellular events that shape the wing blade during tissue flows? To quantitatively address this question, we developed methods to quantify cell shape changes, cell divisions, cell rearrangements and cell extrusions during wing morphogenesis. We imaged three E-Cadherin: GFP-expressing wings at cellular resolution every 5 min between 16 and 32 hr APF. We then extracted and projected the planes containing the apical adherens junctions, automatically detected cell edges, and tracked each cell in the wing over the course of the videos (Figure  3A ,A′ and Videos 4, 5). We designed a relational database (DB) to store information pertaining to all cells in a given video ('Materials and methods', Longterms time-lapse imaging and Data handling and image processing).
We defined a shear rate v ∼ cut that characterizes the anisotropic recoil of the circular cut boundary into an elliptic shape (see 'Materials and methods', Analysis of circular laser ablations). We plotted the projection of this shear rate on the PD axis v ∼ cut xx as a function of the projected average cell elongation Q xx (Figure  9A ). Cell elongation was determined as the average cell elongation within the corresponding region in unperturbed wings. We found that the anisotropic part of the shear rate varied linearly with cell elongation (Figure  9A ). The positive slope of this linear relation indicates that the shear modulus K is positive. In this argument, we use the recoil shear rate v ∼ cut as a measure proportional to tissue stress. A linear fit to this data also has a positive intercept, corresponding to a positive ζ xx in Equation 4.
Long-terms time-lapse imaging Pupae were prepared for live imaging as previously described  (Classen et al. , 2008). Long-term time-lapses were acquired with a Zeiss spinning disk microscope driven by the Axiovision software and equipped with an inverted Axio Observer stand, a motorized xyz stage, and a Zeiss LCI Plan-Neofluar 63× 1. 3 NA Imm Corr lens associated to an objective heater set to 25˚C. The fly pupa was placed in a temperature-controlled chamber set to 25˚C and equipped with a humidifier to prevent desiccation. Images were recorded with an AxioCamMR3 camera (2 × 2 binning). Laser power was measured through a 10×/0. 45 NA lens using a power-meter (PT9610, Gigahertz-Optik). A power of 0. 980 mW was found to be optimal to prevent noticeable bleaching during 24 hr of continuous acquisition with an exposure time of 265 ms.
Data handling and image processing We benefited from the flexible architecture provided by our computer department to handle several TB of data using custom unix bash scripts to archive, compress and store data on tape and retrieve them easily. Image pre-processing steps were mostly performed using custom Fiji macros called from a master bash script to enable parallelization using GNU parallel  (Tange, 2011). A low-pass filter was first applied to images to remove high frequency noise. For each z-stack, the signal of highest contrast was projected using a C++ algorithm as previously described  (Merkel et al. , 2014). Tiles were stitched using Fiji  (Preibisch, 2009; Schindelin et al. , 2012). Stitched images were then loaded in Packing Analyzer v8. 5 (PA8. 5) for cell edge detection by using a seeded-watershed algorithm.
To facilitate cell tracking, cross-correlation of subsequent images was performed in PA8. 5 to calculate local tissue displacement beforehand. Resulting cell-tracking masks were parsed using a custom C++ parser that extracted cell shape properties, cell lineages and cell neighbor relationships for further storage in a SQLite relational DB. Topology was kept in the DB by ordering cell neighbors counter-clockwisely around each cell, namely by following a directed path of cell-cell junctions around each cell. One DB file was created per video. All DB queries were carried out using R (R Development Core Team, 2014) or Python  (www. python. org; Hunter, 2007; P érez and Granger, 2007; van der Walt et al. , 2011). We restricted our analyses to a subset of cells that were trackable throughout the entire course of the video, excluding cells that became visible only at later stages due to the apposition of the two cell layers.
Acknowledgements We are grateful to  Caren Norden ,  Christian Dahmann ,  Jan Bruges  and  Stephan Grill  for critical reading of the manuscript, the  Light Microscopy Facility  for providing support in elaborating the image acquisition pipeline, Scionics for providing optimal strategies for the storage of large data sets and excellent support on the cluster. We thank  Stephan Grill  for giving us access to his laser cutter device. We thank  Tony Ashford ,  Kevin Brychcy  and  Clara Munz  for their help in segmenting cells. RE acknowledges a  Marie Curie fellowship  from the  EU   7th Framework Programme  ( FP7 ). This work was supported by the  Max Planck Gesellschaft , and by the  BMBF. SE  acknowledges funding from the  ERC.
Acknowledgments We thank  Ang elique Vincent  for her help in collecting Pwk/ 129Sv pre-implantation embryos. We thank  Frank Feuerbach  for advices on polyACRNA pull-down,  Khalil Mazouni  for Taf11 primers and  Franck Coumailleau  for the gift of Drosophila larvae. We thank  Michel Cohen-Tannoudji  and  Sandrine Vandormael-Pournin  for their insights on gene expression during oogenesis, for critical reading of the manuscript and for the gift of ovary sections. We thank  Claire Rougeulle  for critical reading of the manuscript. We thank  Martine Chebrout  for her help in collecting SN and NSN oocytes. We thank the  Center d'Immunologie Humaine (CIH-Human Immunology Center)  for allowing and facilitating the use of the Biomark thermocycler. We thank  Ann Holmberg  from the  QLUCORE company  for her help with the analysis of single-cell RT-qPCR results.
Funding This work was supported by a  Marie Curie ERG  grant to CM (# 224945 ), by recurrent funding from the  Institut Pasteur  and the  CNRS (Center National pour la Recherche Scientifique) , and by grants from the  ANR (Agence Nationale pour la Recherche) , the  Epigenome Network of Excellence , the  REVIVE Labex  and from the  Fondation Louis D. Institut de France  to PA. JLD was supported by a postdoctoral fellowship from the  ARC (Association pour la Recherche contre le Cancer).
Phenotyping After harmonizing all item data on neuroticism from multiple instruments, comparable neuroticism scores were obtained for all cohorts. 36 These scores were estimated for all participants after conducting IRT analysis on the available item data for neuroticism from the NEO Personality Inventory, Eysenck Personality Questionnaire, and the International Personality Item Pool inventory, all item data for harm avoidance from the Cloninger's Tridimensional Personality Questionnaire, and all item data for negative emotionality from the Multidimensional Personality Questionnaire (see eMaterials and methods). For the Generation Scotland cohort phenotypes were summed scores on the neuroticism scale of the EPQ Revised Short Form.
Genotyping and imputation An overview of SNP genotyping, quality control (QC), and imputation is given in eTable 2. QC of genotype data was performed in each study independently, using comparable but study specific criteria. Basic QC steps included checks for European ancestry, sex inconsistencies, Mendelian errors, and high genome-wide homozygosity. Checks for relatedness were carried out in those samples that aimed to include unrelated individuals only. Genotype data were further checked based on Hardy-Weinberg Equilibrium (HWE), minor allele frequencies (MAF), SNP and sample call rates. Genotype data were imputed using the 1000G phase 1 version 3 (build 37, hg19) reference panel with standard software packages such as IMPUTE, MACH or Minimac (see eTable 2).
Variance explained by common SNPs-In two large cohorts included in the metaanalysis, the Netherlands Twin Register (NTR, N = 3,599 unrelated individuals) cohort and the QIMR Berghofer Medical Research Institute (QIMR, N= 3,369) adult cohort, Genomic-relatedness-matrix Restricted Maximum Likelihood (GREML) analysis in the GCTA software was applied to estimate the proportion of variance in neuroticism that can be explained by common SNPs. 43, 50 GCTA analysis was based on best guess genotypes obtained in PLINK using a threshold of a maximum genotype probability >0. 70, and additionally filtering on r-squared >0. 80. Next, in estimating the GRM matrix in the GCTA software, SNPs with MAF <0. 05 were excluded. The additive genetic relationship matrix (GRM) for all individuals in the data sets estimated based on SNPs was used to estimate the proportion of phenotypic variance due to additive genetic variance.
g. symptoms of depression or ADHD measured by different questionnaires). The results for neuroticism were predictive for MDD. Future analyses may focus on whether the MAGI1 locus and polygenic variance for neuroticism is also associated with psychiatric disorders that are phenotypically associated with neuroticism, such as Borderline personality disorder, bipolar disorder, schizophrenia, ADHD and substance use disorders. This could be achieved by combining data from with GPC with those available within the PGC  65  and the Social Science Genetic Association Consortium  66. 8] [69]  It is expected that such studies will increase our understanding of the role that emotional instability plays in the occurrence and course of psychiatric disorders and other important health outcomes. Generation Scotland SFHS is funded by the Scottish Executive Health Department, Chief Scientist Office, grant number CZD/16/6.
Acknowledgments We would like to thank all participating subjects. Analyses were carried out on the Genetic Cluster Computer (http://www. geneticcluster. org),which is financially supported by the  Netherlands Organization for Scientific Research  ( NWO 480-05-003 ). ALSPAC We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The  UK Medical Research Council  (grant  74882 ), the  Wellcome Trust  (grant  076467 ) and the  University of Bristol  provide core support for ALSPAC. We thank 23andMe for funding the genotyping of the ALSPAC children's sample.
Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the  GENEVA Coordinating Center  ( U01 HG004446 ). Assistance with data cleaning was provided by the  National Center for Biotechnology Information. Support for collection of datasets and samples was provided by the  Collaborative Study on the Genetics of Alcoholism (COGA ;  U10 AA008401 ) and the  Collaborative Genetic Study of Nicotine Dependence  ( COGEND ;  P01 CA089392 ). Funding support for genotyping, which was performed at the  Johns Hopkins University Center  for Inherited Disease Research, was provided by the  NIH GEI  ( U01HG004438 ), the  National Institute on Alcohol Abuse and Alcoholism , the  National Institute on Drug Abuse , and the  NIH  contract "High throughput genotyping for studying the genetic contributions to human disease" ( HHSN268200782096C ).
Wetherill ,  X. Xuei  ( Indiana University );  Grace Chan  ( University of Iowa );  S. Kang ,  N. Manz ,  M. Rangaswamy  ( SUNY Downstate );  J. Rohrbaugh ,  J-C Wang  ( Washington University in St. Louis );  A. Brooks de Moor  et al. EGCUT AM and TE received support from  FP7 Grants (201413 ENGAGE, 212111 BBMRI, ECOGENE  (No. 205419 , EBC)) and  OpenGENE. AM  and TE also received targeted financing from Estonian Government SF0180142s08 and by  EU  via the  European Regional Development Fund , in the frame of  Centre of Excellence in Genomics. The genotyping of the Estonian Genome Project samples were performed in Estonian Biocentre Genotyping Core Facility, AM and TE thank  Mari Nelis  and  Viljo Soo  for their contributions. AR and JA were supported by a grant from the  Estonian Ministry of Science and Education (SF0180029s08).
ERF The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by  European Commission   FP6 STRP  grant number  018947  ( LSHG-CT-2006-01947 ) and also received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013) /grant agreement HEALTH-F4-2007-201413 by the  European Commission  under the programme " Quality of Life and Management of the Living Resources" of 5th Framework Programme  (no. QLG2-CT-2002-01254 ). The ERF study was further supported by  ENGAGE  consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from  Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research  ( NWO-RFBR 047. 017. 043 ). ERF was further supported by the  ZonMw grant  (project  91111025 ).
Finnish Twin Cohort (FTC) We acknowledge support from the  Academy of Finland Center of Excellence in Complex Disease Genetics  (grant numbers:  213506 ,  129680 ), the  Academy of Finland  (grants  100499 ,  205585 ,  118555  and  141054  to JK, grant  257075  to EV),  Global Research Awards for Nicotine Dependence (GRAND) ,  ENGAGE (European Network for Genetic and Genomic Epidemiology ,  FP7-HEALTH-F4-2007 , grant agreement number  201413 ),  DA12854  to  P A F Madden , and  AA-12502 ,  AA-00145 , and  AA-09203  to RJRose,  AA15416  and  K02AA018755  to  DM Dick HBCS We thank all study participants as well as everybody involved in the Helsinki Birth Cohort Study. Helsinki Birth Cohort Study  has been supported by grants from the  Academy of Finland , the  Finnish Diabetes Research Society ,  Folkhälsan Research Foundation ,  Novo Nordisk Foundation ,  Finska Läkaresällskapet ,  Signe and Ane Gyllenberg Foundation ,  University of Helsinki, Ministry of Education, Ahokas Foundation ,  Emil Aaltonen Foundation.
KORCULA The  CROATIA-Korcula study  was funded by grants from the  Medical Research Council (UK) ,  European Commission Framework  6 project EUROSPAN (Contract No. LSHG-CT-2006-018947 ) and  Republic of Croatia Ministry of Science, Education and Sports  research grants to I. R. ( 108-1080315-0302 ). We would like to acknowledge the invaluable contributions of the recruitment team in Korcula, the administrative teams in Croatia and Edinburgh and the people of Korcula. The SNP genotyping for the CROATIA-Korcula cohort was performed in  Helmholtz Zentrum München, Neuherberg, Germany. LBC1921 & LBC1936  For the Lothian Birth Cohorts, we thank  Paul Redmond  for database management;  Alan Gow ,  Michelle Taylor ,  Janie Corley ,  Caroline Brett  and  Caroline Cameron  for data collection and data entry; nurses and staff at the  Wellcome Trust Clinical Research Facility , where blood extraction and genotyping was performed; staff at the  Lothian Health Board , and the staff at the  SCRE Centre, University of Glasgow.
The research was supported by a program grant from  Research Into Ageing. The research continues with program grants from  Age UK  (The Disconnected Mind). The work was undertaken by  The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology , part of the cross council  Lifelong Health  and  Wellbeing Initiative  ( MR/ K026992/1 ). Funding from the  Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC)  is gratefully acknowledged. MCTFR We would like to thank  Rob Kirkpatrick  for his help running analyses. Research reported in this publication was supported by the  National Institutes of Health  under award numbers  R37DA005147 ,  R01AA009367 ,  R01AA011886 ,  R01DA013240 ,  R01MH066140 , and  U01DA024417. MGS Samples were collected under the following grants:  NIMH   Schizophrenia Genetics Initiative U01s :  MH46276 ,  MH46289 , and  MH46318 ; and  Molecular Genetics  of Schizophrenia Part 1 (MGS1) and Part 2 (MGS2) R01s:  MH67257 ,  MH59588 ,  MH59571 ,  MH59565 ,  MH59587 ,  MH60870 ,  MH60879 ,  MH59566 ,  MH59586 , and  MH61675.
BP is financially supported by  NWO-VIDI  grant no. 91811602. NTR We acknowledge financial support from the  Netherlands Organization for Scientific Research (NWO) : Grants  575-25-006 ,  480-04-004 ,  904-61-090 ;  904-61-193 ,  400-05-717  and Spinozapremie  SPI 56-464-14192. MHMdeM is financially supported by  NOW VENI  Grant No. 016-115-035. Genotyping was funded by the  Genetic Association Information Network (GAIN)  of the Foundation for the  US National Institutes of Health , and analysis was supported by grants from  Genetic Association Information Network  and the  NIMH  ( MH081802 ). Genotype data were obtained from dbGaP (http://www. ncbi. nlm. nih. gov/dbgap,accession number phs000020. v1. p1). ORCADES was supported by the  Chief Scientist Office of the Scottish Government , the  Royal Society , the  MRC Human Genetics Unit, Arthritis Research UK  and the  European Union  framework program 6  EUROSPAN  project (contract no.
Genotyping was partly funded by the  National Health and Medical Research Council (Medical Bioinformatics Genomics Proteomics Program ,  389891 ) and the  5th Framework Programme  ( FP-5 )  GenomEUtwin  Project ( QLG2-CT-2002-01254 ). Further genotyping at the Center for Inherited Disease Research was supported by a grant to the late  Richard Todd, M. D. , Ph. D. , former Principal Investigator of Grant  AA13320. SEM and GWM are supported by the  National Health and Medical Research Council Fellowship Scheme. Further, we gratefully acknowledge  Dr Dale R Nyholt  for his substantial involvement in the QC and preparation of the QIMR GWA data sets. Dr Nyholt also contributed 8% of the GWAS for the QIMR adult cohort (NHMRC IDs 339462, 442981, 389938, 496739). SardiNIA We acknowledge support from the  Intramural Research Program  of the  NIH, National Institute on Aging.