dataesr/scientific-paragraphs-categorization huggingface dataset:
When the data on the hazard ratio was also added (Equation [4]), DIC for trial, SEER, and general population data again barely changed (Table  2 ). Figure  4  suggests a visually good fit of the spline models to each source of data. The estimates complied with the RCT data during the RCT period and with SEER, general population and expert data after the end of the RCT. Using the flexible spline models, we have therefore managed to incorporate all external data, without deterioration in model fit for any of the different evidence sources. The life expectancy gain when all external data sources were incorporated was 4. 7 months [95%CrL: 0. 4; 9. 1] (Table  1 ).
Survival Models The study adds to a growing literature on the difficulties associated with extrapolation of survival curves using standard parametric models. 3, 6   ven the models that fitted the RCT data best without external data fitted the trial quite poorly compared to spline models with external data. Indeed, although it seems a weak requirement that the extrapolated survival in a cancer trial control arm must be less than would be observed in the general population, for this dataset, it was easily strong enough to rule out the standard parametric curves. These distributions do not provide the possibility of ''local'' parameters: the behavior of the tails is strongly determined by data fit at the very beginning, and vice versa. It remains to be seen whether other flexible distributions, such as fractional polynomials,  43  mixture distributions,  [44] [45] [46]  or join-point techniques could represent valid alternatives to the splines, which we found gave rise to numerical problems in some contexts (see Web Appendix).
For example, we assume that conditional survival is exactly the same in the target (trial) population as in the matched SEER cohort. Further analyses of SEER and other registries, such as those shown in Figure  2A , can be used to show how sensitive conditional survival might be to imbalances in cancer site mix, age, and date of diagnosis and Karnofsy score. We used the US SEER data, and, hence, US population data, to supply external data on survival and conditional survival because of the comprehensiveness of this database. Decision makers in other jurisdictions would preferentially use locally relevant population and cancer registration data. If this was unavailable, or partially available, it might be still possible to use SEER data, perhaps making suitable adjustments. Note, however, that conditional survival or relative conditional survival is more likely to generalize across jurisdictions than overall survival.
Detecting corrosion damage Another class of inverse problems is to study some problems of identifiability arising in the domain of non-destructive evaluation. [21]  A sample is given that is marked by some imperfections due to various causes, which are located either on an inaccessible part of its boundary. If we consider the problem of detecting corrosion damage then the goal is to determine quantitative information about the corrosion that possibly occurs on an inaccessible part of the surface of a metallic specimen and on the 'accessible' part, electrostatic data are collected. The coefficient h, in the electrostatic context, represents the reciprocal of the surface impedance. It represents the corrosion damage, and classically, it is interpreted as a coefficient of energy exchange. Usually, it is a generalized Robin conditions where h is a function.
Acknowledgement The support of  EM2C laboratory and of Maison de la Simulation  for the  CEMRACS  project are gratefully acknowledged. The Ph. D. of F. Drui is funded by a  CEA/DGA  ( Direction Générale de l'Armement -French Department of Defense)  grant. The use of the computational  Mesocenter of Ecole Centrale Paris  for some of the simulations is also gratefully acknowledged.
Acknowledgments. VBN is partially supported by the  ANR (Agence Nationale de la Recherche ), projects  Nosevol  n o  ANR-11-BS01-0019  and  Aramis  n o  ANR-12-BS01-0021. JAC acknowledges support from the  Engineering and Physical Sciences Research Council  of the UK grant number  EP/K008404/1 , the  Spanish  project  MTM2011-27739-C04-02 , and the  Royal Society  through a  Wolfson Research Merit Award. GP acknowledges financial support from the  Engineering and Physical Sciences Research Council  of the UK through Grants No. EP/J009636 ,  EP/L024926 ,  EP/L020564  and  EP/L025159.
4 in [15] ). We then prove that this gives an example of a family of foliations with algebraic invariant curves of arbitrarily high degree (see also  [14] ). Acknowledgements. -The author would like to thank both Serge Cantat and Frank Loray for their support and guidance in writing this paper. Gaël Cousin, Karamoko Diarra and Valente Ramirez Garcia Luna contributed through several interesting conversations and offered valuable insight on various topics. Special thanks to Thiago Fassarella for the interest he took in this paper and for suggesting several improvements to its exposition. Funding was provided by the Université de Rennes 1, the École normale supérieure de Rennes and the Centre Henri Lebesgue. Finally, the author thanks the anonymous referee for the time and attention they devoted to reviewing this work.
This paper draws on location theories to statistically identify the relationship between the location of individual business establishments and the characterization of their local economic environment. Taking a microspatial perspective, the paper develops indicators from distance-based measures (DBM) to serve as independent variables in a discrete choice model (DCM). Using a 2006 database of individual business establishments in the Lower-St-Lawrence region-a coherent, nonmetropolitan subsystem of cities in the province of Québec, Canadawe provide an empirical analysis of the determinants of individual establishments' location decisions in relation to their main economic activity within a random utility model (RUM) framework. The results show that distance to nearby centers, co-location (specialization), and the size of establishments are statistically related to location decisions.
However, empirical applications looking at the spatial distribution pattern of economic activities are rarely addressed from a micro The paper is divided into five sections. The first describes factors associated with location, bid-rent theories, and economies of agglomeration, while presenting recent studies on individual firm location decisions using spatial micro-data. The second section sets out the method that we used to establish the possible partial correlations between individual characteristics and location decisions of economic sectors. In the third section, we describe the data used in the course of the analysis and place particular emphasis on our choice of research approach as it relates to the economic sector aggregations, the construction of local indicators, and the underlying hypotheses to be tested.
The Establishments Database The database is derived from the List of Industries and Trade (LIT) from the Québec Ministry of Employment and Social Security (MESS). This source offers data compiled at the establishment level for the entire population of establishments in the LSL region. Information is gathered through local partners, where collection methods may be comparable to an annual census © Southern Regional Science Association 2016. by economic sector where partners try to contact all legal firms listed in their territory during the year. This results in data coverage in particular economic sectors being of better quality than others. Spatial observations (establishments) are provided with their exact address, which allows geocoding and retrieval of the exact geographical coordinates (Figure  1 ).
The number of jobs is given for each establishment, corresponding to the number of employees at the company's busiest period during a full year. The database also reports the main economic activity of the firm by using the NAICS code at the six-digit level. Yet, a comparison with official data sources such as census data and labor force surveys yields a similar distribution once aggregated  (Brunelle et al. , 2013). Despite the fact that the © Southern Regional Science Association 2016. frequency and methods for updating the database vary among different local partners, the organization estimates that nearly thirty percent of establishments on the list are in fact continuously updated on a day-to-day basis, while the remainder is updated once a year. In 2006, the total number of firms (excluding firms in utilities-NAICS 22 3  ) was 9,831, with most of the firms having fewer than five employees (Table  3 ).
RESULTS Before turning our attention to the estimation results, it should be noted that since the models are estimated using a spatial cross-sectional database, the results should not be interpreted as causal relations. It is impossible to ascertain the temporality and, by extension, the direction of the causality of the observed patterns with such data (without an instrumental variables or other means of identification). The results are to be interpreted for what they really tell: the coefficients reflect, at a given point in time, partial correlations among the location choices of establishments according to their main economic activity (the dependent variable), the characteristics of the establishments, as well as the surrounding economic characteristics. Statistical relationships are further investigated through the calculation of the marginal effects (not through values of the coefficients).
Data The dataset extends over the period 1989q1 to 2014q4. The start date of the sample is motivated by the findings of Du Plessis & Kotzé (2010; 2012), who suggest that there is a significant structural change in most macroeconomic variables that would impact on the measure of the business cycle during the mid-1980s. 5 Essentially, we estimate the model with ten observed variables for measures of: domestic output growth, ỹ, GDP-deflator inflation, π, consumer inflation, π c , nominal interest rate, i, nominal wage inflation, π w , nominal productivity, z, nominal currency depreciation, d, foreign output growth, y * , foreign GDP-deflater inflation, π * , and foreign nominal interest rate, i *. All of the data for the South African economy was obtained from the South African Reserve Bank, with the exception of consumer prices, which was obtained from Statistics South Africa.
This paper has benefited from helpful discussions during presentations at various seminars and conferences. We acknowledge funding from  ANR-13-BSH1-0002-AMF. The traditional disclaimer applies. This project is related to the research agenda of the  ADEMU  project, " A Dynamic Economic and Monetary Union ". ADEMU is funded by the  European Union 's  Horizon 2020 Program  under grant agreement  N° 649396  (ADEMU). _________________________ The ADEMU Working Paper Series is being supported by the  European Commission Horizon 2020 European Union funding for Research & Innovation , grant agreement No  649396.
Fig. 2  illustrates the point, using hypothetical values not too dissimilar from the data in our Niger cattle milk off-take study. In calculating total milk off-take from the AMD method one is essentially computing the area of the rectangle ABCD, where AB is the number of months milk was collected and BC is the monthly quantity (in liters) of collected milk. 5  Suppose the true shape of the off-take curve for the respondent was equal to the line BEF, and it becomes evident how AMD would result in an overestimate of milk off-take. The AMD method can be administered for different recall periods, as it is often argued that shorter recall can improve data quality. This is especially true for variables that are characterized by seasonal patterns, which is the case for milk off-take. In the case of the LC method this is not feasible as a, say, 6-month recall period would likely be shorter than the lactation/off-take period, thus complicating the task of the respondent as some of the points in the off-take curve may fall outside of the recall period.
Data The main data set analyzed in this paper comes from fieldwork that took place in the Dantiandou district in Niger, between April 2012 and June 2013, and is referred to here as the Dantlait survey. The fieldwork was managed by two experienced enumerators, and a supervisor. The team monitored the milk off-take of 300 households over 12 months, as well as associated livestock management, together with household consumption and sale of dairy products. The team also administered 6-month recall questionnaires on 200 households, and 12-month recall questionnaires to 400 households. The first 200 household farms were randomly sampled among the 835 household farms documented in 2009 and 2010 for the Livestock Climate and Society (ECliS) project. 6 These 835 households live in 13 villages and associated camps within the district (commune and canton) of Dantiandou (80 km East of the capital Niamey).
A large data base is available on the composition of each household, its economic activities (including cropping, breeding livestock, forestry, and off farm), the composition and number of livestock, milking practice, consumption and sale of dairy products. This data base was used to stratify the households based on the type of dwelling (either village or camp), which largely matches with ethnic affiliation (Zarma/Fulani), and on the size of the cattle herd (less than 5, 5-15, more than 15 adult females). The additional 100 households were selected in 13 additional villages from the district of Dantiandou (5340 households and 45 villages in total), based on the 2008 national census. The monitoring method targeted the assessment of the daily milk off-take in each of the 300 sampled households.
Acknowledgements The authors are grateful to all the individuals who took part to the monitoring and surveys in the village and camps of the Dantiandou district, together with the local authorities, for their patience and active participation to the year round monitoring and the repeated surveys. They are grateful to  Moussa Kaka , to have toured and vaccinated the dairy cows, and to  Oumar Moumouni  and  Seybou Garba  for most ably and carefully conducting the interviews and monitoring of milk production. They would also like to thank  Dr. Mohamadou Gandah  and his colleagues from the ICRISAT administration office for the efficient logistical support they provided to the field team. Alberto Zezza is indebted to  Derek Baker ,  Isabelle Baltenweck ,  Gero Carletto ,  Celine Dutilly ,  Mathieu Lesnoff ,  Nancy McCarthy ,  Jane Poole ,  Steve Staal , and  Matthew Turner  for guidance, discussion, and comments at various stages of this project.
Lastly, we asked the respondent to tell us how they could vote if a referendum was held on the introduction of this toll (If there was a referendum on the introduction on this urban toll, how do you vote? Certainly yes, probably yes, probably no, certainly no or No Answer). The last part of the questionnaire collected sociodemographic data on the respondents (see Table  4 ). Furthermore, we must point out some limitations, in our dataset. Firstly, and because it is both difficult to obtain and quite distinct between France, Sweden and Finland (for example conservative party in France is quite different from conservative party in Sweden), in our questionnaire we do not include question about individuals' political habits (do you vote in general? Do you agree with the current local/regional/national action?).
Principal Components Analysis (PCA) In the case of a survey in which several questions are asked, the Principal Components Analysis (PCA) can be used to group the answers  [43, 66]. It describes the structure of the correlations between the variables, in our case the 14 questions asked in the general part of the questionnaire, by providing a smaller number of independent linear combinations  [18]. This makes it possible to create a new variable that can be substituted for each of the variables of the sub-groups identified without losing much information. With 14 items, we haven't large data sets. However PCA method can produce interesting results in a first step. As Golob and Recker  [18]  write:^It is used to reduce the dimensionality of the data by accounting for redundancy among sets of highly correlated traffic flow variables^(p.
Can the results of a referendum on urban tolls be predicted? We use now an ordered logit estimation to perform a more indepth test of this relation between attitudes to pricing and to voting on the issue of urban tolls. The results of the ordered logit estimation relating to the question on the referendum are presented in Table  7. The referendum question is variable to be explained. As we explain at the end of section 3, 14 variables on general attitudes tested with PCA and significant socio-demographic variables are the explanatory variables coming from OLS estimation. For estimating the ordered probit model  [34, 64] , we use BMass Package Bof the software R  [47]. Nine attitude variables have significant influence on the response to the referendum (cf. Student t test in Table  7 ). Those who are in favor of an environmental tax on air transport, a tax on road transport noise and peak hour tolls to finance roads, and who support environmental values and are in favor of speed camera to save lives, tend to vote rather Bfor^or Babsolutely for^a referendum on the toll.
Cancer registration Only a few population-based cancer registries in the region have high-quality data published in the Cancer Incidence in Five Continents (CI5) series. Although the number of registries in the region has increased from one in CI5 Volume I (published in 1966) to 22 in Volume X (published in 2014), there is still a lack of coverage for most countries. In the latest volume of CI5, only 36% of countries located in Central and South American region were included (Argentina, Brazil, Chile, Colombia, Costa Rica, Cuba, Ecuador, Peru and Uruguay), covering approximately 8% of the population as compared to 96% coverage in the United States  [32]. All the population-based registries are regional registries, except for those in Costa Rica and Uruguay which have national coverage. Cancer mortality data are derived from vital registration systems where the underlying causes of death are certified.
Alongside incidence data, mortality data collected by national vital statistics offices and reported to WHO are also reported. Unlike CI5  [32] , datasets provided by cancer registries were not subject to any quality evaluation process, and all those received from populationbased registries were included in the tabulations and analyses. In one or two instances, where a country had no population-based registries able to report data, information from hospital-based registries were requested and are presented. While this means that the quality of data provided is inevitably variable, largely a function of the age and development in maturity of the registries, the process provides some incidence information on many more regions and countries than in CI5, thus enabling a different perspective on the regional pattern.
Thus incidence data have been made available here from 48 cancer registries in 13 countries, compared with 22 registries in eight countries in CI5 Volume X. Additionally, hospital-based registry data were available for five countries. This compilation of "all available" cancer data will be, we hope, of value to national agencies in governmental and NGO sectors involved in cancer control activities, and will also provide insights into the burden and pattern of cancer in countries and regions of countries which have hitherto been unreported. Quality indicators for the data provided are also presented and are important parameters to consider when making use of the data in this supplement issue. Also, even though there has been no selection based on quality, all datasets received were passed through the same standard data quality control and checking procedures as used in CI5  [32] , and registries were then contacted to correct identified errors and inconsistencies.
Outline This supplement issue of Cancer Epidemiology on "Cancer in Central and South America" comprises, following this introduction, a paper describing the methods employed in the collection, quality control and analysis of the datasets received for the project. This is then followed by a general paper looking at the totality of the data and providing a broad overview of cancer incidence and mortality patterns within the entire region, considering the geographical variation between countries for each site and type of cancer and, to the extent possible, trends over time. Subsequently there is a series of 14 cancer site-specific papers looking in detail at the geographic and temporal patterns for major cancers and accompanying these descriptive analyses with a review of identified risk factors for the cancer concerned and how these may lead to policies for prevention within the region.
Article selection-first step The flowchart describing the literature included/excluded is shown in Fig 1. We initially selected articles by searching the Web of Science (WoS, ISI Web of Knowledge, Thomson Scientific) bibliographic database. We used a Boolean search term including (1) most crops (e. g. cotton, maize, potato) for which GM varieties producing Bt toxins have been commercialized, and (2) most of the pests targeted by Bt crops (e. g. Diabrotica virgifera virgifera, Ostrinia nubilalis, Pectinophora gossypiella). The exact Boolean search term was: ((TI = (rootworm Ã OR Diabrotica OR borer Ã OR Ostrinia OR Diatraea OR armyworm Ã OR Spodoptera OR stalkborer Ã OR Busseola OR Sesamia OR bollworm Ã OR Helicoverpa OR earworm Ã OR Leafworm Ã OR Pectinophora OR budworm Ã OR Heliothis OR "Colorado potato beetle" OR "Colorado potato beetles" OR Leptinotarsa OR Chrysomela OR "Cottonwood beetle" OR "Cottonwood beetles" OR "Diamondback moth" OR "Diamondback moths" OR Plutella OR "Rice leaf folder" OR "Rice leaf folders" OR Cnaphalocrocis OR "Chilo suppressalis" OR "Leucinodes orbonalis" OR "Soybean looper" OR "Soybean loopers" OR "Pseudoplusia includens") AND TS = (maize Ã OR corn Ã OR "Zea mays" OR cotton Ã OR "Gossypium hirsutum" OR soybean Ã OR "Glycine max" OR potato Ã OR "Solanum tuberosum" OR poplar Ã OR Populus OR eggplant Ã OR "Solanum melongena" OR brinjal Ã OR tomato Ã OR "Lycopersicon esculentum" OR rice OR "Oryza sativa" OR broccoli Ã OR "Brassica oleracea" OR tobacco Ã OR nicotiana) AND TS = ("bt" OR cry Ã OR bacillus OR toxin Ã ) AND TS = (transgenic Ã OR GMO Ã OR GMP Ã OR "genetically modified" OR "Bt crop" OR "Bt plant" OR "Bt tree")) OR (TS = ("western corn rootworm" OR "Diabrotica virgifera" OR "European corn borer" OR "Asian corn borer" OR "Southwestern corn borer" OR "Ostrinia nubilalis" OR "Ostrinia furnacalis" OR "Diatraea grandiosella" OR "Diatraea saccharalis" OR "Sugarcane Borer" OR "fall armyworm" OR "Spodoptera frugiperda" OR "African maize stalkborer" OR "Busseola fusca" OR "Sesamia nonagrioides" OR "Mediterranean corn borer" OR "Australian bollworm" OR "Helicoverpa punctigera" OR "Helicoverpa zea" OR "Spodoptera littoralis" OR "Corn earworm" OR "Cotton Leafworm" OR "Pink bollworm" OR "Cotton bollworm" OR "Pectinophora gossypiella" OR "Helicoverpa armigera" OR "Tobacco budworm" OR "Heliothis virescens" OR "Colorado potato beetle" OR "Leptinotarsa decemlineata" OR "Chrysomela tremulae" OR "Chrysomela scripta" OR "Cottonwood beetle" OR "Diamondback moth" OR "Plutella xylostella" OR "Rice leaf folder" OR "Cnaphalocrocis medinalis" OR "Striped stem borer" OR "Chilo suppressalis" OR "Leucinodes orbonalis" OR "Fruit Borer" OR "Shoot Borer" OR "Soybean looper" OR "Pseudoplusia includens") AND TI = (maize Ã OR corn Ã OR cotton Ã OR soybean Ã OR potato Ã OR poplar Ã OR eggplant Ã OR brinjal Ã OR tomato Ã OR rice OR broccoli Ã OR tobacco Ã ) AND TI = ("bt" OR cry Ã OR bacillus OR toxin Ã ) AND TS = (transgenic Ã OR GMO Ã OR GMP Ã OR "genetically modified" OR "Bt crop" OR "Bt plant" OR "Bt tree"))) We searched the WoS database on September 11, 2015.