dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ppm region. To account for differences in sample amount, each integrated region was normalized to the total spectral area. SIMCA-P + software (V13, Umetrics AB, Umea, Sweden) was used to perform the multivariate analyses. Before multivariate analysis, Orthogonal Signal Correction (OSC) filtering  67  was used to remove variation not linked to the infant formula composition. Filtered data were mean-centered and Pareto scaled. Principal Component Analysis was firstly applied to detect outliers and intrinsic clusters. Then, Partial Least Squares -Discriminant Analysis (PLS-DA) was used to assess the relationship between diet (infant formula) and metabolomic profiles. PLS-DA is a supervised method that maximizes the separation between groups. The R 2 (percentage of explained variance) and Q 2 (predictive capacity of the model) criteria were used to assess model performance.
5 h. Sections were then washed with PBS, incubated with a mouse anti-Chromogranin A antibody (1:1000; Abcam, Cambridge, UK) for 3 h, washed with PBS, incubated with an anti-mouse antibody conjugated with Alexa Fluor 488 (1:500; Cell Signaling Technology, Leiden, The Netherlands) for 2. 5 h, washed again with PBS and cover-slipped with Vectashield. Sections were examined by using a fluorescence microscope (Eclipse E400, Nikon Instruments France, Champigny-Sur-Marne, France) attached to a digital camera (Digital Still DXM 1200, Nikon Instruments France). The whole section of the tissue was scanned using digital slide scanner (Nanozoomer 2. 0-RS, Hamamatsu Photonics France SARL, Massy, France). Recorded files were analysed using the software NDPI (Hamamatsu Photonics) for determination of the number of enteroendocrine (chromogranin A-labelled) cells and GLP-1-secreting cells per area of mucosa.
Statistical analysis. Statistical analyses of microbiota and metabolomics data were described above. For all other data, statistical analyses were performed using R Core Team (2016; R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; http://www. R-project. org/). Normality was tested with Shapiro and Wilk test. For parameters with unequal variances between groups, Box-Cox transformations were used. Differences between groups were assessed using a two-way ANOVA (lm function) testing formula, replication, sex and interactions between diet and sex, and between diet and replication, followed by Tukey post-hoc test (Tukey HSD). BW, energy intake, glucose and insulin responses to IVGTT, FFAR expression across sites were subjected to repeated measures ANOVA (lme function) including diet, time (age or min after glucose injection), site (caecum and colon for FFAR expression only), replication, sex and interactions between diet and all other factors, followed by Tukey post-hoc test (lsmeans package).
Acknowledgements The authors acknowledge all the technical staff of Nutrition Gut Brain team and all the staff of Rennes porcine experimental facilities (UEPR, especially  Francis Le Gouevec ,  Julien Georges  and  Mickaël Genissel ) for their professionalism, help, expert assistance and animal care and feeding. They also thank the Nutrition, chemical food safety and consumer behaviour Division of the  INRA  for financing the metabolomics study and the  French National Infrastructure of Metabolomics and Fluxomics (MetaboHUB- ANR-11-INBS-0010 ) for their support. The authors thank  Servane Le Page  for her help in PCR analysis,  Roselyne Gautier  for her help in preparing the samples for metabolomics analyses,  Laure France  for infant formula manufacturing,  Georges Guillemois  and  Jean-François Rouaud  for HE diet manufacturing and  Thibaud Le Mouel  for SCFA assay.
Introduction The overall objectives of the Translation and Cultural Adaptation Special Interest Group (TCA-SIG) are to identify and advance research practices and outcomes of translation and cultural adaptation of patient-reported outcome (PRO) instruments; to provide an evidence database on translation and cultural adaptation of PRO instruments; and to promote visibility of cross-cultural issues in the development and use of PRO instruments in ISOQOL. The copyright subgroup of the TCA-SIG has focused its activities on issues linked to the copyright of original instruments and their derivatives such as translations. The main objectives of this reflection paper are: 1) to help authors of PRO instruments understand basic rules of intellectual property and copyright in order to protect the integrity of their instruments and derivatives; and 2) to provide recommendations to authors and users of PRO instruments in order to prevent any misuse or abuse.
Copyright is regulated by national copyright laws and, at an international level, by the Berne Convention for the Protection of Literary and Artistic Works as revised in Paris on July 24, 1971 (hereinafter referred to as the "Berne Convention"). The Berne Convention was implemented on a country-by-country basis when a member country ratified it and thereby officially became a party to it. For example, the USA ratified the Berne Convention by enacting the Berne Convention Implementation Act of 1988 (H. R. 4262) on October 31, 1988, thereby implementing and joining the Berne Convention on March 1, 1989. Other countries such as Belgium, France, Germany, Italy, Spain, Switzerland and the United Kingdom implemented it on December 5, 1887. A listing of contracting parties to the Berne Convention and the respective dates when it was implemented in a country can be found in the World Intellectual Property Organization (WIPO) database (http://www.
Acknowledgements We thank the ISOQOL  TCA-SIG Copyright Working Group  and the  ISOQOL Board of Directors  for review and comments on the manuscript as well as  Jordi Alonso  and  Montse Ferrer , co-directors of  BiblioPRO (IMIM-CIBERESP, Barcelona, Spain ) for their contribution. We thank  Philippe Masseron ,  Legal Departmental Manager of the Centre Français de Copyright (French Center of Copyright)  for the interview he agreed to give us as well as  Cécile Ouahnon ,  Mapi Legal Advisor  for her thorough review of the manuscript.
'Metropolization' and 'Communities' are not able to achieve this objective, inducing both increased malnutrition and a significant expansion of the agricultural land area with possible loss of natural areas at the global scale. The two other scenarios, 'Regionalization' and 'Households', lead to ambiguous results in terms of both food security and land-use patterns. Among the achievements of Agrimonde-Terra is the coupling of a qualitative and a quantitative analysis of scenarios, which is not common in foresight exercises and deserves to be emphasized. In this regard, the GlobAgri platform, which has been developed as part of the Agrimonde-Terra project and was used to design the specific GlobAgri-AgT database and model for the Agrimonde-Terra foresight, calls for follow-up action. CIRAD and INRA wish that the GlobAgri tool be widely used by the research community.
Introduction The GlobAgri platform was set up by CIRAD and INRA to generate consistent databases and biomass balance models using data from FAOStat as well as data shared by colleagues from different institutions. 4 The databases generated are balanced and account for the links between products (through animal feed or oilseed crushing for instance). Biomass balance models provide a balance equation between resources (domestic production plus imports minus exports) and utilization (food, feed and other) for each region and each agri-food product. In each equation, imports are a linear function of total domestic use and exports are a linear function of the world market size. A world trade balance equation ensures that world imports equal world exports for each agri-food product. The system of balance equations can simulate land-use change in each region induced by changes in the use of agri-food products, provided hypotheses made on changes of a set of variables (such as plant and animal yields, maximum available cultivable land, trade conditions etc.
). The GlobAgri platform has been used to generate a database and a biomass balance model specifically customized for Agrimonde-Terra (specific product and country aggregation, specific rules for co-product handling and specific rules for model closure). The resulting tool is named GlobAgri-Agrimonde-Terra (GlobAgri-AgT). It encompasses 33 aggregates of agri-food products (25 plants, seven animal aggregates and a miscellaneous "Other products") and covers 14 broad regions.
Estimates of available land for agriculture at the world level There are two main databases providing data on current land use for the whole planet at various geographic levels (agro-ecological zones, countries, large regions and the world): FAOStat and the SAGE database (Center for Sustainability And the Global Environment, University of Wisconsin). There are also two databases providing data on the agricultural potential of land all over the world: the GAEZ database (Global Agro-Ecological Zones, International Institute for Applied Systems Analysis, IIASA, Vienna, and FAO) and the above mentioned SAGE database. These databases have been used in a number of studies dealing with land-use change for food security and/or for climate change purposes. One key question in such studies is how much land is currently used for agriculture all over the world and how much additional land could be converted to agriculture.
In the following, we use some of these studies, namely  Ramankutty et al. (2008) ,  Erb et al. (2009 ), Fischer et al. (2011) ,  Lambin and Meyfroidt (2011)  and  Bajzelj et al. (2014)  to provide an evaluation of current land reserves, both globally and in various world regions, that would be suitable for agriculture. We also use Roudart's 2010 analysis, which provides a detailed comparison of the three databases mentioned above and of their results regarding current land use and agricultural potential of land all over the world. Finally, we add the data on land use and the agricultural potential of land that we extracted from the FAOStat and GAEZ databases for the Agrimonde-Terra foresight study, in order to compare the data we used with the data used in existing literature. 14 ❚ ❚ How much land is already used by agriculture around the world? Land used by agriculture comprises cropland (or arable land and permanent crops area according to the FAOStat definition) 15 and pastureland (or permanent meadows and 14.
These data are part of the GlobAgri-AgT database. See Chapter 3 for more details. 15. We also use the term 'cultivated land' for this category. pastures land area according to the FAOStat definition). While the first category is well defined, uniformly named and rather homogenous across studies, the second is more complex, making its manipulation more delicate for comparison purposes. Indeed, the definition of pasture is not clear and is subject to uncertainty. There are many different types of meadows and pastures (cultivated and natural, from poorly productive savannah grassland or rangelands through to highly productive meadows in Western Europe, for example). Consequently, there are many different ways to classify them either as pastureland or in other land-use categories (for example, is grazing in a semi-arid area reported as pastureland or natural land in global databases? Is grazing underneath forest trees reported as pastureland or forest land?).
Fischer et al. (2011). Finally, according to the existing literature, around 12% of the global land area is currently devoted to crops (annual and permanent) and from 20 to 35%, depending on the study, is currently used as pasture. Hence, depending on the study, agriculture currently uses from less than 35% to more than 47% of the global land area. The GlobAgri-AgT database includes the FAOStat data on land use, computed as a 2007-2009 average. Therefore, for Agrimonde-Terra we consider that cropland currently accounts for 12% of the total world land area and pastureland for 26%. Thus, 38% of the global land area is used for agriculture.
For more details about the GAEZ method, see http://webarchive. iiasa. ac. at/ Research/LUC/GAEZv3. 0/docs/GAEZ_Model_Documentation. pdf 18. The suitability index may be calculated considering different sets of crops (crops or crop groups, rainfed or irrigated) for different input levels and management systems, using different aggregation rules, for example. Source: extracted from publications and from the GAEZ database. Note:  In Agrimonde-Terra, we also use the GAEZ approach and data and we chose to consider the land with a suitability index greater than 40% as the land suitable for crop production or as the maximum cultivable land area. As indicated in Table  4. 2, this makes our estimate (4,288 Mha) very close to that of  Fischer et al. (2011). At this stage, it is still not possible to evaluate how much additional land could be converted to agriculture in the future.
Access to land is the second dimension. It depends on land tenure systems and land policy, which are affected by the geopolitical situation, the degree of competition for land, farmers' incomes and access to credit etc. A qualitative analysis of the evolution of access to land can be provided. The distribution of land between different uses is the third dimension. Diets, demography, climate, and international trade are important drivers of this dimension. The fourth dimension is the degree of intensity of land use. far as agricultural land is concerned, on farming practices. This dimension of land use focuses on the linkages between ecosystems and human well-being, and has grown in importance since the 1990s. Most of the data used in this chapter are from FAOStat. Data on land use thus use the.
Other land: land not classified as Agricultural land and Forest area. It includes builtup and related land, barren land, other wooded land etc. Permanent meadows and pastures: land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land). Agrimonde-Terra uses the term "Cultivated area" to designate arable land + permanent crops. Source: GlobAgri-AgT, adapted data from FAOStat (http://faostat3. fao. org/) and GAEZ project (http://www. fao. org/nr/gaez/fr/). Graphic design: Elodie Carl.
❚ ❚ Yields of major cereal crops Average cereal yields over the last 50 years varied from about 373 kg per hectare in Namibia to 6,215 kg per hectare in the Netherlands, and there were 38 countries that had average yields over 3,000 kg per hectare  (Liu et al. , 2015). The average cereal yield increased from 1,258 kg per hectare in 1961 to 2,925 kg per hectare in 2010  (Liu et al. , 2015). A retrospective evolution of maize, rice and wheat yields is provided by  Ray et al. (2012) ,  Iizumi et al. (2014)  and  Grassini et al. (2013) , and despite different datasets and methods, they show quite consistent results and highlight different aspects. After a period of significant yield improvements, three yield trajectories are identified: continued growth, stagnation and decline. Globally, rice and wheat yields have reached upper plateaus (33% and 27% of cases reported by  Grassini et al.
❚ ❚ Farmland: size and access Increasing number of farms of less than 2 hectares One common way to define a farm is its size, as measured by the amount of land used  (Eastwood et al. , 2009; Hazell et al. , 2007) , though some database systems may instead consider the number of livestock or gross sales as criteria. However, depending on the type of production or the agro-ecological zone, farm size in hectares provides only an approximate measure of production potential. The World Bank -like other international organizations -uses 2 hectares as the threshold to define 'small-scale farming'  (World Bank, 2003). While this may be an appropriate limit in many countries, it is not the case worldwide. A Chinese farmer with 2 hectares is considered the head of a large farm, whereas a Brazilian farmer with 50 hectares would be considered to have a small farm in Central Brazil.
❚ ❚ Cropping systems Four distinct pathways for the evolution of cropping systems are considered, including an impasse, each addressing current challenges in different ways: i) Conventional intensification; ii) Sustainable intensification; iii) Agroecology and iv) Collapse of cropping systems (Chapter 11). Two entry variables of the GlobAgri-AgT model are directly concerned with the hypotheses for the future of cropping systems: -Per-hectare yields. -Cropping intensity ratios (as measured by the harvested area over the cultivated area in each region, Chapters 3 and 5). The general rules for building the quantitative hypotheses We adopted three general rules in order to translate the cropping system pathways into quantitative hypotheses for regional per-hectare yield changes: -We calculated current yield gaps for Agrimonde-Terra's crops and regions using potential and observed per-hectare yields provided by the GAEZ database portal.
Ruminant Source: Calculated from GlobAgri-AgT data and  Bouwman et al. (2005)  results. Table  14. 5. Reduction applied between 2010 and 2050 on feed-tooutput ratios in the mixed and pastoral systems for ruminant sectors and in the urban and other systems for monogastric sectors, under the various livestock system pathways, in some regions. GlobAgri-AgT has been used to simulate the impacts on world and regional land use, agricultural production and agricultural trade of the Agrimonde-Terra scenarios. For most scenarios, several hypotheses of change patterns may co-exist for several drivers  (Chapter 13). This means that for these drivers, the world's regions can follow different paths of development, some prevailing in some regions, others being prominent in other regions. For the quantitative analysis, however, one hypothesis is chosen for each driver and applies to all regions.
On a more general ground, Agrimonde-Terra scenarios have shown that the future of livestock systems is a key factor as regard the land use and food security issue. Unfortunately, we lack information and data on the current livestock systems all over the world and their relative technical, economic, environmental and social performance. In addition, there is great uncertainty on the potential development of livestock systems and further work in clearly needed in this area. The situation is similar for current and future pastureland area and production potential. For pastureland, there is a specific need of a clear definition and classification in database. In the same vein, Agrimonde-Terra has shown that we need to add to our knowledge on relative technical, economic, environmental and social performance of the different cropping systems.
Ramankutty et al. (2008)s found in China (400 Mha pasture reported in national statistics versus 290 Mha predicted byRamankutty  et al. ), followed by Australia (320 Mha reported versus 270 Mha predicted) and Mongolia (130 Mha reported versus 90 Mha predicted). Ramankutty et al. (2008)emphasize that these three countries have large amounts of semi-arid land, which may be grazed or not depending on annual climate conditions, and consequently be reported as pastureland or other land according to the various databases.
Acknowledgements Agrimonde-Terra  has benefitted from the knowledge, experience and support of the members of the  Scenario Advisory Committee  who followed the work throughout the foresight process. We warmly thank them for the time they devoted to  Agrimonde-Terra  and for their valuable advices:  LAND USE AND FOOD SECURITY IN 2050  Fanzo J. , 2012. The  Nutrition Challenge in Sub-Saharan Africa. United Nations Development Programme, Regional Bureau for Africa ,  UNDP Working Paper 2012-012 , 70 p. Fanzo J. , 2014. Strengthening the engagement of food and health systems to improve nutrition security: synthesis and overview of approaches to address malnutrition. Global Food Security, 3, 183-192. FAO, 1976. A framework for land evaluation. Soils Bulletin 32. Food and Agriculture Organization of the United Nations, Rome.
Ramankutty et al. (2008)s found in China (400 Mha pasture reported in national statistics versus 290 Mha predicted byRamankutty  et al. ), followed by Australia (320 Mha reported versus 270 Mha predicted) and Mongolia (130 Mha reported versus 90 Mha predicted). Ramankutty et al. (2008)emphasize that these three countries have large amounts of semi-arid land, which may be grazed or not depending on annual climate conditions, and consequently be reported as pastureland or other land according to the various databases. Erb et al. (2009)011) estimate provided byFisher et al. (2011)is 1,239 Mha higher than FAOStat data and 1,800 Mha higher than Ramankuttty et al. 's estimate. One explanation is that the definition of pastureland adopted byFischer et al. (2011)is broader than the one used in both other studies.
In Figure 4. 2, data from 1961 to 2010 are observed data extracted from the FAOStat database. Pastureland area in Figure 4. 2 corresponds to the FAO category 'Permanent meadows and pastures' area. As in Figure 4. 1, as the two studies do not consider the same temporal horizon, the corresponding change in area over the 2010-2050 period has been computed from their results.
MASK is available in 16 languages and deployed in 23 countries. The present paper provides an overview of the methods used in MASK and the key results obtained to date. These include a novel phenotypic characterization of the patients, confirmation of the impact of allergic rhinitis on work productivity and treatment patterns in real life. Most patients appear to self-medicate, are often non-adherent and do not follow guidelines. Moreover, the Allergy Diary is able to distinguish between AR medications. The potential usefulness of MASK will be further explored by POLLAR (Impact of Air Pollution on Asthma and Rhinitis), a new Horizon 2020 project using the Allergy Diary.
More specifically, MASK is focusing on (1) understanding the disease mechanisms and the effects of air pollution in allergic diseases and asthma, (2) better appraising the burden incurred by medical needs and indirect costs, (3) the implementation of multi-sectoral care pathways integrating self-care, air pollution and patient's literacy, using emerging technologies with real world data using the AIRWAYS ICPs algorithm  [28] , (4) proposing individualized and predictive medicine in rhinitis and asthma multimorbidity,  (5)  proposing the basis for a sentinel network at the global level for pollution and allergy and (6) assessing the societal implications of exposure to air pollution and allergens and its consequences on health inequalities globally. The freely available MASK app (the Allergy Diary, Android and iOS)  [26]  is combined with an inter-operable tablet for physicians and other health care professionals (HCPs  [29] ), using the same extremely simple colloquial language to manage AR (Visual Analogue Scale: VAS)  [30, 31].
Geolocation EU data protection rules have changed since the implementation of the General Data Protection Regulation (Art. 4 para. 1 no. 1 GDPR)  [35]. Data anonymization is a method of sanitization for privacy. Anonymization renders personal data "in such a manner that the data subject is not or no longer identifiable"  [36]. The European Commission's Article 29 Working Party (WP29) stated already in 2014 with regards to the Directive 95/46/EC  [37]  that geolocation information is not only personal data but also to be considered as an identifier itself  [38, 39]. Processing personal data by means of an app, like e. g. App Diary, besides Directive 95/46/EC  [37]  also Directive 2002/58/EC  [40]  as amended by Directive 2009/136/EC  [41]  applies. Geolocation was studied for all people who used the Allergy Diary App from December 2015 to November 2017 and who reported medical outcomes.