dataesr/scientific-paragraphs-categorization huggingface dataset:
Health care and technology can be considered good for health (assuming appropriate use) but it has a significant negative impact on the planet. In Australia, it accounts for about 7% of all CO 2 emissions, mainly attributable to hospitals and medicines  [11]. Regarding respiratory and allergic diseases, the Environmental Audit Committee of the UK parliament has requested that by 2022 at least 50% of prescribed inhalers should be of low global warming potential (GWP)  [12]. Planting trees in a Planetary Health. The Good Practice of DG Santé (The Directorate-General for Health and Food Safety) on digitallyenabled, patient-centred care pathways is in line with the objectives of the Finnish Allergy Programme. The ARIAC-ARE-Digital network has been deployed in 25 countries. It represents an example of the digital cross-border exchange of real-world data and experience with the aim to improve patient care.
1 ). 1. Self-care: Patient participation, health literacy and self-care through technology-assisted patient activation  [25]. 2. Pharmacist: Most patients with allergic rhinitis use OTC medications and a guide for the pharmacist, already proposed by ARIA in 2004  [26] , has been updated  [27]. A pocket guide is pending. 3. ICPs for allergen immunotherapy  [28] : Only guidelines for AIT were previously proposed. This is the first ICP. The ARIA-EAACI pocket guide is available. 4. The next-generation guidelines for allergic rhinitis have assessed GRADE recommendations in AR and asthma using real-world evidence (RWE)  [23]. RWE includes not only randomized-controlled trials on treatment effects, but also real-world data (RWD) obtained by mHealth tools-including MASK-and chamber studies  [29]  in order to confirm effectiveness or refine current recommendations  [30].
Integration of IT tools for climate, weather, air pollution and aerobiology There are several models and integration technologies that bring together the key drivers of human pollenrelated allergy and asthma. They include meteorological models, atmospheric composition and air quality models, pollen prediction models, pollen real-time monitoring techniques, and the mobile applications interacting with the allergy sufferers, collecting their self-assessment reports and delivering forecasts of their personalized allergy risk. The POLLAR system is constructed around the MASKair mobile application and its allergy symptom database. It is integrated with the Symptom Forecasting Model developed within the PASYFO (Personalized Allergy Symptoms Forecasting System) project of Copernicus Atmospheric Monitoring Service, which also supplies the meteorological, air quality and pollen information for Europe.
The model will integrate data from SISCAT clinical health records from 2008 to 2018, historical and real-time data from the electronical drug prescription registry as well as meteorological data. This kind of RWD predictive tool returns value to the Catalan society. It offers the opportunity to design preventive measures, plan healthcare needs, improve patient outcomes, and guide new research solutions. • fast, convenient online submission • thorough peer review by experienced researchers in your field.
Objective. In 2010, the principle of proportionate universalism (PU) has been proposed as a solution to reduce health inequalities. It had a great resonance but does not seem to have been widely applied and no guidelines exist on how to implement it. The two specific objectives of this scoping review were: (1) to describe the theoretical context in which PU was established, (2) to describe how researchers apply PU and related methodological issues. Methods. We searched for all articles published until 6 th of February 2020, mentioning "Proportionate Universalism" or its synonyms "Targeted universalism" OR "Progressive Universalism" as a topic in all Web of Science databases. Results. This review of 55 articles allowed us a global vision around the question of PU regarding its theoretical foundations and practical implementation.
Ce honteux commerce, qui fit la fortune de plusieurs ports français (Bordeaux, La Rochelle, Nantes, etc. ), britanniques et hollandais, a laissé de nombreuses traces écrites que l'on peut notamment consulter grâce à une base de données, la Trans-Atlantic Slave Trade Database 1 , qui contient des informations détaillées sur plus de 36 000 voyages effectués entre 1500 et 1900. On peut y lire, par exemple, que la Marie-Gertrude, partie de Saint-Malo le 5 septembre 1719, a embarqué 190 esclaves à Whydah (Ouaidah, Bénin) et a débarqué les 154 survivants à Léogâne (Haiẗi) le 11 juin 1720. On peut même, dans certains cas, accéder à la liste des esclaves embarqués, connaître leur nom africain, leur âge et leur taille… Depuis quelques années, ces données historiques ont pu être complétées par des analyses d'ADN susceptibles de révéler les origines africaines précises de personnes afro-américaines vivant actuellement en Amérique du Nord, du Centre ou du Sud 2.
S. ). Approval to use these pre-existing CT-scans for the present study was granted by the different institutions' IRB and all CT-data were anonymized to comply with the Helsinki declaration  114. Ribcages were segmented through a semi-automatic protocol for DICOM images using the open-source 3D Slicer software (https ://www. slice r. org/) and subsequently reconstructed as 3D models. These 3D models were imported into Viewbox 4. 0 software (www. dhal. com)  115 for (semi-)landmarking using previously published protocols for ribs 1-12 and the thoracic spine  35. The thorax morphology was quantified using 526 tridimensional landmarks and sliding semilandmarks: 7 landmarks and 13 sliding curve semilandmarks were placed on each rib 1-10, and 5 landmarks, and 13 sliding curve semilandmarks on each rib 11 and 12.
Acknowledgements This work was partially funded by the  IdEx University of Bordeaux Investments  for the  Future program  ( ANR-10-IDEX-03-02 ), and the projects  CGL2012-37279  and  CGL2015-63648P  ( Ministry of Economy, Industry and Competitiveness, Spain ) and  PI10/02089  ( Fondo de Investigación Sanitaria, Ministry of Health, Social Services and Equality Spain ). The " Juan de la Cierva Formación" program  ( FJCI-2017-32157 ), from the  Spanish Ministry of Science, Innovation, and Universities , funds DGM. The authors thank the reviewers of previous versions of this manuscript for their positive and fruitful comments.
This was achieved with significant international support and with both philanthropy and know-how support from the Merck organisation in collaboration with NewLink Genetics. The Ebola outbreak, however, did illustrate that the world is ill-equipped to deal with outbreaks that may initially threaten restricted geographical regions but then may broaden to much wider geographical regions. These lessons learnt from the Ebola outbreak were one of the drivers for the creation in 2017 of the Coalition for Epidemic Preparedness Innovations (CEPI) to develop vaccines against emerging infectious diseases. CEPI is funded by donations from public, private, philanthropic and civil society organisations and is supported by the main vaccine manufacturers. Originally focused on the WHO's 'blueprint priority diseases', CEPI has emerged in 2020 as key player in the race to develop a vaccine against COVID-19.
OMVs derived from these recombinant strains, also named GMMAs (generalised module membrane antigens), can be further engineered to over-express heterologous antigens and to form the basis of multivalent vaccines  (Berti and Micoli 2020). The immunogenicity of Shigella sonnei GMMA has been shown in a Phase I clinical trial in humans  (Obiero et al. 2017). The genomic era has changed the vaccine landscape even more. Starting from the first genome sequence of a bacterial pathogen, Haemophilus influenzae type B, the number of sequenced genomes available in the European Bioinformatics Institute (EBI) and National Center for Biotechnology Information (NCBI) databases is of the order of a hundred thousand. The genome revolution has been possible because of the rapid whole genome sequencing technologies that allow an entire genome to be sequenced within minutes and at very low cost.
However, to trace the drivers in case of misbehavior, the TTP need to check each private key stored in its database until it finds a match with the malicious driver's public key. This task is time-consuming, especially in large scale applications. He et al. [9]  proposed an accountable, privacy-preserving authentication framework for wireless access networks. This scheme considers an architecture composed of a network operator; a group manager at the head of each user group; and finally a set of access points allowing the access to the network. Users can anonymously be authenticated in access points and can be traced through a cooperation between the network operator and the group managers. However, and as it has been stated in  [9] , this authentication framework is only applicable to group-based architectures.
7) we emulate the event-collecting module (available in each fog server) as a program that executes our signature verification algorithm (sub-sectionVI-C5) to verify the signature of the reported events, before making them available to the public. 8) we emulate the identity disclosure module as a program (available in the authority) that executes our tracking algorithm (sub-section VI-C6) in order to break the anonymity of misbehaving users. This module interacts with the setuplauncher module to get some setup parameters. Moreover, it interacts with the users-registration module to get information related to users registration. In our event-reporting environment, the authority first executes our setup-launcher module to generate the system parameters. Later on, each fog server runs its subscription module to get the verification parameters from the authority.
B. Signature process In our event-reporting environment, all communicating entities that want to share public information through fog servers, must sign the reported events. Our signature process adopted in each event-reporting module, requires the computation of (div, add) refer to modular division, and addition resp. (P, M e, P m, P a) refer to paring operation, modular exponentiation, elliptic curve point multiplication and point addition resp. (k, r) are two parameters defined in  [15]  and  [25]  schemes resp. one pairing operation, three modular exponentiations, two multiplications and a Hash function. On the hand, existing solutions perform a considerable number of pairing operations, elliptic curve point multiplications (going up to eleven in the case of Mona  [15] ), additions and modular exponentiations in their signature process.
D. Tracking process In our event-reporting environment, the authority tracks users and reveal their identities in case of misbehavior. As shown in the benchmarks of JPBC library  [1] , the computational operations of the tracking process performed in our solution have more or less the same computation time as the operations performed in solutions  [15] ,  [25] ,  [31] , in all elliptic curve configurations. In terms of complexity, table IV shows that each execution of the tracking process in the compared  solutions requires the computation of a constant number of arithmetic operations. Therefore, given n tracking requests formulated in parallel to the tracking module, we can conclude that the complexity is in the order of O(n).
Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task that has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors' location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators.
INTRODUCTION While cities have long been recognized as the cradle of modern civilization by providing a safe place for cultural development, the inequality distribution of wealth and services remain the main pressing issue threatening the sustainability of modern societies. Despite the large technological advances making our life apparently easier, economic inequality has been on the rise worldwide since 1980. This has become such an issue that most recent datasets show that the top 1% of the wealthy population capture twice as much of the global income growth as the bottom 50%  [1]. While such distribution disparity among urbanites and social stratification is currently under deep scrutiny among economists, including the spatial components to such descriptions, it imposes additional methodological difficulties given the vagility of human nature and the heterogeneity of the spatial distribution of resources.
For instance, its precise understanding is often linked to the study objects at hand and the particular methodology employed to study them. Dimensions of inequalities often include the localized concentration of particular groups within cities, the spatial homogeneity of social groups, their accessibility, or more particularly, their distance to downtown  [16]. Hence, devising appropriate tools to characterize the spatial distribution of complex socioeconomic factors may contribute to the urgently needed development of integrative urban planning. The explosive use of Information and Communication Technologies (ICT), such as cellphones and large databases of user spending behavior, has made huge volumes of non-conventional data available for urban research purposes  [17] [18] [19] [20] [21].
Knowing the cellphone tower to which we connect permits the reconstruction of our daily trajectories, providing a surprisingly high spatio-temporal resolution of our social interactions  [22, 23]. This approach has been widely used recently to assess a variety of topics going from individual mobility patterns  [24]  and land use patterns  [25] , to the detection of relevant places of high social activity within the city  [26] , thereby unveiling the structure and function of cities  [25, 27, 28]. Devising an efficient mobility infrastructure has long been known as a means for city integration and the increasing availability of ICT data allows for a new understanding of spatial integration patterns and its relationship to mobility, socioeconomic and ethnic stratification  [29]. Such highly resolved datasets provide a contextual understanding of land use that is readily available to derive new measures of social integration in its spatial context, thereby contributing to accurate, and near-realtime, descriptions of urban dynamics  [30] [31] [32] [33] [34] [35].
We particularly look into mapping the entropy of urban structure using increasingly available mobile phone datasets as a tool to provide highly resolved descriptions of the relationship between attractiveness and several key aspects of the urban environment such as productivity, education and ethnic origin in the Rio de Janeiro Metropolitan Area of Brazil. We focus here on the diversity of visitors' residence to measure the attractiveness of a location and then compare our results to economic and social indicators to assess how entropy effectively relates to socioeconomic indicators. We show that entropy is an important descriptor of socioeconomic complexity across this vastly populated area.
The study area and dataset The study area is the Rio de Janeiro Metropolitan Area (RJMA), the second largest urban area in Brazil with 12,145,734 inhabitants. Administratively, the RJMA is a part of the Rio de Janeiro State, of which Rio de Janeiro city (Rio for short) is the state Capital and the largest municipality with 6,320,446 inhabitants and 1,200,177 km 2. The organization responsible for the demographic census in Brazil is the Institute of Geography and Statistics (IBGE) who follows global standards to aggregate census tracts in sub-district, district, city, state, and country levels such that this partitioning can be used for most regions in the world and at different scales. This study relies on such partitioning, dividing the study area in 49 locations (Figure  1 ) where the city of Rio is divided in 33 sub-districts aggregated into 5 districts as shown in Figure  1.
Districts are called Planning Areas (AP) and represent macro zones of the city with AP1 the center; AP2, the southern zone; AP3, the northern zone; AP4 Barra-Jacarepaguá; and AP5 depicts the western zone. Our analysis is based on mobile phone data provided by a Brazilian telecommunication operator. The dataset was collected during 363 days between Jan-uary and December 2014 across the phone area code 21. We use 2. 1 × 10 9 call records originated from 2. 9 × 10 6 anonymized subscribers. Only outgoing voice call data were made available for this work. We first focused on the identification of user's residence. The algorithm to detect places of residence is based on the analysis of the most frequently visited locations on evenings and weekends (see the Appendix for more details). This step allows us to discard users not living in the RJMA and remove users with no significant activity for the analysis.
In Cidade de Deus, only 13% of the population is living in subnormal agglomerates as it is mostly composed by housing estates building, while its socioeconomic indexes are similar to the favela sub-districts. attractive cluster (C1) as did Cidade de Deus. Complexo da Maré has also many housing estates building and 54% of its population living in subnormal agglomerate. It was classified in the medium attractive cluster (C2), maybe because it is crossed by two of the main expressways that lead to the exit of the city. In the dataset used in this work, a visitor is detected in a given location by a call recorded within the location, such that some detected visitors may be passing-by the location to reach another destination. Figure  4c  and 4d show two social development indexes. In Figure  4c  the Municipal Human Development Index (MHDI), which is an adaptation of the Human Development Index (HDI) for municipalities.
DISCUSSION The impact of socio-spatial inequalities on urban systems has largely been treated in the urban economics and sociological literature, but the increasing availability of large mobile phone databases has open the possibility to provide a clearer picture of how different aspects of urban life impact economic and sociodemographic aspects of cities  [19]. Going into this direction, this work presents the results of the processing of 2. 1 Billion records collected from 2 million users in the Rio de Janeiro Metropolitan Area, Brazil, during the whole year of 2014, placing this research among the largest analysis, to our knowledge, used to relate mobility and its link to socioeconomic complexity in Brazil. We hereby illustrate the potential of combining mobile phone data with entropy-based metrics to measure the attractiveness of a location.
This may prove useful to urban planners and managers when it comes to describe and plan for complex socioeconomic indicators. While it is known that mobility is in fact related to economic activity, this work presents an effective and simple way to measure such relationships from increasingly available ICT data such as mobile phone datasets. While most capital cities in South America suffer from a disproportionate growth compared to other urban settlements  [53] , common patterns of spatial inequalities show that underprivileged populations establish themselves away from highly productive central zones  [30, 54] , with often clear differences among the usage of urban infrastructure  [55]. In this sense, the particular and complex topography of Rio de Janeiro would suggest the existence of shared usage patterns of the city among urbanites coming from different social contexts.
The spatial partitioning employed in our study closely matches IBGE delineation, we are therefore able to compare official statistics with measures derived from CDR data and offer specific insights regarding the usage of ICT as proxies for the spatial distribution of complex socioeconomic indicators derived from mobile phone datasets. Our analysis shows that the attractiveness of a district measured with the diversity of visitors' place of residence is correlated with the income and the number of jobs in spite of the large informal job market of Rio  [32]. We also show that the attractiveness is lower in areas hosting a large percentage of the population with African descent and/or locations where primary school training is prevalent (Figure  6a, c ). While this points to previous descriptions showing how available schooling options closely reproduce residential patterns of socio-spatial segregation  [56, 57] , the spatial mismatch and highly productive Centro area, where work opportunities are concentrated in the RJMA, leads us to think that residential segregation of the poorest is reinforced by new inequalities when taking into account daily mobility opportunities.
This particular characteristic of the city allows to validate the results, as the clusters accurately identified favelas and other socioeconomic isolated districts, as shown in Fig. 4. In summary, this manuscript serves to illustrate the potential of mobile phone data combined with entropy-based metrics for measuring the attractiveness of a location that can be used as a proxy for complex socioeconomic indicators. Even if the spatial partitioning used in this study tends to reduce the level of spatial uncertainty inherent in this type of data sources  [61] , it would be interesting to reproduce the results with different datasets coming form different sources of mobility information.
Identification of the users place of residence The presumed residence of each user was computed as the most visited Voronoi cell between 08:00PM and 06:00AM during workdays and the entire day on Sundays and holidays. We additionally required that the user to be regularly detected in this cell (at least five times) and that the number of visits at the most frequented cell is always greater than the number of visits at the second most frequented cell. The final dataset containing only users with an identified residence ended up to be 350, 685 mobile phone users. As mentioned above, the data were aggregated spatially by assigning each Voronoi cell to one of the 49 districts. The identification of the users place of residence was then evaluated using data from the IBGE 2010 census. As it can be observed in Figure  S4  we obtained a good match between the census data and the residence identified with mobile phone data with a Pearson correlation coefficient equal to 0.
ACKNOWLEDGEMENTS JCC, VFV, MAHBS and AGE acknowledge the funding granted by  The Rio de Janeiro State Research Agency (FAPERJ)  and by the  Getulio Vargas Foundation. The work of ML was funded by the  French National Research Agency  (grant number  ANR-17-CE03-0003 ). HS was funded by  FONDECYT-CONICYT Chile  (grant no. 1161280 ).
General orthographic knowledge, word dictation task Sixty inconsistent or irregular words (see Appendix) were drawn from the Lexique database  (New, Pallier, Ferrand, & Matos, 2001). Thirty words were very infrequent (mean book frequency =. 44, SD =. 29, range =. 07-. 95) and 30 words were very frequent (mean book frequency = 7. 70, SD = 7. 45, range = 1. 15-33. 24). The mean word length was seven letters (SD = 1. 7, range = 4-11), five phonemes (SD = 1. 5, range = 2-10) and two syllables (SD =. 7, range = 1-4).