dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We used 2013 flight itinerary data providing travel volumes of passengers flying between any origin-destination pair of commercial airports in the world (International Air Transport Association (IATA), www. iata. org; Official Airline Guide (OAG), www. oag. com). Starting from the airport of origin, each itinerary reports all connecting airports to reach the final destination and the airline companies handling the connecting flights along the given route. We collected publicly available information on the travel restrictions related to Ebolaaffected regions up to 31 August 2014. We considered both travel bans implemented by national authorities and flight discontinuations by individual airlines (Table ). Restrictions are heterogeneous in terms of start date and target country in the affected area (e.
g. some concern the entire Western Africa area and others just one of its countries). Flight suspensions by airline company A targeting the set of countries C were considered by removing from the flight database all itineraries (and associated travel volumes) to C where A was the dominant airline. Then, travel bans and border closures implemented by country B targeting the set of countries C were considered by singling out all itineraries connecting B with C (in both directions) and reducing by a factor r the associated travel volumes, with r neighbours = 80% for the affected area's neighbouring countries and r others = 90% for all other countries, to model residual human mobility and non-compliance to policies. The resulting overall traffic reduction for each country was obtained by combining the effect of flight discontinuation and country level travel bans.
Other epidemiological parameters were taken from the literature  [9, 12, 13]. The spatio-temporal epidemic evolution is modelled using individual-level dynamics where transitions are mathematically defined by chain binomial and multinomial processes to preserve the discrete and stochastic nature of the processes. Individuals in the latent state are allowed to follow the same mobility patterns and international travel behaviour as those who are not infected. Travel probabilities are calculated based on the integrated flight database and mechanistically simulated travel and commuting patterns. More details on the model and on the parameters' inference procedure are provided in  [9]  and in the supplementary information * (http://www. mobs-lab. org/ebola-eurosurvsup. html). To assess the effect of current travel restrictions on the risk of case importation, we compared the international spread of the EVD epidemic obtained from numerical simulations of the model with and without the travel reductions.
Acknowledgments This work has been partially supported by the  EC-Health  contract no. 278433  ( PREDEMICS)  and the  ANR  contract no. ANR-12-MONU-0018  (HARMSFLU). We acknowledge also funding from  DTRA -1-0910039  and  MIDAS-National Institute of General Medical Sciences   U54GM111274. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods: In 2012-2013, 234 participants of the RECORD GPS Study (French Paris region, median age = 58) wore a portable GPS receiver and an accelerometer for 7 consecutive days and completed a 7-day GPS-based mobility survey (participation rate = 57. 1%). Information on transportation modes and accelerometry data aggregated at the trip level [number of steps taken, energy expended, moderate to vigorous physical activity (MVPA), and sedentary time] were available for 7,644 trips. Associations between transportation modes and accelerometer-derived physical activity were estimated at the trip level with multilevel linear models. Results: Participants spent a median of 1 h 58 min per day in transportation (8. 2% of total time). Thirty-eight per-cent of steps taken, 31% of energy expended, and 33% of MVPA over 7 days were attributable to transportation.
Data collection and processing Population The RECORD participants, recruited during preventive health checkups in 2007-2008 and 2011-2013, were born in 1928-1978 and were residing (at baseline) in 112 municipalities of the Paris Ile-de-France region. In the second study wave  [21] [22] [23] [24] [25] [26] , after undergoing a medical checkup and completing computerized questionnaires at the IPC Medical Centre  [27] , 410 participants were invited, through a standardized information and recruitment form, to enter the RECORD GPS Study (approved by the French Data Protection Authority) between February 2012 and June 2013. Of these, 247 accepted to participate, and signed an informed consent form. Nine participants withdrew from the study and data were incomplete for four participants, resulting in a final participation and completion rate of 57.
Collection of GPS and accelerometer data Participants wore a QStarz BT-Q1000XT GPS receiver  [28]  and an Actigraph GT3X + tri-axial accelerometer  [29]  on the right hip with a dedicated elastic belt for the recruitment day and 7 additional days. They were asked to remove the belt only when sleeping and when they were in contact with water. Participants were instructed to recharge the GPS battery overnight, and to complete a travel diary of their activity locations (with arrival and departure times) over the data collection period, as supporting information for the electronic mobility survey. Two phone calls were made to the participants to reinforce the instructions.
Preprocessing of GPS data The GPS data (one point every 5 seconds) were processed prior to the mobility survey by an ArcInfo 10 Python script  [30]. The aim was to identify the participants' activity locations (any type of activity at a stationary location) over the data collection period from the accumulation of GPS datapoints at certain locations. The algorithm calculates a kernel density surface based on the set of GPS points for each participant, extracts peaks as potentially visited locations, and derives a list of all visits over the period made to each detected location with their start and end times (Additional file 1: Appendix 1). The algorithm automatically uploads the history of visits to locations into the server database of the electronic survey application and generates a set of pdf maps of the tracks that were mailed to the participants prior to the mobility survey.
GPS-based mobility survey The telephone mobility survey was based on the Mobility Web Mapping application (Additional file 1: Appendix 2). With the help of the participants, the survey operator had to geolocate visits to activity locations undetected by the algorithm or for which GPS data were missing, and could modify/remove detected visits to locations that were inaccurate/incorrect. The dates/hours of arrival/departure to/from locations were provided by the algorithm but could be edited. The information collected for each visit to locations included the type of activity practiced at the location and the different transportation modes (19 options) that were used to arrive at the location in a chronological order. In the present study, a trip corresponds to the travel from one destination to the next destination.
Post-processing of GPS and survey data Survey mobility data were automatically checked by a custom SAS program. A report of errors (Additional file 1: Appendix 3) was edited for correcting survey data until no inconsistency remained in the data. The SAS program generated a detailed timetable over 7 days indicating the succession of activity locations and trips between locations with the start/end times of each episode and the corresponding information on activities and transportation modes. This timetable integrates information extracted by the algorithm from the GPS data, and the corrections, additions, and attribute data from the survey. A dummy variable was generated to indicate whether the start and/or end times of each trip were derived from the automatic processing of GPS data or collected/corrected during the survey.
Aggregation of accelerometer data ActiLife 5. 10 with default settings was used to identify episodes of nonwear of the accelerometer (floating windows of consecutive epochs with a 3-axes count equal to 0 for at least 60 min with a Spike tolerance of 2 min of nonzero epochs). Trips that overlapped a nonwear period were flagged. The following accelerometer variables were aggregated for each trip or each visit to locations over 7 days according to the start/end times of the episode: (i) number of steps taken; (ii) time spent in MVPA as the sum of the 5-second epochs with a 3-axes number of counts ≥2,690/12  [29] ; (iii) time spent sedentary (counts per min <150, either determined on an epoch or min basis)  [32] ; and (iv) energy expenditure in kcal calculated from activity counts and participants' weight from three formulas: the Sasaki and Freedson equation  [29] ; the refined Crouter equation  [33] ; and the "Freedson VM3 Combination" equation from the Actigraph website  [34].
Regarding sedentary time, the first approach separately used the data of each 5-second epoch, multiplied each axis counts by 12 to rescale them at the minute level, calculated the rescaled vector magnitude on this basis, and flagged the 5-second epoch as sedentary if the rescaled vector magnitude counts were less than 150. Differently, the second approach aggregated 12 successive 5-second epochs into minute epochs and flagged a minute as sedentary if the cumulated counts were below 150. Details are provided in Additional file 1: Appendix 4. Default processing of GT3X + data involves a filter limiting the sampling to the frequency range of 0. 25-2. 5 Hz to exclude nonhuman accelerations. An optional low-frequency extension filter extends the lower end of the filter (useful when processing data of people who move slowly)  [35].
Study variables Trip-level accelerometry outcomes were analyzed: (i) directly as obtained from the aggregation of data at the trip level; and (ii) standardized for trip length (expressed per 10 min or per km traveled) prior to the modeling. As a proxy of the distance covered during trips, the present article relies on the length in m of the shortest path between the origin and the destination of the trip through the street network (determined with ArcInfo 10 and street data of the National Geographic Institute). The transportation mode variable was defined only among trips made with a unique mode or with a unique mode in addition to walking. We had to exclude trips with two or more nonwalking modes because they could not be attributed to the mutually exclusive categories of modes that were needed to perform the comparison, and because there were much too few trips with each combination of two nonwalking modes to define additional categories.
Relationships between transportation modes and physical activity Additional file 1: Appendix 6 provides summary information on the sample size in which each statistic was calculated and each analysis performed (each statistic was calculated in the sample in which it was the most meaningful, e. g. , summary information on trips in the largest sample). The regression analyses excluded certain trips from the full sample (n = 7,644) to improve the meaningfulness of the analyses. First, data on the start time and end time of each trip were validated at the minute level during the mobility survey. Accordingly, trips of less than 1 minute often had a 0 minute duration in the final database. By definition, such trips (n = 218) could not be used to aggregate accelerometry data and were thus excluded from the analyses.
Trips that were not eligible for a shift of mode included: trips of 0 min of length in the database; trips of less than 5 m of length; trips that started and ended at the same location; recreational walking trips or particularly atypical trips; trips with an excessive duration compared to the distance covered and to the mode used; trips with another transportation mode than those in the classification employed; and trips that included two or more nonwalking modes. trips. For shifts from personal motorized vehicle to public transportation, a penalty of 0. 05, 0. 1, 0. 1, and 0. 2 on the probability scale was applied, respectively, to trips between Paris and the suburb/provinces; trips from the suburb/ provinces to the suburb/provinces; trips on a Sunday; and trips between 0:00 am and 5:00 am.
It was difficult to assess the direction of bias for such percentages, resulting from the relative importance of nonwear of the accelerometer during trips as opposed to during episodes with physical activity at given locations. Fifth, the simulations of scenarios of shift of transportation modes were based on a small sample at the individual level. Such simulations will have to be replicated with a large sample as available in transportation surveys (without precise 7-day GPS, accelerometry, and survey data as in the present study however). Finally, accelerometers worn at the waist were unable to correctly quantify cycling-related physical activity  [37]. This poor estimate did not permit to explore the impact of a shift of mode to biking in the scenarios examined for this population where the prevalence of cycling is low.
Conclusion Overall, the present study is the first to assess the physical activity gains from walking or public transportation use with analyses at the trip level; with information on the start and end times of the trips from GPS data; with systematic information on transportation modes over 7 days from a mobility survey; and with physical activity data from accelerometers. With this innovative approach, we were able to accurately quantify differences in physical activity, steps taken, energy expenditure, and sedentary time between walking, using public transportation, and car driving, for each 10 min or km of trip. Although future research should pay attention to populations facing social and environmental barriers, our assessment of scenarios of shift of transportation modes suggests that promoting walking and public transportation may have a substantial population-level impact on the percentage of people reaching physical activity recommendations.
Acknowledgments This work was supported by  INPES  ( National Institute for Prevention and Health Education ); the  Ministry of Ecology (DGITM) ;  CERTU (Centre for the Study of Networks, Transportation, Urbanism , and Public constructions);  ARS (Health Regional Agency) of Ile-de-France ;  STIF (Ile-de-France Transportation Authority ); the  Ile-de-France Regional Council ; and RATP (Paris Public Transportation Operator). The authors thank the following partners from the funding institutions:  Pierre Arwidson ,  Nadine Asconchilo ,  Annette Gogneau ,  Colette Watellier ,  Laurent Jardinier ,  Tristan Guilloux ,  Mélani Alberto ,  Christelle Paulo ,  Anne-Eole Meret-Conti ,  Cédric Aubouin ,  Benoît Kiéné ,  Hélène Pierre ,  Sophie Mazoué , and  John Séraphin.
Data mobilized and processing The empirical analysis is based on the combination of field surveys and dataset analysis  (Dervillé, 2012). First, field surveys were conducted with various actors from the dairy industry (firms and institutional players) at the national level (10 interviews) and in three case studies (36 interviews) between 2008 and 2011. Semi-structured interviews were conducted with the actors to develop an understanding, on the one hand, of the functioning of the organization, the resources mobilized (including coordination instruments), the projects and difficulties and, on the other hand, of the perception of the change of competition regime and the correlated private and collective issues (adapted innovation strategies to be developed, tensions among actors regarding these strategies and the means required).
Second, the statistical analysis was used to characterize the diversity of business models (farms and firms) and supply chain configurations, as well as the heterogeneity of their environment. Highly exhaustive datasets devoted to dairy farm, dairy industry and natural and socio-economic context data were connected to each other (Table  2 ). All data were geo-located at the municipal level (''commune'', 36,679 units in France, mean area = 1,550 km 2 ). These datasets were used to calculate indicators aimed at characterizing the spatial diversity of dairy farm and firm production and business models, as well as their socio-economic environment. Aggregation took place at three main scales: firm, municipality and department. Data from all these datasets were processed using MySQL software (MySQL, version 5.
The scope of the analysis The French dairy industry encompasses the production of 24 billion liters of cow milk by 81,813 quota holders (source: FAM, 2009), its collection and processing by 782 dairy establishments for a turnover of 23 billion euros (source: YDI, 2006). However, the six major firms process more than 50% of the milk collected. In 2006, 38% of the milk was processed into cheeses, 25% into butter and cream, 18% into various types of powder, 11% into milk and 7% into fresh products. Approximately 28% of these processed products were sold to industries for further processing. Seventeen percent of the turnover corresponded to exported products, mainly in the form of cheeses and towards the EU (48% and 80%, respectively). The indicators calculated on the basis of the combination of the eight datasets made it possible to shed light on the diversity of the French dairy industry  (Dervillé and Allaire, 2014).
Private firms and cooperatives industrialized milk processing. In the Cantal (Case 2), a diversity of systems coexist: grass-based farming systems and local cooperatives inherited from the PDO cheese processing tradition remain, but intensification and industrialization technologies have spread throughout the department since the expansion of dairy production in the 1960s. Generic and specific supply chains are interrelated since PDO specifications are limited. As shown for other PDO (Letablier Farmers with agro-environmental scheme (%) Share of grassland within the overall agricultural land in 2000 (%) a For the three case studies, industry data was updated in 2008 and 2011, with complementary data collected from surveys and reviews of the literature. Indicators calculated based on the combination of the above-mentioned exhaustive datasets.
Acknowledgments This article is based on a PhD thesis carried out at the  INRA Research Center of Toulouse  and jointly funded by the  French Ministry of Research  and the  French Dairy Interbranch Organization  (CNIEL). The authors thank  Mr. Bazin ,  Mme Bouamra-Mechemache  and  Mr. Nieddu  for their valuable remarks, as well as the  Department of Economics and Quality of the CNIEL  and the ODR team for their contribution to the achievement of the project. The authors thank as well the editor and two anonymous reviewers for their constructive comments.
The model with the own funds norm generates a financial bubble with growing indebtedness and no self-stabilizing mechanism. In order to make the program work, it suffices to open the file using Eviews 7 or a more recent version. Once open, the program shows « dialog boxes » with default values (text or numbers). If the user wishes to change these values, she/he may easily do so (however, this does not guarantee the model will work). Afin de pouvoir faire tourner le programme il suffit d'ouvrir le fichier en utilisant la version 7 (ou plus récente) d'EViews. Une fois ouvert, le programme sort de « boîtes de dialogue » avec des valeurs (texte ou chiffres) par défaut. Si l'utilisateur veut les changer il peut facilement le faire (cependant, ceci ne garantit pas que le modèle marchera).
Our main objective is to compare the nature of these two regimes by carrying out demand or supply shocks as well as shocks on the financial side. The paper is organized as follows. Section 1 describes the stylized facts for France from 1979 to 2011 (the longest available time span given official data at the time of writing). In section 2 we give account of the most relevant works on SFC models in our literature review, in order to present the model in detail in section 3. Lastly, we present the results of the model and some scenarios (or shocks, as these are known in the literature) in section 4.
Instead of a general equilibrium taste, these authors gave it a Keynesian/Minskyan flavor that aimed at explaining endogenously created disequilibrium without optimizing behavior from economic agents. Lavoie and Godley (2001)  and  Godley and Lavoie (2007)  account for the most influential works on this type of analyses among Post-Keynesians 4. SFC models consist of systems of simultaneous equations which combine stocks (of debt, capital or deposits) with flows (of production, income or liquid assets) using experimental data under a realistic accounting framework. Considering the fact that all form of wealth 5  in an economy comes from somewhere and goes somewhere, these models have an advantage above other techniques: they are capable of describing the mechanism underlying a shock, either coming from the financial or the real side of the economy, and its effect on macroeconomic aggregates 6.
[14] Academic traditions play an important role of French speaking African researchers to publish outside their country thus African publications in the field of health are faced with a lack of international visibility. [15] World-wide scientific publishing activity over the past decade indicates that most countries in Africa have low levels of publication. [16] Furthermore, the access to the electronic reviews is often hindered in Africa by inconvenient power cuts, lack of computers or services of access to the internet reliable and high expenses of connection. [17] Most African journals are owned by academic institutions, are not indexed in major databases, are poorly funded, have poor circulation and have difficulties maintaining publications schedules. [18, 19] e identification of adequate funding, consensus opinion in this consultation is one of the many constraints faced by the research.
Regional coexistence of symbiont types in a spatially explicit system In the previous sections, we show that invasion of a conditional protective symbiont from rarity is not possible within a single population, but can occur in a two-patch system given a critical number of invading individuals, which is determined by the between-patch productivity gradient. We thus posit that in a broader context, assuming spatial variability in primary productivity (i. e. , the intrinsic growth rate of hosts), regional coexistence between conditional protective mutualists and enemies is possible. To examine this prediction, we simulate the system on a square toric lattice of 10 9 10 patches, with a uniform dispersal kernel from one focal patch to all of its eight neighbors. Computer code (C++11) to reproduce the simulation can be found at https://github.
Methods Our study is part of a larger project to describe mental disorders (such as psychotic disorders, mood disorders, anxiety disorders, SUDs, and PDs) and intellectual disability in newly convicted inmates in a federal prison setting in Quebec. Offenders admitted to the Quebec RRC, a facility responsible for assessing offenders condemned to a sentence of 2 years or more, were approached. Recruitment took place between October 2007 and November 2011. Data were obtained through standardized interviews, consultation of prison records, and laboratory tests. The project was approved by local ethics committees: Comité d'éthique de la recherche de l'Université du Québec à Trois-Rivières (CER-07-122-07. 04), Comité d'éthique de la recherche de l'Institut Philippe-Pinel de Montréal (070216/E/E/A/6), and McGill University Institutional Review Board (A02-B05-08A).