dataesr/scientific-paragraphs-categorization huggingface dataset:
35  To mirror the populations typically targeted in primary prevention efforts for cardiovascular disease, risk model derivation included participants aged 40-80 years without a known baseline history of cardiovascular disease. Follow-up was until the first myocardial infarction, fatal coronary heart disease, or stroke event; outcomes were censored if a participant was lost to follow-up, died from non-cardiovascular disease causes, or reached 10 years of follow-up. Conventional cardiovascular disease risk factors were considered for selection as variables in risk models if they were known to be predictive of cardiovascular disease in different populations, were recorded in available survey data to allow systematic recalibration within each global region,  20, [22] [23] [24] 30  and had been shown to be measurable at low cost in low-income and middle-income countries.
20   e derived two types of new WHO risk prediction models for cardiovascular disease: a laboratory-based model including age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol; and a non-laboratory-based model including age, smoking status, systolic blood pressure, and BMI. Sex-specific models were derived separately for coronary heart disease (defined in the ERFC dataset as non-fatal myocardial infarction or fatal coronary heart disease), and stroke (any fatal or non-fatal cerebrovascular event) outcomes. Details of these endpoint definitions are shown in appendix 1 (p 7). Outcomes were model led separately for coronary heart disease and stroke to allow separate recalibration to the disease-specific incidence in the target populations before combination in a single estimation equation for cardiovascular disease risk (appendix 1 pp 40-41).
Between-study heterogeneity was assessed using the I² statistic. 39 We used meta-regression to assess hetero geneity by geographical region and period of cohort enrolment. 40 or internal validation, we assessed risk discrimination using Harrell's C index. This index estimates the probability of the model correctly predicting who will have a cardiovascular disease event first in a randomly selected pair of participants. 41 To avoid optimism that might result from assessing risk discrimination in the data from which the model was derived, we used an internal-external validation approach in which each study was, in turn, left out of the model derivation and used to calculate a validation C index. 42 The calibration of each model within studies with at least 10 years of follow-up in the derivation dataset was checked by comparing observed and predicted risk across deciles of predicted risk and by calculating a χ² statistic to quantify any evidence of lack of agreement or fit (appendix 1 p 40).
We assessed discrimination using all external validation cohorts by calculating study-specific C indices before pooling by country, weighting by number of events. 45 dditionally, we compared C indices for the same prediction models derived within datasets used for external validation with those calculated for the new WHO models. To compare the proportion of the population at different levels of cardiovascular disease risk, with the WHO models, across multiple countries, we applied the risk models to WHO STEPS surveys data. To allow comparison across countries, we restricted analysis to the latest survey year available for each country and to individuals aged 40-64 years, with total cholesterol between 2•6-10•3 mmol/L, and complete data on relevant variables (appendix 1 pp 8-9). These data were also used to compare risk estimates obtained with non-laboratory-based models with those obtained with laboratory-based models.
Additionally, we observed a good agreement between 5-year predicted and observed risk in the PREDICT-CVD cohort (appendix 1 p 32). The estimated absolute risk for a given age and combination of risk factors differed substantially across regions (figure  2 ). For example, the estimated 10-year cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. Similarly, the 10-year risk for a 60-year-old woman with the same risk factor profile ranged from 9% in Andean Latin America to 23% in eastern Europe, north Africa, and the Middle East. External validation of risk models involved calculation of C indices with use of data from 1 096 061 participants with no previous cardiovascular disease, recruited into 19 prospective cohorts (25 950 cardiovascular disease events observed; appendix 1 p 6).
Discussion We have developed, evaluated, and illustrated the use of revised prediction models for cardiovascular disease risk adapted for low-income and middle-income countries (appendix 2), with the aim of their incorporation into the WHO HEARTS package. 4 These models have been system atically recalibrated to contemporary risk factor levels and disease incidences across 21 global regions, thereby enabling more accurate identification of individuals at high risk of cardiovascular disease in different settings. 46 Because the approach to recalibration that we used allows rapid revision of cardiovascular disease models, it should enable flexible updating of models as relevant new epidemiological data emerge about cardiovascular disease trends in particular geographical areas. ] [22] [23] [24]  In parti cular, the scale and geographical resolution of the datasets analysed have enhanced the validity and generalisability of risk models for each sex-specific and disease-specific (myocardial infarction and stroke) endpoint reported here.
Ideally, however, the derivation of risk models for low-income and middle-income countries would involve nationally representative, large-scale prospective cohort data from several of these countries, each cohort with long-term follow-up and validated fatal and non-fatal endpoints. Unfortunately, however, such data do not yet exist for most low-income and middle-income countries. 21, 29, 52 Therefore, to inform recalibration, we used data from the GBD study and the NCD-RisC, acknowledging that these sources frequently do not have country-specific disease risk estimates because of the paucity or absence of such data. 21, 29, 52 o provide external validation, we analysed data from 19 cohorts distinct from those used in model derivation. However, only one of them (PREDICT-CVD cohort) was nationally representative, whereas some of the other cohorts might have inadequately represented the epidemiology of cardiovascular disease in contemporary national populations of interest.
The coordinating centre was supported by underpinning funding from the  British Heart Foundation  (BHF;  SP/09/002 ,  RG/13/13/30194 , and  RG/18/13/33946 ),  BHF Cambridge Centre for Research Excellence  ( RE/13/6/30180 ),  UK Medical Research Council  ( MR/L003120/1 ), and the  National Institute for Health Research (NIHR; Cambridge Biomedical Research Centre  at the  Cambridge University Hospitals NHS Foundation Trust ). This work was also supported by  Health Data Research UK , which is funded by the  UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England) ,  Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government ),  Public Health Agency (Northern Ireland) ,  BHF , and  Wellcome.
Australia. During the initial 10 years of follow-up Calibration and goodness of fit for the prediction models (3•2 million person-years at risk) 19 333 cardiovascular were good within the ERFC dataset, both overall disease events were observed (table 1, appendix 1 pp 3-5). HRs for myo cardial infarction or fatal coronary heart disease and stroke for each risk predictor included in the WHO models are provided in table 2. Associations of history of diabetes and current smoking status with cardiovascular disease diminished with age, particularly in women, among whom HRs for myocardial infarction or fatal coronary heart disease were reduced from (appendix 1 p 18) and within specific regions and recruitment time periods (appendix 1 p 19). Internally validated C indices ranged from 0•666 (95% CI 0•661-0•672) in men with the non-laboratory-based model to 0•757 (0•749-0•765) in women with the laboratory-based model (appendix 1 p 12).
| Product space and sensory attributes The product space is composed 28 products (P1 to P28) of the same nature ("Cachaças") but differ manly in aging time as well as the recipient used to conserve these products. We have retained 8 sensory attributes related to its taste and aroma, namely: A 1 = "Burnt," A 2 = "Sweetness," A 3 = "Bitterness," A 4 = "Floral," A 5 = "Fruity," A 6 = "Vegetable," A 7 = "Spicy," and A 8 = "Woody. " The sensory attributes were evaluated on a 9-point scale ("1" for not present to "9" for very much present), by a trained panel (see  Serafim et al. , 2013  for more details). In addition, an hedonic test was performed by using 240 consumers of this beverage. To this end, a 9-point scale was used ("1" for "I dislike extremely" to "9" for "I like extremely"). The sensory data and the hedonic score are presented in Tables  2  and 3.
| Mathematical sensory model Each sensory attribute have been expressed has a function of the chemical composition by using a RBF model of the following form: The parameters of this model have been tuned by using the Neural Network Toolbox  (Beal et al. , 2018) , over the database given in Tables  2, 3 , 4, and 5. The Figure  3 , shows the goodness of fit of each sensory attribute. We can see that the coefficient of determination R is very good for each attribute, i. e. , at least 84% of the total variation is explained by the sensory model.
The other attributes have a minor role or have a negative impact on the hedonic score, which is the case for the attributes A 4 and A 3 , with A 4 < A 3. The preference order is then the following one Consequently, to find the best composition we have to solve the following optimization problem (9) Âi (x) = a i,0 + 16 ∑ j=1 a i,j exp ( -||x -m i,j || 2 ∕𝜎 2 i,j ), i = 1, ⋯ ,8 (10) ĥ = 𝛼 0 + 8 ∑ i=1 𝛼 i A i (11) = [5. 77, 0. 007, 0. 331, -0. 268, -0. 114, 0. 0279, 0. 053, 0. 299, 0,417]  The MATLAB code used to obtain a solution to the optimization problem 13 is presented in Appendix S1. (12) (A 8 , A 2 , A 7 , A 6 , A 5 , A 1 , A 4 , A 3 ) TA B L E 2.
This is becoming more significant as calculation capacities become stronger (Bellon-Maurel and Huyghe 2016). Combined with these massive new datasets, the development of serious games such as Rami Fourrager® (literally, Forage rummy)  (Martin et al. 2011)  allows farmers to use simulations to design and evaluate alternatives to their strategy and current practices. Another example is the GECO application developed in France  (Soulignac et al. 2017) , which allows farmers to formalise knowledge and make it available or discuss it within the broader community. The contribution of technological innovations to the AET is therefore largely a function of their capacity to promote the construction of agroecological knowledge and practices. These technological innovations are also fundamentally dependent on farmers' capacities to adopt them or to integrate the results of these technologies into their decision-making systems.
The stakeholders' network was identified on the basis of the researchers' exploratory interviews with the local key actors, and the scientific knowledge drawn from their previous work in these fields. We used an interest/impact diagram to classify local stakeholders according to their willingness and their potential impact on AET  (Therond et al. 2010). A third dimension taken into account in our analysis was stakeholders' position regarding AET (pro or con) (cf. Fig. 4 ). This diagram was used as a decision support tool with local PETR partners to build a stakeholders database for the participatory process. In order to ensure the representativeness of the participatory process outcomes and thus to ensure its relevance, all diagram stakeholder categories were contacted: favourable, unfavour- able and neutral stakeholders, with low, medium or high interest or impact.
Material Collected Twenty-four people were interviewed at the two fields of study for the project: 14 in Midi-Quercy, and 10 in Centre-Ouest Aveyron (cf. chapters "TATA-BOX at a Glance" and "Participatory Methodology for Designing an Agroecological Transition at Local Level"). The people who had participated in the 3 workshops of the process were targeted as a priority (we met with 13 of them). Interviews were also held with 7 people who had participated only in workshop 3, with 3 people present in workshops 1 and 3, and with 1 person present in workshops 1 and 2 (Fig. 1 ). The data were collected during a semi-structured face-to-face interview with each of these people, with five main components: their expectations regarding workshops, how the workshops went, the results, the position of the process compared to others, and its influence.
Data Analysis This data were analysed based on three main components: (i) the course of the participatory process, (ii) its direct results, and (iii) its effects (Fig. 2 ). First, analysing the course of the workshops was intended to identify the real-life experience of the interviewees with regard to the process and the forms of interaction that took place between participants. Discourses were examined both qualitatively and quantitatively. On the qualitative level, the components of the process.
Other Features of the Product Mentioned During the interviews, the interviewees mentioned other features of the workshop outputs. Each of these features was mention by only one or two people. An initial aspect was the realism and degree of justification of some goals. One interviewee thought and liked the fact that the TATA-BOX project "[has] been a federating space, for sharing data to be able to still manage to construct something realistic". He and another person from Midi-Quercy nonetheless questioned the possibility of reducing water consumption, while a third person found, on the contrary, that future constraints, such as the lack of water, were not adequately taken into account during workshops. One interviewee in Centre Ouest Aveyron also saw a bit of utopianism in orienting toward the re-localisation of food and supply chains, taking into account their operating conditions and the current economic environment.
In the End, Will ICT Save or Consume Non-renewable Resources? This chapter has demonstrated that at a territorial scale, many IC tools can support AET as regards their environmental, social, economic and methodological dimensions, during both the design and the implementation phases. However, these demonstrations never considered the steps existing outside the scope of the farmer or territorial community: the construction of the tool, its maintenance and its recycling or treatment as waste. Whatever ICT is considered (sensor, radar, database, website, software, etc. ), those steps have considerable non-virtual costs since they all consume energy and non-renewable resources. From its beginnings, agroecology has been based on the principle that farming systems should use as little fossil-sourced inputs as possible  (Altieri 1995).
This book is based on research funded by the  Agence National de la Recherche [French National Research Agency ], which was carried out under the  AGROBIOSPHERE programme  ( n°ANR-13-AGRO-0006 ), as part of the project "Transition agroécologique des territoires: transition of agricultural territories with local actors). We would like to thank: patory design methodology and of associated boundary objects. agreed to provide their insight in this book through critical analysis;.
These issues are especially important for European ventures. The low level of VC investments in Europe compared with the U. S. and the fragmentation of the European VC market into separated national markets are considered prominent reasons for the underdevelopment of the European high-tech entrepreneurial ecosystem  (European Commission, 2007). Elucidating how the location of European ventures and VC investors influences ventures' demand for VC may help to clarify the source of the European anomaly. Accordingly, our empirical investigation is based on a sample of 533 European high-tech entrepreneurial ventures extracted from the VICO database. The VICO database includes information on young high-tech ventures located in seven European countries -Belgium, Finland, France, Germany, Italy, Spain and the United Kingdom -and is particularly appropriate for our analysis.
VICO is designed to include companies that are very likely to be potential targets for VC investments. The European dimension of the VICO database offers an ideal test bed to assess the impact of national borders on ventures' demand for VC. The availability of VC differs remarkably across European countries and regions. In addition to two large VC hubs located in the London and Paris areas, several other VC hubs are heterogeneously distributed in other countries  (Martin et al. , 2005 , for instance, show that in Germany, the VC industry is spread across six hubs). The panel dimension of the VICO database is also interesting for our purposes. Our dataset includes observations between  1984 and 2009, which  gives us the opportunity to observe the development of the different VC markets over 25 years.
The 533 VICO companies included in our sample are the respondents of a survey that was administered in 2010. The survey asked ventures whether and when they actively looked for external equity 3 in the first 15 years of their existence. We complement company-level information from the VICO database with longitudinal VC market-level data on the availability of VC across European regions and the level of competition in local VC markets. The latter information is extracted from Thomson One. Our results show that proximity to VC hubs where there is a large availability of VC is a key driver of ventures' propensity to look for external equity, but this effect rapidly declines with geographical distance and vanishes beyond 250 km. It also vanishes when crossing national borders. The negative effect of distance from prospective VC investors on the demand for VC varies with the characteristics of those VC investors in terms of their ownership and governance  (Bertoni et al.
Sample We test our predictions on a panel dataset composed of 533 European high-tech entrepreneurial ventures that were potential or actual targets of VC investments and responded to an online survey we administered in 2010. The sample companies were extracted from the VICO database, 7 which includes detailed information for a large sample of high-tech entrepreneurial ventures operating in seven European countries: Belgium, Finland, France, Germany, Italy, Spain and the United Kingdom. All ventures included in the sample were founded after 1984, were independent at their foundation (i. e. , they were not controlled by other business organizations), and operate in the following high-tech manufacturing and service industries: nanotechnology; biotechnology; pharmaceuticals; computers; electronic components; telecommunications equipment; precision, optical and medical instruments; robotics; aerospace; software; telecommunications services; Internet and multimedia services; web publishing; renewable energies; R&D and engineering services.
The VICO database includes two strata of ventures: 759 VC-backed ventures and 7611 non-VC-backed ventures that are "potential targets of VC investments"  (Bertoni and Martí, 2011). VC-backed companies received the first VC round from 1994 to 2004 when they were no more than 10 years old. They were randomly extracted from commercial databases (i. e. , Thomson One, VC-PRO, and Zephyr) and country-specific proprietary databases, including the yearbooks of the Belgium Venture Capital Association and the Finnish Venture Capital Association, the ZEW Foundation Panel (Germany), the Research on Entrepreneurship in Advanced Technologies (RITA) directory and Private Equity Monitor (Italy), the Webriesgo Database (Spain), and the Library House (now called Venture Source; the UK). Moreover, data on VC-backed companies and their investors were cross-checked by a central data processing unit with those available from public sources (e.
g. , websites and annual reports of VC investors, press releases and press clippings, and initial public offering prospectuses). Therefore, data in the VICO database are more reliable than those available in commercial databases. The non-VC-backed firms were included in the VICO database following the same criteria relating to country, age, independence, industry, and legal status used for the inclusion of the VC-backed firms, and they were randomly extracted (conditional on these criteria) from all available years of Bureau van Dijk's Amadeus database. We resorted to additional information sources to improve the coverage of the dataset and assure data reliability (i. e. , industry associations and Chamber of Commerce directories, commercial firm directories, Zephyr, Creditreform, the ZEW Foundation Panel, and the RITA directory).
Ventures in the VICO database are observed from their foundation date to 2010 (or the time at which they ceased operations or were acquired). In February 2010, we sent an online survey to the 5417 VICO companies for which we had a valid email address, to collect information about their demand for external equity. Respondents answered "Yes" or "No" to the question "Has your company ever sought equity financing from sources other than founders, their family members and friends?". To companies that answered "Yes", we also asked when they actually sought financing. The possible non-exclusive answers were "before or at the time of foundation; in the first 2 years after foundation; between the 2nd and 5th years after foundation; between the 5th 7 The VICO dataset was built through the joint effort of nine universities across Europe with the support of the 7th European Framework Program.
For more details on the procedures used in the data-gathering process and on all the variables included in the database, see  Bertoni and Martí (2011). and 10th years after foundation; after the 10th year after foundation". 8  We sent four reminders between February and April 2010. To fill in missing data and check the reliability of the information provided by sample ventures, we complemented the online survey with several phone interviews. Ultimately, we received 814 answers (response rate of 15. 03%). In this paper, we use the data on the 533 ventures for which we have complete information with regards to the variables of interest. We populate an unbalanced panel dataset in which we track yearly information on these companies in the first 15 years of their lives. We exclude companies that are older than 15 years, as VC financing is typically used in the early stages of entrepreneurial ventures' life.
The geographical distribution of VC investments in Europe We retrieved information about the geographical distribution of VC investments in Europe from the Thomson One database. On 26 February 2016, we downloaded the full list of VC investments carried out between 1984 and 2009 by European VC investors (39083 investments in total). Of these investments, we focused on new investments, i. e. , investments in which an investor financed a company for the first time. As we will explain below, we will use these data to measure the availability of VC. We assigned each new investment to a geographical region based on the location of the VC investor 9. We used the NUTS2 (Nomenclature of Territorial Units for Statistics) 2010 classification to identify geographical regions. 10 The European Union's territory is subdivided into 270 NUTS2 regions.