dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The content analysis resulted in 34 categories and the detailed results have been presented elsewhere  (Manchaiah et al. 2015a). Table  2  presents in alphabetical order the list of categories and the frequency of those categories (all countries put together). The most frequently occurring categories included: assessment and management, causes of hearing loss, communication difficulties, disability, hearing ability or disability, hearing instruments, negative mental state, others' attitudes, and sound and acoustics of the environment. Following the qualitative content analysis, the cluster analysis was performed to identify patterns within the dataset. The cluster analysis included 380 responses out of 404 (i. e. 94. 06%) and resulted in five clusters as shown in the dendrogram (see Figure  1 ).
We present numerical simulations results on the injection and acceleration of a 10 MeV, 10 pC electrons beam in a plasma wave generated in a gas cell by a 2J, 45 fs laser beam. This modeling is related to the ESCULAP project in which the electrons accelerated by the PHIL photo-injector is injected in a gas cell irradiated by the laser beam of the LASERIX system. Extensive modeling of the experiment was performed in order to determine optimal parameters of the laser plasma configurations. This was done with the newly developed numerical code WakeTraj. We propose a configuration that benefits of a highly compressed electron bunch and for which the injected electron beam can be efficiently coupled to the plasma wave and accelerated up to 140 MeV, with an energy spread lower than 5%.
This favorable feature has been implemented in the numerical code WakeTraj in which the calculation of the electrons trajectories is performed through a posttreatment of one WAKE-EP calculation. Practically, during a WAKE-EP calculation, tables of values of the field amplitude on a grid are periodically save on disk. A WakeTraj calculation will use these tables to extrapolate the field values at the electron positions. In the quasi-linear regime, the typical length of variation of the field amplitude during propagation is given by Z R , which is rather large (1 cm in our case). We have verified that with a propagation length of Z R /20, between two disk records, we get nearly no-difference between the WAKE-EP and the WakeTraj results. In WakeTraj, there is also the possibility to determine the electric field by applying linear theory formulas  [3] , allowing to quantify the contribution of non-linear effects, as it will be discussed in the next section.
This is the case of the blue curve in Fig. 4 , corresponding to a focal plane at 4 cm of the cell entrance. On contrary, if we start close to the focal plane, d F < dϕ, the electrons are quickly accelerated before changing their relative positions. As they are not initially situated at the maximum of the accelerating field, the final acceleration will be lower. Furthermore, the phase rotation mentioned in the previous sub-section cannot occur, yielding a large energy spread, as observed in the black curve of 4 corresponding to a 1 cm distance of the focal plane. In order to analyze the importance of the non-linear effects the same calculations were also performed using the linear expression of the electric field. In Fig. 5  are represented the percentage of electron accelerated at γ e ≥ 100, calculated by the WakeTraj code using either the WAKE-EP electric field or the linear theory one.
Conclusion A parametric study of the laser plasma acceleration process have been performed within the framework of the ESCULAP project. This has been done using the numerical code WakeTraj, which allows through very fast numerical calculations to investigate the influence of several parameters. Results concerning the laser focal plane position and the delay between the laser and the electrons bunch have presented. From these results an optimized configuration has been found which leads to an efficient coupling between the 10 pC, 10 MeV electron bunch and the plasma wave generated by the 2 J LASERIX laser. Using a 9 cm long gas cell more than 75 % of the bunch electrons can be trapped in the plasma wave and accelerated up to an average energy close to 150 MeV, with a dispersion in energy as small as 4 % a divergence of 1.
Inclusion criteria The target population was defined as follows: families with at least one child aged from 3 to 10 years old, living in the northern neighbourhoods of Saint Denis. Single-parent families were included exclusively from June 2015 to March 2016, then both single parent and couples were included until the recruitment period ended in May 2016. In addition participants had to have incomes below the poverty line, or receive social minima (Active Solidarity Income, Allocation of minimum pension), unemployment, and/or any income-terms allowance. The poverty line threshold was defined after the French National Statistical Institute (INSEE) according to the French incomes data  [54]. The first threshold was fixed at 1. 234 euros per month rounded up to 1. 300 euros for a single-parent household with at least one child aged under 14 years old.
Questionnaires and data collection We included parent-child pairs. Data were collected via face-to-face questionnaires administered by trained interviewers at inclusion, then at 6 months and one year. Volunteer families were interviewed at community centers, or at home in order to sign the consent form, and complete the questionnaires. Questionnaires were adapted from those used in the ABENA study, which was specifically designed to be administered to disadvantaged groups  [26]. A food frequency questionnaire was used to describe the consumption of children and adults in 13 main food groups (cereal products, starches, vegetables, fruits, legumes, dairy products, meats and eggs, fish and sea-food products, fast-food and pizza, salty snacks, sweet products, and beverages). This allowed assessing of the frequency of their specific daily, weekly or monthly consumption in each group.
The proportion of low FV consumers in children (primary endpoint) and adults (secondary endpoint) between the intervention and control groups one year after inclusion were compared using Fisher's exact test. FV consumer categories were established based on the food frequency questionnaire. Participants (adult or children) consuming FV less than 3. 5 times a day were considered low FV consumers. Dietary intakes between intervention and control groups were compared according to the French dietary guidelines boundaries  [10]. End-of-study consumption frequencies were then compared between control and intervention groups using Fisher tests. We first ran a per protocol analysis, based solely on the households which were followed throughout the study. Then, we performed an intention-to-treat analysis for the FV consumption, by replacing missing data on primary and secondary endpoints (i.
e. FV consumption status in children and adults respectively) using a regression multiple imputation method (proc mianalyze of SAS software)  [57] [58] [59] [60] [61] [62]. Missing data were estimated conditionally on the following variables: child's gender, adult's gender, child's age, adult's age, size of the family, income level, professional status, marital status, Epices deprivation score, parent's place of birth, food insecurity status, study group and FV consumption status at inclusion in the both parent and child. Twenty data sets were generated from which a pooled analysis (univariate logistic regression model) was then performed. Participants lost to follow-up were compared to those retained in the study after one year follow-up, according to the main characteristics at inclusion.
Qualitative analysis The qualitative analysis had two main objectives: 1) explore how FV vouchers were used by the households and question their participation in nutritional workshops, 2) understand how the program modified dietary practices and nutritional behaviours over the long-term. The qualitative survey was conducted from March to September 2017. It was performed through semi-directive face-to-face interviews, most often at home, among families from the intervention group. Participants were recruited on a voluntary basis. The number of interviews was determined using the data saturation method, based on the assumption that new collected data do not bring any new element to the understanding of the phenomenon under study. The data were collected by audio recording before being transcribed and anonymized.
Study sample From June 2015 to May 2016, 95 families were included in the study. Given we faced to recruitment difficulties, and although the recruitment period has been extended (from 6 month initially to 12 months), inclusions were stopped before 300 subjects. Three participants were excluded because of missing data on FV consumption. Finally, 92 subjects were included (Fig. 2 ). Follow-up questionnaires and workshops ended in May 2017 (i. e. one year after the last families were recruited). Children aged from 6 to 10 years were more represented than younger children, with a majority of girls. Most of them had lunch at the school canteen. The included adults were mostly women, aged from 21 to 57 years, single-parent with up to 6 children. Most of the included adults (67. 4%) were born in a foreign country, mainly located in Maghreb and Sub-Saharan Africa (N = 29.
On the other hand, Darmon and colleagues have shown that a nutrition education appears inefficient, if not supported by a financially affordable supply of healthy food  [73]. These findings are in line with our qualitative survey, showing that women with a baseline poor knowledge about FV who participated in the workshops durably modified their dietary habits. Furthermore, a financial support for the entire household is relevant (instead of children only), since parents are primary responsible for the food choices of the entire household, they also act as a model for their children  [71]. Our results were consistent with this assumption since the assessment of the adherence to the PNNS dietary guidelines showed that children's dietary profiles were overall similar to their parents' (Table  2 )  [71, 74].
The study was approved by the Ethics Review Committee of the National Institute of Health and Medical Research (Institut National de la Santé et de la Recherche Médicale) (Inserm) IRB00003888 under the number 15-247. The declaration to the National Commission of Data Processing and Liberties (Commission Nationale de l'Informatique et des Libertés) (CNIL) of February 26 2015 was made under number 1838429v0. The study protocol has been registered on clinical trials website under no. NCT02461238.
Acknowledgements The authors thank  Claudia Chahine  and  Cynthia Perlin  dietitians for their important help on this work and for conducting the workshops throughout the study. We thank all FLAM investigators:  Laëtitia Defoi ,  Anita Houeto ,  Anouchka Kponou ,  Lysa Tagherset  and  Marion Genest. We thank  Paul Flanzy ,  Younès Essedik  (computer scientists),  Julien Allègre  and  Frédéric Coffinières  (data-managers).
In addition, serious threats such as African swine fever, which is endemic in the neighboring island of Sardinia  (20) , increase the need to understand and manage wild-domestic pig interactions  (17, 21). The objective of this study is to explore the influence of different farming practices (biosafety, use of natural pastures/forests, ADV between domestic pigs and wild boars (see Study Design: A Two-Step Study on Wild Boar/Domestic Pig Interactions in Corsica). We thus combined different sampling procedures (see Serological Data) to build three datasets for statistical modeling (see Dataset Construction and Data Analysis). study Design: a Two-step study on Wild Boar/Domestic Pig interactions in corsica.
Two-Step Study: Large-scale Approach and Microregional Focus Both virus dynamics may rely on farming practices, such as biosecurity measures (e. g. , fences), vaccination against ADV, and characteristics of the production system: age of slaughtering/hunting (farmers choose the age according to the quality of production output, hunters usually shoot older wild boars, etc. ), mating management or spaying females (i. e. , farmers may avoid encounters between the sow in estrus and male wild boars either by organizing sow mating before releasing them on pastures or by spaying them). These key factors were included in our datasets to be analyzed (see below). To capture the role of farming practices on pathogen dynamics, we combined two different approaches.
Step 1: Study Based on Sampling in Slaughterhouses (Dataset 1) The purpose of this part of the study was to determine the seroprevalence of both pathogens and their geographical distribution and to characterize the potential existing risk factors due to farming practices. To do so, we sampled domestic pigs opportunistically at slaughterhouses to obtain a spatially diversified picture of disease exposure from different areas of Corsica. This sample which was purposive and mainly driven by pragmatic opportunities included 213 pigs originating from 32 farms from several pig production areas across Corsica (Figures  1A, B ): investigators collected blood samples during the slaughtering procedure and interviewed farmers when they came to slaughterhouses to bring their pigs or pick up the carcasses.
Step 2: Study Based on a Focus on a Particular Microregion (Datasets 2 and 3) The purpose was to produce knowledge on pathogen seroprevalence and distribution in domestic pig and wild boar population, targeting traditional outdoor pig farms, supposed to be at risk, from the Boziu-Verde microregion (cf. , Figures  2A, B ). Six farms were particularly selected for their irregular participation in vaccination campaigns (none of them participated to Aujeszky disease management plan implemented in 2011). Only one farm implemented techniques aiming at reducing risk of contact with wild boars (by female castration and reproduction management), and three of them were known to implement "informal" farming practices (i. e. , unofficial farmers and on-farm slaughtering). By focusing on these farms, we hypothesized that the common extensive farming practices (i.
Datasets Three datasets were considered for the statistical analyses: (1) Dataset 1: A total of 213 domestic pigs from 10 different microregions and 32 farms were sampled in slaughterhouses to assess the distribution of HEV and ADV in different parts of Corsica. Pig sera from vaccinated farms were tested for the presence of antibodies to gB and gE using the two ELISA tests to differentiate between vaccinated (gB+/ gE-) and infected (gB+/gE+) animals. Pigs with two consecutive positive results were considered as exposed to ADV (i. e. , seropositive), and those testing negative for gB or positive for gB but negative for gE were considered as noninfected (i. e. , seronegative). In farms that did not vaccinate their pigs, only gB ELISAs were performed and all pigs with gB-positive results were considered as naturally infected with ADV (i.
e. , seropositive). (2) Dataset 2: 80 domestic pigs were sampled on 6 traditional farms located in the Boziu-Verde microregion; these farms were not participating in the official vaccination plan and free ranging pigs shared natural pastures/forests with wild boars. For this sample, only gB ELISA was performed since no vaccination could interfere with surveillance. (3) Dataset 3: A total of 297 wild boars were randomly sampled at seven different locations within the Boziu-Verde microregion during five hunting seasons, from 2009 to 2016. 198 sampled wild boars came from the four areas adjacent to farms sampled in dataset 2, and the 99 others came from the three areas that had no or limited contact with pigs (Figures  2A, B ). Out of these 297 wild boars, 115 were young (i. e. , less than 12 months old).
Dependent and Independent Variables Individuals For both pigs and wild boars, serological data were categorized as positive and negative results (some rare doubtful results were removed) depending on the detection of ADV and HEV antibodies (dependent variables). The age of domestic pigs was determined by ear tags, whereas the age (young or adult) of wild boars was determined by tooth eruption patterns (29) and body size. Sex and the presence or absence of hybrid phenotype were also recorded. In boars, the presence of colors other than black or dark brown in the coat and the shape and length of the ears were considered as indicators of hybridization with domestic pigs  (30). The "hybrid" category thus extended beyond just F1 generation.
Farms For each pig farm, data were collected on its localization (see Figure  1 ), the level of biosafety of the pig farming area (three levels: free ranging, fenced pastures, and closed piggery), the vaccination treatment (three levels: no vaccination, unofficial vaccination, and official vaccination plan), spaying of females (two levels: yes and no), the reproductive management of female during estrus (two levels: yes and no). Among the six traditional farms sampled in dataset 2, we also recorded the use of natural pastures/forests (two levels: seasonal and permanent) and the type of animal [two levels: reproductive animals (breeder pigs) and pigs intended for meat production (fattening pigs)].
Starting from a "complete" model including all the potential explanatory variables for the different datasets (detailed in Table  1 ), we explored simpler models (using the dredge function of the R package MuMin). Considering the QAICc of all the potential sub-models, we used a model-averaging procedure to account for uncertainty in the model selection, because our data corresponded to a small sample size exposed to uncontrolled sampling processes (at the slaughterhouse or of voluntary farms). That means that we could not necessarily retain a "single best model" but rather, a set of potentially "best models" for which the increase in QAICc (delta-QAICc) was less than 2  (31). We finally calculated variable coefficients [i. e. , odd ratios and 95% confidence intervals (CIs)] over this set of "best models" according to the process described by Burnham and Anderson (31) (and using the AICmodavg R package).
Risk Factors Associated with HEV Seropositivity We retained four potential best models according to their QAICc values (delta-QAICc < 2) (additional materials), but significant average effects only concerned pigs' age and the intensity of natural pasture/forests use (full model-averaged coefficients detailed in Table  3b ). Adult pigs were more at risk than young ones (ORadult/ young = 9. 18, 95% CI [1. 97; 42. 73]), and farms permanently using natural pastures/forests showed a higher risk than farms only using them in autumn (ORpermanent/autumn = 8. 02, 95% CI  [1. 33; 48. 32] ). Other effects were not significant and were finally not retained (Table  3b ). seroprevalence and risk Factors of aDV and heV in Wild Boar from Boziu-Verde (Dataset 3) Out of 297 wild boars sampled, 274 analysis could be performed (23 samples could not be analyzed because of sample quality.
In free ranging farms (dataset 2), apparent seroprevalence (30%) was lower than expected when considering data set 1 (>90%); such a gap might correspond to a lower sensitivity of the serological ELISA test when using filter papers (used for dataset 1) instead of sera (used for dataset 2). The effect of domestic pig presence in hunting areas on wild boar seroprevalence suggests that inter-transmission does occur at the wild/domestic interface, possibly through environmental contamination and common attractors (e. g. , contaminated water or food sources), as suggested by previous studies  (16, 17). Nevertheless, the precise environmental risk factors (such as the role of water sources) for HEV transmission are still unclear and should be explored further in the future  (42). Recent studies have suggested that hybrid wild boar populations might be more susceptible to certain infectious diseases than pure specimens  (43, 44) , and this association was also observed in Corsica when the prevalence of HEV was compared in populations of pure and hybrids wild boars  (17).
Parameters estimates among the set of best models (model averaging) for dataset 2 (free ranging pigs from Boziu Verde). freerangingfarms and fenced pasture farms both exhibited a much higher seropositivity compared with closed farms, we finally merged both categories fenced pasture and free ranging categories since these two categories exhibited the same risk level (ORfree ranging or open air/closed = 10. 05,95%CI[2. 61;38. 76]). Other effects were not significant and were finally not retained (Table2b).
3607 1. 4478 1. 4569 0. 934 0. 350327 Zone 2 (vs Zone 1) -0. 0454 0. 6389 0. 6505 0. 070 0. 944364 Zone 3 (vs Zone 1) 5. 9474 1,071. 7341 1,091. 4423 0. 005 0. 995652 Zone 4 (vs Zone 1) 0. 2780 0. 9535 0. 9631 0. 289 0. 772872 Pasture use (seasonal vs permanent) 0. 3833 1. 0516 1. 0627 0. 361 0. 718347 (b) regarding heV age (young vs adult) -2. 2175 0. 7844 0. 7956 2. 787 0. 00532* Pasture use (seasonal vs permanent) -2. 0819 0. 9163 0. 9314 2. 235 0. 02541* Sows management (yes) 0. 1078 0. 3747 0. 3784 0. 285 0. 77582 Sex (male vs female) -0. 3589 0. 6538 0. 6579 0. 546 0. 58538 Reproductive status (yes) -1. 5416 1. 0227 1. 0317 1. 494 0. 13511 seroprevalence and risk Factors in Domestic Pigs from six Traditional Farms in Boziu-Verde (Dataset 2) The analyses were performed on 75 pigs with no missing data for any of the explanatory variables and serological results.
Average seroprevalence in this dataset was 48. 0% (95% CI [36. 7%; 59. 3%]) for ADV and 30. 7% (95% CI [20. 3%; 41. 1%]) for HEV. *Significant p-value. Significant parameters for each data set are indicated in bold. 7 Charrier et al. Domestic/Wild Pig Disease Interface Frontiers in Veterinary Science | www. frontiersin. org January 2018 | Volume 5 | Article 1.
Acknowledgements. We thank Josep González for his help with Lemma 3. 18, and we are grateful to the Banff International Research Station for hosting a May 2017 workshop on Arithmetic Aspects of Explicit Moduli Problems where we worked on this article. Fité is grateful to the University of California at San Diego for hosting his visit in spring 2012, the period in which this project was conceived. Notation. Throughout this paper, k denotes a number field contained in a fixed algebraic closure Q of Q. All the field extensions of k we consider are algebraic and assumed to lie in Q. We denote by G k the absolute Galois group Gal(Q/k). For an algebraic variety X defined over k and a field extension L/k, write X L for the algebraic variety defined over L obtained from X by base change from k to L.
6) H = H 0 ∪ H 0 • (h, τ ) ⊆ G C 0 , where τ is the generator of Gal(M/Q). 3. 2. Moment sequences. We continue with the notation of §3. 1. If C is a k-twist of C 0 , we define the joint and independent coefficient moment sequences M joint (C) := {M n1,n2,n3 [a(C)]} n1,n2,n3 , M indep (C) := {M n [a i (C)]} j,n , where a(C) and a j (C) denote a(Jac(C)) and a j (Jac(C)), respectively, as defined in §1; recall that these moment sequences are defined by and uniquely determine corresponding measures µ I and µ Ij , respectively. Using Lemma 3. 5 and (3. 5), we can apply Corollary 2. 4 to compute the moments M n [a j (C)] for any n, and as explained in §3. 2. 2, it is easy to compute M n1,n2,n3 [a(C)] for any particular values of n 1 , n 2 , n 3. Magma scripts  [Magma]  to perform these computations are available at  [FLS17] , which we note depend only on the pairs (H, H 0 ) (or just H 0 when k = kM ) listed in Tables  4  and 5 , and are otherwise independent of the choice of C.