dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022). Less has been documented, however, about the state of food and nutrition security and what it means for people in similarly stressed regions in Madagascar, including the south-eastern Atsimo-Atsinanana region (but see  Randrianarison et al. , 2020; Moore et al. , 2022). This study intends to inform context-adapted policy and interventions by advancing the understanding of the food and nutrition security situation in the Atsimo-Atsinanana region. To assess this situation, we present survey data from Farafangana District on the food security and diets of 492 rural households. In addition, we explore differences between (i) three seasons within one year, (ii) six household types with different levels of vulnerability, and (iii) different food crops and how they are sourced (own production vs.
95% of the region's population are considered to live in poverty  (INSTAT, 2020) , and of the children aged 0-5 years, 49% are stunted and 12% are wasted  (INSTAT, 2021). Levels of formal education are below the national average, with an adult literacy rate under 50%  (INSTAT, 2020). The data reported in this article were collected by the French NGO 'Inter Aide' , which implemented a development project in rural areas of Farafangana District. This project, called ProSAR (Projet de renforcement de la Sécurité Alimentaire et nutritionnelle et de la Résilience 1 ) is funded by the German Ministry of development cooperation and managed by the German development agency Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ). In Farafangana District, ProSAR aims at improving the food and nutrition security of approximately 5,100 women and their families, for example, by providing trainings on nutrition-sensitive agriculture and by distributing seeds.
This article presents data that were collected both before and after the onset of development interventions, which began in October 2021. We do not aim to provide an impact evaluation, however. Our analysis aims at documenting the varying levels of food security at different time points across a year and how they affect household diets. Potential bias may arise from the fact that, at the second and third wave of data collection, diets and food security levels could already have been influenced by project 1 French for "Project for enhanced food and nutrition security and resilience". In the article, we disclose and critically discuss these potential effects.
Household surveys We collected data from rural households at three moments along the course of one year: the minor lean period, major lean period, and post-harvest period (see Table  1 ). Households were randomly sampled from all 102 target villages of the ProSAR project, located in 14 fokontany of 8 rural communes of Farafangana District in the Atsimo-Atsinanana region of Madagascar. These 8 communes and 14 fokontany had been purposively selected by GIZ and Inter Aide for the ProSAR project on the basis of comparatively weak humanitarian presence in these locations. In each village, individual households were randomly sampled using the following procedure. First, the enumerator gets a general overview of the location of all households belonging to the village. Second, a number between one and ten is randomly drawn.
Third, as the enumerator walks down the village main road and counts houses, the household corresponding to the randomly drawn number is interviewed. Fourth, if the end of the village is reached, the enumerator turns around and keeps counting. Fifth, in case a village is spread across multiple hamlets, the procedure is repeated in every hamlet. Frontiers in Sustainable Food Systems 04 frontiersin. org Our goal was to survey 10% of all households in the 102 villages, with their overall number estimated to be around 3,900. Enumerators occasionally exceeded the 10% target. To avoid disproportionate coverage of certain villages or fokontany, which could affect the regional representativeness of our dataset, we reduced the number of households from villages that exceeded 30% coverage. For these specific villages, we excluded households at random, bringing coverage down to 10%.
Eventually, 492 households were included in the dataset, which represents about 13% of all households in the target villages. The full sample of 492 households was interviewed only in November 2021. To minimize time burden for survey respondents, and to use limited research resources effectively, we split this sample into two sub-samples. By randomly assigning half of the respondents in each of the 102 villages to one group, we created two equally sized sub-samples of 246 households. The first sub-sample was surveyed in April 2022 (where 232 interviews were carried out), and the other in June 2022 (233 interviews, see Figure  2 ). All interviews were conducted using a standard questionnaire implemented in the ODK Collect Android app  (Hartung et al. , 2010)  in local malagasy dialect by female enumerators.
(2018)  and translated into local malagasy dialect. We chose a 30-day recall period, rather than 12 months, to be able to assess fluctuations in food security levels between seasons (see  Fraval et al. , 2019a). We considered households as food secure if the respondent did not answer affirmatively to any question (FIES score = 0). A FIES score of 1-3 corresponded to mild food insecurity, a FIES score of 4-6 was considered moderate food insecurity, and a FIES score of 7-8 indicated severe food insecurity (see  Smith et al. , 2017; Wambogo et al. , 2018). Respondents also provided data on family diets. Meals in rural Madagascar are typically composed of one staple ingredient and one or multiple laoka items (non-staple side dishes). To analyze laoka diversity, we enumerated whether, within the seven days preceding the survey, household meals included any of the following food items: leafy vegetables, other vegetables, pulses, oilseeds, fish or poultry or meat, and eggs.
Household typology To analyze existing heterogeneity among the sample population, we intended to characterize the main types of local livelihoods. To this end, we stratified the 492 households by a typology of household vulnerability. Following deliberation with local experts, we constructed this typology on the basis of three indicators: (i) whether the household owns any cattle, (ii) whether the household owns rice fields, and (iii) how frequently the household generally engages in off-farm wage labor. In our study region, wage labor (on other people's farms) is typically associated with poverty and lack of sufficient own productive resources. The following six household types were defined, and each household was assigned one type, based on data collected in November. We collected these data once, during the minor lean period (November) and assumed that households remained in their respective type for the two following surveys.
Child anthropometry To estimate the prevalence of chronic and acute child malnutrition in the study population, we also collected child anthropometric data once. This data collection was added to the survey enumeration in April, at a time of the year that is considered the harshest in terms of food and nutrition security. We thus expected to observe the annual peak of acute malnutrition. We collected data from all 304 children within the age range of 6-59 months that were present in the 232 sample households that could be reached. To increase regional representativity, however, we randomly sampled another 48 households, adding data on 61 children. This was done to meet our goal of including 7% of the estimated total number of children in this age range in the 14 study fokontany, a coverage suggested by the Emergency Nutrition Assessment (ENA) software, version 2020  (Erhardt et al.
, 2020). The 48 additional households were randomly sampled in four fokontany (out of 14 overall). In these fokontany, the number of children that were part of the households surveyed in November was below the 7% threshold, which is why we sampled additional households there. Overall, we collected data on 365 children. For each child, the following data were collected: age, gender, height, weight, mid-upper Sampling strategy: the full sample (survey in November 2021) was split into two sub-samples for follow-up surveys (in April and June 2022). In April, moreover, anthropometric data were collected from the sub-sample households as well as 48 additional households. Indicators in bold face were collected in all three surveys. HHs, households. The scores were compared to a standardized reference population  (WHO, 2006).
We assume that there was more rounding up (by incompletely tightening the MUAC measuring tape on the child's arm) than rounding down. Because this may also have occurred in cases close to the 125 mm threshold, our results might underestimate the prevalence of wasting based on the MUAC/BLEE indicator. We tested whether the prevalence of any form of child malnutrition was associated with household food insecurity by employing three logistic regressions, using data from the April survey. These models' dependent variables were dichotomous variables of wasting (based on WHZ), stunting, and underweight. The independent variable was the FIES-based food security level of the child's household in April, an ordinal variable. Data analysis was performed with the ENA software following the methodology described by  WHO (2006).
State of child malnutrition In April, in addition to household food security and diet data, we collected anthropometric data from children aged 6-59 months. Our results highlight elevated levels of chronic and acute malnutrition. We found high levels of stunting (57%) and underweight (45%). Acute malnutrition, indicated by the rate of wasted children, stood at 16-17% (Table  4 ). On average, children in households facing more severe food insecurity were also more likely to be malnourished (Table  5 ). The logistic regressions confirmed associations between FIES and all three malnutrition indicators, at the significance levels of 0. 1% (underweight), 1% (wasting), and 10% (stunting; model coefficients provided in the Supplementary material). Two-proportion z-tests on genderdisaggregated data showed that boys were more likely to be stunted than girls, at the 10% significance level.
Acknowledgments The authors thank the  Inter Aide teams at Farafangana (Madagascar) and Versailles (France) , notably  Damien   du Portal  and  Francois Bourgois , for consistent support and constructive feedback. We also thank all survey enumerators and interview respondents who participated in this study, as well as  Marc Giordan  for editorial support. We thank the editor and two reviewers for their comments on an earlier draft. All remaining errors are the authors'.
2023 ). This implies that the AGN torus is misaligned with the galaxy rotation axis, which could be due to a past interaction with the nearby giant lenticular galaxy IC 4329, although we note that such a misalignment is apparently fairly common (e. g. Schmitt et al. 1997 ; Nagar & Wilson 1999 ; Middleton et al. 2016 ). Here, we present the results of an observing campaign on IC 4329A utilizing IXPE as well as the X-ray Multi-Mirror Mission ( XMM-Newton ) and the Nuclear Spectroscopic Telescope ARray ( NuSTAR ). In Section 2 , we detail our data reduction procedure. In Section 3 , we present our polarimetric analysis as well as spectral and spectropolarimetric fits. In Section 4 , we discuss our results in the context of Comptonization models of polarization and multiwavelength observations of the system, and we summarize our results in Section 5.
O B S E RVAT I O N S A N D DATA R E D U C T I O N IXPE observed IC 4329A on 2023 January 5-15 (OBSID 01003601) for a useful exposure time of 458 ks. During this observation, XMM-Ne wton also observ ed the source on January 11-12 (OBSID 0890670201) for an exposure time of 62 ks, and NuSTAR observed it on January 11-13 (OBSID 60701015002) for an exposure of 82 ks. The light curves of all three of these observations are presented in Fig. 2. The following subsections detail the data reduction procedure we employed for the three observatories. Throughout, we make use of FTOOLS from the HEASOFT package (v6. 31. 1).
XMM-Newton XMM-Ne wton observ ed the source with the EPIC CCD cameras: the pn  (Str üder et al. 2001  ) and the two MOS  (Turner et al. 2001 ) , operated in small window and medium filter mode. Data from the MOS detectors are not included in our analysis due to pile-up. The data from the pn camera show no significant pile-up as indicated by the epatplot output. The extraction radii and the optimal time cuts for flaring particle background were computed with SAS 20  (Gabriel et al. 2004  ), via an iterative process which leads to the maximization of the signal-to-noise ratio (SNR) in the 0. 5-10 keV energy band, similar to the approach described in  Piconcelli et al. ( 2004 ). The resulting optimal extraction radii for the source and the background spectra are 40 and 50 arcsec, respectively.
NuSTAR NuSTAR  (Harrison et al. 2013  ) observed the source with its two coaligned X-ray telescopes with corresponding Focal Plane Module A (FPMA) and B (FPMB). The Level 1 data products were processed with the NuSTAR Data Analysis Software (NuSTARDAS) package (v. 2. 1. 2). Cleaned event files (level 2 data products) were Block colours result from a weighted spectropolarimetric analysis within XSPEC that additionally makes use of the new background rejection method. We see that the weighted analysis provides consistent but more tightly constrained results. produced and calibrated using standard filtering criteria with the nupipeline task and the calibration files available in the NuSTAR calibration data base (CALDB 20220510). Extraction radii for the source and background spectra were 40 and 60 arcsec, FPMA spectra were binned in order not to o v ersample the instrumental resolution more than a factor of 2.
5 and to have an SNR greater than 5 in each spectral channel, the same energy binning was then applied to the FPMB spectra. The net observing times for the FPMA and the FPMB data sets are 82. 4 and 81. 5 ks, respectively. FPMA and FPMB spectra are considered separately for the purposes of spectral fitting, but are combined in plots for clarity.
X-ray polarization We first measure the 2-8 keV polarization using the modelindependent pcube algorithm on unweighted data. We find p = 3. 4 ± 1. 6 per cent and ψ = 64 • ± 13 • (1 σ uncertainties) for the PD and PA, and we plot statistical confidence contours in Fig. 3  (blue lines). Currently, pcube does not feature the functionality to impro v e signal to noise by including event weights. Therefore to make use of a weighted analysis, we employ the alternative method of fitting weighted I , Q , and U spectra (produced utilizing the background rejection method) with a simple spectropolarimetric model (equation 1 ) within XSPEC. The best-fitting reduced χ 2  is χ 2 /d. o. f. = 659/614. This is an acceptable fit, although we note that a more complex model is required when we also include XMM-Newton and NuSTAR data (see the following two sections).
X-ray spectrum We jointly fit the IXPE , NuSTAR , and XMM-Newton Stokes I spectra. We consider the usual energy ranges of 2-8 keV and 3-79 keV for IXPE and NuSTAR , respectiv ely. F or XMM-Ne wton , we consider the energy range of 2-10 keV, leaving the more involved treatment of ionized absorbers necessary to reproduce the soft Xrays  (Mehdipour & Costantini 2018 )  to future w ork. We emplo y the following model, referred to hereafter as Model 1: For this we leave, the column density as a free parameter. Finally, mbpo  (Ingram et al. 2017  ) is required to circumvent cross-calibration discrepancies between the three observatories. It multiplies the total model by a broken power law with index 1 for E < E br and 2 for E > E br. A single corrective power law (e. g. 1 = 2 ) has previously been required to jointly model XMM-Newton and NuSTAR data  (Ingram et al.
The other free parameters are inclination angle i , the logarithm of the ionization parameter log ξ (where ξ is in units of erg cm s -1 , and we take log to mean log to the base 10 throughout), the iron abundance relative to solar A Fe , and the reflection fraction f refl. We set the reflection fraction to a ne gativ e value within the model, meaning that the code outputs the reflected component only (conversely, a positive value returns the sum of direct and reflected components). Treating direct and reflected components separately in XSPEC will later allow us to assign different polarization properties to each of them. In the tables, we convert the reflection fraction back to a positive value. Low inclination and a small disc inner radius are preferred, but the contour plot in Fig. 5  shows that a wide range of values are acceptable within 3 σ confidence.
Spectropolarimetric fit We now include IXPE Stokes Q and U into our spectral fit. We first extend Model 1 by assigning a constant (i. e. independent of energy) PD and PA to each component with the polconst convolution model. This is a very crude approximation for the reflection component, which includes an essentially unpolarized iron line and a potentially highly polarized reflection continuum. Because the iron line is the most prominent contributor to reflection in the IXPE band, we first freeze the PD of both reflection components to zero and leave the nthcomp PD and PA as free parameters. This ef fecti vely equates to assuming that the direct coronal emission dominates the 2-8 keV polarization, which is justifiable as this component contributes the most flux in the band (66 per cent). Fitting this model to the spectral-polarimetric data gives χ 2 /d.
Because there is some uncertainty in our spectral fits, we trial three values of the Figure  9. PD of various coronal as a function of the cosine of the inclination angle. For all geometries considered, the PA is perpendicular to the disc plane. The hatched areas represent the observed PD and inclination at 90 per cent (cyan) and 3 σ (grey) confidence. Left: A slab corona extending from 6 r g to 100 r g , calculated using iterative radiative transfer solver COMPPS for three values of electron temperature. Static (circles) and out-flowing (triangles) configurations are considered. Right: Slab and wedge coronal geometries, calculated using the Monte Carlo code MONK , assuming a static corona with an electron temperature of kT e = 40 keV. The slab extends to the ISCO, and two values of black hole spin are considered: a = 0 (black circles, same case as solid black line in the left-hand panel) and a = 0.
The right-hand panel of Fig. 9  presents results from the relativistic Monte Carlo radiative transfer code MONK  (Zhang, Dov čiak & Bursa 2019 ) , assuming a static corona with temperature kT e = 40 keV. The black line with circular markers corresponds to the same geometry as before: a slab extending from 6 r g to 100 r g ( a = 0). We see very good agreement with the COMPPS calculation (black solid line in the left-hand plot) at the low inclination angles of interest, with some discrepancies at high inclinations that will be explored in future work. We also plot results for a slab extending to the ISCO of a maximally spinning black hole (red squares). We see that relati vistic ef fects significantly af fect the polarization for i 60 • , but not at the lower angles rele v ant to IC 4329A. We finally consider a uniform density wedge corona with opening angle 45 • extending from the ISCO of a maximally spinning black hole to 25 r g  (Tagliacozzo et al.
c 2019. 1. 00580. S (PI: Y. Inoue) with a total integration time of 254 s. We used public pipeline-processed data products from the ALMA archive, reduced using CASA version 6. 2. 1. 7. Three spectral windows were combined, and the imaging was done using the CASA task TCLEAN , adopting a Briggs weighting scheme with a robustness parameter of 0. 5. The 12-m array configuration used for the observations allow a maximum reco v erable scale of 1. 2 arcsec, so the images are not sensitive to any emitting structures larger than this. Downloaded from https://academic. oup. com/mnras/article/525/4/5437/7258650 by guest on 06 October 2023.
KAP survey and collection of socio-demographic data The survey questionnaire comprised 147 items which collected socio-demographic data and KAP data. More specifically, eight items concerned socio-demographic information and six concerned sources of information on malaria. With respect to the KAP items nested in the larger questionnaire, six items concerned knowledge about the disease, while a total of 49 concerned associated attitudes and related practices. The questionnaire's other 78 items focused on clinical, ethno-botanical and biological data (not shown). The present study only describes the KAP analysis; sociodemographic, clinical, and biological dimensions are not described.
Construction of variables To construct the variables about patient knowledge, we explored, i) data using statistical methods as flat and spherical principal component analyses (PCA), focused PCA, correlation matrices, and factorial analyses (Appendices 1 and 2) and, ii) the literature on this topic  [11] [12] [13] [14] [15]. For the six explored themes, knowledge about the disease's symptoms, transmission, prevention, and treatment were evaluated based on multiple-choice questions. Each response proposal was recoded into as many binary outputs (Yes/No). A score has been assigned to each of them when they were considered correct or not (See Table  1 ). According to them, "good", "insufficient" or "bad" knowledge categories have been constructed using the different variables listed in Table  1.
Ethical approval and consent to participate The studies on the patients' biological samples were performed in accordance with the relevant guidelines and regulations. The study was approved by the French ethics committee of protection of persons of south-west and overseas 4 N° AM-36/1/CPP15-024. The database was anonymized and declared to the French Regulatory Commission (Commission Nationale Informatique et Libertés, CNIL, n°917,186). Signed informed consent was obtained from all participants involved in the study, following explanations about the study in their native language. For minor patients, informed consent was obtained from a parent and/or legal guardian. Patients who did not received formal education were illiterate. However, illiteracy did not deprive them of the ability to give or withhold informed consent.
Acknowledgements The authors are grateful to the participants of the study in Saint Georges, French Guiana. The NGO DAAC, provided culturally-adapted mediators, offering crucial support throughout this study. Bastien Bidaud ,  Mylene Cebe ,  Franck de Laval ,  Alessia Melzani ,  Olivier Moriceau ,  Gaelle Walter  also contributed to data collection. The authors thank  Frédéric Bouteille  and  Isabelle Leglise  for their excellent advice.
The inert radioactive gas radon-222, ubiquitous in the environment, is the first cause of lung cancer in nonsmokers and a powerful asset to trace geological fluids and constrain atmospheric transport models. In all of these applications, radon flux (RF) from the ground needs to be estimated. However, obtaining a large-scale representative estimate of RF in continental land is a challenging task and a starting basis is to focus on large countries in the world. Here, we collected a total of 2622 direct RF measurements carried out in continental China from 1988 to 2021 using 69 publications in a 494-document pool. Over the whole dataset, the RF results were classified into geographical provinces, time periods and substratum types. Mean RF values yield 180±32, 36. 9±4. 8, 31. 1±1. 0, 66. 1±1. 6 and 1510±100 mBq m -2 s -1 in metamorphic and igneous (n=71), sedimentary (n=24), Quaternary (n=585), faulted areas (n=832) and uranium-related sites (n=898), respectively.