dataesr/scientific-paragraphs-categorization huggingface dataset:
Exposure: food innovations We used patents as a measure of innovations available in each country. We attributed patents to countries by using the designated state(s) in which an (domestic or foreign) inventor asks for protection. We drew our patent data from the Worldwide Patent Statistical Database (Patstat), maintained and diffused by the European Patent Office-EPO (online supplemental appendix pp 1-2 and p 10). This database covers almost all worldwide patents since the 1950s. We first kept only patents with International Patent Classification (IPC) codes related to food processing and food chemistry techniques. This includes IPC codes that directly relate to food products, BMJ Global Health but also those that refer to the extraction of fat or sugar molecules, ingredients or products (online supplemental appendix p20).
We did not select innovations in agriculture, animal husbandry or fishing, as they are not necessarily related to human food consumption. We then restricted the sample to patents for which at least one of the industrial domains of application is the manufacturing of food products or beverages, as defined in the Patstat database. We further counted patents at the level of the 'patent family'. A family is a set of patents that are related to each other by one or several common priority dates filed but may be filed at various national or international offices. Since we were only interested in significant innovations, we kept patent families with applications filed in at least two of the following key patent offices: EPO and its main member states (France, Germany, Italy, Netherlands, Spain, Switzerland and UK), US Patent Office, Australia, Brazil, Canada, China, Japan, South Korea, Mexico, South Africa and Soviet Union/Russia.
Countries can obtain innovations through two main channels: directly, from domestic and foreign inventors asking for protection in their territories and indirectly through food imports. We measured exposure to patents filed in foreign countries through imports of processed foods, as these are likely to embed food innovations. The stock of imported innovations in a country was computed as the weighted sum of patent applications filed in the country's trading partners. The weights were equal to the partner share in the country's total imports of processed foods (online supplemental appendix p 7). Import data were extracted from the United Nations Comtrade database (online supplemental appendix p 4).
Socioeconomic indicators and covariates We conditioned our analysis on a large set of demandside control variables, including country gross domestic product (GDP) per capita, women labour force participation and urbanisation rate. These control variables were obtained from the World Bank World Development Indicators database (online supplemental appendix pp 4-5). We also considered indicators of social and economic globalisation. We used the subindices of the KOF Index of globalisation developed by Dreher  24  , and updated by Gygli et al. 24 25  The economic globalisation index captures changes in economic flows (trade, foreign direct investments, portfolio investments, income payments to foreign nationals) and trade openness (hidden import barriers, mean tariff rate, taxes on international trade, capital account trade openness).
Social globalisation is constructed from data on personal contacts (telephone traffic, transfers, international tourism, share of foreign population and international letters), information flows (internet and telephone users, trade in newspapers), and cultural proximity (number of McDonald's restaurants, Ikea stores, trade in books) (online supplemental appendix p 5). Finally, relying on FAO food balance sheets, we included the share of food imports in total calorie supply, as this variable is likely to have a direct impact of nutritional outcomes, beyond its indirect impact captured through the food innovation measure.
Future work should further enrich food product databases with accurate information on the technologies used in their production, in order to BMJ Global Health document more precisely the causal chain linking innovations, food products and health risks. Furthermore, in line with the initiative 'One world, one health', one could also go one step further and investigate the impact of food innovations on health outcomes (eg, obesity prevalence, cardiovascular diseases and diabetes) Finally, our study investigates the international diffusion of food innovations. A natural extension-relying on additional data-would consist in the exploration of the heterogeneity in the diffusion of innovations within countries. Such approach will help understanding nutritional inequalities within countries and identifying socioeconomic groups most exposed to the effects of food innovations.
Funding This work was supported by  Institut National de la Recherche pour l'Agriculture l'Alimentation et l'Environnement  ( INRAE  grant  DAPP 2020 , project  PITA , and  Metaprogramme DIDIT , project  INNOV 2018-2020 -A-CD  and FE),  AXA Research  ( AXA Research  Award  2015 -FE ), and  Agence Nationale de la Recherche  ( ANR-17-EURE-0001 -A-CD  and  FE ;  ANR-17-EURE-0020 -LR ).
Data availability statement The FAO on food supply and country-level characteristics are publicly available and downloadable from https://www. fao. org/faostat/en/ The World Bank data on country-level characteristics are publicly available and can be downloaded from https://databank. worldbank. org/ The KOF data on globalisation are publicly available data and can be downloaded from https://kof. ethz. ch/en/forecasts-and-indicators/indicators/kof-globalisationindex. html The PATSTAT data are commercial data that can be purchased on the website of the European Patent Office (https://www. epo. org/searching-for-patents/ business/patstat_fr. html). Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed.
Acknowledgements IFMAS ( Institut Français des Matériaux Agro-Sourcés ) and  ANR  are gratefully acknowledged for their financial support. This project was supported by the  French government  as part of the " Programme d'Investissement d'Avenir. " The  project  ARCHI-CM, Chevreul Institute  ( FR 2638 ),  Ministère de l'Enseignement Supérieur et de la Recherche ,  Région Hauts-de-France , and  European Regional Development Fund (FEDER)  are acknowledged for supporting and funding this work.
Parameters derived from chemical analyses included total concentrations of various (trace) elements, stable water isotopes, Se and S speciation obtained via improved (ultra)sensitive methods based on liquid chromatography and inductively coupled plasma tandem mass spectrometry (LC-ICP-MS/MS), as well as organic molecular composition of aerosols based on pyrolysis-gas chromatography mass spectrometry (Py-GC-MS). These analyses are combined with modelling of Se source contributions using the atmospheric aerosolchemistry-climate model SOCOL-AERv2 and air parcel trajectory calculations based on the Lagrangian analysis tool LAGRANTO  (Sprenger and Wernli, 2015; Wernli and Davies, 1997)  with three-dimensional wind fields from the atmospheric reanalysis dataset ERA5 to estimate dominant moisture sources and atmospheric transport patterns.
Determination of organic compounds in aerosol samples The analysis of organic compounds in aerosols was performed with a FrontierLab pyrolyzer equipped with a FrontierLab AS-1020E autosampler and connected to a Thermoscientific Trace 1310 GC coupled to a Thermoscientific ISQ 7000 MS. The operating conditions and subsequent data processing method were done according to  Tolu et al. (2015)  (see detailed description in Supplement S4).
Particle number, black carbon and observational meteorological data A wide range of meteorological and other parameters characterizing the physical properties and chemical composition of the atmosphere at Pic du Midi Observatory are routinely monitored and are available through an internet database (https://p2oa. aeris-data. fr). For this study, we used equivalent black carbon (BC) mass concentrations that were analysed by an Aethalometer (Magee Scientific Co. AE 33) with a time resolution of 15 min as described in  Hulin et al. (2019). The particle number (PN) was analysed by a condensation particle counter (TSI, Inc. , 3010, 3750) with a time resolution of 5 min. Considered meteorological data included air temperature (Vaisala QMH 101), relative humidity and atmospheric pressure (both measured with Vaisala MAWS 301), which were all extracted with a time resolution of 60 min.
Air parcel back trajectories and moisture source analysis To estimate dominant moisture sources of precipitation and atmospheric transport patterns, trajectories were calculated with the Lagrangian analysis tool LAGRANTO  (Sprenger and Wernli, 2015; Wernli and Davies, 1997)  with three-dimensional wind fields from the atmospheric reanalysis dataset ERA5  (Hersbach et al. , 2020)   from a set of five starting points including the location of the Pic du Midi Observatory (42°56'11. 4"N 0°08'31. 1"E) as well as four other points shifted by 0. 5° in the horizontal to take into account uncertainties in the origin of air at the synoptic scale due to turbulent mixing in the boundary layer and the impact of orography. A set of variables is interpolated along the trajectories, namely, specific humidity, relative humidity, as well as the boundary layer height.
Chlorophyll-a exposure in marine moisture source regions To investigate a potential link between marine productivity and Se (speciation) in rainfall, chlorophyll-a (Chl-a) air exposure in marine moisture source regions was diagnosed with the same approach as in  Suess et al. (2019). Sea surface water Chl-a concentrations from the Ocean Colour Climate Change Initiative dataset, version 3. 1 from the European Space Agency (available online at http://www. esa-oceancolour-cci. org/) were used to estimate the air parcels' exposure to Chl-a in marine moisture source regions. The chosen CHL-OC5 Chl-a product (0. 01°x 0. 015° gridded data) is based on daily remote-sensing reflectance data of MODIS/Aqua and MERIS/ENVISAT. 16,17. To minimize the impact of regions with missing data due to cloud cover in all seasons, two-weekly composites were computed to estimate the time mean moisture source Chl-a exposure.
Data sources for thunderstorms Lightning data was used to detect thunderstorm activity in the vicinity of Pic du Midi Observatory. Two-hourly lightning maps and data from the nearest radio antenna stations were extracted from the Meteo60 website (https://www. meteo60. fr/orages-archives/, accessed 24 th November 2022), which take their data from the Blitzortung network (list of stations: https://www. blitzortung. org/en/station_list, accessed 24 th November, 2022). Atmospheric deposition samples were classified as thunderstorm events, if lightning activity was reported in the vicinity of Pic du Midi Observatory (within 3 km) during the time that a sample was collected. The vicinity was defined by comparing detected lightning with on-site observations during the 2019 campaign.
Statistical analysis All statistical analyses were performed using IBM SPSS Statistics 26. Principal component analysis (PCA) was performed on the precipitation data set given in mass per volume (g•L -1 ) or per area (g•m -2 ). Prior to the PCA, the data was converted to Z-scores (average=0, variance=1). Principal components (PCs) with eigenvalues > 1 were selected and extracted using a Varimax rotated solution. A significance threshold value of > 0. 39 was chosen. Other variables, e. g. , precipitation volume, 340 black carbon and total column ice cloud water contents and moisture sources, were included passively in the PC-loading plots by using bivariate correlation coefficients between these variables and the PC scores of each PC. S4 ). We clearly see higher Se concentrations in summer (June-August; 0.
01) and total column ice water contents (TCIW given in mm, integrated over the troposphere from the ERA 5 reanalysis dataset; Supplement S8. 2, Fig. S12 ), which both positively correlate with PC1 groupings (p<0. 01; Fig. 3b ). Both mineral dust and BC aerosols are known to play an important role in heterogeneous nucleation by acting as effective ice nucleating particles  (Kanji et al. , 2017). Apart from the presence of dust, BC, and TCIW, precipitation events with high elemental deposition in this period were associated to thunderstorms (thunderstorms identified both from visual observations during sampling and from lightning data of the Blitzortung's network; Mann-Whitney-U test, p<0. 01; Supplement S8. 2, Fig. S13 ). PC scores of sub-events classified by their 460 precipitation type, including events with rainwater/hail, light rain/cloud water, snow/sleet or thunderstorms, show groupings on PC1 and PC2.
, 2016). In a next step, we investigated the importance of thunderstorms for elemental concentration trends in aerosols in the 2015-2020 time series. Also for this dataset, elemental concentrations in aerosols were significantly higher during weeks in which thunderstorms occurred than for samples that were not related to thunderstorms (selection of major and trace elements in Fig. 4; Mann-Whitney-U test, p<0. 01; data for all analysed elements in Supplement S8. 3). For Se, these highest concentrations were generally underestimated in the SOCOL-AERv2 model (Supplement S8. 3, Fig. S16 ). Significantly higher elemental concentrations associated with thunderstorms were not only observed when considering the full aerosol time series, but also when only considering measurements in summer (Mann-Whitney-U test, p<0.
3, Fig. S17 ). Higher humidity likely favours stronger convection and finally wash-out of aerosols via precipitation. Apart from the finding that deep convective activity during thunderstorms lead to significantly higher elemental deposition, thereby impacting supply of micronutrients and potentially toxic elements to the surface, our results imply that high elemental concentrations do not necessarily indicate a source signal in deposition data. More specifically, our data suggests that while element concentrations in the atmosphere may be primarily determined by the element fluxes at the source, local cloud dynamics of different weather events control the amounts of Se and other (trace) elements in atmospheric deposition fluxes. This implies that variability in elemental concentrations in wet deposition does not reflect changes in strengths of emission sources alone but also weather conditions during atmospheric removal.
Average concentrations of Se VI , Se IV and OrgSe accounted for 43. 7±27. 6 pg•m -3 , 5. 1±4. 0 pg•m -3 and 2. 5±1. 7 pg•m -3 in aerosol water extracts; 2. 7±2. 3 pg•m -3 , 0. 7±0. 8 pg•m -3 , and 0. 3±0. 2 pg•m -3 in cloud water; and 43. 6±55. 3 ng•m -2 , 10. 2±9. 9 ng•m -2 and 1. 9±2. 1 ng•m -2 in precipitation, respectively. We investigated proportions of Se species rather than absolute concentrations for further data analysis to allow comparison between different atmospheric samples, since the use of species proportions removes influences by e. g. , variability of aerosol loading or precipitation volume (dilution effect) among specific seasons and events. The main Se species in all analysed atmospheric samples is Se VI , followed by Se IV and OrgSe (Fig. 5 ). Se VI accounts for 53. 4±12.
Inorganic Se speciation is not only influenced by emission source but also by pH To get insights into processes and/or sources affecting inorganic Se in atmospheric deposition, we investigated the species variability in different atmospheric samples and their potential links to different source indicators. Fig. 5  shows the proportions of Se species (% of total Se concentrations) in the water-extracts of the 2015-2020 aerosol time series as well as in precipitation and cloud water from the campaign in 2019. No clear seasonal differences were observed for proportions of Se VI , but Se IV had significantly lower proportions in aerosol samples collected in summer (7±7%) than in autumn (11±9%). To test for potential sources of inorganic Se, we studied correlations between inorganic speciation, moisture sources and Py-GC-MS data in aerosols.
411, p<0. 05). Notably, these relatively high Se IV proportions changed significantly during sub-events (see evolution of Se IV proportions during P17 in Fig. 6b  and relative changes in moisture sources during sub-events in Fig. 6c ). No HMS was detected in sub-events that showed relatively high proportions of Se IV , indicating a different origin of Se IV in wet deposition than for the 2015-2020 aerosol dataset. Although we can link inorganic Se speciation to specific marine/coastal sources, we further found an effect of pH in wet deposition (pH in aerosols was not determined). Oxidation-reduction reactions are strongly influenced by the redox potential (pe) and pH in natural waters. From the pe-pH stability field diagram  (Séby et al. , 2001) , it is clear that the speciation of Se is likely quite sensitive to pH under atmospheric conditions.
Conclusions and environmental implications Our approach of combining multiple measurement and modelling techniques to study the atmospheric trace element cycling offers new insights into the complex processes influencing the atmospheric deposition of trace elements in general, and Se in particular. Past research in this field has often been based on the assumption that significant positive correlations between elemental concentrations in atmospheric deposition solely reflect specific source contributions. Our data shows that total Se concentrations (and other elements) in atmospheric deposition rather mirrors a combination of contributing source emissions, atmospheric processing during local versus long-range transport and local cloud dynamics associated with specific weather events (i. e.
Acknowledgments Samples and observation data were collected at the Pyrenean Platform for Observation of the Atmosphere P2OA (https://p2oa. aeris-data. fr). We especially acknowledge  Francois Gheusi  and and  Serge Soula  for managing observational measurements onsite and maintaining the P2OA database, as well as the technical support from the  UMS 831 Pic du Midi Observatory  team. The authors are grateful to their colleagues at  Eawag and ETH , especially  Björn Studer  for carrying out the stable water isotope analysis,  Elyssa Beyrouti  for her contributions to the Py-GC-MS measurements,  Pauline Béziat  for providing the DMSeP standard and  Caroline Stengel  for general lab support.
Financial support This study was funded by the  Swiss National Science Foundation  (project number  179104 ) and internal funds from  Eawag and ETH Zurich. I. T. was partially funded by the  Swiss Polar Foundation  (project  DAWATEC ). P2OA facilities and staff are https://doi. org/10. 5194/egusphere-2023-1135 Preprint. Discussion started: 6 June 2023 c Author(s) 2023. CC BY 4. 0 License.
https://doi. org/10. 5194/egusphere-2023-1135 Preprint. Discussion started: 6 June 2023 c Author(s) 2023. CC BY 4. 0 License. weather systems (e. g. , enhanced convection or frontal passages over mountainous terrain), on trace element and moisture source cycling. We created a unique dataset of aerosols samples (n=134) taken at a weekly resolution over 5 years, as well as samples of precipitation (n=26), cloud water (n=56), and aerosol (n=48) taken at high temporal resolution (sub-event based sampling of wet deposition or separate day and night sampling of aerosols). Our objective in this study is to comprehensively investigate the role of different source factors and weather dynamics on the observed variability in trace element concentrations and Se speciation in atmospheric deposition. Specifically, the combined investigation of aerosol, cloud water and precipitation offers unique insights into the evolution of atmospheric deposition, by looking at effects on trace element cycling by changing air masses as well as in-cloud and below-cloud processes.
6 mm, 3 μm, Agilent Technologies, USA) and a gradient elution with a mobile phase consisting of 0. 1% aqueous formic acid (A) and 50:50 v/v methanol: acetonitrile (B). The multisampler and the column compartment were set at 4 and 25 • C, respectively. The injection volume was 20 μL. The Agilent 6495 Triple Quadrupole mass spectrometer detector operated in ESI negative ionization mode and performed multiple reaction monitoring with unit mass resolution. Mass spectrometer control, data acquisition and data analysis were performed with MassHunter Workstation software (B. 07. 00). Each analyte was detected using two specific MRM transitions. The most abundant one was used for the quantification (Q) of the target compound whereas the other was used for its identification (q). A deviation ≤ ± 0.
Statistical analysis GraphPad Prism (v. 9. 0) and MetaboAnalyst v. 5. 0 (Wishart Research group, The Metabolomics Innovation Centre (TMIC), University of Alberta, Canada (http://www. metaboanalyst. ca)) were used for the statistical data analysis. The statistical relationship between variables was examined by Pearson's correlation. Univariate (non-parametric Wilcoxon and Kruskal-Wallis test) and multivariate analysis ((PCA and cluster analysis  [29] ) were performed on the dataset. The original data were pre-processed (data integrity and missing value check) and normalized (i. e. , data transformation and data scaling) before the statistical analysis. Continuous variables with a normal distribution were reported as mean ± standard deviation, whereas variables with skewed distribution were described by the median with lower (25th percentile) and upper (75th percentile) quartiles.
Preliminary data on heart failure, hypertensive, and obese patients Within the framework of the clinical pilot study, AHF (n = 17) and CHF (n = 7), hypertensive (n = 7) and obese (n = 10) patients were enrolled. AHF subjects were monitored at hospital admission (due to a re-cutisation episode), at hospital discharge and one month later during the follow-up visit. Hypertensive and obese subjects were monitored twice a month, for a total of 91 samples collected and analysed. For these patients, clinical parameters and oxylipin levels did not change significantly (p > 0. 05) within that month thus samples collected at those collection points were considered as biological replicates of the same population. Demographic and clinical data of both AHF and CHF, hypertensive and obese patients are reported in Table  2.
006), lower full blood count (p < 0. 001) and iron (p = 0. 001) content compared with CHF, hypertensive and obese patients. As expected, AHF patients showed very high levels of NT-proBNP which differed significantly (p < 0. 001) hypertensive and obese patients. When patients were grouped in one of four categories in the NYHA functional class, lipoxin-B 4 , DHA and omega-3 DPA content differed significantly among the four classes (p = 0. 02). Oxylipins were quantified in the saliva samples collected from all the participants. Thirty-four out of sixty oxylipins were excluded from the dataset as the concentrations were below the limit of quantification for more than 50% of the samples. This may be expected since only a fraction of the free oxylipins circulating in blood may be transferred in oral fluid depending on the physical and chemical properties of the compound (e.
Acknowledgement We would like to acknowledge the support of the  KARDIATOOL  project (Grant Agreement No. 768686 ), which received funding from the  EU 's  Horizon 2020 research and innovation program. We would also like to thank  Dr. Christina Petersen  from the  Cardiology Division, Fondazione Toscana Gabriele Monasterio (Pisa, Italy)  for managing the saliva sample collection.