dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge financial support from the following sources:  FACCE JPI MACSUR2  through the  Italian Ministry for Agricultural, Food and Forestry Policies  (D. M. n. 24064/7303/15 ); the  European Union 's  Horizon 2020 research and innovation program  under the Grant Agreement No. [727247]; the  Biotechnology and Biological Sciences Research Council , through the  Designing Future Wheat (DFW) program  ( BB/P016855/1 ); the  AgriDigit-Agromodelli  project (DM n. 36502 of  20/12/2018 ), funded by the  Italian Ministry of Agricultural, Food and Forestry Policies and Tourism ; the project  PR. AVA2018. 051 ( Innova-Clima ) funded by the  European Regional Development Fund (FEDER).
Contributions: Conceptualization, M. L. and B. P. ; strains, M. L. and B. P. ; cultures and product extraction, M. L. and J. M. ; SEC-MALS analyses, E. B. ; NMR analyses, S. D. ; enzymatic analyses, S. A. ; original draft preparation, B. P. ; supervision, B. P. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by a CIFRE fellowship from ANRT (Association Nationale de la Recherche et de la Technologie), grant number 2016/0814; Labex ARCANE and CBH-EUR-GS (ANR-17-EURE-0003), Glyco@Alps (ANR-15-IDEX-02), and PolyNat Carnot Institut (ANR-16-CARN-0025-01).
Author Contributions: Conceptualization, M. L. and B. P. ; strains, M. L. and B. P. ; cultures and product extraction, M. L. and J. M. ; SEC-MALS analyses, E. B. ; NMR analyses, S. D. ; enzymatic analyses, S. A. ; original draft preparation, B. P. ; supervision, B. P. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by a CIFRE fellowship from ANRT (Association Nationale de la Recherche et de la Technologie), grant number 2016/0814; Labex ARCANE and CBH-EUR-GS (ANR-17-EURE-0003), Glyco@Alps (ANR-15-IDEX-02), and PolyNat Carnot Institut (ANR-16-CARN-0025-01).
The latter period corresponds to the end of conditioning in trained animals. The potential impact of pheromone exposure on odorant coding and differentiation was evaluated by comparing the overall activation signal averaged over all observed glomeruli, and the distinguishability of the odor-coding patterns quantified in terms of the Euclidean distance. Data analyses were performed with MATLAB (R2018, MathWorks). Glomerular response signals were deduced from relative changes in fluorescence, with respect to a background signal: -ΔF(t)/F. The background fluorescence F was obtained in a dynamic way by applying a moving average filter with a long time constant (7 s) to the fluorescence time series. Ten glomeruli were identified for each bee. Glomerular responses were averaged over the first 600 ms of olfactory stimulation and over the three repeated stimulations for each odorant.
Individual conditioned responses were examined using GLMMs with a binomial error structure-logit-link function-glmer function of R package lme4 57. When necessary, models were optimized with the iterative algorithm BOBYQA  58. In the sucrose-responsiveness model, "bee response" was entered as a dependent variable, "treatment" (GER, 2H, or mineral oil) as a fixed factor, and "sucrose concentrations" as covariates. In the learning models, "bee response" was entered as a dependent variable, "treatment" (GER, 2H, or mineral-oil exposure) and "CS" as fixed factors, and "conditioning trial" as covariate. In the memory-retention models for bees exposed before conditioning, "bee response" was entered as a dependent variable, and "treatment" and "testing time" (2, 24, or 72 h) as fixed factors. In all cases, "individual identity" (IDs) was considered as a random factor to allow for repeated-measurement analysis.
These GLM models were designed as described above with the exception that the fixed factor "treatment" (exposure) was replaced by the fixed factor "pharmacological treatment". In all the GLM models, "individual identity" (IDs) was considered as a random factor to allow for repeatedmeasurement analysis. In many analyses, several models were run and compared to identify significant interactions between fixed factors and/or covariates, and the significant model with the highest explanatory power (i. e. , the lowest Akaike information criterion (AIC) value) was retained. When AIC values were very similar, the most significant model was retained. Interactions, wherever significant, are indicated in the text. Tukey's post hoc tests were used to detect differences between the different groups (glht function from R package multcomp 59 ).
Acknowledgements We thank  Alex Kacelnik  for useful discussions on the role of pheromones. This work was supported by the  French National Research Agency  (Project  PHEROMOD ,  ANR-14-CE18-0003 ), the  Institut Universitaire de France , and the  IDEX Program  "Excellence Chairs" of the  Université Fédérale Toulouse Midi-Pyrénées. A. C. and A. H. acknowledge funding by the  Autonomous Province of Bolzano  (Project  B26J16000310003 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Measurement of AChE activity (see supplementary Materials & Methods). Zebrafish larval locomotor activity. Larvae locomotor activity was measured, as previously described  14  , using a Zebrabox (View Point), an automated infrared tracking device, with ZebraLab 5. 13. 0. 240 software (https ://www. viewp oint. fr/en/home). Five dpf larvae were individually dispatched in a 96-well plate in approximately 200 µl of E3 medium containing either 1% DMSO or 15 µM DFP. The plate was then placed in the recording chamber for a 30-min habituation in the dark and in silence. Locomotor activity was then recorded for 4 h using the following settings: animal color was set to black and threshold detection to 12. The locomotion activity was quantified as the sum of all pixels showing intensity changes during the recording time and plotted as "acting units".
Immunohistochemistry (see also Supplementary Table Quantification of NR2B and gephyrin puncta labeling. Sections stained with anti-NR2B-NMDA and anti-gephyrin antibodies were imaged at full resolution (voxel size: 0. 063 × 0. 063 × 0. 3 µm) using a Leica SP8 laser scanning confocal microscope equipped with a 63 × /1. 4 oil-immersion objective. Images were then processed with AutoQuant X3. 1 software (https ://www. media cy. com/autoq uantx 3) and the density of NR2B or gephyrin labeled puncta was quantified using a homemade ImageJ macro (Zsolt Csaba, Inserm UMR1141). After stacking all images, a median filter was applied, a threshold on the fluorescence intensity was then set to select NR2B-NMDA or gephyrin puncta. Puncta were identified as accumulation of more than two pixels (0. 126 µm) in diameter, which lead to an average punctum size of 0.
The seasonality of giant gourami reproduction was analyzed as a function of seasonal variation in rainfall, rearing water temperature and day length. Based on a total of 413 collected spawns, the multiple linear regression models did not show any significant relationship between giant gourami spawning frequency and the three environmental variables studied. In contrast, the number of eggs per spawn was highly and positively related to rainfall (p < 0. 0001), moderately and negatively related to water temperature (p < 0. 001), and weakly and negatively related to day length (p < 0. 01). The proportion of clear eggs was a positively related to day length (p < 0. 01). Overall, this study represents one of the first comprehensive datasets on the reproductive phenology of giant gourami, and as such will be helpful in formulating broodfish management strategies for the species.
, 2006). Nevertheless, to our knowledge, an accurate description of the seasonal variation in reproductive performance of captive giant gourami broodfish is still lacking in the scientific literature. Such information would help to rationalize and optimize hatchery operations, as in many intensively farmed finfish species  (Migaud et al. , 2013). Because there are considerable interspecific variations in the patterns of reproductive seasonality  (Wootton and Smith, 2014; Paugy et al. 2017) , such investigation would also be useful to further document the influence of the environment on the reproductive phenology of freshwater tropical fishes for which, with the exception of some families (mostly cichlids and clariids), availability of accurate datasets remains limited. The objective of the present study was to assess seasonal fluctuations in the reproduction of giant gourami (spawning frequency, number of eggs in a spawn and egg quality), based on nearly two years of follow-up in ponds at the Tasikmalaya Center for the Development of Giant Gourami Culture (BPPSIGN), the largest giant gourami fry production center in the West Java Province, Indonesia.
Monitoring of environmental conditions Throughout the 20-month experiment, fluctuations of three environmental parameters were considered: rainfall, pond water temperature and day length. Pond water temperature was monitored continuously using a data logger (Onset HOBO) placed in the center of the ponds at 20 cm depth. A rain gauge was installed in the immediate vicinity of the ponds to record the daily rainfall and the information related to day length was collected from Time and Date (2019).
Data treatment and statistical analysis Prior to statistical analysis, fish reproduction and environmental data were compiled and, for each pond and each variable, monthly means of the recorded values were calculated. Statistical analyses were then performed on these average monthly values. The non-significance of the variable "pond", initially included in general linear models (GLM) with quantitative environmental variables was confirmed. Then, stepwise multiple linear regression models (MLR) were used to test for relationships between female spawning frequency, egg numbers or clear egg proportion in spawns and environmental variables (rainfall, water temperature and day length). The assumptions underlying the MLR were checked by visual observation of the model residuals and by statistical analysis (Goldfeld-Quandt test, Rainbow test and Shapiro-Wilk test).
Results At the BPPSIGN Tasikmalaya Center, day length varied annually between 11 h 41 min and 12 h 33 min, and pond water temperature ranged from 22. 6°C to 32. 7°C. The cumulative monthly rainfall ranged from 5 to 528 mm during the experiment, from May 2016 to December 2017. Overall, the monthly mean of the pond water temperature showed limited variation (between 25. 4°C and 28. 0°C), whereas the monthly rainfall was much lower during the second than during the first half of the experimental period (from 331 to 528 mm vs. 0. 5 to 288 mm, respectively; Figure  1A ). During this 20-month period, as determined from the 413 spawns collected in the two experimental ponds (206 and 207 spawns per pond), the reproduction of giant gourami occurred continuously. Because no effect of the duration of egg production periods (successive periods of 3 or 7 months) was detected for any variable (spawning frequency, egg number and quality), the data were pooled for analyses.
Figure8. The total number of hot pixels per year (a) and per month (b) within the savannas of French Guiana. The hot pixels were quantified between 2008 and 2017 from shapefiles of the occurrence of burns obtained from the databases of the Instituto Nacional de Pesquisas Espaciais (https://prodwww-queimadas. dgi. inpe. br/bdqueimadas/). Based on these shapefiles and using ArcGIS v10. 4. 1 (Environmental Systems Research Institute, 2011), the total number of hot pixels was quantified in accordance with the vegetation map of savannas of FrenchGuiana (ONF, 2017). The classification in seasons of the year based on precipitation was based on Vezenegho et al. (2016). In March, classified as part of the "short dry season," there were no hot pixels. Average precipitation per month was adapted from https://fr.
publication from an " Investissements d'Avenir " grant managed by  Agence Nationale de la Recherche ,  Centre d'Etude de la Biodiversit e Amazonienne, ref. ANR-10-LABX-25-01 ;  William D. de Carvalho  is supported by a postdoctoral scholarship from  CAPES-PNPD (Coordenac¸ão de Aperfeic¸oamento de Pessoal de N ıvel Superior ,  Programa Nacional  de P os Doutorado); and  Karen Mustin  received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  706680. ://orcid. org/ 0000-0003-0596-1161Marianne Palisse //orcid. org/0000-0002-4554-0372.
Sections were then washed with PBS, incubated with the secondary antibody (1/400, 633-conjugated goat anti-rabbit or anti mouse IgG) for 2 h at room temperature in the dark before being washed with PBS and mounted in Dako fluorescence mounting medium (Dako, Carinteria, USA) and cover-slipped. The primary antibody was omitted for negative control. Before immunofluorescence level examination using a confocal laser-scanning microscope (Leica TSC SPE-Mannheim, Germany), samples were protected from lights. Image J software (NIH, Bethesda, Maryland, USA) was used to perform analysis of immunofluorescence signals.
Cassava may, therefore, become more relevant under climate change  (Jarvis et al. , 2012; Rosenthal and Ort, 2012). A new simulation model for cassava, MANIHOT, was incorporated in the Decision Support System for Agrotechnology Transfer package (DSSAT;www. DSSAT. net) (Hoogenboom et al. , 2019a (Hoogenboom et al. , , 2019b;; Jones et al. , 2003). MANIHOT is based on the earlier CROPSIM cassava model  (Matthews and Hunt, 1994). The changes to the CROPSIM model required new or modified GSPs. The definition and function of each GSP is specified, but we do not know the range each can take for different cultivars nor how it might interact with other GSPs. Moreover, MANIHOT has only been evaluated for a limited number of different regions. We undertook a sensitivity and uncertainty analysis of MANIHOT to identify which parameters contribute the most to model uncertainty.
MANIHOT  (Moreno-Cadena, 2018) , includes a new drought stress factor based on soil water content rather than the ratio between potential and actual transpiration as used in several other DSSAT crop models  (Ritchie, 1998). The drought stress factor affects the germination, leaf appearance, branching, leaf size and biomass increase. The sensitivity to drought can be different for germination but it is the same for the other processes. Appendix A includes a brief evaluation of the results. The source code is available from GitHub under the 3-Clause BSD License (https://github. com/DSSAT/dssat-csm-os).
Input parameters and their distributions MANIHOT currently has 18 GSPs. The effects of photoperiod (photoperiod sensitivity, PPS1) and nitrogen (nitrogen concentration in the storage roots, SRN%S) so far have not implemented in the code and were, therefore, omitted from this study. The distribution of the remaining 16 GSPs was defined based on a literature review of 35 publications and an unpublished database of the International Center for Tropical Agriculture (CIAT) (Table  1  and Fig. 1 ). Where necessary, we converted reported chronological time to thermal time using the mean temperature of the study site. The distribution of each GSP represents the structure of the variability among cultivars including different timing and patterns of branching, leaf sizes and leaf retention. We fitted different distributions to the data of each GSP using the R fitdistrplus package.
Study sites and model input data The sensitivity of a simulation model to the variation of a GSP depends on the growth environment  (Confalonieri et al. , 2010b; Silvestro et al. , 2017; Vanuytrecht et al. , 2014). We, therefore, conducted the global sensitivity analysis of MANIHOT under combinations of warm and cool temperatures under either rainfed or no water limitations (subsequently termed unlimited). We selected two sites in Colombia, Popayan (2. 4278 °N, 76. 6208 °W, 1750 masl), and Cereté (8. 8397 °N, 75. 8019 °W, 20 masl) with contrasting temperatures because of their different altitudes. Both are important national cassava areas and represent 27 % and 62 % of the global and equatorial cassava production, respectively  (IFPRI and IIASA, 2005; Kottek et al. , 2006). The mean temperature for the growing season from late April to February was 18 °C in Popayan (cool), and 28 °C in Cereté (warm).
Rainfall and mean solar radiation during the growing season were 890 mm and 16 MJ/m 2 / day for Popayan and 1130 mm and 17 MJ/m 2 /day for Cereté (Table  2 ). We obtained 30 years' data of maximum and minimum temperature, precipitation and solar radiation for each site  (Popayan, 1984 -2013 , Cereté, 1980 -2009). The weather data for Cereté were obtained from IDEAM, the Colombian National Institute of Meteorology; the weather data for Popayan were obtained from NASA/ POWER  (Stackhouse et al. , 2018; Van Wart et al. , 2015). We quality checked the data  (Esquivel et al. , 2018)  with the RClimTool software (Llanos-Herrera, 2014). The soil data for Cereté were from the IRI database  (IRI et al. , 2015)  and for Popayan the site's profile data in the DSSAT database (see Tables  C1  and C2  in Appendix C).
Global sensitivity analysis by enhanced sampling Uniformity (eSU) We used eSU to rank GSPs according their relative influence on MANIHOT's outputs of the simulated processes. We selected eSU because it creates multiple combinations of GSPs with improved sample uniformity, sample spread and screening efficiency compared with other methods  (Chitale et al. , 2017; Khare et al. , 2015). We used the eSU procedure in the MATLAB package  (The MathWorks Inc, 2015)  to create a set of 408 combinations within 8 levels of each of the 16 GSPs (k) specifying 24 trajectories (r). Trajectories are defined as the succession of points within the sampling space that create the r*(k+1) combinations  (Chitale et al. , 2017). We specified 8 levels of each GSP, which is more than 4 that is commonly used for these analyses  (Chitale et al.
5 in the warm environment with water unlimited. In contrast, only seven of them had μ* values above 0. 15 in the cool environment. As expected, branching behavior simulated by MANIHOT is sensitive to the GSPs B01ND and B12ND (data not shown). Like μ*, the values of σ under rainfed and unlimited conditions were similar, but differed between warm and cool environments (Fig. 3 ).
Leidy Moreno-Cadena (2018) compared MANIHOT simulations with data from ten experiments with nine varieties at three sites in Colombia. The model represented leaf number with a root mean square error (RMSE) less than 13 for both the calibration and evaluation data sets, tending to underestimate at the end of the growing season. Simulated yield of total dry matter was more variable (RMSE up to 3600 kg/ha and a coefficient of determination (R 2 ) of 0. 62) (Fig. A2 ). Although both GUMCAS  (Matthews and Hunt, 1994)  and LINTUL  (Ezui et al. , 2018)  reported higher R 2 values (0. 76 and 0. 92), they were for just one variety at one (GUMCAS) or two (LINTUL) sites. 1 The photosynthesis factor was set to 1 although the original value in the database of DSSAT is 0. 9.
Acknowledgments This study was supported by the  International Center for Tropical Agriculture (CIAT)  and the  International Institute of Tropical Agriculture (IITA)  as part of the  African Cassava Agronomy Initiative  (ACAI, http://acai-project. org/) project funded by the  Bill and Melinda Gates Foundation  [grant number  OPP1130649 ]. The weather data for one of the locations were from the  NASA Langley Research Center (LaRC)   POWER  Project funded through the  NASA   Earth Science/ Applied Science Program  (https://power. larc. nasa. gov/).
Abstract Based on the GRAFS method of biogeochemical accounting for nitrogen (N), phosphorus (P) and carbon (C) fluxes through crop, grassland, livestock and human consumption, a full description of the structure and main functioning features of the French agro-food system was obtained from 1850 to the present at the scale of 33 agricultural regions. For the period since 1970, this description was compared with the results of an agronomic reconstitution of the cropping systems of the Seine watershed based on agricultural census and detailed enquiries about farming practices at the scale of small agricultural regions (the ARSeine database), which were then used as input to an agronomical model (STICS) calculating yields, and the dynamics of N and C. STICS was then coupled with a hydrogeological model (MODCOU), so that the entire modelling chain can thus highlight the high temporal inertia of both soil organic matter pool and aquifers.
For each product, the food systems map visually represented the supply chain, showing the main actors involved in production, processing, trade and consumption, as well as an estimation of the different flows (in percentages) linking these actors. These maps were developed drawing from official statistics and expert knowledge obtained from key informant interviews. They were refined and validated using information from a household survey with farmers (see  Guarin et al. , this issue)  and through focus group discussions with diverse stakeholders in each region. See Table  1  for details on numbers of interviews, surveys and participants in focus groups. Data was collected between 2016 and 2018. In all regions, identical protocols and reporting templates were used in order to ensure the comparability of the data.
For the comparative analysis, the information from the 91 system maps was coded and converted into a database. We codified the information about the supply chain, including the different actors, the connections between them, and the relative magnitude of the flows within them. In addition to this information, we included data on the products' regional production and consumption, as well as general regional characteristics such as GDP or number of small farms. To compare the importance of small farms across different products and regions, we developed a simple model. For each product, this importance was assessed along two axes: first, the share of regional production that comes from small farms. This indicates the contribution of small farms to the total regional output. And second, the share of what small farms produce that stays in the region.
This is an indication of their relative importance with regard to regional consumption. We carried out a further analysis to understand what factors are associated with the importance of small farms along the two axes described above. Why do small farms account for a higher share of production in some products and some regions than in others? And why do more small farms' products stay in some regions than in others? We used a Random Forest algorithm  (Freeman et al. , 2015)  to organize, in order of importance, the characteristics/variables that explain the variability of the two main dependent variables described above. Random forest classification was implemented using the R package randomForest  (Liaw and Wiener, 2002). A similar technique has been used to explain different relationships between farms and crop portfolios  (Weigel et al.
Acknowledgements This work was carried out under the  SALSA (Small farms, small food businesses and sustainable food and nutrition security ) project, funded by the  European Union  - Horizon 2020 programme (Project ID :  677 , 363 ). We would like to thank all the farmers and stakeholders involved in the interviews, focus groups and workshops for their time and availability. We are grateful to  Bill Vorley  and two anonymous reviewers for helpful comments on earlier drafts and to S� ergio  Godinho  for his help with the Random Forest analysis.