dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement All data and MATLAB™ scripts used in this study is available from a publicly available repository at https:// doi. org/10. 5281/zenodo. 8085934, with scripts also available from GitHub at https://github. com/thomasaubry/ IVESPA_GRL2023_scripts. The core data used is from the Independent Volcanic Eruption Source Parameter Archive (IVESPA) Version 1. 0 at http://ivespa. co. uk/data. html. IVESPA is curated by the IAVCEI Commission on Tephra Hazard Modeling and supported by the British Geological Survey.
Thermal analysis of PLA films The analyses of the crystallinity degree (χ c ) and the glass transition Abbreviations: Cast -film produced by casting; MP -film produced by melt processing; Pfilm with plasticizer; Bix -films with bixin. temperature (T g ) were carried out using a Q100 DSC (TA Instruments, France) equipped with an intercooler. An indium standard was used for equipment calibration. The film samples (3-5 mg, loaded in TZero aluminum crucibles) were analyzed using a heat-cool-heat cycle from 10 • C-180 • C with a rate of 10 • C. min -1 under N 2 flow (50 mL min -1 ). The glass transition temperature (T g ) was measured at the second heating scan and χ c calculated from the data of the first heating scan: χ c = ΔH m-ΔH cc ΔH f (1 -x) × 100, (1) where ΔH m is the enthalpy of melting, ΔH cc is the enthalpy of cold crystallization, ΔH f is the melting enthalpy of 100% crystalline PLA (93.
Thermal degradation products of bixin caused by heat and shear treatment The film production by casting aimed to evaluate the thermal stability of bixin stability, while the melt processing evaluated the stability to both, heat and shear forces. The initial bixin extracted from annatto seeds and the extract of the PLA/bixin films collected during the release study was analyzed with the help of UHPLC-MS in the aim to identify the main degradation products. The characteristics of the observed substances are detailed in Table  3 (compounds 1, 2, 3  and 4 ). The main degradation reaction was isomerization to trans-bixin. The identification of trans-bixin and 9 ′ -cis-bixin was realized using literature data considering the major ions characteristic of bixin fragmentation, the peak retention times and absorbance maxima  (Scotter, 1995).
The crystal structure was solved by molecular replacement with Phaser  [35]  using the coordinates of the deposited structure of wt Af FNR (pdb code 5E44)  [11]. Next, Refmac  [36]  and Phenix  [37]  were used for rigid body, positional and temperature factor refinement. Further model improvement was obtained via manual corrections using Coot  [38]. The refinement statistics are included in Table  S1.
Spectroscopy UV-visible absorbance and CD data were acquired using a Jasco V500 spectrometer (Jasco UK Ltd. , Heckmondwike, UK) and a Jasco J810 spectropolarimeter, respectively. EPR spectra at the X-band were acquired using a Bruker EMX spectrometer (Bruker UK Ltd. , Coventry, UK) equipped with an ESR-900 helium flow cryostat (Oxford Instruments, Abingdon, UK). The quantification of the paramagnetic species was carried out using a Cu(II) EDTA standard, as previously described  [24].
Rapid Reaction Kinetics of FNR Proteins EcFNR: UV-visible stopped-flow experiments were performed using an Applied Photophysics (Leatherhead, UK) Bio-Sequential DX. 17 MV spectrophotometer and the data were analyzed as previously described  [24, 39]. Prior to use, the stopped-flow system was flushed with ~30 mL of anaerobic buffer and wt FNR was used to used to confirm the absence of residual O 2  [40]. Wild-type and L28H-Af FNR: Experiments were performed using a Biologic SFM-3000/S and MOS-200 rapid kinetics spectrometer installed in a glove box. Anaerobically purified protein at 72 µM in 50 mM Tris and 300 mM NaCl, pH 8. 0, without a reducing agent, was mixed in a 1:1 volume ratio with the same buffer previously exposed to air. A flow rate of 16 mL/s was used for mixing to a final volume of 150 µL; absorbance changes were detected at 420 nm with a path length of 1.
Molecular Dynamics (MD) Simulations of wt and L28H-AfFNR Model setup: All calculations were performed with the Schrödinger suite  [43]. Our starting models were (i) a further refined wt structure (from PDB code 5E44  [11] ) and (ii) the [4Fe-4S] L28H-Af FNR crystal structure (this work). All hydrogen atoms were constructed and the protonation states of each residue were optimized using the Protein Preparation Protocol. In the Schrödinger suite, regardless of the force field chosen, the charges of [Fe-S] clusters are formal, i. e. , +2 or +3 for the Fe atoms and -2 for the inorganic S atoms connecting them. We know from an extensive quantum study on [4Fe-4S] clusters  [44]  that the actual charges on the Fe atoms and the inorganic S atoms are greatly reduced due to covalency  [44]. Accordingly, we modelled the [4Fe-4S] cluster in its oxidized form with a total charge of +2, and we used charges of +0.
Since we wanted to compare the intrinsic dynamics of the loop of wt and L28H-Af FNR, we constructed two additional models of wt and L28H-Af FNR, without the [4Fe-4S] cluster, and we protonated the four Cys thiolate groups. MD simulations: The Desmond program  [47]  included in the Schrödinger suite was used for all molecular dynamics simulations. The default protocol was used to equilibrate the system and heat it from 0 to 300 K. The simulations of the wt and the [4Fe-4S] L28Hdimer models were then run for 1 µs with a time step of 2 fs in the isobaric, isothermal (NPT) ensemble, with a temperature of 300 K and a pressure of 1 atm. The Cys20-Cys29 loop dynamics were analyzed in chains A and B for both models. To evaluate whether the cation-π interaction between Arg184 and His28 can occur in the [4Fe-4S] and apo L28H-Af FNR proteins, the simulated trajectories were analyzed, calculating the radial distribution functions (RDF) between the Cζ atom of Arg184 and the center of mass of the imidazole ring of His28.
Spectroscopic, O 2 Reactivity and DNA-Binding Properties of [4Fe-4S] L28H-EcFNR The purification of L28H-EcFNR resulted in a brown solution that gave UV-visible and CD spectra that are not only characteristic of a [4Fe-4S] cluster, but also almost identical to those of wt-EcFNR  [16]  (Figure  1 ). Iron and sulfide analyses of the as-isolated protein revealed a cluster incorporation level of ~60% (Table  1 ). Similar observations were made via Mössbauer  [30]. Thus, spectroscopic techniques that are highly sensitive to the cluster environment indicated that the replacement of Leu28 by His did not result in any significant structural changes in the EcFNR cluster. L28H-AfFNR, without the [4Fe-4S] cluster, and we protonated the four Cys thiolate groups. MD simulations: The Desmond program  [47]  included in the Schrödinger suite was used for all molecular dynamics simulations.
[4Fe-4S] L28H-EcFNR Reacts Rapidly with NO Stopped-flow absorbance was used to follow the reaction of L28H EcFNR with excess NO. Changes at 360 and 420 nm were measured, corresponding to the maxima of the final nitrosylated product and the initial iron-sulfur cluster, respectively (Figure  4A, B ). The data were similar to those previously reported for wt EcFNR in that three distinct kinetic phases were observed at both wavelengths. While this suggested that there are three steps in the overall reaction, fitting the A 360 nm and A 420 nm data to exponential functions separately and simultaneously (Figure  5A, B ) demonstrated that the middle phase has quite different kinetic characteristics at the two wavelengths. Similar observations were previously made for wt EcFNR  [24] , Steptomyces coelicolor WhiD and Mycobacterium tuberculosis WhiB1  [28].
2018; Weggleret al. 2016). Systems biology approaches including phenotypic screens are increasingly used to uncover the mechanisms of action of mostly single compounds and are also embraced to support systems toxicology also known as toxicology of the 21st century  (Bellet al. 2017; Kleinstreueret al. 2014). Differential regulation of transcripts, proteins and metabolites by chemical perturbants can be investigated at a global scale by OMICS technologies  (Viantet al. 2019; Waters and Fostel 2004). Bioinformatic tools allow to discover individual as well as networks of pathways which are deregulated by toxicants  (Fortinoet al. 2022; Wooet al. 2015). More and more databases are available which can be queried for hazard assessment of single chemicals and to extrapolate from in vitro experiments to the in vivo situation  (Huanget al.
2016; Kohonenet al. 2017). Data is also compiled by text mining tools as in case of the Comparative Toxicogenomics Database (CTD) and potential relationships between certain chemicals or even chemical moieties, gene expression and human disease might be inferred  (Duran-Frigolaet al. 2014; Daviset al. 2017 ). In the field of drug discovery, millions of gene expression profiles for thousands of mainly pharmaceuticals at different concentrations and time points were recorded which can be compared to the signatures of novel compounds to deduce their mode of action via the so-called connectivity map (CMAP)  (Lambet al. 2006; Subramanianet al. 2017). Interestingly, in the field of hazard assessment the CMAP approach has rarely been employed. Connectivity mapping has been used as a tool to support read-across hypotheses  (De Abrewet al.
Paired end reads (2 × 50 nucleotides) were obtained on a Hiseq1500 using SBS v3 kits (Illumina, San Diego, CA, USA). Cluster detection and base calling were performed using RTAv1. 13 and quality of reads were assessed with CASAVA v1. 8. 1 (Illumina, San Diego, CA, USA). The mean Phred quality score was greater than 30 for all samples. The reads were mapped against the human genome (GRCh37) using topHat version 2. 0. 11  (Kimet al. 2013 )with the options "-r 180 -mate-std-dev 80 -b2sensitive --no-novel-juncs -a 5 -p 5 -library-type fr-unstranded" and using known exon junctions (Ensembl release 75). Gene expression was determined by counting for each gene the number of reads that overlapped known genes (Ensembl release 75) with HTSeq version 0. 5. 3p3  (Anderset al. 2013). Differential expression was computed using the R package DESeq2  (Anderset al.
2015). If not indicated otherwise, a transcript was considered to be differentially regulated if the adjusted pvalue is < 0. 001 and the absolute log2-fold change is ≥ 1. 3485 and 2099 DEGs were identified for A549 and BEAS-2B, respectively. As during the course of the project the genome annotation as well as the mapping software were updated, we re-analyzed our RNA sequencing data using the genome version GRCh38 and Ensembl release 108 together with STAR  (Dobinet al. 2013 ) and confirmed the initially identified DEG and GO terms. Sample quality control metrics are provided in Supplementary Table  S2  and Supplementary Fig S1.
Bioinformatic analyses Correlations with NextBio Datasets: datasets related to combustion areosols and genotoxins with diverse mode of actions were downloaded from the NextBio database during December 2016-February 2017. The data was used as retrieved, i. e. only significant DEG are included, but no further filtering criteria were applied. The data were compared to the top 2000 (by p-value) DEG after wood smoke exposure (aerosol vs control). The intersecting set of DEG (based on gene symbols present in both compared datasets) was used for correlation plots of the log2-fold changes. Correlation with cigarette smoke exposure: eight-to-ten-week-old and twelve-month-old female C57BL/6 mice were whole-bodyexposed to active 100% mainstream cigarette smoke for 50 min twice per day for 6 months as previously described  (Conlonet al.
2020). Twenty-four hours after the last exposure to cigarette smoke, mice were killed. The right lung lobes were snap frozen in liquid nitrogen, homogenized and total RNA isolated and amplified using the Illumina TotalPrep RNA Amplification kit (Ambion), and finally hybridized to Mouse Ref-8 v2. 0 Expression BeadChips (Illumina). Microarray data was retrieved from GEO (GSE125521) using the R package 'GEOquery'. The retrieved data was log-transformed and probes which did not achieve a hybridization intensity of 3 in at least 6 samples (out of 27 total) were removed from the expression set. Analysis of DEG was performed with all samples from the 6-month timepoint using limma (v 3. 50. 3) with R (v 4. 1. 3) using standard parameters. The intersecting set of DEG of wood smoke and cigarette smoke, with an adjusted p-value <=0.
05 in each set, was used for plotting the correlation on the basis of identical gene symbols. In case multiple transcripts were represented by the same gene symbol, a single value was created by taking the mean. The top regulated 2000 DEGs in A549 cells in response to wood smoke were compared with the NextBio datasets of DEGs recorded for human prostate cancer cells (PC-3, number of genes for comparison 8823) exposed to camptothecin (11. 4 μM, 6 h), for human spherocytic spleen cells (TK6, number of genes for comparison 4463) exposed to formaldehyde (200 μM, 4 h), of A549 cells (number of genes for comparison 5116) to acrolein (1 h) and of rats (number of genes for comparison 4914) exposed to formaldehyde via nasal instillation (200 μM, 6 h). In case of mice chronically exposed to cigarette smoke for 6 months, 1456 DEGs were compared to 7095 DEGs identified in A549 cells upon exposure to wood smoke.
GO-Term enrichment analysis: Ensembl Gene IDs of significant DEG were compared to a background list which consisted of all Ensembl Gene IDs which were counted at least once in each of the sequence samples using the web version of DAVID. Enriched GO-Terms are presented with adjusted p-values in the figures. Percentage of regulated transcripts refers to the fraction of DEG found within all Gene IDs in the background list which belong to the respective GO-Term. Enrichment analysis using BioJupies: raw read count data of A549 cells exposed to complete and filtered wood smoke and clean air was uploaded to BioJupies (https://maayanlab. cloud/biojupies/) (Torreet al. 2018). Aerosol exposure and air controls were selected for DEG analysis and enrichment analysis (via Enrichr) was performed with the upregulated DEG using the automated workflow in BioJupies (accessible at https://amp.
H2AX foci determination Gamma-H2A foci formation was determined by immunostaining using the protocol described in  (Kochet al. 2017 ) with some modifications for cells grown on insert membranes. After exposure, the cells were fixed by applying 4% formaldehyde/PBS directly in the Transwell inserts. Pieces of roughly 1 cm 2 were cut out and treated like cells grown on coverslips according to the immunostaining protocol. Microscopy slides were prepared by sandwiching membrane pieces with mounting medium (VectaMount, Vector Laboratories) between standard glass slides and cover slips. For each sample, 20 images were acquired using µManager 1. 4. 21  (Edelsteinet al. 2014 ) with a Leica DM IRE2 fluorescence microscope [63x objective and an ORCA-ER (Hamamatsu) camera]. Foci were counted automatically by the ScanR image analysis software (Olympus, Hamburg, Germany) by detecting focal gamma-H2AX signals within the nuclei.
γ-H2AX immunostaining and counterstaining with DAPI for identification of nuclei of A549 cells exposed to clean air, complete aerosol and particle-free gas phase (B) and quantification of γ-H2AX foci (C). Foci counts are the cumulative results from 3 experiments with 2 replicates each, with a total of at least 1300 analysed cells per condition. For better visualization, counts per cell beyond the 99th percentile were omitted. Boxplots show the mean (coloured square), median (bold line), first and third quartile (box) and 1. 5-fold IQR (whiskers). DNA single strand breaks in A549 cells measured with alkaline unwinding (D), data from 3 experiments with 4 replicates each is shown as boxplots (C). *** = p < 0. 001 according to Tukey's HSD post hoc test for ANOVA. Activation of p38 (E), JNK (F) and ERK (G) in A549 cells was monitored by western blotting.
Overall, if we want to preserve a livable planet, we must leave our well-trodden disciplinary paths and search for novel inter-disciplinary solutions and approaches. Moreover, not only national but overarching transnational funding programs need to be implemented to develop or adapt solutions to local specificities. PlantACT! aims to urgently accelerate these new inter-disciplinary interactions and solutions by stimulating new forms of working and funding (Box 4). Organization of the United Nations, https://www. fao. org/3/cb3808en/cb3808en. pdf). The FAO data includes greenhouse gases other than CO2, converted to CO2 equivalents. Adapted from  [26]  with data from  [4]  and  [11]  (created with BioRender. com).
Farm model design The farm model was designed to simulate the strategic decisions of farmers by optimizing farm activities under multiple constraints and toward a single farmer strategic objective of maximizing farm income (Fig. 2 ). Fig. 1. The approach used to assess benefits and constraints that new options at field level may generate at farm level. The model represented different farm types and the key interactions between farm structure and its environment. These interactions were captured by building a structural typology of maize-based farms. The structural typology of farms was obtained using the data from 120 farm household surveys (see "Light survey", Fig. 1 ) conducted in six villages of the Kham Basin (Dokham, Laeng, Lé, Houat, Xay and Nadou). The 120 households chosen for this light survey were selected from a census conducted in 2016 on all farmers of the six villages  (Lestrelin and Kiewvongphachan, 2017).
All the households selected were at the same level of market integration for maize, but differed in land size and land allocation. The data recorded on the 120 households included information about farm land allocation, family size and workforce, farm equipment (motorized equipment for agricultural activities and for transport), the farmers' on and off-farm activities, and income generated for the period 2016-2017. We performed a principal component analysis (PCA) and a hierarchical cluster analysis to obtain relatively homogeneous types of farms. We then conducted in-depth surveys and farm visits with a subsample of 16 farms (Fig. 1 ). The number of farms selected per farm type (2 to 8 farms per type) was proportional to the distribution of farm types in the 120-household sample. During these 4 to 5-h-long in-depthsurveys we collected detailed data on farm productions, on crop/livestock management, seasonality of work, input/output prices, seasonality of family expenses and treasury, soil types, surfaces, distances between fields, and cattle herd size.
(2004)  regardless the soil type, considering a 5-years fallow period (managed by slash-and-burn before sowing) between two crop cycles on the same field. The model was built using GAMS software (version 22. 5). The single goal of maximizing one-year farm income was considered in the model. However, beside income maximization we considered a number of constraints applied to five time periods in which a year was split: land, labour, cash, rice needs, and livestock feed (see supplementary material, appendix A). The model was designed to consider distinct simulations for each of the 16 farms surveyed in the in-depth survey. Each farm was characterized by the arable land available in the different land types, its household size and composition (representing mouths to feed and labour force available), initial cash and rice stock available for consumption at the beginning of the year.
Acknowledgement We thank the  Department for Agricultural Land Management (DALaM)  and the  Provincial Agriculture and Forestry Organisation (PAFO) in Xieng Khouang  for their support and assistance. We are grateful to the farmers in the villages of Xay,  Leng  and Nadou for their warm welcome. Many thanks to AR for her help on figure 1. This research was implemented as part of the  Eficas  project funded by the  Directorate-General for Development and Cooperation -EuropeAid  ( EuropeAid/132-657/L/ACT/LA ) and the  Agence Française de Développement (Conservation Agriculture  within the  Northern Upland Development Programme , NUDP). Write-up and publication of this work was also supported by the  OneCGIAR Global Initiative on Sustainable Intensification of Mixed Farming Systems  (SI-MFS) (https: //www.
The MS E experiment mode was set to high definition MS E (HDMS E ). Data acquisition covered an m/z range of 50-1000 at 0. 2 s per scan. Data acquisition and processing was performed with UNIFI version 1. 9. 4 (Waters). Samples were injected by species series (V=10 µl) and every fourth samples a blank was interleaved to check for extrolite remanence on the column. [ 13 C 18 ]-sterigmatocystin peak areas were observed for each sample to verify the analysis. Extrolites were determined by the precursor mass ions the peak detection and the most intense isotopic ions for quantification.
Acknowledgements ABTE-ToxEMAC is a member of the  FHU (University Hospital Federation) RESPIRE : ' Pathogènes, Environnement et Hôte: une approche integrative en santé respiratoire '. This study was supported by the  EREM expertise unit (Expertises, Reconnaissances et Études en Mycologie) , the  European Union  within the framework of the  ERDF/ESF 2014-2020 Operation Program  and the  Regional Council of Normandy  (RIN  BEE  project).
(2021b)  and Prada-Salcedo,  Wambsganss, et al. , 2021. Retrieved raw bacterial and fungal sequences were extracted based on their unique barcodes. The bioinformatics workflow was based on software package DADA2 and MOTHUR classifier  (Schloss et al. , 2009; Callahan et al. , 2016; Weißbecker et al. , 2021)  implemented in the pipeline dadasnake  (Weißbecker et al. , 2021 ; https://github. com/a-h -b/dadasnake/). Primers were detected allowing maximum a 20 % mismatch and were cut using cutadapt 1. 18. Bacterial sequences were filtered by adaptingtrunc_quality:11, trunc_length:0 and max_EE:0. 5. During the filtering of fungal sequences, trunc_quality:11, trun-c_length:0, max_EE:3, and minLen: 70, were adapted. Taxonomic annotation of the high quality reads per amplicon sequence variant (ASV) was assigned with Bayesian classifier against Silva 132  (Quast et al.
, 2013)  and UNITE v2014  (Nilsson et al. , 2019)  data bases for bacteria and fungi, respectively. One ASV was therefore considered representative of one taxon (i. e. ASV is the lowest taxonomical unit). FUNGuild V1. 0 tool was used to parse fungal taxonomy and establish ecological guilds  (Nguyen et al. , 2016) , here called functional subgroups, and we classified bacterial taxa according to their potential capacity to cope with drought conditions as described in Naylor and Coleman-Derr (2018) (see Table  S3  & S4). Third, diversity and statistical analyses were performed using the software R and its packages phyloseq  (version 1. 16. 2, McMurdie and Holmes, 2013)  and vegan (version 2. 5-6,  Oksanen et al. , 2019). Considering data type, data normalization was done by rarefying to 20, 000 and 10,000 depth number of sequences among the bacterial and fungi samples, respectively and rarefaction curves confirmed a sufficient sequencing depth reaching the saturation for each sample (see Fig.
Statistical analyses All statistical analyses were run with the R software, figures were made using the 'pirateplot' function from the YaRrr! package (version 0. 1. 5, Phillips, 2018), the function 'fviz_pca_biplot' from the factoextra package (version 1. 0. 6, Kassambara and Mundt, 2019) , and the ggplot2 package (version 3. 2. 0, Wickham, 2016). Permutational multivariate analysis of variance (PERMANOVA) were performed on the ASV richness, relative abundance, and H' of the bacterial functional groups and of the fungal functional groups as well as on the 'used' substrate respiration rates using the function 'adonis' in the vegan package to explore the influence of DRW, tree species mixing, their interaction, root CWM PC1, and soil PC1 at a multivariate level. When PERMANOVA results showed significant (p-value < 0.