dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein localization analysis A KUP9 protein fusion with the GFP reporter was generated to localize the KUP9 transporter. The coding sequence of KUP9 was PCR amplified (forward primer: ATGGCGGAAAGAGTCGAAGCATC, reverse primer: CTAAACATAAAAGACTTGTCCAACG) on Col-0 cDNA synthetized from 1 pg RNA with SuperScript™ III kit (Invitrogen). Sequencing of PCR products (GATC Biotech, Konstanz, Germany) reveals that the amplified fragments correspond to the splicing variant named At4g19960. 2 in TAIR database (Fig. 1 ). This coding sequence was inserted into PCR-XL-TOPO optimizing this way its further insertion into the entry vector pDONR P2r-P3 (attB2r forward primer: GGGGACAGCTTTCTTGTACAAAGTGGTCATGGCGGAAAGAGTCGAAG, attB3 reverse primer: GGGGACAACTTTGTATAATAAAGTTGCCTAAACATAAAAGACTTGTCCAACG) for cloning using Gateway® technology.
Localisation of the KUP9 transporter Cellular localization of the KUP9 transporter was attempted generating transgenic A. thaliana, which expressed N-terminal GFP fusions with KUP9 transporter under the control of the 35S promoter (pro35S:GFP-KUP9) or under the control of the KUP9 promoter (proKUP9:GFP-KUP9   2020). Sequencing of the PCR products from KUP9 cDNA in Col-0 used for these constructions corresponded to the splicing variant named At4g19960. 2 in the TAIR database. Unfortunately, even though no errors were detected in sequencing, these constructions led to the synthesis of a cleaved protein (37 kDa protein detected in western blot, Appendix S2 and Fig. S2 ), and we were not able to detect GFP signals in transgenic lines. In a recent work,  Zhang et al. (2020) , however, localized the KUP9 transporter from the At4g19960.
3 splice variant (according to the TAIR 10 database) mainly in the endoplasmic reticulum. Predicted proteins from the four KUP9 splice variants differ in their amino acid sequences length (Fig. 1 ). Different isoforms of a protein can have distinct functions or localization  (Brummell et al. 2011 , Kriechbaumer et al. 2012 , reviewed in Syed et al. 2012 ). However, deeper analyses are needed to establish the prevalence of each splicing variant (which can depend on tissue, developmental stage or environmental stresses,  Syed et al. 2012)  or the impact of splicing choice on protein function.
2015)  and in KUP7 activity  (Han et al. 2016). Querying database for phosphorylation sites in plants (dbPPT,  Cheng et al. 2014) , did not results in any hits for the KUP9 transporter. Computational prédictions (Musite,  Yao et al. 2012) , applied on the KUP9 peptide sequence, pointed out at least one phosphorylation site with high specificity (> 95%). It would be interesting to further investigate these putative phosphorylation sites and their contribution to KUP9 activation under different K+ conditions.
1 M). Briefly, a mass of 50 mg of material was dispersed in a volume of 75 mL of NaNO3 (0. 01 M) and degassed by nitrogen bubbling overnight; then, the pH was adjusted to ca. 3 using an HCl solution (0. 1 M) and titrated by NaOH using a Titrino Plus system, Methrom. The functionalities were determined using the SAIEUS software applied on the proton affinity versus pH curves  [22].
8 mL/min and the sample injection volume was 50 μL. LabSolutions software from Shimadzu was used for the identification and quantification of the molecules. The concentration of the compounds was calculated based on the relative peak areas using standards of known concentrations. Equilibrium adsorption isotherms were also carried out in 25 mL of solutions-with an initial concentration ranging from ca. 5 to 150 mg/L-containing 2. 5 mg of adsorbent to obtain a range of 0. 05-1. 5 g compound/g adsorbent. The isotherms were carried out over the course of 3 h (equilibrium time based on the kinetic study). Regarding the determination of the adsorption capacity at lower concentrations (imposed by the EU legislation  [16] ), 500 mL of ESA and OXA aqueous solutions (0. 001 and 0. 01 mg/L) were stirred at 400 rpm in glass flasks with a mass of 1 mg of adsorbent.
Biological Sampling The dataset includes 19 specimens from 16 species of chondrichthyans (Table  1 ) and covers six of the eight orders of extant sharks, the four orders of extant batoids, and one of the three families of extant Chimaeriformes  (Naylor et al. , 2012; Ebert et al. , 2013; Last et al. , 2016). The samples were kindly provided by the University of Montpellier, by the Aquarium of Montpellier (Planet Ocean Montpellier), or were bought at fish markets. No handling of live specimens was necessary for this study. The ontogenetic stages were determined based on literature data, using the total length (TL) or disc width (DW) as proxies (Table  1 ). When no data were available about the ontogenetic stages of the species sampled, the TL, DW, or both were provided (Table  1 ). When fresh or frozen material was available, the vertebra or neural arch were sampled and fixed in 4% paraformaldehyde in Phosphate-Buffered Saline 1X (PBS 1X).
ACKNOWLEDGMENTS We thank  Sylvain Adnet ,  Henri Cappetta , and  Guillaume Guinot  from the  Institut des Sciences de l'Evolution de Montpellier  and the aquaria  Planet Ocean, Montpellier  ( Nicolas Hirel ) and  Marineland Parcs ,  Antibes  ( Jean-Philippe Cateau ) for providing biological samples. We thank  Renaud Lebrun  for his help with microCT scans and  Camille Martinand-Mari ,  Nicolas Leurs , and  Yann Bayle  for insightful proofreading. 3D data acquisitions (microCT facilities) and slide scanning (nanozoomer) are hosted in the MRI platform, a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 , Investments for the future), and of the  Labex CEMEB  ( ANR-10-LABX-0004 ) and  NUMEV  ( ANR-10-LABX-0020 ).
We restricted our analysis to 43 actors from this list (Supplementary Materials 1), in line with our resources. The 43 selected actors were identified based on a preliminary internet search of academic and grey literature and a brainstorming process with four academic experts of the region representing different disciplines. We then iteratively updated this list over the course of field research by purposive snowballing (Bryman 2016). The composition and distribution of the selected actors reflect to a certain extent the composition of the agricultural sector in Maurienne, so we consider the interview dataset as a representative sample of the 400 actors relevant to its Beaufort production system (SPM 2020). We classified actors into two groups, to determine whether specific cooperative structures built around Beaufort production foster different perceptions of NCP co-production activities and the associated collective capabilities than actors not directly linked economically to the Beaufort production system.
Data collection We conducted 44 semi-structured interviews, representing the 43 selected actors. We selected interviewees based on their involvement in their institutions. They included farmers, officials (e. g. agricultural chamber), collective institutions (e. g. associations for agricultural development, pastoral land associations) or skiing resorts (Supplementary Materials 1). For collective actors or regional authorities, we selected interviewees in either leading or managing positions, or actively involved in Beaufort production. For instance, we conducted interviews with chairs of six pastoral associations. Thirteen interviewees were full-time farmers. Consistent with other Alpine agricultural systems, most of them had representative, managing, leading or technical roles (often voluntary) in collective institutions (e.
g. cooperatives, pastoral land associations), in addition to managing their farm  (Schermer et al. 2010). To elicit interviewees' perceptions of agriculture, semistructured interviews focused on the role and background of respective institutions, on their professional position, their perceptions and relations to the Maurienne agricultural landscape, including the agricultural sector and Beaufort production (Interview guide in Supplementary Materials 2). With institutional representatives, we specifically asked interviewees to explain the role of their organisation. Interviews took place from February to September 2019 in the actors' working environment, with an average duration of 60-90 min. We transcribed and uploaded the dataset of 350 pages in the original language (French) into the qualitative data analysis software NVivo, release 1.
Analysing perceptions and associated sentiments about Beaufort co-production In a second step, we analysed the associated sentiments expressed by actors about different co-production activities. Sentiment analysis is widely used for marketing or communication purposes (Twitter, customer-related review sites), but less commonly for qualitative interview data  (Liu and Zhang 2012; Lee et al. 2020). In socialecological research, it has been applied to measure placebased perceptions of non-material NCP (Jones et al. 2020). We used sentiment analysis to test whether actors share similar positive or negative sentiments about Beaufort coproduction activities. We used a four-level Likert scale (very positive, moderately positive, moderately negative, very negative) to code and systemize interviewees' overall feeling associated to each of the identified activities.
e. residuals are greater than we would expect if the variables were truly independent). In contrast, residuals with values close to zero mean that there is no association. The sign of the residuals describes the nature of the association (positive or negative)  (Agresti 2007). Standardised residuals were represented as mosaic graphs  (Meyer et al. 2006 ). The mosaic plot was drawn with the vcd package and R version 4. 0. 2 (Meyer et al. 2006 (Meyer et al. , 2020;; Zeileis et al. 2007 ; R Core Team 2020). All graphs were created with R, and the tidyverse  (Wickham et al. 2019) , ggpubr  (Kassambara 2020a) , rstatix  (Kassambara 2020b ) and viridis  (Garnier 2018)  packages.
Methodological considerations: unravelling the links between nature's contributions to people co-production, collective capabilities and local identities with sentiment analysis We evaluated the role of collective capabilities in the different steps of Beaufort cheese co-production in the French Alps from interviews of 43 actors of the Maurienne agricultural system, depending on whether actors were directly involved Beaufort cheese production chain or not. Our mixed method enabled integrating qualitative and quantitative information. Word frequency, commonly used to analyse general themes in interviews or large corpus datasets  (Droste, D'Amato, and Goddard 2018) , enabled a precise and exhaustive identification of key terms. Post-interpreting sentiment from in-text context rather than explicitly asking interviewees to express their sentiment about terms and activities is common practice  (Lee et al.
2020). Such qualitative information is crucial in sentiment analyses as automated algorithms (e. g. NVivo) attributing predetermined sentiment scores to "sentimentally loaded words" (Jones et al. 2020) do not always correctly reveal people's preferences and value-systems. Although manual coding was in some cases a difficult and time-consuming task, as subtle, less pronounced emotions can be difficult to detect in written datasets, we believe it was more efficient for determining sentiment scores depending on context. This way, some terms received both positive and negative scores, like milk whose production is crucial for Beaufort cheese (and justifies positive sentiments), but whose quota restrictions and the associated work load are often seen as constraints by farmers (negative sentiments).
This virus also displays two glycoproteins at its surface, E1 and E2, but only the structure of E1 in its postfusion form has been reported  [21]. There are not enough orthologs of E2 for AlphaFold to make reliable predictions, and, therefore, the question of whether it belongs to the canonical type of class II AP remains unanswered. Despite an extensive literature on class-II MFPs, comparatively little is known about their APs, partly because their structures have remained elusive for many years. Here, we review what is known about the structural biology of the class II AP. To provide a broader perspective, we have used AlphaFold, a machine learning approach that has recently emerged as a breakthrough in the prediction of protein structures  [22, 23]. The AlphaFold software predicts protein structures with atomic accuracy without requiring structural homologs as a template, provided that multiple alignments of enough orthologs are available to evaluate covariance.
Hantaviruses Hantaviruses are rodent-borne viruses belonging to the Orthohantavirus genus of the Hantaviridae family in the Bunyavirales order of segmented, negative-strand RNA viruses. This genus includes many important pathogens distributed worldwide. Their transmission to humans can cause two severe diseases: hemorrhagic fever with renal   [30] ) and Gn (PDB: 6ZJM  [16] ) in side view. Domains A, B, C, and the β-ribbon are indicated and colored in red, yellow, pink, and blue, respectively. The right panels show topology diagrams of E2 (top) and Gn (bottom) with the domains colored as mentioned previously.
The precise organization and specificity of Gn/Gc spike interactions have remained elusive because of an inherent malleability of the viral particles, which deviate from having perfect icosahedral symmetry. This feature has limited high-resolution cryoEM structural studies. To date, the cryo-EM map with the highest resolution reaches just beyond 8 Å. This map was obtained by applying a localized reconstruction procedure [54] on particles previously fixed with formaldehyde  [19]. The models of RVFV Gn H and Gc We used AlphaFold  [22]  to predict the structure of full length Gn (residues 154-690) of the RVFV polyprotein precursor (Uniprot code: P03518). The program produces a per-residue confidence metric termed pLDTT on a scale from 0 to 100  [22]. pLDTT values higher than 70 reflect reliable models with correct backbone predictions, and those with values higher than 90 correspond to models with both reliable backbone and side-chain orientation predictions.
pLDTT values lower than 50 are a strong predictor of disorder, and regions with such values are either unstructured under physiological conditions or only structured as part of a complex. The AlphaFold prediction of the RVFV Gn fold (Figure  2 , central panel) has very high confidence with an average pLDTT of 85. 6, and revealed that the head domain, which aligns closely with the structure of RVFV pGn H determined by x-ray crystallography (PDB: 5Y0W), is followed by an extended linker of nine amino acids, a β-sandwich with the same "s-type" IgSF fold observed in domain C of alphavirus E2 and hantavirus Gn, followed by a C-terminal, and an amphipathic α-helical hairpin stabilized by two intramolecular disulfide bonds. The structure of the latter segment is very similar to the MPER present on hGn, and we, therefore, term it the phenuivirus Gn MPER (pMPER, Figure  2 ).
To explore this idea, we used AlphaFold to predict the structure of the complete TSWV Gn protein (residues 31-368, Uniprot code: R9RUV9). The model thus generated predicted the formation of a hairpin similar to that reported for hantaviruses, and suggests that the segment ( 310 IYKQTACINFS 320 ), which the UniProt database partially predicts as part of a TM segment (underlined) and contains an N-glycosylation sequon (in bold), is actually the C-terminal helix of the tospovirus Gn MPER (Figure  3A ). ). To date, there is no experimental structure for Gn of any peribunyavirus, and only the prediction by AlphaFold is displayed in B. The disulfide bond in the MPER regions is indicated with green lines, and the location of the putative N-glycosylation site with green arrowheads. These models are available as a Supplementary Items in this manuscript.
Nairoviruses The Nairoviridae family of tick-borne bunyaviruses contains 7 genera with 47 species reported to date by the ICTV  [48]. Crimean-Congo hemorrhagic fever virus (CCHFV, genus Orthonairovirus) is the most relevant nairovirus, and is the prototype of the family. It is transmitted to humans upon a bite by an infected tick, or by contact with blood or tissues of infected livestock. CCHFV is not pathogenic in most vertebrates, but, in humans, it causes hemorrhagic fever with a case fatality rate up to 30%  [63]. The genome is made by three segments of negative-sense ssRNA, termed S, M, and L, which code for a nucleoprotein (S segment), an RNA-dependent RNA-polymerase (L segment), and a poly-glycoprotein precursor of the envelope proteins (M segment). The biogenesis of the nairovirus fusion machinery is more complex than that of the bunyaviruses discussed above, and requires several cellular proteases for processing the precursor poly-glycoprotein.
gp38 folds as an N-terminal 3-helix bundle followed by a β-sandwich composed of a seven-and a four-stranded β-sheet facing each other (Figure  4 ). The structure is further stabilized by four disulfide bonds, three of them conserved across the family, indicating a similar protein fold. A search of the PDB database for structural homologues showed no matches, but an alignment of the gp38 and Gn sequences of several nairoviruses revealed that some sequence and structural elements were conserved in the region corresponding to the β-sandwich, suggesting that both proteins may have the same fold, and may have arisen through a gene duplication event  [68]. We used AlphaFold-2  [22]  to predict the structure of the CCHFV poly-glycoprotein segment comprising gp38 and Gn (residues 248-842, Uniprot code: Q8JSZ3), and obtained a reliable model with an average pLDTT of 75 (gp38 has a pLDTT of 81 and the Gn ectodomain 66) (Figure  4 ).
about 100 nm with spike-like projections of 10 nm with tetragonal symmetry, similar to those observed in hantavirus particles. The only glycoprotein for which the structure is known is gp38  [68] , although its function remains unclear. It is not highly conserved throughout the family, but gp38 is the target of the monoclonal antibody 13G8, which is non-neutralizing, but was shown to protect against a heterologous CCHFV challenge in a STAT1-knockout mouse model  [68]. gp38 folds as an N-terminal 3-helix bundle followed by a β-sandwich composed of a seven-and a four-stranded β-sheet facing each other (Figure  4 ). The structure is further stabilized by four disulfide bonds, three of them conserved across the family, indicating a similar protein fold. A search of the PDB database for structural homologues showed no matches, but an alignment of the gp38 and Gn sequences of several nairoviruses revealed that some sequence and structural elements were conserved in the region corresponding to the β-sandwich, suggesting that both proteins may have the same fold, and may have arisen through a gene duplication event  [68].
The left panel is a cartoon representation of the CCHFV gp38/Gn complex obtained using AlphaFold. The portion corresponding to gp38 is identical to its x-ray structure (PDB: 6VKF  [68] ), and is colored according to domains, the N-terminal α-helical bundle in teal, and the C-terminal β-sandwich in orange. The portion corresponding to Gn, for which there is no experimental data, is colored according to domains as indicated. In the right panel, the topology diagram is colored in the same way, and has a dotted outline for the region for which no experimental structure is available. The disulfide bond in the predicted MPER region is depicted and labelled, with the corresponding cysteines labelled in the right panel. As the mucin-like domain (MLD) is intrinsically disordered, it is not included in the diagram.
In the model for the The left panel is a cartoon representation of the CCHFV gp38/Gn complex obtained using AlphaFold. The portion corresponding to gp38 is identical to its x-ray structure (PDB: 6VKF  [68] ), and is colored according to domains, the N-terminal α-helical bundle in teal, and the C-terminal β-sandwich in orange. The portion corresponding to Gn, for which there is no experimental data, is colored according to domains as indicated. In the right panel, the topology diagram is colored in the same way, and has a dotted outline for the region for which no experimental structure is available. The disulfide bond in the predicted MPER region is depicted and labelled, with the corresponding cysteines labelled in the right panel. As the mucin-like domain (MLD) is intrinsically disordered, it is not included in the diagram.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3 390/v13122368/s1. Models of Gn from RVFV (residues 154-690, Uniprot code: P03518), TSWV (residues 31-368, Uniprot code: R9RUV9), LACV (residues 14-299, Uniprot code: Q8JPR1), and CCHFV (residues 248-842, Uniprot code: Q8JSZ3) were generated using a local copy of AlphaFold-2  [22] , installed using the open-source code available at https://github. com/deepmind/alphafold (accessed on 1 September 2021). Runs were performed on a Centos 7 workstation with 64 CPUs and 4 GeForce RTX 2080 Ti GPUs, using the casp14 preset and including all PDB templates present in the database. We have deposited the structures as Supplementary Items for this manuscript.
| Herbaceous plant data Along each runway-transect, we collected quadrat data at two distances from the runway-transect: over the runway ("on runway") (runways are about 8 cm wide) and 25 cm from the runway edge ("off runway"). Each quadrat was a square cardboard frame of 10 cm 2 , which we laid on the ground over the sample and photographed with digital cameras in autofocus mode at a distance of approximately 1 m from the ground. Five quadrats of on-runway samples were recorded every 2 m along the runway-transect at odd-numbered meter marks. Five quadrats of off-runway samples were recorded at even-numbered meter marks. Quadrats alternated to the left and right of the transect.
In some cases, the dishes of oats gained up to 2 g of water from dew. The amount eaten is thus precise to within ≤2 g. We did not observe ant activity at the oat dishes. Spillages were noted and not treated as eaten. Just prior to data collection, another experiment started in the same research site, which involved baiting Sherman traps with oats, and some of these traps were close to one of our central plots. Thus, we did not collect foraging data from this plot.
In fact, the two most abundant of the three non-native species observed at our field site, Camissonia sp. and Erodium cicutarium, are considered to be desert-adapted species and poor competitors with grass  (Ehleringer et al. , 1979; Holmgren et al. , 2000; Schutzenhofer & Valone, 2006; Stamp, 1984) , while only E. moschatum, which was much less abundant, is considered ruderal (IUCN Invasive Species Database, http://www. iucng isd. org/gisd/speci es. php?sc=518). If an important impact of degus is via soil aridity in grazing areas, this might also account for  Madrigal et al. (2011)  finding that the degu presence at their site resulted in a numerical dominance of non-native species. Their research site is more arid than ours, and a further increase in soil aridity may more strongly favor non-native desert-adapted herbs.
We did not anticipate that ecosystem engineering affecting soil properties (soil aridity) might explain the expansion of non-native species, since the F I G U R E 4 Diagram of the mechanisms of disturbance and their accumulation over time into an ecosystem engineering effect, which favors non-native dryadapted species majority of literature on non-native herbs in central Chile focuses on their adaptations to herbivory rather than aridity, and disturbance literatures focus on the mechanisms of competition and biomass loss, without linking to ecosystem engineering. However, preexisting measurements of soil hardness and moisture at the same degu colony, and the desert adaptations of the most abundant non-native species, provide evidence for the hypothesis that ecosystem engineering of the soil conditions contributes to the spread of non-native herbaceous species on degu colonies.