dataesr/scientific-paragraphs-categorization huggingface dataset:
The samples were then pooled, fresh masses weighed, and soils sieved through a 2-mm mesh. Fine roots were picked, washed, and dried to estimate fine root biomass. Aliquots of fresh sieved soil were weighed and dried (85°C, 48 h) to calculate the fresh to dry weight ratio and the soil water content. Other aliquots were used to measure the soil amino acid content, gross rates of protein depolymerization and microbial amino acid consumption rates (and of organic N mineralization and nitrification), microbial biomass carbon and nitrogen, and potential enzyme activities (dataset:  Maxwell et al. , 2021).
Concentrations and 15 / 14 N isotope ratios of NO 3 -in the 1 M KCl extracts were determined by converting NO 3 -to NO 2 -with vanadium (III) chloride (VCl 3 ) under acidic conditions and further reduction of NO 2 -to N 2 O by sodium azide (NaN 3 )  (Lachouani et al. , 2010). Concentrations and 14 / 15 N isotope ratios of the resulting N 2 O were determined by PT-IRMS with a cryo-focusing unit (GasBench II coupled to Delta V Advantage, Thermo Fisher Scientific). Parts of the data of gross rates of inorganic N cycling processes (reduced treatments, and for the July harvest only) have previously been published and analyzed  (Séneca et al. , 2020).
| Statistical analyses Statistical analyses were performed with R 3. 1. 3 (R Development Core Team), and graphs were generated using the R "ggplot2" package  (Wickham, 2016). Supplementary graphs were generated using Sigma Plot 12. 0 and the 3D plots were generated with the R package "rsm"  (Lenth, 2009). The experiment consists of two different approaches, a response surface approach, including the three levels of atmospheric temperature and CO 2 concentration manipulation (including ambient conditions), and an ANOVA design, including ambient and a "future environmental scenario" (combining highest levels of eCO 2 and eT) ± drought. We also applied a correlation approach to study the main variables explaining the protein depolymerization rates.
| Response surface regression approach To test effects of eT and eCO 2 on the N pools and processes (protein depolymerization, microbial amino acid consumption, mean residence time of amino acids, organic N mineralization, and nitrification) across seasons, we first used a quadratic generalized least squares (GLS) model with the R package "nlme"  (Pinheiro & Bates, 2020). We accounted for autocorrelation between the sampling dates (R function corr = corrCompSymm) and used an additional weights function to allow for heterogeneous variance between sampling dates, similar to  Simon et al. (2020). We included all the levels of temperature and CO 2 (ambient, +1. 5°C, +3°C; and ambient, +150 ppm, +300 ppm), enabling us to test both first and second-order factors in order to evaluate possible nonlinear responses to multiple levels of temperature and CO 2  (Piepho et al.
| Correlations with protein depolymerization rates We ran repeated measures correlation analyses to assess the relationship between gross protein depolymerization and other edaphic and vegetation parameters, including plant and microbial descriptors, N pool sizes, and enzyme activities. This was done using the "rmcorr" package  (Bakdash & Marusich, 2017) , which accounts for nonindependence among observations, using analysis of covariance (ANCOVA) to statistically adjust for interindividual variability. This was done in order not to violate the assumption of independence due to the repeated measures in our experimental design. P-values in the "rmcorr" package were obtained by a bootstrapping approach  (Bakdash & Marusich, 2017). The normality and homoscedasticity assumptions were verified visually (as described above) and data were log-transformed when necessary to meet the model assumptions.
| Controls of soil protein depolymerization Across the entire dataset, we found a strong negative correlation between soil water content (SWC) and gross protein depolymerization (n = 102, ρ = -0. 710, p <. 0001) (Figure  S7 ) and amino acid consumption rates (ρ = -0. 558, p <. 0001). Season had a strong effect on SWC. Excluding the drought treatment, which had the lowest SWC of all, the soils were driest in May (0. 10-0. 25 g H 2 O g -1 dm), wettest in July (0. 31-0. 36 g H 2 O g -1 dm), and intermediate in soil water content during October (0. 22-0. 34 g H 2 O g -1 dm). After running repeated measurement correlations of protein depolymerization with several other parameters, different patterns emerged between the "eT eCO 2 " data subset (ambient, eT, and eCO 2 plots across seasons) and the "drought" data subset (ambient and "future scenario" at the drought onset, peak drought, and postdrought dates) (Figure  4 ).
In the "eT eCO 2 " dataset, there was a strong positive correlation between protein depolymerization and plant parameters, including aboveground net primary productivity (ρ = 0. 576, p <. 0001), belowground biomass (ρ = 0. 463, p =. 0005), and root turnover time (ρ = 0. 454, p =. 0006). There was also a strong negative correlation between protein depolymerization and total soil N (ρ = -0. 395, p =. 0034), but a positive correlation with free amino acids (ρ = 0. 541, p <. 0001), which are released by protein depolymerization. Subsequent process rates such as ammonification and gross nitrification were positively (ρ = 0. 293, p =. 0335) and negatively (ρ = -0. 393, p =. 0036) related to protein depolymerization, respectively. Focusing on the "drought" data subset, the relationship of protein depolymerization with aboveground net primary productivity was lost (p =.
3716), while belowground plant biomass and root turnover time had a much smaller, but still significant positive relationship with protein depolymerization. However, in the "drought" dataset, there was a strong positive relationship between protein depolymerization and dissolved organic N (ρ = 0. 443, p =. 0038), and with microbial biomass N (ρ = 0. 479, p =. 0015). We observed a negative relationship between protein depolymerization and several soil microbial parameters, such as microbial turnover time (ρ = -0. 443, p =. 0037), gram-negative and gram-positive bacteria (ρ = -0. 454, p =. 0028; ρ = -0. 372, p =. 0165, respectively), and arbuscular mycorrhizal fungi [AMF] biomass measured by root NLFA analysis (ρ = -0. 3450, p =. 0272) and by PLFA markers (ρ = -0. 341, p =. 0290). The relationships between protein depolymerization and these soil parameters were mostly nonsignificant for the "eT eCO 2 " subset, except for gram-positive bacteria (ρ = -0.
| Drivers of protein depolymerization and indications of substrate limitation When correlating protein depolymerization with other potential drivers in the "eT eCO 2 " dataset (ambient conditions, eT, and eCO 2 ), we observed multiple positive correlations between protein depolymerization and plant-related variables, such as ANPP, net primary productivity (NPP), belowground biomass, and root turnover time (Figure  4 ). This suggests that microbial N limitation triggers an increased allocation of resources toward soil organic N mining and therefore protein depolymerization, when plant N uptake or rhizodeposition and priming processes prevail in the system. Indeed we found higher protein depolymerization rates with high root turnover time (slow root turnover rate), low root and shoot δ 15 N (proxies for a more conservative ecosystem N cycling)  (Robinson, 2001) , and low soil total N and thus high soil C:N (Zechmeister-Boltenstern  et al.
, 2015). When focusing on the "drought" dataset (ambient versus "future scenario," ± drought), we found a strong shift in the explanatory variables of protein depolymerization rates compared to the "eT eCO 2 " dataset. The positive correlation between protein depolymerization and ANPP from the "eT eCO 2 " dataset was lost and the positive relationship with belowground biomass and root turnover time became markedly weaker (Figure  4 ). During drought conditions, gross primary production and plant biomass is reduced along with C input to soil  (Meeran et al. , 2021). Instead, many relationships with microbial-related parameters became more significant. We suggest that this indicates a shift from plant control in the "eT eCO 2 " subset to soil microbial substrate control, accelerating protein depolymerization during drought/rewetting.
This penetration depends on the soil composition. For clay soils or rich clay soil content, even in case of extreme dryness, the waves are reflected in the first centimeters, and the SM retrieval on the surface is well correlated to the in situ SM probe at 5 cm depth  (Vey et al. , 2016; Zhang et al. , 2018). For sandy soils, the penetration is more critical and exceeds the meter in conditions of intense drought. Our approach estimating SM is based on state-of-the-art GNSS-R for SM retrieval. It is combined with the phase unwrapping, generally used in SAR imagery  (Benoit et al. , 2020; Lu & Zebker, 1998)  for very weak values of SM, which induces the intense penetration for the sandy soil of the study area. The method using SNR data incorporates both direct and reflected signals, continuously collected by the geodetic antenna.
Interference Pattern Technique Interference Pattern Technique (IPT) is a method based on analyzing the multipath signature present in the GNSS SNR data recorded continuously by a GNSS receiver and its associated single geodetic antenna (Figure  1a ). Multipath propagation affects the SNR recorded by a GNSS receiver by generating frequency oscillations with lower amplitudes than the direct signals. These frequency oscillations depend on the satellite elevation (SE) angle and vary from the intermediate frequency at Low Satellite Elevation (LSE) to the high frequency at High Satellite Elevation (HSE). Because the antenna gain pattern prefers the direct signal over the reflected signal by construction, and during reflection will also reduce the signal energy; the primary influence of the structure of the SNR time series is the direct one (Figure  1b ).
Data used for validation The SSM is monitored by an SM station located about 500 m away from the GNSS receiver (Figure  7a ). Some authors (e. g. Baup et al. , 2007; Rosnay et al. , 2009)  have shown that in semiarid Sahelian areas, with equivalent soil and vegetation, in situ SM measurements are representative of much larger area. This is why, we consider in this case of very homogeneous geological/ soil (Faye et al. , 2017), the moisture station represents a large area, including the GNSS-R station and can be used for calibration. Soil content of the Dahra experiment is ~95% of sand associated with few percent of organic matter in surface and few percent of clay. In order to avoid surface disturbances (presence of this small concentration of organic matter), we took soil samples at 10 cm depth.
Importance of the unwrapping in sandy soil For sandy soils of Dahra, we have a very noisy phase dataset (Figure  8a ) without any correlation with the in situ probes. So we applied the unwrapping technique to this phase dataset and we make an inspection of the unwrapped/wrapped signals to identify possible fake slips due to the noise. To calculate the number of fake slips, we compute the difference between two adjacent samples of the wrapped signal. If the computed difference is upper than twice the noise variance threshold criteria (TC), we assume we have a fake slip. In that case, the signal after the fake slip is affected by "miss wrap" due to the error propagation. This part of the time series is affected by a phase offset that can be positive or negative depending on the noise difference sign.
We need to apply the unwrapping process to correct these harmful effects until all the computed differences are lower than TC. The resulting phase correlates with the in situ VSM measurements (P 5cm , (1) Figure  7a ) with a calibration function in this type of soil of VSM~1 x unwrapped phase where the bias is close to 0 (Figure  8b ) which shows that we do not need to calibrate the phase to obtain VSM because of VSM ~Φunw. The rest of the study will use the Φ unw dataset to compute the antenna height parameters (see 4. 2). October to December 2016 in the study area. We do not use P 1m due to the weak correlation with subsurface SNR m measurement. This observation shows that the wave penetration depth is less than 1 m, which agrees with the theoretical model for VSM upper than 2. 5% (Figure  2a ).
97 over the study area (95% of sand). Furthermore, the combination of all GPS/GLONASS/GALILEO/ BEIDOU satellites in the SNR data treatment can be applied to generate a complete series of time precision, which considerably increase the spatial and temporal resolution from 1 day to 10 min. Currently, GNSS networks exist in various countries and can be applied GNSS-R tool for free. Another critical point is the number of GNSS satellites which increase continuously from 1990 (~40 compared to more than 120 in 2030  (Gao & Enge, 2012) ). This factor increases the spatiotemporal monitoring of the Earth, except in the pole areas due to the structures of GNSS orbital planes. GNSS-R SM maps can be established continuously worldwide and can provide powerful time series. Monitoring SM by GNSS-R will be a crucial parameter to improve the understanding of spatial variability of droughts at all spatial and temporal scales in the context of global climate change.
Acknowledgments This research was funded by the  Vietnam National Foundation for Science and Technology Development (NAFOSTED)  under grant number  105. 08-2020. 17 , by the  University of Toulouse , and by the foundation of  Thematic Network for Advanced Research (RTRA) in Science and Technology for Aeronautics and space (STAE)  under the  MOMMHYS  ( Mesures d'Opportunités pour   la Modélisation Météo-Hydro-Surfaces continentals)  project.
Data processing and analysis were performed using the Topspin ® 4. 0 and CcpNmr version-2 software  [50]. To record the spectrum of the completely denatured protein, the buffer was supplemented by 6 M urea. The 15 N R 1 and R 2 relaxation rates and { 1 H}- 15 N heteronuclear nuclear Overhauser effects (NOEs) were measured at 303K. The 15 N R 1 and R 2 relaxation experiments were based on the refocused 1 H- 15 N HSQC relaxation experiments and recorded in an interleaved pseudo-3D method with an inter-scan delay of 4 s. For the determination of R 1 relaxation rate constants, 11 total data sets were collected at relaxation delay times of 10, 50, 100, 200, 400, 500, 600, 800, 1000, 1500, and 2000 ms. For the determination of R 2 rate constants, 12 data sets were collected at delay times of  17, 34, 51, 68, 84.
8, 102, 136, 170, 204, 237, 271 , and 305 ms. R 1 and R 2 spectra were recorded as 144 × 2024 complex data points. For the backbone { 1 H}-15 N heteronuclear NOEs, two different spectra were recorded as 256 × 2048 complex data points in an interleaved manner with and without a 4 s proton saturation pulse. The R 1 and R 2 rates, heteronuclear NOE values, and their associated errors were determined from the peak intensities using the CcpNmr version-2 software  [50]. The amide proton chemical shift temperature coefficients (∆δHN/∆T (ppb/K)) were calculated using the Shift-T web server (http://biophysical. science/shiftt, accessed on 22 September 2022)  [51].
Assignment is missing for the three N-terminal amino acids (M23-R25) and for residues H67-K68, R81, H102, D116-S118, R122-H125, M139-Q140, M161-R162, and H169. We note that the five histidine residues of Nter-BsRNaseY (H67, H102, H117, H125, and H169) are localized within these segments. The Nter-BsRNaseY 13 CO, 13 C α , 13 C β , 1 H N , and 15 N chemical shifts were then used as input for the TALOS-N software  [53]  to predict the protein backbone torsion angles along the sequence. The TALOS-N results reveal that several regions of the protein (residues 39-61, 73-88, 122-157, and 171-191) have the highest propensity to form α-helices at 300K (Figure  3A  and Table  S1 ). In regular turns and α-helical polypeptide chains, sequential amide protons close in space (HN i -HN i+1 ) yield to cross-peaks of high intensity in the NOESY spectra, while protons farther away (HN i -HN i+2 ) give peaks of lower intensity.
The highest structural homology was found for the coiled-coil domain of SAS-6, a centriole protein (PDB code 6YRN, 11% sequence identity, Z score = 7. 9, root mean square deviation (rmsd) of 3. 9 Å for 111 aligned Cαs)  [59]. SAS-6 fragments were shown to be organized as two-stranded parallel coiled-coil domains that could form higher-order interactions: nine SAS-6 dimers associated via interactions between their N-terminal globular head domain to form a ring. In addition, asymmetric parallel association between coiled-coil domains of SAS-6 was found to be important to form a cartwheel structure and provide polarity to the assembly.
AlphaFold Model of the Full-Length B. subtilis RNase Y To complement our NMR study, which was performed using a truncated B. subtilis RNase Y protein that lacks the C-terminal globular domain (residues 193-520), AlphaFold was used to generate a model of the full-length B. subtilis RNase Y, which was shown to exist as a dimer and possibly also as higher oligomeric forms  [12]. Interestingly, in the model of the full-length protein, the coiled-coil structure observed for the Nter-BsRNAseY construct was extended by ten residues (Figure  S12A, B ) that were previously thought to belong to the C-terminal domain. Analysis of the assemblies with the Proteins Interfaces Structures and Assemblies (PISA) program  [60]  indicates that the coiled-coil structure contributes to ~72% of the buried surface area in the full-length dimer.
Moreover, the models unveiled the probable fold of the C-terminal domain, which was unknown until this point. Intriguingly, the pLDDT values, especially for region 130-190, were lower for the full-length RNAse Y (Figure  S11C-E ) than for Nter-RNAseY (Figure  5D-F ). This suggests an effect of the C-terminal domain on the conformation of residues 130-190. The analysis of the best AlphaFold model of the C-terminal domain of B. subtilis RNase Y with DALI  [58]  revealed high structural similarity with proteins of known structure containing the HD or KH domains (Figure  1 ). Indeed, the highest structural homology was found for a protein predicted to belong to the HD hydrolase superfamily (PDB code 2PQ7, 24% sequence identity, unpublished), with a Z-score of 12. 3 and an rmsd of 3. 4 Å for 139 aligned Cαs.
Interestingly, the latter protein was crystallized in the presence of divalent Fe atoms and superposition of the HD domains of both proteins highlights the potential binding mode of divalent metal ions to B. subtilis RNase Y, involving conserved His/Asp residues of the HD domain (Figure  S16 ). Indeed, it is known that cleavage by RNase Y requires the presence of Mg 2+ ions, which can be replaced by Mn 2+ or Zn 2+  [6]. In addition, the C-terminal domain of B. subtilis RNase Y showed a high structural homology with the putative RNA-binding protein of the exosome complex (PDB code 2Z0S, 21% sequence identity, unpublished), with a Z-score of 12. 2 and an rmsd of 1. 8 Å for 81 aligned Cαs, and with the KH-containing RNA-binding protein RRP4 of the archaeal exosome (PDB code 2BA0, 21% sequence identity), with a Z-score of 11.
8 and an rmsd of 1. 7 Å for 78 aligned Cαs  [61]. The exosome is a large multi-subunit RNase complex that is required for 3 /5 processing of ribosomal RNA in archaea and eukaryotes and thus it has a similar function to RNase Y. The exosome structure consists of six RNase phosphorolytic (PH) domain subunits forming a hexameric ring, which associates with three KH and/or S1-containing subunits to form a regulatory RNA recognition platform that restricts entry to the catalytic chamber to unstructured RNA substrates. The analysis of the C-terminal region with DALI identified several cation-binding proteins as close homologs, such as the cation efflux protein MamM (PDB code 3W64, 14% sequence identity, Z-score of 9. 3, rmsd = 2. 0 Å for 73 aligned Cαs)  [62]  and the cytoplasmic C-terminal domain of zinc transporter protein YiiP (PDB code 3H90, 11% sequence identity, Z-score of 7.
8, rmsd = 2. 3 Å for 72 aligned Cαs)  [63]. MamM is one of the main ion transporters of magnetosomes, i. e. , bacterial organelles that enable magnetotactic bacteria to orientate along geomagnetic fields. It was shown that the cytosolic domain of MamM forms a stable 'V-shape' dimer that undergoes distinct conformational changes upon divalent cation binding (  [62] , whereas the C-terminal domain of YiiP adopts a metallochaperone-like fold that allows it to deliver zinc ions to protein targets. Finally, the C-terminal region of B. subtilis RNase Y was found to be homologous with proteins that are components of megadalton-sized ring-shaped complexes, such as protein PRGK (PDB code 2Y9J, 16% sequence identity, Z-score of 7. 2, rmsd = 2. 0 Å for 69 aligned Cαs)  [64] , or the flagellar M-ring protein FliF (PDB code 6SD3, 9% sequence identity, Z-score of 6.
Conclusions A coiled-coil is a structural element that is remarkable with respect to the diversity of conformations that it can adopt and for the range of functions that it exhibits  [56]. As a result, long coiled-coil proteins encode an enormous repertoire of surface epitopes, in addition to potentially linking functional domains or communicating conformational changes  [70]. There are several examples of proteins that have coiled-coils that act as a scaffold for interaction with other proteins or other domains. For example, the flexibility of the coiled-coil was shown to regulate the function of soluble guanylate cyclase  [71, 72]. Elongated structures, such as coiled-coils that are under-represented in the PDB or structures that have never been observed previously, are difficult to predict using AlphaFold.
Author Contributions: Conceptualization, N. M. , C. v. H. and B. G. -P. ; methodology, N. M. and C. v. H. ; software, N. M. ; formal analysis, N. M. , C. v. H. and B. G. -P. ; investigation, P. H. , N. A. , N. M. and B. G. -P. ; data curation, N. M. and C. v. H. ; writing-original draft preparation, N. M. and B. G. -P. ; writingreview and editing, N. M. , C. v. H. and B. G. -P. ; visualization, N. M. ; supervision, C. v. H. and B. G. -P. ; funding acquisition, C. v. H. and B. G. -P. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the French State Program 'Investissements d'Avenir' (Grants "LABEX DYNAMO", ANR-11-LABX-0011 and ANR-11-BSV8-026). Financial support from the IR INFRANALYTICS FR2054 for conducting the research is also gratefully acknowledged.
Acknowledgments: We thank  Djemel Hamdane  for performing the CD data collection and analysis,  Christophe Velours  for performing the SEC-MALS experiments, the  Macromolecular Interaction Platform of I2BC  for use of its facilities,  Charles Bou-Nader  for initiating the  NMR  project and for fruitful initial discussions,  Ludovic Pecqueur  for preliminary experiments and fruitful discussions, and  Harald Putzer  for discussions relative to RNase Y.
We included litter C:N rather than N concentration, because it was shown that potential N transfer not only depends on N concentration, but also on the C available to decomposer organisms  (Handa et al. , 2014; Manzoni et al. , 2008). Traits were measured in five individuals per species and 10 leaves per individual. For 30 species, the mentioned functional traits were retrieved from  Canessa et al. (2021). For other species not present in that study, data of Fp, C:N, tannins and total phenolics were measured, following the same methodology used in  Canessa et al. (2021). All traits were averaged at the species level.