dataesr/scientific-paragraphs-categorization huggingface dataset:
The field data was inverted with using Res2dinv software  [29, 30]. A robust inversion  [34]  was applied to the field data. The inversion results are shown in leakage zone no. I-II and the CCR cross-section (which is discussed in Section 3. 3) were also superimposed. In the resistivity cross-sections, the relatively low anomalies ("L1" to "L5") and high resistivity anomalies from "H0" to "H3" are distinguishable. The high resistivity anomalies are in blocky form and were spotted for x = 22-33, 40-58, 89-110 m of the profile line. The anomalies are characterized by resistivity values equal to approximately 10,000 Ωm. It is worth noting that the "H0" anomaly, located between the H2 and H3 anomalies, has a very similar shape to the other "H" anomalies but the resistivity value equals approximately 250 Ωm.
The CCR measurements were carried out in the continuous mode. The OhmMapper geometry was set up using three receivers (Rx1-Rx3) and a standard single transmitter Tx. The applied settings are shown in Figure  12  below. The detailed geometry of the OhmMapper array was shown below in Table  1. The detailed geometry of the OhmMapper array was shown below in Table  1. In the next stage, the obtained field data was analysed using OhmImager (Data analysis applet). In the software, the following steps were applied: firstly, the data was examined, then the accuracy of the geometry was checked and, in some cases, adjusted. Secondly, the erroneous data was removed in two ways by deleting the individual pieces of data and by using a median filter  [47]. Finally, the CCR data was exported in a format compatible with Res2dInv software, where the inversion process was performed.
All of the inversion parameters were identical to those in the ERT data case. In order to enable a comparison of the CCR and ERT data, both cross-sections were shown (Figure  13 ). Additionally, the ERT depth investigation was limited to 5 m below the ground surface, which is the CCR maximum depth of the investigation. It is worth noting that the measurements were not carried out at the same time. The CCR measurements were performed on 15th October and the ERT measurements on 5th November 2020. Similar to the previous case, the relatively high resistivity anomalies ("H1" to "H4") and the low resistivity anomalies, which are interpreted sequentially as sandstones and mixture of sands and clays, were detected. In general, the considered resistivity sections are quite similar up to roughly the 80th meter of the profile line.
Seismic Surveys High resolution seismic reflection surveys were carried out as supplementary measurements for the GPR, ERT and CCR surveys. The GPR techniques delivered information about the geological medium to a maximum depth of 16 m, but the main anomalies were detected between the surface and a depth of approximately 10 m. The ERT and CCR techniques allowed for the detection of anomalies to a maximum depth of 8 m. In such situations, seismic surveys should deliver data from depths greater than 10 m. As described in the introduction, the geological investigations revealed in greater depths the presence of sandstones and mudstone shale formations. Additionally, in the subsoil of the dam, faults and highly fractured rocks occurred. Therefore, the goal of seismic surveys was the detection of the main geological layers and the identification of the faults.
The nearest offset, chosen with respect to the depths of the researched boundary "A", was set to 1 m. According to the principles used for the research of anomalies  [51] , the geophone spacing was set to 1 m, yielding a CDP resolution of 0. 5 m. The profile was processed using Winses18 software  [52]  and applying the HRRS CDP processing flow, namely: input band pass filtering, first arrival and air-coupled wave muting, FK filtering, scaling, optimum offset window apply (offset windowing), velocity analysis, NMO correction, CDP stacking, post-stack band pass filtering. The acquisition geometry and obtained resolutions in layer I are summarized in Table  2. The obtained seismic sections and its interpretation are shown in Figure  14. During signal processing it was found that the OOW for boundary "A" fits the offset interval between 3 m and 8 m, however for the subsequent boundaries, the OOW starts from 15 m.
Acknowledgments: The work presented in this paper is performed in the framework of the grant EMMA (i. e. , E -Mobility, sustainable MAterials and Technologies ), no. PPI/APM/2018/1/00027/U/001task  no. 9, within cooperation between the  Cracow University of Technology (Poland)  and the  Ecole Centrale de Lille (France) ; the project was supported by the  Polish National Agency for Academic Exchange research program. We also appreciate to "Poli Doctus" scholarship as part of the project:  POWER-"A Way to Perfection-a comprehensive university support program ".
Funding: This research was funded by the grant EMMA (i. e. , E-Mobility, sustainable MAterials and Technologies), no. PPI/APM/2018/1/00027/U/001-task no. 9, within cooperation between the Cracow University of Technology (Poland) and the Ecole Centrale de Lille (France); the project was supported by the Polish National Agency for Academic Exchange research program. The works were also supported by the "Poli Doctus" scholarship as part of the project: POWER-"A Way to Perfection-a comprehensive university support program".
Results For the following scenario simulations, we use Euler implementation in MATLAB with a 0. 1-time step to solve our coupled system using the parameter values given in Table  S1  (see SI). In the subsequent discussions, we consider that users of different functions are maximizers of benefits in the sense that they do not care about damaging and degrading other functions. For example, timber harvest users only care about extracting wood regardless of the impact on the forest scenic beauty perceived by the tourists. In addition to the long-term equilibrium states of the simulations, we have chosen to represent the transient dynamics for a relatively short period (50 years,  Mathias et al. , 2015) , to highlight the trade-offs that can occur between the different functions. Moreover, to quantify the wood removal function, we take into account the annual wood extracted from tree cuttings and the deadwood collected from the forest.
Acknowledgments This research has been funded by the  ANR (Agence Nationale de la Recherche)  under the  VIRGO  project ( ANR-16-CE03-0003 ) and the region of  Auvergne. Moreover , this research was conducted on the  Long-Term Socio-Ecosystem Research platform LTSER "Zone Atelier Alpes ," a member of the  ILTER-Europe network.
, 2011; Zitellini et al. , 2020; Thorwart et al. , 2021). Similar observations have been made in the bay of Cadiz  (Gràcia et al. , 2003). In spite of numerous detailed studies led in the last years offshore, no synthetic work has described the evolution of the structural style and of the geometrical properties along the Algerian margin and the deep basin, even if a clear margin segmentation has been reported  (Cattaneo et al. , 2010; Leprêtre et al. , 2013; Khomsi et al. , 2019; Strzerzynski et al. , 2021). Here we use a large dataset, including seismic lines from different origins to map the most relevant tectonic structures marking the inversion of the Algerian margin: faults, flexure and potentially buckling. The goals of this study are therefore to (1) identify the major active fault systems by direct and indirect effects imaged on depth-converted seismic sections (Figure  2 ) and (  2 ) image, characterize and compare the main structural features of the submarine fold-and-thrust belt located off Algeria.
, 2015; Bouyahiaoui et al. , 2015; Aïdi et al. , 2018). A total of 103 seismic profiles were included into this analysis and 156 intersections between the main seismic sections were used to tie the reflectors picked in adjacent profiles. Unfortunately, the oldest seismic lines are lacking resolution, especially below the basement, limiting the amount of horizons that can be correlated when they were the only crossing sections  available. Along the margin, we also used sections from the MARADJA seismic experiments. They are of high resolution, but penetrate only down to the Upper (UU), Mobile (MU) or sometimes lower (LU) Messinian units. Since their contribution in this work is limited, we did not include them on our data location map (Figure  2 ) to avoid overwriting. All sections were visualized and analyzed using the Kingdom Suite© seismic and geological interpretation software.
These uncertainties remain mostly local, and often associated with Messinian salt diapirs. In the absence of such structures, the depth-converted horizons conform to previous depth-converted data from the wide-angle seismic profiles of the SPIRAL project. To avoid misleading interpretations of the basement/sediment deformations potentially caused by diapirism, we were also careful to discard the short wavelengths (few kilometers) associated with diapirs in our interpretations and only report on longer wavelengths (Table  2 ). Finally, this time-to-depth conversion approach, in spite of obvious intrinsic limitations, allowed us to obtain for the first time a set of depth maps of major layers based on 59 depth converted seismic sections spread over the Algerian margin and adjacent basin (Figure  5 ).
Seismic Section 6 The foot of the margin is marked by a basement high between km-20 and km-28, separating the margin from the basin, that might be related either to fault activity or to an inherited structure. The opposite end of Section 6 (Figure  8 ) to the north reveals a deepening of the salt base, a thickening of MU and intense salt tectonics that may support the presence of a fault around km-95. Without any other seismic section from our dataset extending far enough in the basin to image this area, we cannot further strengthen these hypotheses. In the basin, the basement depth increases by 1. 5 km over a distance of 60 km, from the north to the base of the continental slope, which gives a minimal amplitude and half-wavelength of the DCF in the eastern zone. The top of the basement is marked by at least three undulations, from km-0 to km-22.
ACKNOWLEDGMENTS We would like to thank all the people involved in the  SPIRAL  project, from  CRAAG  ( Centre de Recherche en Astronomie, Astrophysique et Géophysique ),  DGRST (Directorate-General for Scientific Research and Technological Development) ,  CNRS  ( Centre National de la Recherche Scientifique ),  Ifremer  ( Institut Français de Recherche pour l'Exploitation de la Mer) ,  IRD (Institut de Recherche pour le Développement) ,  Universities of Brest and Nice , as well as Sonatrach (Société nationale pour la recherche, la production, le transport, la transformation, et la commercialisation des hydrocarbures). We also thank  Juan I. Soto  ( University of Granada, Spain ) for his help on improving the interpretation of the salt tectonics and  Marie-Odile Beslier  ( CNRS, Géoazur Nice ) for her useful advice on improvement to our interpretation.
1B. In this analysis, the clinical isolates were incubated with pooled normal human plasma. Surface-adhered proteins were enriched via centrifugation and quantified by data-independent mass spectrometry analysis (DIA-MS). The data were stringently filtered, resulting in the identification of 92 surface-bound plasma proteins in total, which were further grouped into six major protein families according to their functional roles, i. e. , apolipoproteins, cell adhesion and cytoskeleton proteins, coagulation, complement, immunoglobulins, and other plasma proteins (Fig. 2A ; see also Table  S3  in the supplemental material). The clustering of the strains in the heat map is based on the Z-score, where the Z-score measures the standard deviation of a protein intensity from the mean intensity of that protein across all strains.
TX-MS involves the use of cross-linking mass spectrometry and computational models to determine protein-binding interfaces. In HDX-MS, the protein complex is subjected to deuterated water, and protected regions, such as protein-binding interfaces, are tracked using MS. The site engaged in binding will block the deuterium exchange, which is then used to map the binding interface. As the input, we first generated a computational model of full-length M28, which was determined using the Rosetta comparative modeling (RosettaCM) protocol  (45) , based on the previously reported model of the M1 protein  (27). This model was further used to provide protein-protein docking decoys using structures deposited in the Protein Data Bank for IgA (PDB accession number 6LXW) and C4BP (PDB accession number 5HYP).
For M28, the affinity tag was removed, and the untagged protein was mixed in solution with either C4BP or the Fc domain of IgA, followed by TX-MS and HDX-MS analyses. For C4BP, we found one interaction site supported by both TX-MS and HDX-MS. The cross-linked peptides observed between M28 and C4BP overlapped the interaction interface resolved using X-ray crystallography (46) (Fig. 5A  and BI, Fig. S4 , and Table  S2 ). The cross-links were observed between two C4BP residues (K28 and K67 under PDB accession number 5HYP; the corresponding residues are K72 and K111 in the full-length C4BPa chain) and K50 on our M28 construct (Fig. 5A  and BI and Table  S1 ). No cross-links from C4BP to the crystallized M28 segment were observed (Fig. 5A  and BI), most likely due to the lack of stereochemically favorable lysine residues at the interaction interface.
Using HDX-MS, we identified a 14-amino-acid stretch (aa 23 to 36) in the HVR domain of M28 interacting with C4BP (Fig. 5A  and BII), enclosed between the C4BP-binding site in the crystallized complex (PDB accession number 5HYP) and the cross-linked site (K50) identified by TX-MS (Fig. 5A ). For IgA, we identified two different cross-linked sites by TX-MS, and similarly, HDX-MS analysis of the M28-IgA Fc domain interaction showed strong protection against deuterium uptake at the same two distinct sites. The first site was supported by four interprotein cross-links and overlaps the previously identified M22-based streptococcal IgA-binding peptide (SAP)  (28). (Fig. 5A  and CI, Fig. S4 , and Table  S2 ). At a 1:1 ratio of M28 to IgA, the reduction in deuterium uptake was observed at the SAP and the overlapping region, as identified by TX-MS (Fig.
The interaction interface on the crystallized M28 segment (PDB accession number 5HYP) is shown in cyan, and the SAP interacting with the IgA Fc domain is in yellow. Cross-links are observed between lysine residues K72 and K111 (numbered based on the full-length C4BPa chain) and K50 on our M28 construct. The cross-links are depicted as dotted lines, with the labels corresponding to a given spectrum in Fig. S4  and Table  S2  in the supplemental material. Due to the dimeric nature of M28, several combinations of cross-links are possible. The HDX-MS-mapped binding site of C4BP on M28 is represented in pink. (II) Deuterium uptake graph for amino acids 23 to 36 on M28 alone (black) and on M28 and C4BP (red). This amino acid stretch is colored in pink in the M28 model in panel BI. (CI) Closeup view of the IgA Fc-binding interface on M28 identified by TX-MS.
MATERIALS AND METHODS Cloning, expression, and purification of recombinant proteins. The M proteins were cloned, expressed, and purified at the Lund Protein Production Platform (LP3) (Lund, Sweden) and at the University of Oslo (Norway). The recombinant M proteins used in this study lacked the signal peptide and the LPXTG cell wall-anchoring motif. The open reading frames corresponding to the mature M proteins M1 (UniProt accession number Q99XV0; aa 42 to 448; gene emm1), M3 (UniProt accession number W0T370; aa 42 to 545; gene emm3), M5 (UniProt accession number P02977; aa 43 to 456; gene emm5), M28 (UniProt accession number W0T1Y4; aa 42 to 358; gene emm28), M49 (UniProt accession number P16947; aa 42 to 354; gene emm49), and M89 (UniProt accession number W0T3V8; aa 42 to 360; gene emm89) were cloned into a pET26b(1)-derived vector carrying a 6ÂHis-HA-StrepII-TEV (six-histidinehemagglutinin-Strep-tag II-tobacco etch virus protease recognition site) tag.
SA-MS and AP-MS data analysis. MS raw data were converted to gzipped and Numpressed mzML (59) using the tool MSconvert from the ProteoWizard v3. 0. 5930 suite  (60). All data were stored and managed using openBIS  (61). SA-MS DDA-acquired spectra were analyzed using the search engine X! Tandem (2013. 06. 15. 1-LabKey; Insilicos, ISB)  (62) , OMSSA (version 2. 1. 8) (63) , and COMET (version 2014. 02 rev. 2) against an in-house-compiled database containing the Homo sapiens and S. pyogenes serotype M1 reference proteomes (UniProt proteome identifiers UP000005640 and UP000000750, respectively) complemented with common contaminants from other species, yielding a total of 22,155 protein entries and an equal number of reverse decoy sequences. AP-MS DDA data were analyzed using the same search engines as the ones described above, against an in-house-compiled database containing the Homo sapiens and S.
07), and peptide spectrum matches (PSMs) were filtered with the protein FDR set to 1%, resulting in a peptide FDR of ,1%. The SA-MS and AP-MS DIA data were processed using the OpenSWATH pipeline  (65). For DIA, spectral libraries from the above-described DDA data set were created in openBIS (61) using SpectraST (version 5. 0, TPP v4. 8. 0 Philae, build 201506301157-exported [Ubuntu-x86_64]) in TPP  (66). For DIA, raw data files were converted to mzXML using MSconvert and analyzed using OpenSWATH (version 2. 0. 1, revision c23217e). The retention time extraction window was 6300 s, and m/z extraction was set at a 0. 05-Da tolerance. Retention time was then calibrated using iRT peptides. Peptide precursors were identified by OpenSWATH (2. 0. 1), and PyProphet (2. 0. 1) was used to control false discovery rates of 1% at the peptide precursor level and 1% at the protein level.
Next, TRIC (67) was used to align the runs in the retention time dimension and reduce the identification error by decreasing the number of missing values in the quantification matrix. Further missing values were requantified by TRIC  (67). The resulting DIA data sets were analyzed using Jupyter Notebooks (version 3. 1. 1). For DIA, proteins identified by more than 3 peptides and enriched with a log 2 fold enrichment of. 1 (2-fold) with an adjusted P value of ,0. 05 using Student's t test were considered true interactors. However, for the saliva-plasma dilution DIA data, TRIC was not enabled. The intensities of the proteins were estimated by summing the intensities of the most intense three peptides for each protein relative to the total peptide intensities (without iRT) for that protein. The AP-MS data for commercial IgA with M28, M89, and GFP were analyzed in MaxQuant (1.
0375, 0. 075, 0. 15, and 0. 3 mM were prepared. For C4BP as the analyte, concentration series of between 0 and 96 nM were prepared. The analytes were injected into the active (Fc_2) and reference (Fc_1) channels at the same time. Triplicate injections were done for each concentration series. The association time was set to 120 s, while the dissociation time was kept at 600 s. For the regeneration of the surface, 10 mM glycine-HCl (pH 2. 5) was used at a flow rate of 10 ml/min. Evaluation of SPR analysis. For the evaluation of the SPR analysis, the kinetic parameters were determined using Biacore Evaluation software (v. 2. 0) for binding analysis based on curve-fitting algorithms, which employs global fitting. The data collected for each experiment were analyzed according to a 1-to-1 fitting model using the kinetic fitting programs that yield association rate (k a ), dissociation rate (K d ), and K D values and also by fitting the data to a heterogeneous binding model.
TX-MS data analysis and computational modeling. The UniProt accession numbers used for the S. pyogenes M28 protein and human C4BPa, C4BPb, IGHA1, and IGHA2 are W0T1Y4, P04003, P20851, P01876, and P01877, respectively. The tertiary structure of the M28 protein was characterized using the Rosetta comparative modeling (RosettaCM) protocol (45) from the Rosetta software suite (68) based on the previously generated full-length model of the M1 protein  (27)  as the homologue structure. For IgA and C4BP, PDB accession numbers 6LXW and 5HYP, respectively, were used. To analyze the interactions of M28 with IgA and C4BP, the TX-MS protocol was employed  (18) , through which computational docking models were generated and filtered out using distance constraints derived from MS-DDA data. A final round of high-resolution modeling was performed on the top selected models to repack the side chains using the RosettaDock protocol  (69).
HDX-MS sample preparation and data acquisition. HDX-MS was performed in two separate runs on M28 with IgA Fc and M28 with C4BP. In each experimental run, HDX-MS was first performed on pure untagged M28 (1 mg/ml). Next, the following mixtures of M28 with the different ligands were prepared in PBS and subjected to HDX-MS: M28-IgA(Fc) at a 1:1 molar ratio, where each sample consisted of 1 ml of M28 (75 pmol/ml) mixed with 1 ml of PBS and 3 ml of IgA(Fc) at a concentration of 20 pmol/ml; M28-IgA(Fc) at a 2:1 molar ratio, where each sample consisted of 2 ml of M28 (75 pmol/ml) mixed with 3 ml of IgA(Fc) at a concentration of 20 pmol/ml; M28 (pure), a run where each sample consisted of 1 ml of M28 (75 pmol/ml) mixed with 4 ml of PBS; M28-C4BP, a run where each interaction sample consisted of 2 ml of M28 (75 pmol/ml) mixed with 5 ml of C4PB (1 to 2 pmol/ml); and M28 (pure), where each sample consisted of 2 ml of M28 (75 pmol/ml) mixed with 5 ml of PBS.
HDX-MS data analysis. PEAKS Studio X (Bioinformatics Solutions Inc. , Waterloo, Canada) was used for peptide identification after pepsin digestion of undeuterated samples (i. e. , 0-s time point). The search was done on a FASTA file comprising only the sequences of the analyzed proteins, and search criteria were a mass error tolerance of 15 ppm and a fragment mass error tolerance of 0. 05 Da, allowing fully unspecific cleavage by pepsin. Peptides identified by PEAKS with a peptide score value of a log P value of. 25 and no modifications were used to generate peptide lists containing the peptide sequence, charge state, and retention time for the HDX analysis. HDX data analysis and visualization were performed using HDExaminer version 3. 01 (Sierra Analytics Inc. , Modesto, CA, USA). Due to the comparative nature of the measurements, the deuterium incorporation levels for the peptic peptides were derived from the observed mass differences between the deuterated and nondeuterated peptides without back-exchange correction using a fully deuterated sample.
HDX data were normalized to 100% D 2 O content with an estimated average deuterium recovery of 75%. Peptide deuteration was determined from the average of all high-and medium-confidence results, with the first two residues of each peptide set to be unable to retain deuteration. The allowed retention time window was set to 60. 5 min. Heat map settings were uncolored proline and heavy smoothing, and the difference heat maps were drawn using the residual plot as a significance criterion (61 Da). The spectra for all time points were manually inspected; low-scoring peptides, e. g. , obvious outliers, and peptides where retention time correction could not be made consistent were removed.