dataesr/scientific-paragraphs-categorization huggingface dataset:
The area-equivalent modal diameter was determined by fitting a Gaussian distribution and this fitting contributes to the uncertainty budget. This uncertainty u fit was directly given by the data evaluation software used (here Origin 2019b) and was reported for each measurement. The composed uncertainty U SEM (k = 1) for the measurement of the area-equivalent modal diameter of NP population by SEM is then given by: U SEM = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ u 2 beamwidth + u 2 cal + u 2 repeat + u 2 sampling + u 2 fit √ (5).
TEM image treatment The equivalent diameter of NP imaged by TEM was calculated from Crouzier et al. [2]. For this, a binary image of the particle acquired by TEM was obtained by thresholding the signal at the base of the nano-object. Isolated pixels on the binary image related to the noise of the TEM image were automatically removed by the processing program (Matlab® script). The number-weighted area-equivalent modal diameter (D NWAE ), measured by TEM, was evaluated by fitting a Gaussian distribution to the equivalent diameter distribution histogram of the NPs population.
LE to be applied in order to limit the bias NP area-equivalent diameter measured by SEM for the NPs populations imaged in this study. co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. TEM images were obtained with the TEM OSIRIS, Plateform Nano-Safety, CEA-Grenoble, operated by François Saint-Antonin. This work was supported by the French Agence National Research Agency (ANR), program 'Investissements d'Avenir'. Contract reference ANR-10-EQPX-39.
Echocardiographic analysis Before the initiation of sacubitril/valsartan and at the 12-month follow-up, all patients underwent transthoracic 2D echocardiography using standard equipment (Vivid 9 or 95; GE Healthcare, Horten, Norway), supplied with a 3S or M5S 3. 5-mHz transducer, with the same imaging protocol. Bidimensional, colour Doppler, pulsed wave, and continuous wave Doppler data were stored on a dedicated work station for offline analysis (EchoPAC; GE Healthcare). Cardiac dimensions and functions were measured according to the current recommendations. 15 he RV function was described using tricuspid annular plane systolic excursion (TAPSE), Doppler S velocity, RV free wall strain in apical RV-dedicated 4-chamber view, RV fraction area change (FAC) and Tei index (myocardial performance index (MPI)).
Statistical analysis Data are presented as mean ± standard deviation (SD), median (interquartile range (IQR)) and frequency/percentage depending on the nature of variables. Prior to the analysis, missing data were imputed using the singular value decomposition (SVD) impute function within the impute package in R software (The R Project for Statistical Computing, Vienna, Austria). The percentage of missing values ranged from 0% to 50% (for TAPSE/ sPAP ratio). Before the cluster analysis was performed, the Gower dissimilarity was used to measure the closeness between each observation. This measure implies standardization, which is set to range for interval and ordinal variables. A hierarchical cluster analysis was conducted in PROC CLUSTER (SAS v. 9. 4; SAS Institute Inc. , Cary, USA), using Ward's minimum variance method with standardization.
1 ). This arbitrary classification was used as we could not determine residual cancer burden in all cases because of a lack of complete breast and lymph node material in some cases treated outside of Centre Jean Perrin (external participants in the trials). Interpretable targeted panel sequencing (MSK-IMPACT) data were obtained for 45 patients, 26 from the PTMB trial and 19 from the CTX trial. For all patients who reached pCR (n = 13), only the pre-therapy sample was assessed, whereas pre-NAT and post-NAT matched samples were available in 17 of the 32 non-pCR patients. For 15 non-pCR patients, only the post-NAT sample was analyzed (Table  2 ). Gene expression analysis by gene arrays was performed in 41 pre-NAT samples, whereas NanoString technology was used to analyze 28 post-NAT samples (Table  2  and Supplementary Fig.
Patients and tumor samples The available material (formalin-fixed, paraffin-embedded (FFPE) tumor tissues or extracted DNA) from the patients enrolled in clinical trials NCT00933517 (panitumumab trial, PTMB) and NCT00600249 (cetuximab  trial, CTX) was used. Details on the inclusion criteria, treatment, response, and non-genomic predictive biomarkers are previously published  22, 23. The clinical follow-up of 5 years after surgery ended in August 2017. Recurrence was defined as the appearance of any invasive cancer deposit. All local and most distant recurrences were histologically confirmed, except brain metastases which were diagnosed by imaging. ATR DNMT1 SYK PGR RUNX1 IRS2 PIK3R2 DOT1L PRDM1 PDCD1 TBX3 ERBB2 GLI1 NOTCH3 PIK3CD EPHA7 CDKN2A BCOR RPTOR RICTOR MALT1 EIF4A2 PARP1 HIST1H3G BCL6 ESR1 BRIP1 LATS1 GRIN2A FGFR3 INSR EPHA5 DAXX NOTCH4 BRCA1 EPHB1 PTCH1 FIP1L1 RASA1 KDR ARID2 PIK3R1 DNMT3A RARA E2F3 BRCA2 SMARCA4 NCOA3 MEF2B CARD11 SMO APC NBN NCOR1 INPP4B AMER1 MTOR MSH6 ATM MAP3K13 INPP4A MED12 CREBBP AURKB ZFHX3 RBM10 ARID1A FLT4 ERBB3 MDC1 BLM BRD4 PBRM1 KMT2C FAT1 PTPRD RECQL4 TET2 CDK12 KMT2A AKT1 RB1 TSC1 MGA NSD1 CIC NOTCH1 PIK3CA SETD2 KDM6A KMT2D The greatest dimension of post-NAT breast residual tumor and the number of involved axillary lymph nodes were retrieved from the pathology reports received from the participating centers in the trials.
Gene expression by gene arrays Nucleic acids were extracted from frozen tumor tissue (pre-NAT samples) using AllPrep DNA/RNA mini kit (Qiagen France SAS, Courtaboeuf, France) according to the manufacturer's protocol. RNA quality was verified using the 2100 BioAnalyzer (Agilent Technologies). The extracted material was sent to Helixio (Saint-Beauzire, France), where it was hybridized with gene arrays (Human SurePrint, Agilent Technologies France, Les Ullis, France). The raw data were transferred to Memorial Sloan Kettering Cancer Center for bioinformatics and statistical analysis. Limma package  60  was applied to process Agilent microarray data and identify differentially expressed genes between progressive tumors and tumors with pathological complete response. Background correction was performed on expression intensities before normalization by the parameter of "expnorm".
Gene expression by NanoString RNA was isolated from FFPE samples of post-NAT residual tumors using the High Pure FFPET RNA Isolation Kit (Roche Diagnostics, Meylan, France), according to the manufacturer's instructions. RNA purity and concentration were determined using a spectrophotometer (BioPhotometer, Eppendorf). Only the samples with a purity of ≥1. 6 were used. One hundred nanograms of RNA was loaded for hybridization with nCounter ® PanCancer Progression Panel, according to the manufacturer's instructions (NanoString, Seattle, WA, USA). The hybridization signals were analyzed by the nCounter ® FLEX Analysis System (NanoString). Raw count data, which include 740 endogenous genes, 6 positive controls, 8 negative controls, and 30 housekeeping genes, were preprocessed using the R package NanoStringNorm  67.
Raw counts were merged by patients by taking an average of samples. Specifically, geometric mean-based normalization was used to normalize for technical assay variation, followed by background adjustment based on negative controls, where "mean. 2sd" is used. RNA content normalization was performed by all 30 housekeeping genes with the parameter "housekeeping. geo. mean". Finally, log2-transformed data were used for downstream analysis. One sample was excluded for the analysis due to the high positive normalization factor examined by NanoStringNorm  67. Differential expression analysis was performed using the R package Limma  60.
DATA AVAILABILITY The assembled prospective somatic mutational data from ctDNA and tumors for the entire cohort have been deposited for visualization and download in the cBioPortal for Cancer Genomics (http://cbioportal. org/). MSK-IMPACT DNA sequencing data is available under SRA accession code PRJNA750756, gene array RNA sequencing data is available under GEO accession code GSE180775 and nanostring sequencing data is available under GEO accession code GSE180717. All other data supporting the findings of this study are available from the corresponding author on reasonable request.
This dataset is combined with companion deep seismic profiles and industrial onshore-offshore seismic lines to provide a robust scenario for the formation and evolution of the LM. Our P-wave velocity model consists of an upper sedimentary sequence of weakly compacted sediments including intrusions and lava flows in the NNV while contourites and mass transport deposits dominates the eastern edge of the LM. This sequence covers a thick acoustic basement that terminates as a prominent basement high just west of the contourites and mass transport deposits domain. The acoustic basement has a seismic facies and velocity signature typical of a volcano-sedimentary basin and appears widespread over our study area extending toward the eastern MCP and NNV. Based on industrial well logs that calibrate our tectono-stratigraphic analysis we constrain its age to be pre-Neocomian.
Multichannel Seismic (MCS) data Ifremer's SolidQC software was initially used for processing MCS data. It allows data quality control, 2D geometry setup and SEG-Y file generation. Further processing was performed using CGG-Veritas Geocluster software. This sequence included external mute of direct and water wave arrivals, large band filtering (1-8-64-92 Hz), predictive deconvolution (440 ms operator, 252 ms IO, 32 ms gap), 4 ms resampling, seafloor multiple attenuation using Surface-Related Multiple Elimination  (SRME)  and RAMUR in the tau-p domain, time variant band-pass filtering, and pre-stack Kirchhoff migration. Velocity picking in https://doi. org/10. 5194/se-2020-209 Preprint. Discussion started: 2 February 2021 c Author(s) 2021. CC BY 4. 0 License. done after each major processing step in order to refine the final velocity model and build a coherent pre-stack Kirchhoff time migrated section (PSTM) as shown in figure  2a  for MZ3 profile.
Travel-time uncertainty for each phase was automatically calculated based on traces signal to noise ratio. They range from 0. 025 s for high ratio to 0. 25 s for poor ratio. MZ6MCS MZ6MCS MZ2OBS17 MZ2OBS17 MZ5OBS05 MZ5OBS05 MZ4OBS08 MZ4OBS08 MZ3OBS01 MZ3OBS02 MZ3OBS03 MZ3OBS04 MZ3OBS05 MZ3OBS06 MZ3OBS07 MZ3OBS08 MZ3OBS09 MZ3OBS10 MZ3OBS11 MZ3OBS12 MZ3OBS13 MZ3OBS14 MZ3OBS15 MZ3OBS16 MZ3OBS17 MZ3OBS18 MZ3OBS19 MZ3OBS20 MZ3OBS21 MZ3OBS22 MZ3OBS23 MZ3OBS24 MZ3OBS25 MZ3OBS26 MZ3OBS27 MZ3OBS28 MZ3OBS29 MZ3OBS30 MZ3OBS31 MZ3OBS32 MZ3 Almirante Leite Ridge We used the iterative procedure of two-dimensional forward ray-tracing followed by the damped least-squares travel-time 225 inversion of the RAYINVR software  (Zelt and Smith, 1992). Our modeling proceeded following a top-to-down strategy of arrival times fitting of both wide-angle reflected and refracted phases identified on OBS data.
MCS data pre-stack depth migration (PSDM) A pre-stack depth migration and a residual moveout analysis were performed in order to convert MCS data from time to depth and verify the accuracy of the wide-angle velocity model. The Kirchhoff pre-stack depth migration procedure is described in details by Schnürle et al. and the resulting section for MZ3 profile is shown in figure  C1a. As for the pre-stack time migration (figure  2 ) the full sedimentary cover is well imaged up to the top of the SV1 layer. Below, the section is mostly transparent with only intermittent and tiny events within the SV1 layer. In the common image gathers (CIGs: figure  C1b ), the top of Sv1 coincides with a strong low wavelength reflector, followed by poorly coherent and under migrated events. The semblance panels (figure  C1c ) is similarly well focused in the upper sediments and top of SV1 interfaces while it is blurred at greater depth and beneath the ALR.
The most prominent basement high, located at 310-340 km model distance, is clearly an isolated basement structure controlled on each side by steep faults. To its SW, the fault zone appears wider at the basement surface suggesting a deep fault rising as a flower system (Figure  2 ). Internally the acoustic basement show chaotic and incoherent signal in some places (60-120 km, 300-420 km, Figure  2 ) but also clear basement-parallel or dipping reflectors of variable amplitude in others  (-60 to -20 km; 120-260 km). This already suggests that the top of the acoustic basement is not the roof of the crystalline basement, at least up to the basement high at 330 km. Its internal structure is indeed only partially resolved by the MCS. OBS data further constrain a 3 to 4 km thick unit made of two layers (SV1 and SV2 layers in green on figure  2b ).
Rift to drift evolution and vertical movements In the alternative East-Gondwana 'fit' proposed by  Thompson et al. (2019) , the continental domain to the south-west of the Astrid Ridge on the Antarctica plate is the conjugate of the African corner made by the MCP and the Beira High (Figure  151515a-b ). Further south the Grunneghona craton is facing the NNV. The exact seaward limit of the Antarctica continental crust is inferred at the location of a strong positive free-air gravity anomaly  (Mueller and Jokat, 2019; Scheinert et al. , 2016)  since transitional crust is observed further offshore on seismic data  (Jokat et al. , 2004). At this stage, the Antarctica plate overlaps the corridor which will develop progressively during rifting (Figure  15c ) and before oceanic spreading in the MB starts from Chron M25 ( 155 Ma) onwards (Figure  1515d ).
Acknowledgements. We thank the captain, crew, and  MCS  technical team of the  R/V Pourquoi-Pas. We also thank the OBS technical team who maintain and constantly improve our OBS pool, as well as the land stations deployment team. A Leprêtre  and  F. Verrier  respective post-doc studies and contract were co-funded by TOTAL and Ifremer as part of the  PAMELA (Passive Margin Exploration Laboratories) scientific project. We thanks  WesternGeco ,  INP  and  Total  for permission to make and publish line-drawings of seismic data. The GMT (Wessel and Smith, 1998),  Seismic Unix  (Cohen and Stockwell, 2019;Stockwell, 1999), and Geocluster (CGG-Veritas) software packages were used extensively in this study. 29 https://doi. org/10. 5194/se-2020-209Preprint. Discussion started: 2 February 2021 c Author(s)  2021.
Data availability. Seismic datasets of the PAMELA-MOZ3  (Moulin and Aslanian, 2016)  and PAMELA-MOZ5  (Moulin and Evain, 2016)  cruises are archived and referenced in Ifremer SISMER database and can be requested at: https://doi. org/10. 17600/16009500 and https://doi. org/10. 17600/16001600.
Results and discussion A first 11-minute MinION sequencing run generated 92,000 reads, including 229 M. tuberculosis complex reads and 224 M. tuberculosis sensu stricto reads (Fig. 1 ) analysed online using Oxford Nanopore tools designed for real-time sequencing and the EP2ME software  (version 2019. 11. 11-2920621)   (Lu et al. , 2016). After two hours of sequencing, the blast of generated reads after assembling on a Canu assembler available online on Galaxy Europe (Version 1. 8) yielded a M. tuberculosis strain FDAARGOS-756 (GenBank accession no. : CP054014). Total reads were extracted and mapped to the M. tuberculosis strain FDAARGOS-756 complete genome using CLC Genomics Workbench software version 7. 5. 0 (Qiagen). Of the 744,732 reads in total, 2,811 were mapped to the reference genome and 4,143,715 nucleotides were matched to the reference sequence, resulting in a 93.
Ethics All data were generated as part of routine work at the Assistance Publique-Hôpitaux de Marseille (Marseille University hospitals), and this study is the result of routine clinical management. No specific clinical sampling was performed in the course of this study. positive by Ziehl-Neelsen staining after 2 wk of incubation. The shell-vial supernatant (500 mL) was inactivated, centrifuged for 15 min at 13,000 g and the pellet was incubated in 150 mL of G2 buffer and 15 mL of proteinase K overnight at 56°C. After treatment with glass powder and incubation at 100°C for 10 min, DNA was extracted from 200 mL of supernatant using an EZ1 DNA kit (Qiagen) and eluted in 50 mL. A 47-mL volume of DNA was used for NGS library preparation according to the Oxford Nanopore protocol. Real-time data analysis was performed using EP2ME software and the M.
Funding Madjid Morsli  is PhD student supported by the " Fondation Medit erran ee Infection ". This work (lab equipment and reagents) was supported by the  French Government  under the  Investissements d'Avenir  (Investments in the Future) programme managed by the  Agence Nationale de la Recherche  ( ANR ,fr:  National Agency for Research)  [reference: M editerran ee Infection  10-IAHU-03 ].
Gray  [1]  defined it as 'the natural range (diversity) of geological (rocks, minerals, fossils), geomorphological (landform, processes) and soil features. It includes their assemblages, relationships, properties, interpretation and systems'. After some refinements (e. g. ,  [3, 13] ), Gray  [14]  added that geodiversity is considered as the backbone of geoheritage, valuing abiotic nature, and geoconservation and shows a strong connection to modern society. The Chaîne des Puys has been recognized as an area of great geodiversity (e. g. ,  [15] ). Quantitative geodiversity assessment models have evolved with the concept of geodiversity. They mainly use cartographic and spatial database sources to calculate an index score of an evaluated area  [2] [3] [4] [5]. In most cases, national coverage data sources were available to the authors.
Two types of LiDAR data were available for this area: in the central part, a 0. 5 m resolution dataset was used (LiDARVERGNE from the CRAIG open dataset:  [34] ), while in the more distant parts, the DTM had 5 m resolution (LiDAR Clermont Metropole  [34] ). Both resolutions provide a dense point cloud that gives an image of a surface showing a detailed microtopography. The ca. 30 m resolution Shuttle Radar Topographic Mission (SRTM) data are available in the area, but this, being a digital surface model, may considerably differ from the ground surface in forested areas. Furthermore, the resolution of this dataset is low in relation to our objects. In the statistical assessments of DTM tests, it is a common problem that the results may depend on the resolution of the input data. If the evaluation contains calculations about slope angle this is especially true, the derivative of the lower resolution DTMs tends to underestimate steeper slopes.
In the case of volcanic edifices, the slope distribution is a key feature to describe the cone, because the slopes are related to the type and age of the cone. Statistical studies always face this problem: examining the study area at different resolutions, it has been seen that the slope angle values derived from the lower resolution DTMs are further (smaller) from the values actually measured  [35]. We therefore use the highest resolution datasets only, which provides us with 26 edifices, 24 scoria cones, and two lava domes, which have similar morphometry to scoria cones  [36] , to be examined (shown in Figure  4 ). Puy de Dôme and Petit Puy de Dôme is a distinct complex morphological landform  [31]. We decided not to have it in the analysis, as we are concentrating on simple scoria cones.
Calculation of Geodiversity Indices: Traditional Approach The quantitative methodology of this study is mainly based on Pereira et al. [4]. Th is a modified version (e. g. ,  [5] ), but the original framework suits our purposes for volca areas. During the assessment process, various subindices are determined. These repres geodiversity elements: geology, geomorphology (relief and hydrology), pedology, pa ontology, and mineral occurrences. The sum of these is the geodiversity index of a cert area. During the evaluation, only open access software (QGIS 3. 16. 0 [QGIS. ORG, Gr Switzerland] and GRASS GIS 7. 8. 4 [Grass Development Team, OR, USA]) was used ensure reproducibility. The first step was to slice up the area into a grid of 500 m × 500 m square cells. T resolution was chosen because of the characteristics of the sample area: the main geom phological features (scoria cones) can be analyzed and highlighted this way.
We tr other cell resolutions as well: the choice of the 500-m cell size is a result of integration widely varying resolution of the input layers; a higher resolution introduces local effe of some input components. The geological subindex was calculated using the 1:50,000-scale vector geolog map of France  [37]. It contains lithological/stratigraphical units. The number of differ units in a grid cell is considered the subindex. The vector dataset was converted to a ras shown with the 500 m × 500 m data grid (described in Section 2. 6). Thematic data were determined for every cell. Source of the DTM:  [34]. Red outlines are indicating the extents of the studied scoria cones.
Calculation of Geodiversity Indices: Traditional Approach The quantitative methodology of this study is mainly based on Pereira et al. [4]. There is a modified version (e. g. ,  [5] ), but the original framework suits our purposes for volcanic areas. During the assessment process, various subindices are determined. These represent geodiversity elements: geology, geomorphology (relief and hydrology), pedology, paleontology, and mineral occurrences. The sum of these is the geodiversity index of a certain area. During the evaluation, only open access software (QGIS 3. 16. 0 [QGIS. ORG, Grüt, Switzerland] and GRASS GIS 7. 8. 4 [Grass Development Team, OR, USA]) was used to ensure reproducibility. The first step was to slice up the area into a grid of 500 m × 500 m square cells. This resolution was chosen because of the characteristics of the sample area: the main geomorphological features (scoria cones) can be analyzed and highlighted this way.
We tried other cell resolutions as well: the choice of the 500-m cell size is a result of integration of widely varying resolution of the input layers; a higher resolution introduces local effects of some input components. The geological subindex was calculated using the 1:50,000-scale vector geological map of France  [37]. It contains lithological/stratigraphical units. The number of different units in a grid cell is considered the subindex. The vector dataset was converted to a raster image ('Rasterize' tool in QGIS), and the 'Variety' attribute of the 'Zonal statistics' tool presented the amount of different raster values in a certain overlaid grid cell. The pedological subindex was calculated with the use of European Soil Database v2. 0  [38]. The process was similar to the geological index: after rasterization, the different values were counted in each cell.
As this data source is small-scale (1:5,000,000), it does not have a large effect on the final geodiversity index. As paleontological features are not common in volcanic areas, the value of this subindex is 0 in every grid cell. Mineral occurrences were searched for in the EGDI Mineral Resources Map  [39] , although no feature was recorded within the sample area. The geomorphological subindex is the composition of relief and hydrological indices. Queries were made to obtain watercourse features and lakes in the territory from Open-StreetMap database with overpass turbo. Only a few cells were affected by this. The relief index was formerly produced with the use of geomorphological maps. The different morphostructural units were recorded as scores in each cell. As we lack a good large-scale geomorphological map of the area, the concept of geomorphons  [40]  was applied.