dataesr/scientific-paragraphs-categorization huggingface dataset:
Maximum deformation towards the satellite is located in the western flank of Cumbre Vieja, at the south of the eruptive vents. Both datasets show good agreements in terms of deformation spatial distribution, having higher mean velocity values the SNAPPING processing because the studied period is shorter. The complete time series of both datasets in a 200 m buffer around the LP03 permanent GNSS station from the IGN regional network (located in the area of maximum displacements) are shown in Fig. 16. The SNAPPING displacement time series were smoothed using a moving average window of three acquisitions, but only before and after the eruptive event to maintain the discontinuity due to the volcano activity. Displacements also show good agreement (Fig. 16 ). Pre-event period has a linear trend with oscillations in a range of less than 3 cm.
Discussion SAR data from 4 different satellites has been used to generate three SAR-derived products to monitor the evolution of the morphology of the volcanic building, the extension of lava flows and ground deformation   In this analysis we evaluate the technical performance and the accessibility to data. The technical performance measures the quality of each SAR derived result to map and monitor volcanic features, whereas the accessibility to data evaluates the velocity at which SAR images are available during the emergency. To evaluate the technical performance result we consider two variables: (1) Detection capacity: this binary variable evaluates if the satellite SAR data is useful (1, capable) or not (0, not capable) to generate the targeted SAR-result. Note that during La Palma crisis Capella satellite did not allow to generate InSARphase products because of the satellite configuration.
(2) Quality of the result: expert criterion is used to assess this variable in Low, Medium or High quality (1/2/3). Note that this assessment is based on the usefulness of the SARderived result which depends not only on the spatial resolution of the images. In our case study, the best quality (level 3) regarding the morphology of the volcanic edifice is achieved using the Capella and CSK-Spotlight data (Table  3 ). Cone morphology analysis based on SAR amplitude data takes advantage of the spatial resolution. Ultra-high resolution of Capella satellites (0. 5 m) data allows to interpret most of the cone and lava flow features (vents, cliffs, channel levees, cracks) even better than UAV photo-interpretation (Fig. 17a, e  and f ). Capella data increase the contrast revealing occult features and is not affected by ash plume.
Other source of high resolution data are CSK and PAZ constellations. They operate in two modes (spotlight and stripmap) both useful for the cone morphological characterization but preferring spotlight due to higher resolution (Fig. 17 ). Stripmap data (Fig. 17b  and c ) still reveals most of the features detected by UAV avoiding that technique limitations. Sentinel-1 amplitude images (Fig. 17d ) have lower resolution, only allowing to see the main shapes. The best quality lava flow detection products are produced using CSK-Spotlight products (Table  3 ). The detection of lava flow perimeters and active areas uses interferometric coherence, to generate false color MTC maps. Rough external perimeter is easily detected by all the satellites due to the significant changes between the areas affected by lavas from those that are not.
Contrarily lava flow cooling compaction, is as a phenomenon limited to a well-defined area that takes advantage of the high resolution and sensitivity of X-band data avoiding the influence of the errors impacting the regional analysis. To assign the level of technical capability, we use Eq. (  1 ), which can result in final values 0, 1, 2, 3 (0 no technical capability, 1 low, 2 medium, 3 high) Technical Capability = Detection*Quality (1) Table  3  shows the results for each dataset. The other aspect we have evaluated is data accessibility, which refers to the easy, quick and continuous access to the SAR images that are necessary to support the emergency management. During an emergency scenario any difficulty, delay or uncertainty for accessing the data for every study area will reduce the usefulness of the retrieved results.
-Data Latency: to express the time gap between data acquisition and user receipt, being either hours (2) or days (1). -Acquisition Cycle: to assess data repeatability and consistency of the acquisition. Being continuous and on-demand (3), continuous (2) or discontinuous (1). To assign the accessibility value, we use Eq. (  2 ). Accessibility = Access + Latency + Acq. Cycle Max Value (2) where Max Value is equal to 8. Table  4  shows the results for each dataset. Finally, to evaluate the most appropriate dataset (operativity), we combine both factors (Eq. (  3 )): Operatibity = Accessibility*Technical Capacities (3) Considering both, accessibility and technical capacity, the optimal SAR dataset to generate each product are shown in Table  5. Operativity values are dimensionless and must be considered only for ranking purposes.
Acknowledgements The work is under the framework of the e-shape project, which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  820852. This work was also supported by the  Spanish Ministry of Science and Innovation (MCIN), Spain  and the  State Agency of Research (AEI) , under project  SARAI  (Project  PID2020-116540RB-C22  funded by  MCIN/ AEI /10. 130 39/501100011033 ) and by the  "Severo Ochoa" extraordinary grants for excellence IGME-CSIC  ( AECEX2021 ). We thank the  European Space Agency  for access to Sentinel-1 data and the  Geohazards Exploitation Platform  (under the  ESA Network of Resources Initiative ). This work includes material © CCME (2021), provided under COPERNICUS by the  European Union  and  ESA , all rights reserved.
[49] Based on the various positive pre-clinical results,  [34]  AGuIX USNs were transferred to the clinic in five trials worldwide (Table  1 ). The successful clinical translation of AGuIX can be explained by different factors including i) the scale-up method to produce kilograms of clinical grade batches, ii) the controlled hydrodynamic diameter close to 5 nm ensuring renal elimination, and iii) their biodegradability in diluted medium that facilitate elimination. The preliminary reported PK data confirmed the short half-life of AGuIX USNs in rats (t 1/2 = 0. 31 ± 0. 034 h)  [50]  (Table  S1 , Supporting Information), as well as in cynomolgus monkeys, and in humans (t 1/2 = 1. 29 ± 0. 27 h). [25, 51] When converted in equivalence dose for a human of 60 kg (based on the FDA conversion charts-FDA report "Guidance for Industry Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers, 2005"), we observed that most of the area under the curves (AUC 0-inf ) and concentration max (C max ) are within the same range despite some slight differences.
[56] linically, AGuIX USNs are injected either once  [25]  or multiple times  [57, 58]  over the course of the treatment. The results of NanoRad Phase I clinical trial have demonstrated the feasibility and safety of administration of unique injection at doses ≤ 100 mg kg -1. [56] The need to perform multiple reinjections confirms the pre-clinical studies were a decrease of the MRI contrast in the tumor were observed and were performed following NanoRad trial. [14] Such decrease of imaging signal has also been observed clinically despite a longer retention time in brain metastasis over one week (NCT03818386). [25] This loss of signal is proportional to the leak of the USNs from the tumor; for this reason, but also to increase the tumor uptake, mAb-targeted, and peptidetargeted AGuIX USNs were developed to improve the specificity of the USNs to bind specifically to a cell type within the tumor microenvironment (TME).
AGuIX USNs Demonstrate Optimal Tumor Targeting in Various Cancer Models Despite the short circulation time of the AGuIX USNs in the body (vide supra), at 24 h post-injection, a moderate fraction (1. 19 ± 0. 87% ID g -1 ) remains passively targeted to the tumor during their first passage (Figure  2A, B ). Importantly, this amount is nonsignificantly different to what can be achieved with larger passively targeted organic NPs developed for drug delivery purposes (3. 31 ± 2,82% ID g -1 ) and inorganic NPs (3. 16 ± 2. 48% ID g -1 ) (data extracted from Mittelheisser et al. ,  [18]  p-value = 0. 113 and 0. 075, respectively, one-way ANOVA) (Figure  2A ). Owing to their single passage in the body before renal clearance, the tumor neovascularization dictates the AGuIX USNs passive tumor uptake. This was validated when we compared their propension to target passively tumors, where AGuIX NPs tend to accumulate better in subcutaneous (SC) tumor mouse models than in orthotopic tumor mouse models (p-value = 0.
Importantly, with a half-life <30 min for both approaches, these results confirm that the AGuIX USNs does not recirculate in the body and are processed during their first passage, which is at the opposite of the common expectation when mAbs are employed for drug delivery. This result, in addition to the biodistribution data in the liver, spleen, and kidneys (p-value = 0. 243, 0. 231, and Table  2. Summary of the main grafting strategies on N-terminal, C-terminal, and sulfhydryl function commonly present on the biomolecules with their major advantages and drawbacks. The three functions annotated with an asterisk (*) in the second column are the most common reactions used in bioconjugations. [107-109]   0. 082, respectively, paired t-test) (Figure  4E-G ) confirm that the pharmacokinetic properties of AGuIX USNs are more influential than the conjugation moiety in determining the biodistribution of the conjugate, as recently emphasized by Mittelheisser et al.
5 drug delivery, melanoma fluorescent dye, a PEG coating, radionuclide therapy 2016: and a 124 I radiolabeled NCT01266096 (Ph I/II): Malignant brain Targeting strategy: c(RGDyC) and recurrent metastatic melanoma targeting peptide 2018: NCT03465618 (Ph I): Brain cancer 2019: NCT04167969 (Ph I): Prostate cancer NanoTherm Composition: iron oxide 15 nm Therapy: magnetic Approved in Europe (2011): Brain tumor, [36, 40-42] (magForce) nanoparticles coated with hyperthermia Glioblastoma aminosoloxane NU-0129 Composition: spherical nucleic 13 nm Therapy: Immunotherapy, 2017: [36, 43, 44] acid (SNA) gold nanoparticle Targeted molecular NCT03020017 (Ph I): Glioblastoma formulation composed of small therapy, chemo, or multiform and interfering RNAs (siRNAs) radiation therapy gliosarcoma-completed (2020) Targeting strategy: Bcl-2-like protein 12 (BCL2L12) SPIONS/SMF Composition: Superparamagnetic 12-21 nm Therapy: Chemotherapy 2020: [45] iron oxide (SPIONs) NCT04316091 (Ph I): Osteosarcoma nanoparticles with spinning magnetic field (SMF) Diagnostic Inorganic FerroTrace Composition: super-paramagnetic 13 nm Diagnostic: MRI, NIR 2021: iron-oxide nanoparticle surgical camera, and NCT05038098 (Ph I): Gastric (SPION) polymer coated magnetometer adenocarcinoma Used in combination with NCT05092750 (Ph II): Colorectal cancer indocyanine green dye (ICG),.
e. , the statistical uncertainty due to the small datasets)  (Straub and Schubert, 2008; Ravanel et al. , 2017). Bootstrap analysis  (De Biagi et al. , 2017)  and Monte Carlo simulations  (Jaboyedoff et al. , 2021; Strunden et al. , 2015)  are usually used to evaluate the uncertainty of magnitude-frequency relationship fittings. These fast and straightforward approaches obtain the confidence bounds of the fitted distributions to evaluate the associated uncertainties. A significant population of samples could improve a robust fit. In the context of quantitative risk assessment of rock slope instability, a comprehensive understanding of the various potential failure scenarios (referring to reasonably expected potential slope failure sources and corresponding volumes) and their corresponding frequencies or return periods (referring to the recurrence interval at which a particular event is expected to occur) assumes critical significance in the effective prediction and protection against hazards  (De Biagi et al.
Data acquisition Following the reactivation of the Brenva rock avalanche scar in September 2016, SfM surveying was implemented to characterize the subsequent rock slope failure activity. Five helicopter flights performed between July 2017 and September 2021 enabled the manual acquisition of five series of high-resolution photographs of the scar using single-lens Elevations are in m a. s. l. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) reflex cameras. Further details of the surveys are shown in Table  1. Of the cameras used, only the Canon 7D Mark II had an integrated GPS system. However, this feature was not utilized during the data collection process. Additionally, none of the cameras were connected to any external GPS devices during the survey.
3D points cloud model generation All the photographs were processed with Agisoft Metashape Professional (version 1. 6. 2) (AMP) software  (Agisoft, 2020) , a dedicated photogrammetric program that uses SfM-MVS (Multi View Stereo) algorithms to generate high resolution 3D models. The main workflow followed to generate 3D point clouds by AMP includes photo preinspection and importation, high accuracy photo alignment, and the generation of high-quality dense point clouds  (Cook and Dietze, 2019). Due to the differences in camera resolution, the number of photos and the shooting distances, the density of the final SfM point clouds over the interesting part on the Brenva rock avalanche scar is variable. As a result, the surveys conducted in July 2017, August 2017, October 2018, September 2020, and September 2021 had average point cloud densities of 83 pts.
The transformation matrix obtained by this process was applied to the remaining portions of the datasets that were considered as unstable. The 3D mesh was generated using the Poisson Surface Reconstruction algorithm  (Kazhdan and Hoppe, 2013)  and was completed in 3DReshaper software  (Hexagon-Technodigit, 2016)  and CloudCompare. It is worth noting that to address the absence of GCPs, the points cloud was divided into several smaller parts that were aligned independently by the ICP algorithm one by one.
, 2017). The value from neighbours was chosen for the denoising process for all the datasets in our study. Then, change detection thresholds were set to twice the standard deviations of the C2M distance for the parts supposed to be stable  (Abellán et al. , 2009) , resulting in detection thresholds of 0. 28, 0. 48, 0. 50, and 0. 50 m for the 4 successive comparisons, respectively.
Structural analysis Using georeferenced 3D point cloud data obtained with SfM photogrammetry and Coltop3D software  (Jaboyedoff et al. , 2007) , we created a 3D shaded, coloured relief map that reveals topographic orientation (slope aspect and slope angle). This approach enabled us to conduct topographic structural analysis, which is crucial for studying rock mass slopes at high elevations. These areas are often remote and inaccessible, making conventional field investigations challenging. By employing this method, we detected discontinuities responsible for rock slope instabilities. On the Brenva rock avalanche scar (Fig. 4 ), we identified a total of seven main joint sets (rock face (RF) and J1 to J6). To gain a better understanding of how discontinuities contribute to progressive slope failure, it is essential to determine the families of discontinuities involved in past events.
, 2002). Assuming that the rockfall volumes follow a power-law frequency distribution, and because of the limitation of the short span of monitoring in natural environments, which would lead to epistemic uncertainties in the statistical model and prevent an exact prediction of the return period for a particular volume for a rock failure event  (Straub and Schubert, 2008) , we used Monte Carlo simulations to estimate the confidence intervals (uncertainties) of the return period of the potential rock failure scenarios. The simulation principle is done by simply inverting a certain sets of the power-law generating volumes, rather than removing a certain amount of sample from the large database and repeating the simulations, as suggested by  Strunden et al. (2015) : V = ( F min + rand × (F max -F min ) a ) -1 b (4) where rand is a random number between 0 and 1, constant a and b hold same meaning as described in Eq.
(  2 ), and F min is the minimum frequency corresponding to the approximate time separating the 1997 event from subsequent events. F max is the maximum frequency corresponding to the high rockfall frequency between 2017 and 2021 in this case. In this approach, 1 × 10 5 datasets of rockfall inventories were simulated. It's important to emphasize again that the validity of this proposed statistical approach depends on the assumption that the rock failure volume distribution follows a power law.
Conclusions Through five SfM photogrammetry surveys conducted in the Brenva rock avalanche scar (Aosta Valley, Italy), we identified 39 rockfall occurrences ranging from 11 to 13,250 m 3 between July 2017 and September 2021. This resulted in a high rock wall retreat rate of 15. 5 ± 2. 3 mm/year, possibly attributed to permafrost degradation. Monitoring data indicated a decrease in rockfall activity, with erosion rates reducing from 44 ± 5. 5 mm/year to 0. 33 ± 0. 2 mm/year. Nevertheless, cyclical patterns in rockfall activity were observed, as exemplified by the potential activation or acceleration during heat waves in 2022 within the Mont Blanc massif. Applying a power law-based volume-frequency relationship, we estimated the return period and corresponding uncertainties for seven potential instability volume scenarios derived from the integration of the SLBL concept and geo-structural analysis.
Mozley [1934] ). Propertius' account of the episode also implies that the Naiads seized Hylas' arm when he dipped his hands into the water (1. 20. 43-47): tandem haurire parat demissis flumina palmis / innixus dextro plena trahens umero. / cuius ut accensae Dryades candore puellae … prolapsum leviter facili traxere liquore ('at length, with lowered hands he prepares to cup the water, leaning on his right shoulder to draw a full measure. When the tree-nymphs, fired by his beauty … pulled him nimbly through the yielding water' , trans. Goold [1990] ). Furthermore, it is very likely that Statius was inspired by the representation of this myth in private paintings and mosaics, where a nymph seizing Hylas' arm is a ubiquitous element. (Hylas is sometimes also portrayed with an urn to suggest the reason that he came to the spring, but this is not the rule.
Western blot analysis Proteins were separated by SDS-PAGE (10-15%) and transferred to a PVDF membrane, which were incubated (overnight, 4°C) with antibodies: anti-EPAC1 (1/500), anti-caspase 3 (1/500), anti-caspase 9 (1/1000), anti-RAP1 (1/1000), and anti-H 2 AX-pS 139 (1/1000) from Cell Signaling Technology (Saint-Cyr-L'Ecole, France), anti-TopIIβ (1/1000) from Abcam (Paris, France), anti-SERCA2A (1/1000), and anti-actin (1/50,000) from Santa Cruz Biotechnology (Heidelberg, Germany), anti-calsequestrin (1/2500) from Thermo Fisher Scientific (Les Ulis, France), and anti-tubulin (1/1000) from Sigma (St Quentin Fallavier, France). Proteins were detected on the iBright FL1000 Imager (Thermo Fisher Scientific, Les Ulis, France) with ECL and protein band intensity was calculated using ImageJ software and normalized by actin.
Calcium handling measurement Cells were loaded with Fluo-3 AM as previously described  (Llach et al. , 2019). [Ca 2+ ] i transients were recorded following electrical stimulation at 2 Hz (two parallel Pt electrodes). Images were obtained with a laser scanning confocal microscope (TCS SP5X, Leica Microsystems, Nanterre, France) equipped with an ×40 water immersion objective in the line scan mode (1. 43 ms/line). Fluo-3 AM was excited with a white laser fitted at 500 nm, and emission measured at wavelengths above 510 nm. Image analyses were performed by IDL 8. 2 software (Exelis Visual Information Solutions, Inc) and homemade routines  (Llach et al. , 2019).
Acknowledgements We are grateful to  Dr. E Hirsch  and  Dr. A Ghigo  ( University of Torino, Italy ) and  Dr. D Hilfiker-Kleiner  ( Marburg University Medical School, Germany ) for providing cancer cell lines, and to  V Domergue  and the IPSIT platform for animal housing. Sources of funding: This works was supported by grants from  Agence Nationale de la Recherche  ( ANR-13-BSV1-0023  and  ANR-15-CE14-0005 ),  LabEx LERMIT  ( ANR-10-LABX-0033 ),  DHU TORINO ,  Leducq Foundation for Cardiovascular Research  ( 19CVD02 ), and  EU   MILEAGE  project # 734931. AL was recipient of a  Lefoulon Delalande fellowship.
Acknowledgements This work was funded through the  French Investment Program SINAPS @ project by the  Commiseriat à l' Énergie Atomique  and the  Institute de Radioprotection et Sûreté Nucléaire , and was hosted by the  LRC Yves Rocard  ( Laboratoire de Recherche Conventionné CEA-ENS-CNRS). TJC also thanks the  Royal Society  (under  URF\R1\180088 ) for financial support during the final stages of this project. EC acknowledges funding from the  Institut Universitaire de France. We thank  K. Lambeck  for making the  ANU-ICE  model available, and  C. Gruetzner  for his assistance with Rhine Graben data. Figures were created using the Generic Mapping Tools software package. We thank the editor and four anonymous reviewers for their comments, which have helped improve the manuscript.
This Science Project (SP) contributes to the estimation of the impacts of the invasive species on the European Biodiversity and Ecosystems. This topic is important for European Green Deal and the new European Biodiversity Strategy. The SP is also linked with the socio-economic issues because of the NIS implications to the local ecosystems and their services, and their societal goods and services. Since many of the above impacts may be of local scale, they may alter common practices in circular economies. The SP is implemented by: (a) Combining different sources of data and information; (b) Using a dual workflow to analyse the data; (c) Integrating its resources with core EOSC services and potentially horizontal services available; (d) Engaging the relevant scientific communities. The users will be able to: (a) Analyse distribution patterns of invasive species from different sources of data; (b) Compare the above patterns; (c) Provide managerial suggestions to relevant authorities; (d) Build on the existing infrastructure to address more complex questions (e.
Such approaches require access to big datasets (from genomics to in situ and satellite borne environmental data) and high computational power, especially for those models with iterative algorithms. Objectives: 1) to integrate data from different scientific disciplines in the marine subdomain (e. g. chemistry, physics, biodiversity, ecosystems, genomics, socio-economics) into an analytical framework in order to advance our knowledge on the impact of NIS on European marine biodiversity and ecosystems; 2) to connect the analytical framework and federate access to relevant data infrastructures at the EOSC portal in order to mobilise and empower a larger community of researchers and potential data providers; and 3) to demonstrate and promote the benefits and potential of web-based science using EOSC.