dataesr/scientific-paragraphs-categorization huggingface dataset:
Table. 1 Summary table of the performance of a cdom (443) inversion models (CDOM-KD2, A2018, C2017, S2011) on the different evaluation data sets (DS2 and DS3) considered as a whole (classes 1-17) or focusing on non-turbid waters (classes 3-17). For each in situ validation subset descriptive statistics are provided (N: number of data points, X: mean values, m: median value, std. : standard deviation value). The former statistics are also provided for the a cdom (443) data estimated from each inversion model together with additional model performance statistical descriptors (RMSD (m -1 ), MAPD (%), MR). CDOM and Chl-a might tend to artificially strengthen the co-variation between these two variables (Supplementary Fig. 1 ). Moreover, the a cdom (443)/a cdm (443) ratio values estimated at global scale using S2011 (for a cdom (  443 )) and GSM (for a cdm (  443 )) present numerous unrealistic values, the mean and standard variation values being of 1.
The CDOM-KD2 algorithm was also applied to OLCI L3 4-year data at global scale. OLCI a cdom (443) (Supplementary Fig. 2 ) globally shows similar spatial patterns with GlobColour data although OLCI a cdom (443) reaches more extreme end-member values than the GlobColour 10-year average merged data (lower values in the ultra-oligotrophic and higher ones in eutrophic waters, respectively). Several works have considered the a cdom (443)/a cdm (443) ratio as spatially and temporally quasi invariant or constant assuming CDOM to be the major contributor (> 80%) to CDM in the blue spectral domain  (Nelson et al. , 1998; Swan et al. , 2009). However, the GlobColour 10year average map shows that the ratio a cdom (443)/a cdm (  443 ) is highly variable in space, ranging from about 0. 2 to almost 1 (Fig.
Acknowledgments The study was funded by the  European Commission's Copernicus Program  and  EUMETSAT , contract number  EUM/CO/17/4600001980/ EJK  as well as by the  French Spatial Agency (CNES)  through the  TOSCA program  in the frame of the  DOC  project (PI. H. Loisel). The PhD funding of  Ana Gabriela Bonelli  was covered by  CNES  and  ACRI-ST. The authors acknowledge the two anonymous reviewers and  Aurea Maria Ciotti  for their valuable suggestions which have helped to improve the quality of the manuscript. The authors also acknowledge  NASA , and especially  Antonio Mannino , for providing access to GOCAD dataset. The authors would like to thank all participants and voluntary contributors for collecting data that have been assembled in the NOMAD data set. We would also like to recognize the time and effort done to collect the in situ measurements gathered in this study, which were collected by different research groups and for different areas across the globe.
We annotated miRNAs using the state-of-the-art tool for identification and annotation of miRNAs, miRDeep2  [16]  and filtered candidate miRNAs against tRNAs and rRNAs (see Material and methods for details). In the case of cnidarians living in symbiosis with dinoflagellates, Symbiodinium miRNAs were filtered out using the available genome data (S. microadriaticum, S. kawagutii and S. minutum)  [18] [19] [20]. Following the core algorithm of miRDeep2, we additionally ran the package's quantifier module to detect conserved miRNAs not found by the previous strategy and to compensate for incomplete genome assemblies. miRDeep2 initially predicted 56 candidates in A. viridis from which 16 are bona fide miRNAs. In A. millepora 285 candidates were predicted, which were subsequently reduced to 50 bona fide miRNAs.
A. millepora is a close relative to A. digitifera, for which 26 miRNAs were estimated recently  [11]. To provide methodological consistency, we re-annotated the species' miRNAs based on small RNA sequences of A. digitifera larvae, two replicates of an adult single colony and two replicates of pooled colonies  [11]  yielding 292 candidates, from which 42 passed manual inspection using the same criteria as for the other species. We took a similar approach for the published small RNA datasets of the sea anemone E. pallida  [12]  and the stony coral S. pistillata  [9]  miRNA datasets, which yielded 47 and 23 bona fide miRNAs, respectively. For E. carnea, S. callimorphus and M. senile no genome assemblies are currently published. Hence, in these cases, conserved miRNAs were detected using the quantifier module via homology alone.
These datasets enabled the use of a custom pipeline rather than conventional prediction tools in order to find miRNA targets shared between Nematostella and other species investigated. We took advantage of the verified miRNA target interactions in Nematostella and H. magnipapillata  [10]  and extracted stringent targeting rules by visualizing the binding properties of cnidarian miRNAs and their functionally modulated targets by the using RNAhybrid  [38]. Bona fide miRNAs were mapped to the respective transcriptome by applying these rules using a customized version of an established mapping algorithm  [39]. A set of homologous targets was selected based on reciprocal best BLAST hits to Nematostella genes whose target sites were verified using previously published degradome data  [10]. Putative targets resulting from this analysis were, like their Nematostella and Hydra miRNA targets previously, subjected to deeper inspection by RNAhybrid  [38]  and removed if they did not fulfil the set rules.
(c) Annotation of miRNAs For N. vectensis sequences of bona fide miRNAs were taken from recent Argonaute immunoprecipitation results (PRJNA658931  [17] ). miRNA identification for species with available genomes  [40, [51] [52] [53]  was performed using miRDeep2  [16]. The adapter trimmed reads of A. digitifera (PRJNA298449) and small RNA reads S. pistillata and E. pallida were downloaded from the SRA (PRJNA234072 and SRX1351928 respectively). The following procedure was essentially identical for all species. Small RNA reads of the replicates and different stages were pooled. Adapter sequences were trimmed (ATCTCGTATGC for S. pistillata, TGGAATTCTCGGGTGCCAAGG for E. pallida and A. millepora AGATCGGAAGAGCACACGTCT for E. carnea, M. senile and S. callimorphus) and reads processed to discard short reads with scripts included in the miRDeep2 package.
The remaining reads were mapped to the respective genome using Bowtie (v. 0. 12. 7 as part of the miRDeep2 package) and a list of miRNAs known from other cnidarian species  [8] [9] [10] [11] 54]  as input file. A script shared by Yi Jin Liew was used to filter out candidates with a miR-Deep2 score below 10 (B. Z. 2017, personal communication)  [9]. These candidates were validated and further filtered manually according to criteria summarized by Fromm  [54]. These criteria include the requirement of a 2-nucleotide overhang on the 3 0 end of the precursor miRNA, 5 0 consistency of the mature miRNA strand (at least 90% of the reads have to be starting from the same position), and at least 16 nucleotide complementarity between mature and star strand. In addition, criteria of significant RNAfold p-value and a minimum of 10 reads had to be fulfilled to qualify as bona fide miRNA.
(e) Prediction of miRNA targets Transcriptomes of E. pallida, S. pistillata, A. millepora, A. digitifera and A. viridis were assembled previously  [40, 52, 58, 59]. Total RNA of three pooled M. senile individuals, six pooled parasitic individuals of E. carnea and one individual of S. callimorphus was extracted with Trizol (Thermofisher Scientific). Library preparation and transcriptome sequencing was performed at GENEWIZ on an Illumina HiSeq 2000 platform for M. senile and for S. callimorphus, and at VBCF on a Hiseq 2500 platform for E. carnea. De novo transcriptomes for E. carnea, M. senile and S. callimorphus were assembled with the Trinity package  [60]. To assess the quality of the transcriptomes, BUSCO analysis was performed  [61]  (electronic supplementary material, file S11). For miRNA target prediction, we defined rules for the targeting mode of cnidarian miRNAs according to the previously verified miRNA target interactions in Nematostella and H.
Reads were mapped to the Nematostella genome using bwa  [62]. Annotated mRNAs containing a putative miRNA target site which was exactly 10 bp downstream and antisense of a degraded read's 5 0 end mapping location were considered as verified targets. Homologous miRNA targets were identified by running reciprocal BLAST searches against a set of the verified target sites of Nematostella. We validated the obtained homologous miRNA targets by assessing the annealing properties to their miRNA with RNAhybrid  [38]  and only miRNA target pairs passed which obeyed the defined rules. Data accessibility. The transcriptomes of E. carnea, S. callimorphus and M. senile were submitted to the sequence read archive (SRA) under accession PRJNA430035 and the miRNA samples for A. millepora, E. carnea, A. viridis, S.
Sports activity was performed after 6-8 months. Functional results were evaluated by two independent observers who were blinded to the type of device used, through International Knee Documentation Committee (IKDC) scoring and Lysholm scoring  [17]. Lachman tests and pivot shift tests were used to evaluate knee stability  [18, 19]. Measurement of the quadriceps wasting was done at a point taken 15 cm proximal to the superior pole of patella  [20]. The assessment was done 2 years after surgery. SPSS software, version 20. 0 (IBM Corp. , Chicago) was utilized for the statistical assessment. The unpaired Student's t test (b) and the paired t test (c) were applied for continuous data, while the Fisher's exact test (a) was applied for categorical data. Statistical significance was considered for a p value < 0.
METHODS Four CCAs with compositions listed in Table  1  were synthesized by arc-melting from high purity (Cr greater 99. 2%, all other metals greater than 99. 9%) elements and suction cast in a water-cooled copper hearth under argon atmosphere. Alloys were flipped five times to ensure chemical homogeneity before encapsulation into quartz tubes under argon and a 5 h annealing treatment at 1,070°C followed by a water quench. The compositions and heat treatment were chosen to promote the formation of an fcc matrix while balancing cost, density, and passivating element concentration with machine learning methods discussed elsewhere. 2, 7 The Al 0. 3 Cr 0. 5 Fe 2 Mn x- Mo 0. 15 Ni 1. 5 Ti 0. 3 system was also modeled with CALPHAD utilizing ThermoCalc † 2019b software and the TCHEA3 database for Mn concentrations between 0 and 25 at% to predict the thermodynamically stable phases for each synthesized alloy.
7 eV) on a PANalytical Empyrean † Diffractometer at a scan rate of 0. 15 °/s. The samples were characterized through scanning electron microscopy (SEM) using backscattered electron (BSE) imaging on an FEI Quanta 650 † operating at an accelerating voltage of 15 keV, a spot size of 4, and working distance of approximately 10 mm. The chemical compositions of the samples were evaluated via point scans and mapping with energy dispersive spectroscopy (EDS) analyzed using Oxford Instruments AZtec † software. Area fractions of second phase regions were calculated with ImageJ † using a brightness threshold to isolate at least 300 precipitates across multiple micrographs per alloy. The calculated area fraction of second phase was taken to be equivalent to the volume fraction of second phase in the material.
25 V SCE for 40 ks, verified to be within the passive range of all tested alloys via potentiodynamic polarization. The Z img was continually characterized every 6 s at a frequency of 1 Hz and an AC voltage of 20 mV rms to monitor oxide growth during the potentiostatic hold. The potentiostatic EIS described above was carried out at -0. 25 V SCE to characterize the surface after the potentiostatic hold. EIS spectra were fit to a simplified Randles' circuit model containing a CPE with Gamry Echem Analyst † software to obtain the polarization resistance of the oxide film. To examine the effects of a longer time of corrosion, 1 cm diameter buttons with a thickness of approximately 0. 25 cm were measured for mass to 0. 01 mg precision and fully immersed for 25 d in 0. 1 M NaCl. Corrosion products were rinsed with nitric acid under ASTM Standard G1 C.
Cation fractions were normalized with relative   A)  Bolded terms indicate values associated with single-phase stability in CCAs. (2) Although oxide thicknesses could be quantified with a dielectric constant and resistivity estimated from XPS-based surface analysis, such assumptions were not made to characterize the film during polarization due to the changing composition of the oxide film. Therefore, the trends in the Z img measured during polarization and/or potentiostatic holds should be regarded as qualitative descriptions of the oxide film thickness. sensitivity factors obtained from Phi Multipak † software. The overlap between the Al 2p series and Cr 3s series was accounted for by fixing the magnitude of Cr 3s signal based on the intensity obtained in the Cr 2p 3/2 region normalized by relative sensitivity factors.
ACKNOWLEDGMENTS This work was supported by the  U. S. Office of Naval Research  through award no. N00014-19-1-2420  under the directorship of  Dr. Airan Perez  and  Dr. Dave Shifler. Author K. Ogle  and the AESEC experiments were supported by the  Agence Nationale de Recherche , grant no. ANR-20-CE08-0031  ( Tapas 2020 ). Utilization of the  PHI VersaProbe III, Quanta 650 SEM , and  Empyrean X-ray Diffractometer  within the  UVA Nanoscale Materials Characterization Facility (NMCF)  was fundamental to the completion of this work. The PHI VersaProbe III system was supported by  NSF  Award no. 162601.
Apatite Fission Tracks (AFT) We performed AFT analysis at the University of Padua, Italy. Apatite grains were mounted in epoxy resin, polished, and etched at 5. 5 M HNO 3 for 20 sec at 20 °C to reveal spontaneous tracks. The samples were analyzed by applying the external detector method  (Gleadow, 1981)  using low-uranium muscovite foils as an external detector to cover apatite mounts and then irradiated at the Radiation Center of Oregon State  University with a nominal fluence of 9 × 10 15 neutrons/cm 2. After irradiation, we etched mica detectors for 40 min in 40% HF at 20 °C to reveal induced tracks. We counted tracks and measured track length distribution using an Olympus optical microscope at a magnification of ×1250. We carried out age calculations and statistics with Trackkey software  (Dunkl, 2002).
Apatite grains were dissolved for 3 h at 70 °C in a 50 µL HNO 3 5N -solution containing a known content of 235 U, 230 Th, and 149 Sm, and additional 50 µL HNO 3 5N -and then filled with 0. 9 mL of ultrapure MQ water. The final solution was measured for U, Th, and Sm concentrations by quadrupole inductively coupled plasma (ICPquadrupole) mass spectrometry (collision cell technology [CCT] Thermo-Electron at LSCE, Gif/Yvette, France). The analysis was calibrated using external age standards, including Limberg Tuff and Durango apatites. Mean AHe ages of 16. 0 ± 1. 4 Ma and 31. 1 ± 2. 1 Ma have been measured for the Limberg Tuff and yellow Durango apatite, respectively, which agree with published data (i. e. , 16. 8 ± 1. 1 Ma and 31. 0 ± 1. 0 Ma;  Kraml et al. , 2006; McDowell et al. , 2005). Single ages were corrected using the calculated ejection factor FT, determined using the Monte Carlo simulation technique of  Ketcham et al.
Time-Temperature Modeling In this work we used the QTQt software (version PC 5. 7. 0) to model 30 samples, including those never before modeled, from  Savignano et al. (2016). The program allows inversion of the AFT annealing and AHe diffusion parameters with the Markov chain Monte Carlo method  (Gallagher et al. , 2009; Gallagher, 2012). The inversion code incorporates kinetic models of He diffusion in apatite  (Flowers et al. , 2009; Gautheron et al. , 2009)  and AFT annealing in the multi-kinetic model  (Ketcham et al. , 2007). The modeling procedure is detailed in  Gallagher (2012). (3) age of the unconformity (Lower Jurassic) on top of the basement; and (4) present day temperature (10 ± 10 °C for all of the rocks). Thermal history simulation results were obtained taking into account the influence of α-recoil damage, grain size, and apatite kinetic properties.
RESULTS A homogeneous single grain AFT population (χ 2 test passed) was detected in all of the samples, whereas significant intra-sample variability was observed in only two cases among the AHe data despite the large variety of rock types and U-Th-Sm composition (in both these latter samples, apatites derive from Paleozoic rocks from the foreland). No particular relationships occur between age, eU (eU = [U] + 0. 235[Th] + 0. 0 047[Sm]), and grain radius. AFT ages obtained are in all instances older than AHe ages, as is expected given the closure temperatures. The regional picture is characterized by a marked thermochronological age difference between the Patagonian Precordillera and the broken foreland (Figs. 1, 2,  and 4 ). In fact, the samples from the Precordillera show consistent middle Miocene to Pliocene cooling ages.
, 2011; Maloney et al. , 2013). Coeval retroarc magmatism was widespread  (Pilcaniyeu Belt, El Maitén Belt, and Somuncura Plateau; De Ignacio et al. , 2001; Echaurren et al. , 2016; Iannelli et al. , 2018). Cooling ages and thermal histories derived from thermal modeling indicate distinct evolution patterns for the Andean chain and the broken foreland during the Neogene. Indeed, middle Miocene-Pliocene exhumation is solely recorded by our data in the Patagonian Precordillera, although the cooling pattern is not homogenous throughout the area investigated. In fact, a few samples collected from sub-volcanic bodies show a rapid cooling following emplacement at shallow depths during the Cretaceous  (Adriasola et al. , 2006). Other samples were affected at the same time by minor heating, which was possibly related to the intrusion of the North Patagonia Batholith.
ACKNOWLEDGMENTS Funding for this work was provided by the  University of Padova (Progetto di Ateneo 2015 ,  CPDA158355 ) and the  Universidad Nacional de Río Negro  ( PICT-FONCyT 2013-2916 ,  PIP-CONICET 330 ,  U. N. Comahue ). S. Boesso ,  R. Pinna , and  L. Bordier  are thanked for sample preparation and for their assistance during the analyses. The thoughtful and constructive review by  B. Horton  and the useful comments of the associate editor are gratefully acknowledged. B. Guillaume  is thanked for his useful comments.
The compositions of the matrix and the constituting particles were obtained using EDS scans run at 20 kV using a BRUKER detector. EBSD maps were captured using an EDAX detector in the FEI-SEM with 15 kV beam voltage, 1 μm step size and 200x magnification. The scans were run at a distance of minimum 300 μm inside from the border of the samples in order to avoid the possibility of drifting very close to the border. Orientation Imaging Microscopy (OIM) software was used to analyze the EBSD images. First of all, a "grain" was defined by setting the grain tolerance angle to 15 • , the minimum grains size to 10 pixels and the minimum confidence index (CI) to 0. 1. For better presentation of images, the inverse pole figures (IPF) shown in this work do not incorporate the grain definition. However, all other analyses such as grain orientation spread (GOS) and grain boundary rotation maps are  obtained by considering only the grains that satisfy the pre-defined conditions.
Acknowledgements This research work is a part of the  ENABLE  project funded by the  European Union ' s Marie Skłodowska-Curie Actions (MSCA) Innovative Training Networks (ITN )  H2020-MSCA-ITN-2017  under the grant agreement No  764979. The authors would like to thank  Alumeco  for providing the material used for experimental studies. They are also grateful to the  Division of Materials Science, Luleå University of Technology, Sweden  and the  Centre des Matériaux, MINES ParisTech, France  for allowing to use their laboratories and equipment for carrying out the experiments. Finally, the authors would like to thank  Dr. Valdimir Esin ,  Centre des Matériaux, MINES ParisTech, France  for his helpful comments on the EBSD analysis.
DATA AND METHODS The analyzed data primarily consist of seismic reflection lines recorded during various marine geophysical cruises performed in the Western Ionian Basin (Figure  2 ). Interpreted seismic data consist of high-resolution seismic profiles sounded in the frame of the CIRCEE-HR project (R/V le Suroît, October 2013, see also  Gutscher et al. , 2016)  and Poseidon expedition POS496 (R/V Meteor, March-April 2016, see also  Krastel, 2016). The CIRCEE-HR seismic data were acquired using a 450 m long, 72 channel Sercel seismic streamer with an average geophone spacing of 6. 25 m. The seismic source was a six mini-GI airgun array with a total volume of 111 cubic inches fired at a cadence of once every 6 s, for an average shot spacing of 16 m and a 24-fold coverage for each common midpoint.
Quality control of the seismic data, including processing of the navigation files (shot position and streamer geometry), was performed with the SISPEED software (Ifremer). The seismic data were subsequently bandpass filtered (70-425 Hz), stacked, and time migrated (water velocity of 1500 m/s), using the Seismic Unix software package. During cruise POS496, an 80channel digital solid-state Geometrics GeoEel streamer with a group interval of 1. 5625 m was used for seismic acquisition. A Sercel Mini GI-Gun seismic source, with a total volume of 0. 4 L, was shot in a harmonic mode. The shot interval was set to 6 s, resulting in a shot distance of ∼12 m at a ship's speed of four knots. The seismic profiles were processed by using the commercial software package Schlumberger Vista Seismic Processing.
The processing workflow includes bandpass filtering with corner frequencies of 40/80/600/1000 Hz, despiking, debias filtering, CMP binning, and normal-move-out (NMO) correction. All data were time migrated by using the software's finite difference migration. Due to the relatively short streamer and the high water depths, no dedicated velocity analysis could be applied during NMO correction and migration. Hence, a constant velocity of 1500 m/ s was applied. Moreover, other published seismic dataset  (Argnani and Bonazzi, 2002; Argnani and Bonazzi, 2005; Argnani et al. , 2012; Polonia et al. , 2016; Polonia et al. , 2017)  and high-resolution bathymetry  (Gutscher et al. , 2016; Gutscher et al. , 2017)  were considered to better constraint the spatial extension of faults at the seafloor and their in-depth geometry.
Only the most representative seismic lines will be described in detail in this article (see Deformation Pattern and Fault Activity), whereas basic line drawing of the POS496 dataset (TWT) and of the entire time to depth-converted seismic dataset (including published seismic lines) can be found in the Supplementary Material (Supplementary Figures  1  and 2 ). To obtain geometric realistic parameters of the faults, seismic profiles (Supplementary Figure  3A ) were time/depth converted by assigning average acoustic velocity to the two-way travel time sequence thickness (see Seismic Stratigraphy). The adopted acoustic velocity model was achieved from previous studies in the area  (Gallais et al. , 2011; Gross et al. , 2016; Kokinou et al. , 2013; Le Meur, 1997; Maeasano et al. , 2017) , and it is reported in Supplementary Table  1.