dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization A Panalytical X'Pert PRO X-ray diffractometer (Royston, UK) was used to assess the nature of the nanoparticles and the crystalline structure of the CNF aerogels. The XRD anode material was Cu with Kα at a wavelength of 1. 54 nm. The generator voltage and current were 40 kV and 40 mA, respectively. The scan step size and 2θ range were 0. 05 • s -1 and 10-80 • , respectively. The baseline correction, smoothing, and background subtraction for the XRD data were performed using Origin Pro 2021 software. The surface morphology of CNF aerogels before and after IONP immobilization was assessed using a Zeiss Nvision 40 scanning electron microscope (SEM; Oberkochen, Germany). The aerogel samples of a thickness of 2-4 mm were prepared by slicing through the middle section using a sharp blade.
The data obtained for CNF and CNF-IONP aerogels were normalized with respect to the wavenumber 1055 cm -1 , which represents the stretching vibration of the cellulose backbone (not altered by the crosslinking reaction). A Nano ZS90 Zetasizer (Malvern, UK) was used to measure the zeta potentials of CNFs and IONPs in water. The isoelectric points (IEPs) were determined by measuring the zeta potentials at pH 3-11. Relatively low concentrations (~0. 1 wt. %) of CNF and IONP suspensions were used with a constant ionic strength of 0. 01 M NaCl for all the zeta potential measurements. The density (ρ, g cm -3 ) and porosity (%) of the aerogels were calculated by measuring the void volume (v 1 ) with an Accupyc II gas pycnometry system (Norcross, GA, USA), the total volume (v 2 ) using a digital caliper, and corresponding mass (m) according to Equations (  1 ) and (2), respectively.
Acknowledgments: This work is financially supported by  United States Department of Agriculture (USDA) Forest Services. This project was also supported by the  USDA National Institute of Food and Agriculture , McIntire-Stennis Project number  ME042120  through the  Maine Agricultural & Forest Experiment Station. Maine Agricultural and Forest Experiment Publication Number 3860. The authors would like to acknowledge  Brian Frederick  and  George Bernhardt  for their help with BET and XRD analyses. The authors would also like to thank  Kelly Edwards ,  Michael Handley , and  Emma Perry  for their help and training on SEM and ICP-MS.
Acknowledgments: Two PhD studies by Dr. Yutaka Mori and Dr. Tamon Kusumoto on ion tracks in PADC were completed during the collaborative framework between the research team at Kobe University and the research team at the CNRS in France, one of which was conducted as a cotutelle program between The University of Strasbourg and Kobe University. This is to acknowledge that the progress presented in this contribution would not have been possible without these two research projects. This work was supported by the JSPS KAKENHI Grant Number 19K12643. This study was made as part of the Research Project with Heavy Ions at NIRS-HIMAC (H138). The authors express sincere thanks to the staff of the NIRS-HIMAC for their support during the experiments. The authors express their thanks to the NIRS-Cyclotron crew for providing excellent beams.
Pattern analysis Fig. 5  presents eight different patterns selected for the analysis in our study. For each pattern, each kind of printer and paper, 100 copies were printed and captured by Zeiss Microscope. In total, the database has 100 × 8 × 2 (paper) ×3 (printers) = 4800 samples. From these different patterns, the objective is to understand if the direction of the printing process affects the degradation of printed patterns. The study on the single dot was carried out under the micro-scale  ( Nguyen et al. , 2018 ). The printed parts are the main concern in the experiment, thus the binary images are required. There are a number of threshold methods to convert grey-scale images to binary images  ( Kittler and Illingworth, 1986; Li and Tam, 1998; Otsu, 1979 ). Each method, for a specific purpose, has some advantages and also few drawbacks, based on the requirement of the application.
Random Forest multi-class for classification One of the most popular and applicable tree-based model for classification is Random Forest, which was discovered on top of the ideology of ensemble learning (known as a divideand-conquer technique used to restrict the learning biases and push the performance of the prediction to the magnitude which is generally infeasible to common machine model). Basically, RF model is learned by stacking randomly multiple decision tree models on slightly different parts of data, then take advantage of these models' powers to perform the prediction. These sub-samples of the considered dataset are generated via the procedure of bootstrap resampling. Then, we train a decision tree model associated with each sample, and since it is learned only in the particular subset, it is a biased classifier which can capture distinct trends in the data.
In the final step, a majority-voting process is applied in order to retrieve the final prediction output as the highest average class probability of all the individual trees in the forest. We can imagine RF as a committee of experts of different fields and have some certain knowledge and understanding over the target topic (the classification problem) and they can only make decision on the problem based on voting. The package random-Forest ( RColor  Brewer and Liaw, 2018 )  is used to implement the RF classification algorithm in R.
Experimental setup The proposed experiment is conducted with three printing technologies on two types of printing paper, which mean that there are totally six printing sources. Details are summarised in Table  4. For each pattern in eight patterns, Fig. 5  , we implement the classification model for six printing sources. For training and evaluating the SVM and RF classification, the ratio between the training dataset and testing dataset is 3:2. In other words, for each pattern, sizes of training and testing dataset are 320 and 240, respectively. For this dataset, we used the radical basis function (RBF) kernel, and the parameters are tuned with the 10-fold cross-validation. The optimal parameters are derived as C = 10 , γ = 0. 1. With the RF algorithm, the hyper-parameter is also tuned with the 10-fold cross-validation, the chosen value is ntree = 500.
The experiment indicates that a good design of printing patterns and printing source can be developed for authentication application, especially for forgery detection of printed documents. The size of this dataset is relatively small and the pattern form is simple, yet the classification algorithm still performs well. Moreover, thanks to the simplicity and efficiency of SVM, the classification model is light and easy to implement in commercial production, hence our proposed study is expected to develop a lightweight model which can be integrated to IoT devices, smart-phone, etc. , for print authentication.
Incidence of cancers in France We collected information on the overall incidence of cancers in France from the French National Cancer Institute (INCA) website. In 2018, there were 382 000 new diagnoses of solid tumor cancers in France, and 157 400 cancerrelated deaths (41%) (https://en. e-cancer. fr/). Raw nationwide data from the publicly available health care system were also used as of 24 November 2020 26 to extrapolate the reduction in new cases attending Unicancer centers at the national level.
FUNDING This work was supported by  LYRICAN  ( INCA-DGOS-INSERM-12563 ), the  French National Research Agency [LabEx DEvweCAN  ( ANR-10-LABX0061 )],  RHU4-DEPGYN  ( ANR-18-RHUS-0009 ),  INCA&DGOS (NetSARC, RREPS, RESOS , no grant number),  INCA  ( InterSARC , no grant number),  European Commission  ( EURACAN-EC739521 ),  Fondation ARC  (no grant number),  PIA Institut Convergence François Rabelais PLAsCAN  ( 17-CONV-0002 ),  La Ligue contre le Cancer  (no grant number), and  La Ligue de L'Ain contre le Cancer  (no grant number) to JYB. The funding sources had no role in the making of the manuscript.
Here, we combine InSAR data spanning 2005-2010 and 2014-2019 from ENVISAT and Sentinel-1 satellites, respectively, with local seismicity from the Afar rift to investigate the plate-boundary kinematics of the Afrera linkage zone, the junction between the Erta Ale and Tat Ali magmatic segments in Northern Afar (Ethiopia). We obtain time-series of cumulative InSAR Line-Of-Sight (LOS) displacements that show deformation is accommodated by a series of active en-echelon faults striking ∼NS and characterized by normal slip associated with a left-lateral strike-slip component. Additionally, we observe spatial variation in fault behavior with stick-slip and creep. The faults in the center of the linkage zone behave primarily in a stick-slip mode (with abrupt fault displacements up to ∼40 mm) and fault motions are associated with earthquakes of M L > 5.
, 2017; Illsley-Kemp et al. , 2018; La Rosa et al. , 2019). A recent study by La  Rosa et al. (2019)  used InSAR, seismicity, and structural geology to investigate a fault slip in Afrera associated with a M L 5. 0 in 2007 and proposed a model of rift linkage consisting of a right-lateral, NW-SE-trending transfer zone where deformation is accommodated by dominant ∼NS-striking faults with oblique kinematics characterized by both normal and left-lateral components (Figure  1 ). Here, we processed a large SAR data set from ENVISAT and Sentinel-1 (S1) catalogs spanning the time periods 2005-2010 and 2014-2019 to produce time-series of cumulative displacements and the related uncertainties. We identified not only deformation along different faults with both stick-slip faulting, in January 2018 but also fault creep throughout the observation period.
Modeling of both types of faults showed motion along ∼NS-striking planes with oblique kinematics, similar to the 2007 event (La  Rosa et al. , 2019). We also investigated how seismicity accompany the observed deformation in space and LA ROSA ET AL. time across the linkage zone. To this aim, we not only used the available catalogs 2005-2010  (Belachew et al. , 2011; Illsley-Kemp et al. , 2018; La Rosa et al. , 2019)  but also analyzed new data from two local seismic networks  (Doubre, Leroy, et al. , 2017; Keir et al. , 2017; La Rosa et al. , 2021)  to study the seismic sequence in January 2018 and we identified two major earthquakes with M L > 5 (Figure  1a ). This study presents one of the few direct observations of the fault kinematics in a rift linkage also detailing the temporal evolution of the strain.
InSAR and Seismic Data We analyzed a vast data set of SAR images by the European satellites ENVISAT and S1 covering the Afrera zone and spanning the time periods 2005-2010 and 2014-2019, respectively, to investigate how deformation is distributed across the linkage zone. We finally analyzed the local seismicity recorded by two temporary seismic networks active in Ethiopia during 2017-2018 (Figure  1a )  (Doubre, Leroy, et al. , 2017; Keir et al. , 2017; La Rosa et al. , 2021).
InSAR Data Processing We formed 92 interferograms from SAR images of the ENVISAT satellite in both ascending (track 028) and descending (track 049) geometries from 2005 to 2010. ENVISAT interferograms were generated using the ROI_PAC software developed by JPL/Caltech  (Rosen et al. , 2004). Topographic phase contributions were removed using an external 3-arc sec (∼90 m resolution) SRTM DEM  (Farr et al. , 2007). Interferograms were then filtered using a power spectrum filter  (Goldstein & Werner, 1998)  with strength of 0. 6 and unwrapped using the ICU branch-cut algorithm  (Goldstein et al. , 1988). The unwrapped interferograms were then geocoded using the same SRTM DEM. We also produced 142 interferograms from S1 satellite images in both ascending and descending tracks (014 and 079) for the 2014-2019 period, using the JPL/Caltech/Stanford InSAR Scientific Computing Environment software package  (Rosen et al.
InSAR Time-Series Analysis In order to minimize sources of errors in the interferograms, we estimated time-series of cumulative satellite Line-Of-Sight (LOS) displacements and their uncertainties for each InSAR track, using the Π-RATE software  (Wang et al. , 2012). We analyzed 51 interferograms for ENVISAT ascending track 028, 41 for EN-VISAT descending track 049, 55 for S1 ascending track 014, and 87 for S1 descending track 079 (Figures  S1  and S2 ). Before the time-series analysis, we multilooked the S1 interferograms to a pixel size of 90 m × 90 m to reduce noise, this is also the same resolution as for the ENVISAT interferograms. Unwrapping errors were identified in ENVISAT data by adopting a phase closure method on Minimum Spanning Trees  (Wang et al. , 2012) , while the excellent level of coherence in S1 data and lack of dense fringe patterns ensure that there are no unwrapping mistakes in this study.
Seismic Analysis To better characterize the stick-slip events, we analyzed the seismicity accompanying the fault slip identified by InSAR in January 2018 and recorded by the seismic network deployed in Afar in  2017 -2018 (Doubre, Leroy, et al. , 2017; Keir et al. , 2017; La Rosa et al. , 2021). We inspected 1 month of continuous seismic recordings, from January 1 to January 31, 2018 to identify all the earthquakes during that period. We found a total of 499 earthquakes with the first P wave arrival at station N009 (located at Afrera) and manually picked both P and S waves for earthquakes recorded by four or more stations. We then located the earthquakes using the Oct-Tree search algorithm implemented into the NLLoc software  (Lomax et al. , 2000)  and a 2. 5D velocity model of Afar. We finally estimated local magnitudes (M L ) by measuring the zero-to-peak amplitude on simulated Wood Anderson seismometers and applying the distance correction for the Danakil region from Illsley-Kemp et al.
5 mm. This supports the hypothesis the only the M L 5. 1 of January 2018 contributes to the observed InSAR displacement, as also indicated by the very good correspondence between LA ROSA ET AL. the hypocentral depth estimated from both InSAR inversion (2. 6 km) and seismic data (2. 0 km) for the January 11 earthquake. The cumulative seismic moment release (Figure  8b ) shows earthquakes occurring in the 2 days (January 8 and 9, 2018) preceding the main earthquake. We attempted to process focal mechanisms for the earthquakes with M L > 4. 5. However, due to the large azimuthal gaps of the seismic network in the study area, we have not been able to obtain unambiguous solutions. The majority of the aftershocks following the major earthquakes are shifted to the west (Figure  8a ) likely suggesting that some small slip along nearby fault segments was triggered by the mainshock.
Coulomb Stress Changes Calculation Earthquakes modify the stress conditions on nearby faults by either promoting or inhibiting slip (e. g. ,  Harris, 1998; King et al. , 1994; Lin & Stein, 2004; Toda et al. , 2005). These stress changes can be quantified with the Coulomb failure criterion, whereby positive CSCs caused by a fault slip (called the source fault) on a pre-stressed fault (called the receiver fault) can bring it to fail, while negative CSCs unload faults inhibiting their rupture. We explore whether some stress-triggering effect occurred between the three major earthquakes, the M L 5 in 2007 and the two M L ∼ 5 in 2018. For the CSCs calculation, we used the Coulomb 3 software (https://www. usgs. gov/software/coulomb-3), and faults are modeled as Okada shear dislocations within a uniform, elastic halfspace with the same elastic moduli as used in the InSAR modeling.
Conclusion In this study, we provided one of the few direct observations on how deformation is distributed across a linkage zone between two active magmatic segments. We combined a vast InSAR data set from ENVISAT and Sentinel-1 satellites, spanning 2005-2010 and 2014-2019, respectively, with local seismic recordings from the Afar rift. We show that deformation at the AP, between the EA and TA segments, is accommodated by several en-echelon, ∼NS-striking, oblique faults, which are arranged in two main structural architectures: dominant east-dipping faults characterize the center of the AP while dominant west-dipping faults are observed close to the eastern edge, where the AP meets the TA segment. Various fault segments are active at different time periods, showing great variability in their behavior.
Acknowledgments This study was developed in the framework of the  PhD  project of A. L. R. (XXXIII cycle of the  Dottorato Regionale Pegaso in Earth Sciences ) and is supported by the  Ministero Università e Ricerca (MiUR)  through  PRIN  grant  2017P9AT72. A. L. R. and C. P. acknowledge partial support by the  University of Pisa  grant  PRA_2018_19. H. W. was supported by the  NFSC  project (41672225). ENVISAT ASAR and.
Study Population Patients were retrospectively identified through a French nationwide survey. The diagnosis of encrusted cystitis and pyelitis was based on imaging features including thickening and calcified lesions of the bladder and/or renal calyces and pelvis. Diagnosis of isolated encrusted cystitis was made by ultrasonography while diagnosis of encrusted pyelitis with or without cystitis was based on computed tomography (CT) scan. The identification of corynebacteria in the urine was a major diagnostic criterion. The presence of Gram-positive bacilli, struvite crystals, and/or alkaline urinary pH ($8) was considered as an alternative criterion in the absence of corynebacteria. 3 ta Collection Demographic, clinical, and biologic data were recorded retrospectively at diagnosis, during and at the end of follow-up for all patients.
Statistical Analysis The statistical analysis was carried out with STATISTICA software (version 13. 3) from TIBCO Software Inc. (Palo Alto, CA, USA). The values for each experimental condition were reported as the mean ± standard error of the mean (SEM) of six independent repetitions. The effect of nitrite/nitrate treatment on the dry-cured fermented sausages was assessed by a one-way analysis of variance (ANOVA): (0 NO 2 /NO 3 ; 80 NO 2 /NO 3 ; 200 NO 3 ; 120 NO 2 /NO 3 ). The correlation matrix and ANOVA was performed using a confidence level of 1%, and the post hoc test used was the Tukey test.
Background: Detection of the ROS1 rearrangement is mandatory in patients with advanced or metastatic non-small cell lung cancer (NSCLC) to allow targeted therapy with specific inhibitors. However, in Spanish clinical practice ROS1 determination is not yet fully widespread. The aim of this study is to determine the clinical and economic impact of sequentially testing ROS1 in addition to EGFR and ALK in Spain. Methods: A joint model (decision-tree and Markov model) was developed to determine the cost-effectiveness of testing ROS1 strategy versus a no-ROS1 testing strategy in Spain. Distribution of ROS1 techniques, rates of testing, positivity, and invalidity of biomarkers included in the analysis (EGFR, ALK, ROS1 and PD-L1) were based on expert opinion and Lungpath real-world database. Treatment allocation depending on the molecular testing results was defined by expert opinion.
Decision-tree parameters All the inputs that are used in the decision-tree submodel are listed in Table  2. As shown in Fig. 1 , the results of the biomarker determinations can be informative (positive or negative) or invalid, mainly due to insufficient sample. The positivity rates for EGFR, ALK and ROS1 determinations were obtained from Lungpath database while the PD-L1 positivity rate (considering TPS > 50% as the threshold for positivity) was agreed by the expert panel, given that the PD-L1 positivity rate obtained from Lungpath probably reflects a mixture of positivity rates with different thresholds depending on the center. Invalid rates for each biomarker were obtained from the Lungpath database. On the other hand, based on the experience of the experts, repeating invalid results does not usually give informative results, so it was assumed that invalid results would be direct candidates for re-biopsy (considered successful by experts in only 33.
3% of cases). When re-biopsy is unsuccessful, patients receive doublet of chemotherapy if the molecular diagnosis of EGFR is unknown (due to an invalid result at the beginning of the sequential determination), or chemo-immunotherapy (the same received by patients with TPS < 50% or unknown PD-L1) if the invalid result was obtained for ALK or ROS1 but the diagnosis of EGFR is known and negative. The current distribution of ROS1 determination techniques in Spain included in the model was obtained from the panel of experts, as the data provided by Lungpath reflects clinical practice in 2008 and does not correspond with current guideline recommendations where reflex to FISH is mandatory for ROS1 determination. In the base case, the accuracy of the techniques is not taken into account, so that the specificity and sensitivity of all ROS1 determination techniques were assumed to be 100%.
Thirdly, since the main objective of the analysis was to determine the significance of ROS1 determination and not so much the validation of the testing techniques, a specificity and sensitivity of the techniques of 100% was assumed and validated by experts, which is far from real clinical practice. However, this limitation was evaluated in one of the alternative scenarios. Fourth, Markov models have some limitations when reproducing at a long term the clinical management of NSCLC patients. On the one hand, as in all costeffectiveness models where long-time horizons are used, extrapolations are necessary with the associated limitations. On the other hand, as described above, in the absence of individualized data for all treatments included in the analysis, it was assumed the use of exponential models for all treatments, as conducted in  Nadal.
Conclusions In conclusion, despite the low prevalence of ROS1 rearrangements in advanced NSCLC patients, our analysis shows that testing ROS1 in these patients in Spain is a strategy that would increase and improve patients' lives and that it is a cost-effective strategy for the Spanish NHS. For this reason, this analysis should encourage that not even a single ROS1-positive patient goes undiagnosed. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
After training on a large simulation dataset, the network shows a satisfactory approximation error on a complementary test dataset with a MAPE of 32. 8%. The convolutional part is pre-computed and the emulator is defined as the remaining part of the network, saving significant computational time. On a 32-core machine, the emulator has a speed-up factor of several thousands compared to the simulator and the overall relationship between its inputs and output is consistent with the expected physical behavior of fire spread. This reduction in computational time allows the computation of one-hour burned area map for the whole island of Corsica in less than a minute, opening new application in short-term fire danger mapping.
The land use field comes from Corine Land Cover data  (Feranec, Soukup, Hazeu, & Jaffrain, 2016)  coupled with data from the IGN (Institut Géographique National) product BD TOPO R for road and drainage networks. The elevation field is extracted from another IGN product: BD ALTI R , which has originally a 25-m resolution. A fuel parameterization is used to assign reference fuel parameters to each type of vegetation (referred to as "fuel type" in the following) in the land use data for ROS computations. Data used for simulation also include 2D fields of wind speed vectors at a resolution of 200 m that were pre-computed for average wind speed vectors with the mass conserving preconditioner from the atmospheric forecasting system Meso-NH  (Lac et al. , 2018)  to account for orographic effects.