dataesr/scientific-paragraphs-categorization huggingface dataset:
Scanning Transmission Electron Microscopy (STEM) To perform STEM acquisitions, a volume of 10 µL of diluted hybrid suspension (0. 5 g/L in CNC content) was deposited on glow-discharged carbon-coated grids (200 meshes, Delta Microscopies, Microscopies, Mauressac, France) for 2 min, and the excess was then removed by Whatman filter paper. The grids were dried overnight in air and a 0. 5 nm platinum layer was then deposited by an ion-sputter coater (LEICA AM ACE600, Oberkochen, Germany). Images were recorded at 10 kV with a Quattro Scanning electron microscope (Thermo Fisher Scientific, Waltham, MA, USA) using a STEM detector. ImageJ software was used to analyze the STEM images. For each sample, several images were examined and the AgNP Feret's diameter (i. e. , the greatest distance of two tangents to the contour of the measured particle) of approximately 100 AgNPs was evaluated to obtain an average value.
X-ray Diffraction Spectroscopy (XRD) A Bruker D8 Discover diffractometer was used to record XRD diffractograms of the CNC/AgNP dried samples (10 min of acquisition). Cu-Kα1 radiation (1. 5405 Å) was produced in a sealed tube at 40 kV and 40 mA, parallelized using a Gobël mirror parallel optic system and then collimated to produce a 500 mm beam diameter. The data were collected in a 2θ angle range from 3 • to 70 •. The AgNP crystallite size (CS) was determined using Scherrer's equation  [59] : CS = Kλ β cos θ where K is the shape factor (0. 9), λ is the X-ray wavelength (1. 54 Å), β is the full-width at half-maximum (FWHM), and θ is the angle of the diffraction peak of the crystalline phase (Bragg's angle). The FWHM was determined considering the AgNP characteristic peak at 2θ = 38 •.
Ag + Relesase Analysis Conductometric measurements were performed to determine Ag + release in pure water from AgNPs in hybrid suspensions. The conductivity of dialyzed CNC/AgNP hybrid suspensions was monitored at 22 • C at different times (i. e. , 48, 96, 120, and 168 h) using a Metrohm 856 Conductivity Module and recorded by Tiamo TM Titration Software. The concentration of Ag + ions (mM) was deduced using a calibration curve obtained by the analysis of various amounts of a silver standard solution (1000 µg/mL, Chem-Lab NV, Belgium) diluted in a 2 g/L CNC suspension (i. e. , Ag + concentrations from 0 to 2. 4 mM).
For each hybrid suspension, a controlled volume of 3 µL was immediately deposited on the paper disks. All the tests were performed working at neutral pH. Ultra-pure water and 70% ethanol were used as negative and positive controls, respectively. After 48 h at 30 • C, the average diameter of the inhibition zone was measured using ImageJ software. It was confirmed that the pure CNC did not display any biocidal activity (data not shown). For the biocidal activity persistence test, the biocide-impregnated paper disks from the above experiment were recovered, layered on a fresh bacteria plate exactly as described above, and rehydrated with 3 µL of water. The plates were grown at 30 • C for 96, 120, or 168 h, and the average diameter of the inhibition zones was measured. It was estimated that 168 h represented a good compromise between the requirements for investigating a long-term biocidal activity and the feasibility of the experiment associated with the necessity of paper disk rehydration.
89-3722). Other characteristic Ag peaks with weaker intensity were detected at 44. 1 • and 64. 2 • , corresponding to (200) and (220) crystalline planes, respectively, thus confirming the isotropic nature of the crystals  [28]. The average crystal size (CS) was estimated to be equal to 3. 3 ± 0. 3 nm. It is known that AgNPs are composed of metallic Ag 0 , but they could also contain a certain Ag + ion content  [2, 55] , thus influencing the final AgNP oxidation state. The oxidation state of AgNPs in the hybrid system was investigated here by the analysis of XANES spectra. As reported in Figure  S1c , the XANES data referring to the hybrid sample were compared to the XANES spectra of pure silver references. The analysis of these data using the Athena software based on the LCF procedure allowed precisely quantifying the Ag + and Ag 0 fractions in AgNPs.
Acknowledgments: We acknowledge SOLEIL for providing access to its synchrotron radiation facilities on the  SAMBA beamline. We gratefully acknowledge  S. Durand  ( BIA-Nantes ) for support in FTIR measurements,  F. X. Lefevre  and  N. Guichard  ( Université de Nantes ) for support in AAS experiments,  B. Pontoire  ( BIA-Nantes ) for performing XRD acquisitions,  B. Novales  for helping in acquisition of STEM images, and  S. Haouache  ( BIA-Nantes ) for helping during SAMBA beamtime. This work is a contribution to the  Labex SERENADE  (n •  ANR-11-LABX-0064 ) funded by the  French government program , " Investissement d'Avenir ", of the  French National Research Agency (ANR)  through the A* MIDEX  project (n •  ANR-11-IDEX-0001-02 ).
(  4 ) to Eq. (  6 ), a MATLAB function that computes the total reflection coefficient of multilayer absorber was developed. This code allows the calculation of the reflection coefficient using the complex dielectric permittivities and/or magnetic permeabilities of the layers composing the multilayer absorber. In our case, the elaborated composites are pure dielectric materials (µ r = 1), so only complex dielectric permittivities are taken into consideration. Fig. 2  Reflection coefficients of the multilayer absorber.
At each iteration, the fitness function compares the computed (Γ computed ) to the targeted (Γ Target ) reflection coefficients; this operation is repeated until the goal of the best absorption performance (lowest reflection coefficient) is reached. Here, the GA will stop in two cases: when the number of generations exceeds the imposed maximum limit of iterations or when the GA achieves its goal (Γ Target ). The introduced parameters for the optimization program are the number of layers in the multilayer absorber, the thickness limits (min and max) of each layer, the frequency range, the material database and the targeted reflection coefficient (Supplementary Data 2). The optimization by the developed code takes only a few minutes to be finished. After this, the reflection coefficient of the proposed design is computed using CST for the oblique incidence of 30° as the developed program deals only with the normal incidence.
Multilayer absorber design by using genetic algorithm optimizer In this part, the analytical method, associated with GA, is used for the optimization of the multilayer absorber. The database that was used for this optimization is composed of the same samples used before (Fig. 3 ). It consists of unloaded (0 wt. %) and the loaded epoxy foam composites with 12 mm CF length using different weight percentages between 0. 0125 wt. % and 0. 4 wt. %; our database is therefore composed of 10 composite materials as layers' choice for the GA optimizer. The optimization of the multilayer absorber is conducted in the frequency range between 0. 75 and 18 GHz with a targeted reflection coefficient of -20 dB. The used parameters for this optimization are listed in Table  1 ; the number of layers was set to 5 with a limit of thickness, between 10 and 25 mm for each layer, thus, the total thickness of the absorber was limited between 50 (as a minimal thickness) and 125 mm (as a maximal thickness).
mm Table  1  Parameters used for the optimization of the multilayer absorber (with 5 layers) using MATLAB code with the GA The optimized multilayer absorber MLA98-GA, proposed by the GA, presents a total thickness of 98 mm; the chosen layers and their thicknesses are shown in Fig. 8. This design is composed first, of 10 mm thickness of the unloaded epoxy foam composite as a matching layer; this layer has the closest dielectric properties to that of air, so it will ensure a very good transition of the EM waves between the air and the first layer of the absorber. Then, and for the other four layers, the GA has proposed layers loaded with increasing rates of CFs: 0. 0125 wt. %, 0. 025 wt. %, 0. 075 wt. % and 0. 4 wt. % for the second, the third, the fourth and the fifth layer, respectively. The last layer is the most loaded layer among all used layers; this layer presents the highest losses and will ensure a total absorption of the EM waves.
We obtain a very good concordance between simulated and measured reflection coefficients. Close absorption bandwidth and performance to that of the commercial absorber (AH 125), in the entire studied frequency range, were obtained with our prototype. These obtained results confirm that CF loaded epoxy foam materials with very low CF rates (from 0. 0125 wt. % to 0. 075 wt. %) are suitable to achieve a planar multilayer absorber. The second used strategy to design a planar multilayer absorber is to optimize the choice of the composition and the thickness of each layer by using the genetic algorithm optimizer. A database of ten materials is used and a minimum and maximum thickness of 10 mm and 25 mm, respectively, for each layer, are imposed to the optimizer program. MLA98-GA prototype is thereby proposed with gradual load of compositions (0 wt.
In this article, we exploit the same idea to leverage pleiotropy effects, especially because the method copes with the challenge of detecting small possible opposite effects. The method is compared to two well established statistical methods in genetic studies. The first one, ASSET  [6]  extends standard fixed-effects meta-analysis methods for detecting effects in opposite directions from a same genetic trait. The second one metaSKAT  [7]  permits to carry out gene-based meta-analysis extends SKAT and SKATo methods for meta-analyses. The developed statistical approaches will be applied to real dataset for enriching our insights about the genetic mechanisms of thyroid and breast cancer types. We are interested into exploring gene-level and pathway-level associations for each cancer type as well as for both cancer types together.
We propose to illustrate the methods on real datasets, by investigating the pleiotropic effect of genetic variants from candidate pathways in breast and thyroid cancers. Beluhca dataset includes data from CECILE, a french case-control study on breast cancer (1 125 cases, 1 172 controls) and from CATHY a french case-control study on thyroid cancer (463 female cases and 482 female controls). All these individuals were genotyped using a customized microarray including 8 716 genetic variants from 28 candidate pathways (648 genes) selected from KEGG database and from a literature review (SNPs are located at +/-50 kb from the gene boundaries). After quality controls, we retained 6 677 SNPs available for both type of cancers. Missing values were imputed using the median among cases or controls and data were centered to µ = 0.
When 2 SNPs were correlated at r 2 = 1 , one of the SNP was removed and couple of extremely correlated ( r 2 > 0. 98 ) SNPs belonging to same genes were eliminated. As group sparse dimensionality reduction methods such as sgPLS and joint-sgPLS need to be extended in case of overlapping groups of variables  [47] , 10 non-overlapping pathways were selected and only the 3766 SNPs related to those groups were kept in the final database. After all these preprocessing, the new dataset is composed of 3766 SNPs, grouped in 337 non-overlapping genes and 10 non-overlapping pathways. The list of the pathways and genes is displayed in Tables  5  and 6  in Appendix 1. The methods implemented in this article are: ASSET, metaSKAT, sgPLS and joint-sgPLS. For metaSKAT, sgPLS and joint-PLS, SNP-level, gene-level and pathway-level results are given by the methods whereas ASSET gives only SNP-level results.
Hence, in the case of ASSET, genes corresponding to selected SNPs are considered. For each SNP i, an univariate logistic model for gene-disease association can be considered separately for thyroid data and breast data (thyroid and breast cancer, Fig. 4 ). As it has been presented before, for sgPLS and joint-sgPLS, a calibration of the parameters is generally performed through a cross-validation procedure. This process relies on the definition of a measure of performance: the error of prediction of the model. However, in genetic studies, the effects are small and the prediction performances based on genetic units are usually very low. The prediction performance of sgPLS and joint-sgPLS are not different enough from one set of penalization parameters to another. In order to facilitate the interpretation, we present the results for calibration parameters set to 20 genes and 3 pathways and α = 0.
5. We explore the stability of the methods using the bootstrap strategy described in the section method. Figures  5  and 6  present this rate for preselected and non-preselected features. A gene and resp. a pathway is kept in the final selection if and only if it is preselected and its rate of selection among the bootstraps is higher than any other gene (resp. pathway) that is not preselected. We can see that for joint-sgPLS less genes are selected than for other methods (4 against resp. 20 and 18 for metaSKAT and sgPLS on both data). Results of the selection are presented in Table  3  where the name of genes and pathways is presented. "sgPLS single" stand for the use of the sgPLS on thyroid and breast data separately while "sgPLS both" stands for the use of the method on a concatenation of both data set standardizing by study.
Genes selected for thyroid data sets and selected by meta-analyses are: MAP2K2, GTF2H1 and CYP2F1. Those genes are then related to thyroid cancer but meta-analyses suggest they may be involved with breast cancer in a common effect. Genes selected for breast data and c Percent of selection of genes for sgPLS on all data, 100 bootstraps d Percent of selection of genes for joint-sgPLS on all data, 100 bootstraps Fig. 5  continued selected by meta-analyses are: PLAG2G6, ERCC3, ERCC6, MUTYH, MTHFD2, IL13, NAT2. Meta-analyses suggest that these genes may also be involved with thyroid cancer in a common effect. We can see that joint-sgPLS selects a lower number of genes (resp. 4) compared to ASSET, metaSKAT and sgPLS (resp. 19, 20, 18). Method sgPLS and joint-sgPLS select the glycan pathway and folate metabolism pathway and sgPLS Table  3  Selected data sets in terms of genes and pathways.
Computational performances Computation performances are presented on simulation cases 1 and 2 which represent data having 500 predictors and 1 output (Table  4 ). The number of observations is respectively n = 200 and n = 400. The methods sgPLS and joint-PLS penalization hyperpa- rameters are estimated with the grid used for the simulation. Mean running times over 50 replications are given. We can see overall that has the smallest running time. The methods sgPLS and joint-sgPLS have the most expensive computational. This is due to the estimation of the penalization parameters as hyperparameters. However, this calculus consist in successive applications of the same method. It can then be paralellized.
Acknowledgments The authors would like to sincerely thank the many conservation and fisheries managers, practitioners and researchers in the PICTs working within the seagrass or seagrass related fields for sharing their expert knowledge and information. We also thank  Lucas Langlois  ( JCU ) for his assistance with Fig. 2. We sincerely thank the two anonymous reviewers for the time they spent on the careful reading of our manuscript and their many insightful comments and suggestions.
The exclusion criteria were BCLC stage C, portal vein thrombosis (complete or partial obstruction of the portal vein, due to the presence of a chronic, acute or neoplastic thrombus in the vasal lumen), extrahepatic metastasis, previous systemic treatment, a platelet count \ 50,000 and a bilirubin level [ 3 mg/dL. All treated lesions had previously been untreated. The patient database reported in this manuscript has never been published before. The B-TACE population included 96 patients (179 nodules). Five patients were excluded since they were lost to follow-up (Fig. 1 ). In the B-TACE population, 22 patients were treated with cTACE (B-cTACE) and 69 with DEM-TACE (B-DEM-TACE). In the B-TACE patients, the mean BOASP was measured for the majority (65/96) of the patients and a comparative analysis between BOASP values and response to B-TACE was performed.
Statistical Analysis Data are presented as means, ranges and frequencies. The Chi-squared, Fisher's exact, Student's t and Mann-Whitney U tests were used. Patients were matched in a Propensity Score Matching (PSM) procedure by a one-toone ratio for age, gender, number of nodules, Child-Pugh score, and type of TACE (conventional or DEM-based) with a match tolerance of 0. 2, giving priority to exact matches. The baseline characteristics were analysed before and after the PSM. All the tests were two-tailed; a P-value \ 0. 05 was considered statistically significant. All the statistical analyses were carried out using IBM SPSS 25. 0 (SPSS Inc. , Armonk, NY, USA).
Results and Discussion To describe in more detail the role of HSA on the expression of competence-associated genes leading to natural transformation, we must briefly review previous transcriptomic data obtained by our lab  (35) (36) (37) (38). Specifically, data previously obtained for A. baumannii A118 and AB5075 cultured in the presence of HSA or human fluids with different amounts of HSA, such as human pleural fluids (HPF) and cerebrospinal fluid (CSF), was analyzed. Based on the Vesel et al. publication  (30) , forty-one competence and type IV pilus associated genes were selected for further examination. (30). These genes participate in type IV pilus synthesis and regulation, DNA uptake and translocation, and ssDNA binding and recombination (Fig. S1  and Table  S1 ). The expression of 30 competence and type IV pilus associated genes was enhanced when A.
86 GeV at an off-axis angle of 1. 5 degrees. As described in Sec. 2. 2, the WAGASCI module and the INGRID Water Module are basically the same except for the detector position and electronics. In the future, we will use both detectors to measure neutrino cross-sections at an off-axis angle 1. 5 degrees. Hereafter, we will describe the experimental apparatus, the Monte Carlo simulations, the datasets, the event selections, the analysis method, the systematic uncertainties, and the results.
Neutrino Beam The accelerator complex J-PARC in Tokai (Japan) is composed of a linear accelerator (LINAC), a rapid cycling synchrotron (RCS), and the main ring (MR). The 30 GeV proton beam is extracted from the MR every 2. 48 s. The beam spill consists of eight bunches with 581 ns interval. The protons impinge onto a graphite target fixed in the most upstream electromagnetic horn. Produced charged hadrons are focused by three electromagnetic horns into a 96 m-long decay volume where they decay preferentially producing ν µ (ν µ ) and µ + (µ -). By changing the polarity of the horns, the beam mode can be switched between the neutrino mode and anti-neutrino mode. In this article, the data are collected in the anti-neutrino mode with a beam power of about 470 kW.
Monte Carlo Simulation To estimate backgrounds, neutrino fluxes and signal detection efficiencies, a set of Monte Carlo (MC) simulations is used as follows: • JNUBEAM  [13]  (13a) for neutrino fluxes, • NEUT  [14]  (5. 3. 3) for neutrino interactions with nuclei, 7/29 Fig. 1:  The WAGASCI module before the installation into the water tank is shown on the left side. Detectors installed at the J-PARC neutrino-monitor building (right). Fig. 2 : Typical neutrino event display for a simulated neutrino event in the Proton Module. The beam axis corresponds to the z-axis. The muon angle is defined as the scattering angle with respect to z-axis. • GEANT4  [15] (v9r2p01n00)-based software for the transport and detection of secondary particles. Software settings for the simulation are the same as those used in  [5].
The neutrino energy spectra at the WAGASCI and Proton Module positions predicted by JNUBEAM, with hadronic processes tuned from the NA61/SHINE measurements  [16] , are shown in Fig. 7. The mean neutrino energy is 0. 86 GeV, and the peak is at 0. 66 GeV with 1σ spread of +0. 40 -0. 25 GeV. The flux-integrated CC cross-sections per nucleon predicted by NEUT are summarized in Table  1. To compare predicted neutrino cross-sections in Sec. 7, an alternative event generator, GENIE  [17]  (2. 12. 8), is also used. In both generators, a Relativistic Fermi-Gas (RFG) model  [18]  is used, but the Bodek-Ritchie modifications  [19, 20]  are implemented in GENIE. In NEUT, random-phase approximation (RPA)  [21] , and multi-nucleon (2p2h) 8/29  interactions  [22]  are considered. In addition, they use the Rein-Sehgal model  [23, 24]  for the single-meson production, the Berger-Sehgal model  [25]  for the coherent-pion production, and Glück-Reya-Vogt-1998 (GRV98)  [26]  parton distributions with Bodek-Yang modifications  [27, 28]  for the deep-inelastic scattering.
Acknowledgements We thank the  J-PARC  staff for the superb accelerator performance. We thank the  CERN NA61/SHINE Collaboration  for providing valuable particle production data. We acknowledge the support of  MEXT, Japan ;  NSERC  (Grant No. SAPPJ-2014-00031 ),  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy ;  National Science Centre (NCN)  and  Ministry of Science and Higher Education, Poland; RSF  (Grant # 19-12-00325 ) and  Ministry of Science and Higher Education, Russia ;  MINECO  and  ERDF funds, Spain ;  SNSF  and  SERI , Switzerland; STFC, UK; and  DOE, USA. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system,  NII  for  SINET4 , the  WestGrid and SciNet consortia in Compute Canada , and GridPP in the United Kingdom.
In addition, participation of individual researchers and institutions has been further supported by funds from  ERC  ( FP7 ), " la Caixa Foundation  ( ID 100010434 , fellowship code  LCF/BQ/IN17/11620050 ), the  European Unions Horizon 2020 Research and Innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 713673  and  H2020  Grant No. RISE-GA644294-JENNIFER 2020 ;  JSPS, Japan ;  Royal Society, UK ; and the  DOE Early Career program, USA.
Data collection We combined archival tag data and visual observations of tagged individuals at the surface to record the behaviour and movements of humpback whales before, during, and after playbacks of killer whale sounds. During each experiment, 1 humpback whale was tagged and identified as the focal individual  (Altmann 1974). In 2011 and 2012, we deployed version 2 Dtags  (Johnson & Tyack 2003) , and in 2016, we deployed version 3 Dtags. Dtags contain a suite of sensors (250 Hz 3-axis accelerometer, 250 Hz 3-axis magnetometer, 50 Hz pressure sensor) and 2 hydrophones (sampling frequency: 96 kHz, sampling width: 16 bits). In 2017, we used 'mixed tags', a version 3 Dtag sensor suite in a custom-made housing which includes a GPS logger  (FastLoc™, SirTrack, Wildlife Tracking Solutions). We attached all tags to the animals temporarily with suction cups using a long pole or a pneumatic launching system (ARTS™, LKARTS).
(2015)  dataset in our analysis. The 2011 and 2012 killer whale play backs were conducted after the focal humpback whales had been exposed to naval sonar signals as part of a parallel project (detailed by  Kvadsheim et al. 2015). The 2016 and 2017 fieldwork campaigns only included killer whale playback experiments and control noise broadcasts. In 2016 and 2017, we waited on average 1 h 34 min 37 s ± 25 min 26 s (range 1 h 9 min 22 s-2 h 27 min 3 s) from the tag deployment to the first playback. In 2011 and 2012, an average recovery period of 2 h 3 min 12 s ± 1 h 10 min 2 s (range where aa are the first initials of the genus and species, BB are the last 2 digits of the experiment year, CCC is the Julian date of the experiment, and d is the number of the tag deployment within the day from a to z.