dataesr/scientific-paragraphs-categorization huggingface dataset:
16). Other recent studies had similar findings;  3, 4, 15  i. e. , significant improvement in PFS for the investigational arm, but not in OS time. In a study by Ribas et al. , monotherapy of tremelimumab (a checkpoint inhibitor) vs. standard-of-care chemotherapy in advanced melanoma was evaluated, where the tremelimumab arm showed a significant benefit in PFS (35. 8 vs. 13. 7 months) while OS was not statistically different between standard-of-care and tremelimumab (10. 7 vs. 12. 6 months, P = 0. 127). 15 In the tremelimumab study, patients had the opportunity to receive ipilimumab (also a checkpoint inhibitor) as a second-line treatment, and the authors suggested that this could be one of the reasons for decreased power of the study to demonstrate an OS benefit. The aim of the current study was to develop a multistate model framework, using data from IMpower131 study, to investigate the confounding effect of second-line immunotherapies on the overall survival analysis.
Influence of second-line treatment on survival Introduction of second-line immunotherapy after progression is a potential confounding factor in the survival analysis as the benefit of the single treatment regimen cannot be defined separately. In a multistate model, the hazard of death from second-line (λ 45 ) and from progression ( 35 ) can be allowed to be different. To investigate the influence of the second-line treatment on OS, 1,000 trials were simulated in R (The R Foundation for Statistical Computing,c/o Institute for Statistics and Mathematics, Vienna, Austria) (https://www. r-proje ct. org/) using the final multistate model (using mrgsolve package  19  ) and the observed baseline covariate distribution. In the simulations, the transition hazard of second-line to death was set to be the same as the estimated hazard of progression to death of the corresponding arm (λ 45_A = λ 35_A ), as if the patients had not re- ceived the second-line immunotherapy.
Model development and evaluation Population models were developed using the nonlinear mixed-effect modeling software NONMEM (ICON Clinical Research LLC, Gaithersburg, Maryland). 21 Model development was assisted by Pirana (version 2. 9. 9) (Pirana, 100 Overlook Center, Suite 101, Princeton, NJ) for run management, the Perl-speaks-NONMEM (PsN) toolkit for handling NONMEM run commands, R (version 3. 6), and Xpose (version 4. 1) (Andrew C. Hooker, Uppsala University, Sweden) for model diagnostics and graphical analysis. 22 The objective function value (1) S1 0 = 1 dS1 dt = -S1 • λ 12 +λ 13 +λ 15 (2) (-2•log-likelihood) and graphical diagnostics were used in the evaluation of model performance. An objective function value decrease of 6. 64 (P < 0. 01) was considered significant for the addition of one parameter (1 degree of freedom) while testing predictors in the multistate model.
Participants We conducted the experiment on a sample population of 30 young participants and 32 healthy older participants. We removed 2 older participants from the analysis because they performed below chance level, and then we cannot ensure their comprehension of the task. In addition, 4 other participants (2 older and 2 young participants) were excluded due to excessive artefacts in the EEG data as assessed by signal-to-noise ratio calculation and a careful visual inspection of the signals. Analyses were finally conducted on 28 young participants (mean age: 23. 93 years old; SEM = 0. 64; range: 19-35; 14 females) and 28 older adults (mean age: 71. 25 years old; SEM = 1. 01; range: 61-81; 18 females). Participants were right-handed, had no history of neurological or psychiatric disorders and they self-reported normal or corrected-to-normal vision.
Stimuli and procedure Visual stimuli were created using the Unity Engine software (Unity Technologies version 2019. 2. 0. 0f1) and presented on an iiyama ProLiteB2791HSU monitor  (1920x1080, 30-83khz)  placed at eye level and 60 cm away from the participants. Stimuli were presented using the open-source PsychoPy software (v2022. 13), implemented on a Dell Precision 7560 computer (Intel® Xeon® W-11955). The environment was adapted from a previous fMRI experiment on healthy aging  (Ramanoël et al. , 2020). It was a three-arm maze (Y-maze) consisting of three corridors: one branch containing a goal materialized by a gift box, 2 identical starting branches, and 3 three-dimensional (3D) objects positioned at the intersection serving as landmarks (a cube, a ball, and a pyramid). The experimental paradigm was divided into 3 tasks: learning, reorientation, and control (Figure  1 ).
EEG recording and preprocessing EEG was sampled at 500 Hz from 64 Ag/AgCl electrodes mounted in a cap (Waveguard™ original) connected to an amplifier (eego™ mylab, ANT Neuro) and digitized at 24-bit resolution. Data were referenced to CPz electrode, with AFz as ground. Electrode impedances were reduced to at least 20 kΩ, with most values falling below 10 kΩ. Synchronization of EEG recording, and stimulus presentation was ensured by the LabStremingLayer software Labrecorder (LSL Labrecorder version 1. 13). Offline preprocessing was performed with MATLAB (R2021a; The MathWorks Inc. , Natick, MA, USA) using custom scripts based on the EEGLAB toolbox version 14. 1. 0b  (Delorme & Makeig, 2004 ) and adapted from a previously used processing pipeline  (Delaux et al. , 2021). We first downsampled the data to 250 Hz and corrected the time points for the software delay using the trigger time added to a fixed delay of 50ms for the hardware delay.
A short break was proposed to all participants between blocks proposed optimal range between 10 and 100  (Chang et al. , 2020). We then temporally high-passed the cleaned dataset using a 1. 5 Hz Finite Impulse Response (FIR) filter with a Hamming window (with 0. 5 Hz transition bandwidth, 1. 25 Hz passband edge, and 1650 order)  (Klug & Gramann, 2021)  before applying independent component analysis (ICA) using the Adaptative Mixture Independent Component Analysis (AMICA) algorithm  (Palmer et al. , 2008). Next, each independent component (IC) was assigned a dipolar source reconstructed with the equivalent dipole model (DipFit ;  Oostenveld & Oostendorp, 2002). We used the ICLabel algorithm  (Pion-Tonachini et al. , 2019)  to classify components into 7 classes, using default percentages for classification.
Event Related Potential and time-frequency analyses Analyses were restricted to the occipito-parietal electrodes previously associated with scene-selective regions  (Harel et al. , 2016; Kaiser et al. , 2020) , corresponding to P6-P8-PO8 for the right hemisphere and P5-P7-PO7 for the left hemisphere. Further data analysis was performed using custom MATLAB scripts with functions from the Fieldtrip toolbox  (Oostenveld et al. , 2011). For the Event Related Potentials (ERPs), the baseline was identified from -200 ms to image onset corresponding to the recommended minimum of 10 to 20% total epoch length  (Luck, 2014) , and mean baseline activity was subtracted from the data. Peak amplitude was calculated using the mean amplitude, corresponding to the average of the most positive value surrounded by two lower values.
Statistical analysis For the ERSPs, we performed a non-parametric cluster-based permutation test using a Monte-Carlo estimate to deal with the multiple comparisons problem  (Maris & Oostenveld, 2007). We chose the most robust and least conservative method among several modalities, which involved 10 000 permutations with weighted cluster mass  (Hayasaka & Nichols, 2004)  and a cluster-level alpha of 0. 005 to account for multiple comparisons. All other statistical analyses were performed using R Statistical Software (version 4. 2. 1, R Foundation for Statistical Computing, Vienna, Austria) with R studio (version 2022. 07. 02+576). After comparing different models using the Akaike information criterion  (Akaike, 1974) , we decided to use the linear mixed model from the lme4 R package  (Bates et al.
, 2014) , with participants included as random intercept. Then, we used the anova function to compute a type III Analysis of Variance (ANOVA) using Satterthwaite's method. The reported results are estimated marginal means computed with the emmeans package in R, using a type III sum of squares, and finally post-hoc Tukey's Honest Significant Difference tests were performed. To ensure normality of residuals and homoscedasticity, both were carefully visually inspected using quantile-quantile plots and boxplots, respectively. Finally, we conducted correlation analyses between ERP peaks and behavioral data using the Bonferroni correction to account for multiple comparisons.
Acknowledgements The authors gratefully acknowledge  Maud Saulay-Carret  for her help with acquisitions and also the voluntary participants involved in this research. This work was supported by the  French government  through the  UCA JEDI  project managed by the  National Research Agency  ( ANR-15-IDEX-01 ) and, in particular, by the interdisciplinary  Institute for Modeling in Neuroscience and Cognition (NeuroMod) of Université Côte d'Azur. It was also supported by the  Fondation pour la Recherche sur Alzheimer , the  French National Research Agency  ( ANR-18-CHIN-0002 ), the  LabEx LIFESENSES  ( ANR-10-LABX-65 ), and the  IHU FOReSIGHT  ( ANR-18-IAHU-01 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Methodological approach The principal research question raised by this study can be presented as follows: How Lebanese leaders build resilience during crisis time? According to Dubé and Paré  [32]  and Yin  [33] , how questions were most frequently posed in exploratory case research. Thus, given the objectives of the study, a qualitative exploratory method is likely to be appropriate to explore successful practices and processes developed by Lebanese leaders to promote resilience in the food service sector. We adopted a positivist approach since we worked within a theoretical perspective  [32]  and used existing theoretical constructs (e. g. , resilient leadership, crisis management) to guide our research. Finally, we analyzed data generalized from semi-structured interviews by using deductive thematic analysis while comparing findings with the extant literature.
Aim, design and setting of the study This retrospective, multicenter study was conducted in France on patients whose data were prospectively collected in the Traumabase® between January 2012 and December 2021. The Traumabase® group (www. traumabase. eu) is a French severe trauma registry initiated in 2011. Patients included in the Traumabase® are suspected of severe trauma from the scene based on national triage criteria and admitted (primarily or secondarily) to one of the 22 participating trauma centers  (14). Sociodemographic, clinical, biological, therapeutic, and evolution data from prehospital scene to hospital discharge are systematically collected for all consecutive trauma patients. The structure of the Traumabase® integrates algorithms for consistency and coherence. Data monitoring is performed by a central administrator  (15).
Characteristics of participants All consecutive trauma patients registered in the Traumabase® during the aforementioned period were included. Patients without traumatic brain injury, managed after secondary transfer, aged < 15 years, pregnant, with anticoagulant or antiplatelet therapy prior to trauma, with transfusion of any blood product prior to hospital admission and those with missing data on coagulation at admission were excluded. Patients categorization: the Abbreviated Injury Score (AIS)(16) was used to de ne the study population and to score trauma severity. The AIS is an anatomically-based injury severity scoring system that classi es each injury by body region (head and neck, face, chest, abdominal, extremities, external) on a six points scale (ranging from 1 = minor, to 6 = maximal = currently untreatable).
We select unaffected counties with population size in the top 25% of the distribution (i. e. population size ≥ 67976), for a total of 50 unaffected counties. Those counties will be considered as a baseline in our analyses. To analyze human behavior, we consider the weekly indoor seasonality index σ it by county computed in Ref. [16]  and available here 2. The index takes values higher (lower) than 1 if, on average, indoor (outdoor) activities are preferred to outdoor (indoor) ones. The data is based on SafeGraph Weekly Patterns data  [24]  from 2018 to 2021, and 4. 6 million POIs are sampled in all years of our study from all locations where people can spend their time, excluding home locations. The dataset is anonymized, and information on individual mobile devices is omitted. Data are representative across various demographics, including race/ethnicity, educational attainment, and income  [25] , and it has also been shown not to be significantly biased by age and gender  [26] , except for individuals younger than 16 years of age are not included  [27].
Dilution vs scavenging Elements released from the hydrothermal vents can be diluted in seawater or scavenged onto sinking particles  23. We used dissolved manganese (dMn) as an index to assess the importance of Hg removal processes following venting. Owing to its slow oxidation rate, dMn has been used as a conservative tracer of the dilution of vent fluids along hydrothermal plumes. During the cruise transit, particulate manganese (pMn) was lower than dMn with concentrations consistent with oceanic background levels  24  (Extended Data Fig. 3 ). Average pMn considering all stations and depths within the plume was 2. 5% of the total manganese (tMn) (Extended Data Fig. 4 ). Following previous studies  19, 25, 26  , we thus use dMn as a conservative tracer along the plume, being affected only by dilution.
Mercury release from TAG hydrothermal vent site We collected hydrothermal vent fluids, plume waters and seawater surrounding TAG on three dedicated oceanographic cruises. The unfiltered tHg concentrations of the vent fluid samples were 4,206 ± 262, 2,968 ± 87 and 367 ± 18 pmol l -1. During hydrothermal venting, magnesium (Mg) is quantitatively removed from seawater by precipitation as magnesium-hydroxy-sulfate hydrate. We used magnesium (Mg) concentrations of the vent fluid samples (4. 59 ± 0. 046, 32. 9 ± 0. 329 and 48. 1 ± 0. 481 mmol l -1 ) and their respective tHg concentrations to extrapolate the Mg-free vent fluid end member to 4,966 ± 497 pmol l -1 (Extended Data Fig. 1 )  18. The good correlation between tHg and Mg (R 2 = 0. 91) suggests that the tHg variations in the measured vent fluids are mainly due to mixing with surrounding seawater, with contributions from a single vent or fluid type.
Previous studies at deep hydrothermal sites reported vent fluid tHg concentrations ranging from 5 to 11,000 pmol l -1 (refs. 10-13). Differences in geological settings may account for the variation in tHg concentrations of vent fluids. However, the vent fluid end member tHg concentration was not calculated in previous studies so different degrees of mixing with seawater may also explain the variability. The calculated vent fluid end member is around 6,000 times enriched in tHg compared with the median tHg concentration of the Atlantic Ocean (0. 80 ± 0. 42 pmol l -1 ; n = 1,917; Extended Data Fig. 2  and Extended Data Table  1 ). Seawater and plume waters were collected during the HERMINE cruise (GEOTRACES GApr07) following the TAG plume flow dispersion in the water column. The tHg concentrations vary from 0.
The tHg dilution efficiencies tend to increase as the distance from the vent site increases, reaching 90% 10 km from the vent. Considering all plume depths and all stations, the dilution efficiency is 73 ± 20%, suggesting that tHg is largely conservative at TAG. Assuming that only dilution and scavenging account for losses from the plume, scavenging would remove the remaining 27% tHg. To assess the validity of the dilution model, the dilution efficiency of dFe and tFe were also calculated. Calculations were made using the average seawater dFe (0. 70 nmol l -1 ) (ref. 28), our previously reported Fe concentrations of the vent fluid end member (5. 4 mmol l -1 ) (ref. 27)  and plume Fe values  19  (Extended Data Fig. 3 ). The average dilution efficiency considering all stations and depths within the plume is 14 ± 7% for dFe (Fig.
2 ) and 39 ± 8% for tFe (Extended Data Fig. 6 ). These low dFe and tFe dilution efficiencies are in good agreement with the previously reported non-conservative behaviour of Fe at TAG  19, 23. Although Hg dilution efficiencies are relatively high, there is still potentially substantial deposition of Hg near the vent field. To further corroborate our observations, we analysed surface rocks and drill-core samples. The lithology of the active mound can be divided into six layers (Fig. 3a ). The uppermost layer (Fe oxyhydroxides and cherts) displays Hg concentrations varying from 74 to 409 ng g -1 with a median of 112 ± 145 ng g -1 (circles in Fig. 3 ) and includes typical supergene lithologies including red ochres, Fe oxides, Mn-Fe oxyhydroxides and sulfides with different degrees of alteration (Extended Data Table  2 ).
Sample collection High-temperature hydrothermal vent fluids were collected unfiltered into gas-tight titanium samplers using the Remotely Operated Vehicle Jason II. Three samples for tHg from the vent fluids were collected and subsampled into 40 ml glass vials. All glassware used was acid cleaned with 3% v/v double-distilled HCl, rinsed with ultrapure water (18 MΩ cm, MilliQ), dried under a flow hood and then muffled at 450 °C for 4 hours. Surface rock samples (Extended Data Table  2 ) were collected by a series of dives of the submersible Alvin to the TAG active mound. Drill-core samples (Extended Data Table  4 ) are from drilling sites TAG1, TAG2, TAG4 and TAG5. As all the drilling sites are located in the active 200 m diameter TAG black smoker complex, all drilling locations are considered one site.
More than one drill core was done at each location to recover as much stratigraphic section as possible. Cores were described and subsampled onboard. Rock samples were ground and homogenized with an agate mortar before Hg analyses. Seawater and plume waters were sampled by means of a trace metal clean carousel consisting of a polyurethane powder-coated aluminium frame attached to a Kevlar line. A conductivity temperature depth sensor (CTD, Seabird 911plus) was mounted on the trace metal clean rosette, and suspended matter data were processed in real time. The direction of the plume was monitored by a Lowered Acoustic Doppler Current Profiler deployed on a CTD/rosette before each trace metal clean cast. The trace metal clean carousel was equipped with 24 Teflon-lined 12 l GOFLO bottles with external closing.
Acknowledgements This project was supported by the  French National Funding Agency  project  HydrOThermal Mercury  ( ANR-21-CE34-0026 ) and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie GMOS-Train , GA grant agreement number  860497. R. S. acknowledges the fund provided by the  National Natural Science Foundation of China  ( 42173011 ). We thank  A. Lestari  and  F. Ni  for assisting with the tHg analysis,  S. G. Kohler  for proofreading and  J. Torres Sanabria  for the illustrations in Fig. 4. We thank the sampling teams, fellow scientists, crew and captains of the three research expeditions. The HERMINE cruise was funded by the  IFREMER REMIMA  project. Special thanks to  M. Roudaut ,  V. Guyader ,  H. Whitby ,  A. Gourain  and  T.
Extended Data Fig. 2 | 2 Atlantic Ocean database statistics and comparison with the present study. a, Histogram showing the distribution of tHg data for the Atlantic Ocean in grey and this study tHg in blue. b, Histogram showing the distribution of MeHg data for the Atlantic Ocean in grey and this study MeHg in red. Statistical parameters Q1, Q3, and maximum value (Max) are shown with vertical lines in both histograms. The maximum value is determined as 1. 5 times the interquartile range. Data points beyond are considered outliers. In the right part of the histograms a comparison between this study data and the Atlantic Ocean without outliers. https://doi. org/10. 1038/s41561-023-01341-w.
Acknowledgments -We thank  Estelle Crozat ,  Hans Geiselmann ,  Calin Guet ,  Thomas Hindré  and  Bianca Sclavi  for fruitful discussions. We acknowledge funding from  FRM   AJE20160635870 ,  IRIS OCAV Idex   ANR-10-IDEX-0001-02   PSL ,  ANR-18-CE12-0012 ,  LabEx Memo-Life  and  PSL-Qlife.
Soon after their first deployment in rivers in the early 2000s, RFID tags rapidly became the reference technology for bedload tracing in rivers. We can estimate from the literature that during the last 20 years, more than 30,000 RFID tracers have been injected in gravel-bed rivers all around the world to study bedload transport. Many field experiments have been reported in a great diversity of fluvial environments, complemented by many laboratory experiments and methodological developments. This paper proposes a review of these works, notably based on the compilation of more than 350 RFID surveys, complemented by 97 magnetic surveys, for a total of 125 study sites. The meta-analysis of this database shows that RFID tracers have improved our understanding of sediment transport in fluvial environments with rapid bedload dispersion.
These range from small boulder-bed streams (<5 m in channel width, w) to large gravel-bed rivers (w > 200 m) and from semi-arid to temperate and tropical climatic settings, including all types of hydrological regimes  (intermittent, pluvial, Mediterranean, snowmelt and glacial-melt). Several flume experiments using RFID tags have also been reported. The thematic field covered by this corpus is very rich (Figure  1 ), with many contributions on physical mechanisms and properties of bedload transport (referred to here as 'physical studies', n = 35), on the monitoring of river management and restoration works ('management applications', n = 22) and on methodological works using experimental facilities and/or field tests ('methodological advances', n = 13). Based on this corpus of published papers and additional conference proceedings and academic reports presenting results from RFID tracers, a database of 362 RFID surveys and 97 magnetic surveys was compiled, for a total of 125 study sites (see the supporting F I G U R E 1 Time evolution of the number of scientific publications reporting RFID applications in fluvial bedload tracing between 2004 and 2021.
Only papers published in peer-review scientific journals were considered. Selection of papers was based on an ad hoc procedure combining the Scopus database and citations from selected papers. Papers were classified in three groups (methodological advances, physical studies and management applications) according to the primary objective of the study (see Tables  S1  and S3 ). information for details about the corpus inventory and data compilation). This database was used to provide a comprehensive and synthetic meta-analysis of RFID studies of the last 20 years, by addressing successively (i) how RFID tags have been deployed and surveyed in river channels and what have been the main achieved methodological advances, (ii) what has been the contribution of RFID tags to the observation and understanding of bedload transfer in gravel-bed channels, (iii) how RFID tags have been used for the monitoring of river management and restoration works and (iv) what are the key challenges of RFID bedload tracing for the next years.