dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, all together, the herein described bPEI600-based CDs evoked a dose-dependent lung inflammation. This toxicological effect occurs in the same dose range as that evoked by other carbonaceous nanomaterials, namely carbon black  [42, 43]  and carbon nanotubes  [18]. Furthermore, it is in keeping with the data reported by other groups on ethylenediamine-based CDs  [31] , confirming that polyamine surface-passivated cationic CDs trigger adverse outcome in the lung. response found in BALFs (Figure  2c ). Thus, all together, the herein described bPEI600based CDs evoked a dose-dependent lung inflammation. This toxicological effect occurs in the same dose range as that evoked by other carbonaceous nanomaterials, namely carbon black  [42, 43]  and carbon nanotubes  [18]. Furthermore, it is in keeping with the data reported by other groups on ethylenediamine-based CDs  [31] , confirming that polyamine surface-passivated cationic CDs trigger adverse outcome in the lung.
CD Cell Target in the Airway Lumen We then focused on the identification of the cells targeted by the CDs in the airways lumen of mice by analyzing cells recovered in BALFs by FACS. The staining and gating strategy made it possible to measure both the CD cell uptake (Figure  3 ) and mortality (Figure  4 ) while discriminating the different cell types. Mice received one administration of CDs (100 µg) or saline, and BALFs were collected after 4 h, 24 h, 7 d, 14 d or 21 d. As shown on Figure  3a  for the 4-h time point, macrophages (CD45+CD11c+GR-1-) and neutrophils (CD45+CD11c+GR-1+) were the two main cell populations present in BALFs from mice exposed to CDs, which is in agreement with cytological data of Figure  2a. To investigate CD uptake, the CD-associated fluorescence was measured into these two cell populations.
No internalization was measured within neutrophils (data not shown). In contrast, an important CD uptake was observed within macrophages (Figure  3a , panel internalization within macrophages). Examination of BALF cells from mice exposed to CDs by optical microscopy showed CD-loaded macrophages (Figure  3b ), which confirmed the FACS data. Regarding the kinetics of CD internalization by macrophages, more than 50% of cells had internalize CDs as early as 4 h after instillation of the NPs in the lung of mice, and this internalization increased further at 24 h, with more than 75% of CD-loaded macrophages (Figure  3c ). Presence of CDs into macrophages started to decrease from day 7 after administration, but the NPs were still present at day 21, suggesting that they are not rapidly cleared. confirmed the FACS data.
5 µg/10 3 macrophages taking into account a number of macrophages of 250. 000 in mouse airways, as determined by cell counting in BALFs of naïve animals. As shown on Figure  6a , a decrease in cell viability of 35-85% was observed at CD concentration ranging from 25 to 100 µg/mL, and a maximum loss of viability was reached at 200 µg/mL. Thus, in agreement with our previous studies on bPEI25k-based CDs, we found that bPEI600-based CDs are toxic to macrophages  [32, 36]. A lactate dehydrogenase (LDH) release was measured in the cell culture supernatants of macrophages exposed to CDs (data not shown), suggesting that the cell death induced by CDs probably occurs by a necrosis mechanism. In this type of CDs, the presence of polyamine groups at the CD surface results in a highly cationic surface of the particles, which may promote the interactions of CDs with the negatively charged cellular membranes and then their internalization, as discussed above.
Although the exact mechanisms leading to lysosome damage are not fully understood, this trafficking to the lysosomes, associated with loss of lysosomal integrity, has been reported for NPs of various chemical composition including carbonbased NPs  [61, 62]. These data are also consistent with our previous work indicating that bPEI25k-based CDs were addressed to the lysosomes after their uptake by macrophages and caused a loss of lysosomal integrity  [36]. A second major organelle KE induced by bPEI600-based CDs in the present study is mitochondrial dysfunction. Mitochondrial dysfunction is reported as a crucial mechanism leading to toxicity of NPs  [57] , including carbon-based NPs  [42]. In line with our data, a recent study reported that N-doped CDs caused a decline in mitochondrial activity in hepatocytes  [33].
Conclusions In this study, the combination of in vivo and in vitro experimental approaches was used to propose an AOP related to the acute lung injury induced by polyamine-based cationic CDs (Figure  7 ). Based on our data, we propose that uptake of CDs by macrophages constitutes the MIE of this pathway, followed by the loss of lysosomal integrity, mitochondrial disruption, oxidative stress, and inflammasome activation as KEs. All these effects may trigger macrophage death, leading to lung inflammation. This putative AOP is aligned with the recommended KEs that emerged from the network of inflammation-related AOPs  [69]. Besides, as lung inflammation may lead to acute lung injury, lung fibrosis or lung cancer, it could be extended and/or connected to other larger networks in the field of inhalation toxicology in the AOP-Wiki database.
Acknowledgments: The authors thank  Pascal Didier  for its help with  CLSM (Laboratoire de Bioimagerie et Pathologies ,  UMR 7021 ,  Faculté de Pharmacie ,  CNRS-Université de Strasbourg, Illkirch, France) , as well as  Claudine Ebel  and  Muriel Koch  for their help with FACS experiments (Cytometry platform, IGBMC, Strasbourg).
Funding: This work was supported by the Agence Nationale de Sécurité Sanitaire de l'alimentation, de l'environnement et du travail (ANSES-Grant number: EST-2015/1/005) and the Agence Nationale de la Recherche (ANR-Grant number: ANR-18-CE34-0005-01). Institutional Review Board Statement: Animal experiments were conducted in compliance with the European legislation (Directive 2010/63/EU). Experimental protocols were approved by the local ethics committee (CREMEAS, agreement number #4674).
Context. 0. 1-10 MeV observations of the black hole microquasar Cygnus X-1 have shown the presence of a spectral feature in the form of a power law in addition to the standard black body (0. 1-10 keV) and Comptonization (10-200 keV) components observed by INTEGRAL in several black-hole X-ray binaries. This so-called "high-energy tail" was recently shown to be strong in the hard spectral state of Cygnus X-1, and, in this system, has been interpreted as the high-energy part of the emission from a compact jet. Aims. This result was nevertheless obtained from a data set largely dominated by hard state observations. In the soft state, only upper limits on the presence and hence the potential parameters of a high-energy tail could be derived. Using an extended data set, we aim to obtain better constraints on the properties of this spectral component in both states.
Methods. We make use of data obtained from about 15 years of observations with the INTEGRAL satellite. The data set is separated into the different states and we analyze stacked state-resolved spectra obtained from the X-ray monitors (JEM-X), the gamma-ray imager (IBIS), and the gamma-ray spectrometer (SPI) onboard. Results. A high-energy component is detected in both states, confirming its earlier detection in the hard state and its suspected presence in the soft state with INTEGRAL, as seen in a much smaller SPI data set. We first characterize the high-energy tail components in the two states through a model-independent, phenomenological analysis. We then apply physical models based on hybrid Comptonization (eqpair and belm). The spectra are well modeled in all cases, with a similar goodness of the fits.
2006; Jourdain et al. 2012 ) and IBIS  (Laurent et al. 2011; Rodriguez et al. 2015, hereafter L11, R15) , but so far has remained undetected by INTEGRAL in the HSS  (Jourdain et al. 2014, hereafter J14). This might be both due to the intrinsic faintness of the component and the rather short exposure time of INTEGRAL in the HSS. Observations obtained between 2003 and 2011 contained ∼ 90 % of LHS data (e. g. , R15). Thanks to a spectral transition to a long-lasting HSS in 2011  (Grinberg et al. 2011) , follow-up studies such as the present one will be increasingly sensitive to the high-energy tail emission of Cyg X-1 in the HSS. Detection and investigation of the high-energy tails in both states of Cyg X-1 with INTEGRAL are possible thanks the mission's combination of a large effective area with the capability to measure polarization -a key tool for breaking degeneracies in the quest for the physical origin of the high-energy tail emission.
We are therefore not only interested in assessing the existence of a high-energy tail in the HSS but also in investigating its origin with the extended amount of data available. Our approach, accumulating data until 2017, increases the count statistics by a significant amount over all earlier works and allows us to study the parameter space of hybrid models not only in the LHS but also, for the first time, with INTEGRAL in the HSS up to 2 MeV. This paper is organized as follows. In Sect. 2, we outline the selection criteria for data considered in this work and the extraction of data products. In Sect. 3, we present a set of techniques and models used to describe the data products. In Sect. 4, we discuss the results and in Sect. 5 we provide our conclusions.
State classification and data selection We consider all Cyg X-1 observations made with INTEGRAL from 2003 June 9 to 2017 November 7 (Modified Julian Day, MJD 52799 to 58054) corresponding to the satellite revolutions (hereafter revs) 54 to 1882. We restrict our analysis to "science windows" (hereafter scws), that is, individual INTEGRAL pointings of typically 1800-3600 s duration, where Cyg X-1 is less than 10 • off axis. This results in a total of 7288 scws corresponding to 11. 4 Ms of dead time-corrected exposure. Similar to R15, we separate the whole data set into three states (soft/HSS, intermediate/IS, and hard/LHS) using the model-independent classification of  Grinberg et al. (2013). This classification defines LHS, HSS, and IS based on count rates and hardnesses measured by all-sky monitor instruments (RXTE/ASM, MAXI, and Swift/BAT).
Spectral extraction: INTEGRAL/IBIS/ISGRI Data from INTEGRAL/IBIS' upper detector layer ISGRI 1  taken before December 2015 (INTEGRAL revolution 1627, MJD 57387) were reduced with version 10. 2 of the Off-line Scientific Analysis (OSA) software. Data obtained after that date were extracted with OSA v11. This split is necessary because (at the time of writing) specific versions of calibration data -specifically for the gain correction of the instrumental energy scale-are required for both software versions 2. The long-term IBIS light curve (with a resolution of individual scws) is shown in the top panel of Fig. 1. The different gain correction used before and after December 2015 is seen as an overall increase in the 30-50 keV count rate. For comparison, the 30-50 keV Crab count rate is ∼87 counts s -1 for OSA v10.
2 and ∼134 counts s -1 for OSA v11. IBIS/ISGRI data were reduced with the standard procedure 3. For each satellite revolution we first produce a mosaic image to identify the brightest sources (here defined as those with a detection significance greater than 7σ in the 30-50 keV mosaic image) of the field. We then extract light curves and spectra on a scw-by-scw basis. For each scw, we construct a sky model including the brightest sources as defined above (usually Cyg X-3, Cyg X-2, GRO J2058+42, and KS 1947+300, in addition to Cyg X-1). Taking the background into account, the analysis software then reconstructs the sky image and corresponding source count rates by deconvolving the shadowgrams projected on the detector plane. The roughly 250 scws from the time period of the 2015 June outburst of V404 Cyg (INTEGRAL revolutions 1554 through 1558) remain unclassified and were therefore ignored in our analysis.
The resulting number of pointings is 2358 and 1771 for the LHS and HSS, respectively, with a total dead-time-corrected observation time of 3. 4 Ms and 4. 7 Ms, respectively. The resulting exposure map using all good pointings is shown in Fig. 2. This map shows Cyg X-1 in the center of the chosen exposure and which (known) sources are exposed and how they might contribute in the fit. We consider single events (SEs) below 530 keV and pulseshape discriminated (PSD) events above that energy. This removes strong "electronic noise" features between 1. 4 and 1. 7 MeV and allows for a smooth spectral extraction 4. We also extract SE spectra up to 755 keV to establish consistency between the two data types. The derived PSD fluxes are then scaled by a factor of 1/0. 85 to account for the change in efficiency 4.
For the spectral extraction, we define 21 logarithmic energy bins between 25 keV and 755 keV for the SE range, and 8 logarithmic bins between 530 keV and 2000 keV for the PSD range. The spectra are obtained bin by bin from maximum-likelihood fits accounting for Poisson-distributed data. These fits include the two-component background model as described by  Siegert et al. (2019) , which is based on the SPI BackGround and Response DataBase (BGRDB,  Diehl et al. 2018) , as well as Cyg X-1 and Cyg X-3. We use the OSA spimodfit  (Strong et al. 2005)  to perform these fits. Given the large unknown in the SPI data reduction processes, and although Cyg X-1 is not formally a source sufficiently bright to present the caveats described by Roques & 4 https://www. isdc. unige. ch/integral/download/osa/doc/ 11.
Conclusion In this paper, we advance the long-lasting investigation of the nature of the hard MeV tail of Cyg X-1 for both major spectral states. We accumulate data from 15 years of INTEGRAL (JEM-X, IBIS and SPI) observations of the source and reach unprecedented count statistics in the 3-2000 keV range, not only in the LHS, but also in the HSS, which allows us to firmly confirm the suspected presence of a high-energy component -extending to at least 600 keV-in the spectra of the source. We follow two approaches with the modeling of these data. In the first, empirical model, we fit a thermal Comptonization spectrum independent of a simple power law, describing the MeV tail between hundreds of keV and ∼1 MeV. The slopes of the MeV tails appear compatible between the LHS and the HSS. The origin of this high-energy emission can be tackled with single physical spectral models where the data are self-consistently considered.
Acknowledgements. We thank  D. Kantzas ,  M. Lucchini ,  A. Chhotray , and  S. Markoff  for extensive discussions on the origin of the high-energy tail emission based on physical jet models developed at the  Anton Pannekoek Institute Article number , page 10 of 14 Cangemi et al. : Origin of the Cygnus X-1 high-energy tails for  Astronomy ,  Univ. of Amsterdam. FC, JR, PL acknowledge partial funding from the  French Space Agency (CNES) , and the  French  programme national des hautes énergies (PNHE). FC and VG acknowledge support from the  ESTEC Faculty Visiting Scientist Programme. TS is supported by the  German Research Society (DFG-Forschungsstipendium   SI 2502/1-1 ). VG is supported through the  Margarete von Wrangell fellowship  by the  ESF  and the  Ministry of Science, Research and the Arts Baden-Württemberg.
Acknowledgements Financial support from the  Capes/Brazil  to  A. B. S. N. (Grant No. 88881. 190581/2018-01 )and  M. G. A. C. (Grant No. 99999. 006712/2015-00 ) is gratefully acknowledged. The authors thank  M. Aouine  ( HR-TEM ) and  Y. Aizac  (XRD) of IRCELYON.
Preseismic Transient Displacements Captured by GPS We process GPS data of 68 stations in South America from several networks (CSN, LIA Montessus de Ballore, Ministerio de Bienes Nacionales, RAMSAC, RBMC-IP, IGS, IGM Bolivia, see supplementary information for references). Processing is done using a differential approach  (Herring et al. , 2018)  including tropospheric delays and horizontal gradients. The results are computed in the ITRF 2014 reference frame  (Altamimi et al. , 2016)  and converted in a fixed South-America frame  (Nocquet et al. , 2014). We use daily solutions except for the last position before the mainshock, which is obtained from data up to 1 h before the event. We remove a trend corresponding to interseismic motion from the time-series by fitting a linear regression in a 4 months time-window before the mainshock.
In order to assess uncertainties associated with the CMT solution of the largest M W = 6. 0 foreshock, we perform a new CMT inversion within a Bayesian framework, following  Duputel et al. (2012 Duputel et al. ( , 2014)). Each source of uncertainty considered here is integrated in the problem as a covariance matrix. The covariance matrix C d , associated with observational errors, is derived after a first CMT inversion. From this inversion, an average correlation function is derived from residuals between synthetic and observed waveforms at each station. This allows us to estimate the correlation between neighbor data samples, and include it into C d. The standard deviation for each channel is fixed to 4 times the corresponding average absolute residuals. This empirical procedure provides a conservative estimate of observational uncertainty associated with each waveform.
e. , aseismic displacement only). To build a fault geometry, we use the CSI package to mesh the Slab 2. 0 model with triangles of variable sizes as shown in Figures  4e  and 4f. We invert for slip values at the triangular nodes using AlTar, a Markov chain Monte Carlo sampler based on the algorithm described by  Minson et al. (2013). Continuous fault slip distribution is represented as a linear interpolation of the slip values at the triangular nodes. Green's functions are computed in the same stratified elastic model used for our CMT catalog (Figure  S4 ). Given the limited amount of available observations, we enforce a positive Laplacian prior distribution with a scale parameter of 1 m. Such sparsity-inducing prior will favor "simple" models with slip only where it is requested by the data.
The GEDI mission constitutes an unprecedented opportunity to generate new forest related products compared to the ones derived from previous remote sensors. GEDI has a much higher firing frequency than the previous ICESat-1, and therefore has a much higher sampling density  (Dubayah et al. , 2020). For reference, in its first 18 months of operation, GEDI managed to acquire over 25 billion unfiltered shots globally, requiring a storage space of more than 90 TB. Moreover, since GEDI is a FW LiDAR system, sophisticated processing of the waveforms is still required for the generation of the useful metrics. The Land Processes Distributed Active Archive Center (LP DAAC) offers main GEDI data as two distinct datasets  (Dubayah and Luthcke, 2020a,b). The L1B data product contains the geolocated waveforms, while the L2A data product contains the extracted metrics.
Assessment of the spatial transfer behavior of the competing approaches Table  7  reports the results achieved by the different methods considering the spatial transfer experiment. We can observe that the accuracy on the estimation of the H dom variable degraded considering both approaches when only samples from MS were considered at the training stage. For the CNN based model, an increase in RMSE between 5 (ES region) and 44 cm (MA region) is observed, and between 5 (BA region) and 28 cm (ES region) with the RF model. Conversely, we can observe that the RF model had an increase in the estimation performance of H dom over SP when this method is trained only with the MS data. Indeed, for SP, we observed a decrease in RMSE from 1. 71 m (Table  5 ) to 1. 56 m (Table  7 ). Leveraging an additional region to enrich the training datasets (GEDI waveforms data from MS and SP) does not seem to increase the estimation performance of H dom using either the 2D-CNN or the RF approaches.
Statistical Analyses The statistical analysis was performed using the Statistical Package for Social Sciences (SPSS 21. 0, International Business Machines Chicago, IL, USA). Descriptive statistics are presented as mean ± standard deviation (SD). Levene and Shapiro-Wilks tests were performed to check for homogeneity and normality of the data, respectively. A Student's t-test for unpaired data was used to evaluate differences between groups. Additionally, the standardized mean differences were calculated using Cohen's effect size (ES) (95% confidence interval) for all comparisons. Threshold values for ES statistics were as follows: >0. 2 small, >0. 5 moderate, >0. 8 large  [29]. The different correlations between the parameters were evaluated using Pearson's correlation (r). Significance level was set at p ≤ 0.
Material and Methods PCR on Nasal Swabs and Blood. PCR was performed on nasal swabs with the Alinity kit (Abbott). PCR was performed on blood with the SIMPLEXA COVID-19 direct assay (DiaSorin). Antigenemia. Serum N antigen concentrations were determined with the COV-QUANTO ELISA kit (AAZ). IFN-α2 Determination by Simoa. Serum IFN-α2 concentrations were determined with Quanterix platforms, as previously described  (36). RNA-seq. We collected whole blood from the patient on days 1, 3, 4, and 14, with a PAXgene Blood RNA tube. The blood sample was subjected to hemoglobin RNA depletion. Samples were sequenced on the Illumina NextSeq platform with a single-end 75-bp configuration. The RNA-seq fastq raw data were inspected to ensure that they were of high quality. The sequencing reads were mapped onto the human reference genome GRCh38 with STAR aligner v2.
ACKNOWLEDGMENTS. We thank  Ivona Aksentijevich  for critical reading of this manuscript. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  HHMI , the  Rockefeller University ; the  St. Giles Foundation ; the  NIH  ( R01AI088364 ); the  National Center for Advancing Translational Sciences ;  NIH   Clinical and Translational Science Award program  ( UL1TR001866 ); a  Fast Grant from Emergent Ventures ;  Mercatus Center at George Mason University , the  Yale Center for Mendelian Genomics  and the  Genome Sequencing Program Coordinating Center  funded by the  National Human Genome Research Institute  ( UM1HG006504  and  U24HG008956 ); the  Fisher Center for Alzheimer's Research Foundation ; the  Meyer Foundation ; the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ); the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ); the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ); the  FRM  and  ANR   GEN-COVID  project; the  ANRS-COV05, ANR GENVIR  ( ANR-20-CE93-003 ),  ANR AABIFNCOV  ( ANR-20-CO11-0001 ), and  ANR   GenMIS-C  ( ANR-21-COVR-0039 ) projects; the  European Union 's  Horizon 2020 research and innovation program  under Grant  824110  ( EASI-genomics ); the  Square Foundation ;  Grandir-Fonds  de solidarit e pour l'enfance; the  SCOR Corporate Foundation for Science; Fondation du Souffle ; INSERM; and the  University of Paris.
The images covered a longer daytime period between June 2018 and August 2018, from 0600 to 2000, and were taken 15 or 30 min apart (depending on the camera location). In total, more than 14 000 photos were taken between 7 August 2017 and 15 August 2018. Each was analyzed individually to monitor the accretion/collapse of the snow cornice on images from camera TAS1 and to detect snow avalanche deposits on images from cameras TAS2 and TAS3. Observations of the snow cornice formation and evolution over the winter of 2017-2018 were only made at the camera TAS1 location, and do not apply for the entire crest length. The cameras provided data throughout the winter, but the spatial range was limited to the camera views.