dataesr/scientific-paragraphs-categorization huggingface dataset:
In the latter case the amplitude is markedly underestimated over the sMoUS subregion. For both groups 1* and 2*, the upwelling is broadly reproduced in these subregions, with an overestimation of UI sst amplitude in MPI-ESM1 over the sMoUS. Thus, the only increase in the at-   (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005)  corresponds to the Ekman process computed from CCMP, T geo from the AVISO SSH product, and MLD from  de Boyer Montégut (2004). Data 2 and data 3 represent the Ekman process from ERA5 and NOAA-20CR-v3 over 1985-2014 respectively, T geo from GODAS, and the same MLD used in data 1. The discontinuous horizontal lines highlighted the observational range. mospheric resolution in models produces no clear impact on upwelling representation.
This suggests that higherresolution winds lead to an improved Ekman transport. Similar conclusions are generally drawn over the SMUS region for groups 1* and 2*, whereas group 2 shows a better agreement with the validation data sets than group 1. The latter again overestimates the amplitude of the Ekman transport with respect to the reference values. This situation has also been documented in Castaño Tierno (2020). We consider now the ability of the different model configurations to reproduce the seasonal variability in the wind stress curl (Fig. 4 ). The models reveal sometimes noisy patterns, making the interpretation of the effect of model resolution complex in these cases, but some conclusions can be drawn. Group 2 reproduces the expected larger features of the W ek seasonal cycle in the different subregions, and a comparison with group 1 reveals differences in structure and intensity, particularly over the southern flank (MoUS and SMUS).
Group 1 shows generally a rather sharp and unrealistic seasonal cycle in the SMUS, which is longer than that identified in the validation data sets. The improvement in group 2 may be linked to that found by  Ma et al. (2019) : a finer horizontal resolution of climate models enables better representation of low-level coastal jet structure, with stronger and closer alongshore wind stress and curl leading to a more realistic representation of upwelling. The refinement of just the atmospheric resolution (group 2*) also leads to an improved wind stress curl.
Models of group 2 are generally in the range of observational estimates, and group 2* shows a small improvement with increased resolution. The CMCC-CM2-VHR4 is as close to the validation data sets as its LR version, while MPI-ESM1 always overestimates UI total , although the value is smaller in the HR version than in the LR version. In the sMoUS subregion the differences among model resolution are less marked than in the previous regions. Thus, it is difficult to directly relate the representation of UI total with model resolution. Finally, over the SMUS domain and as seen in Fig. 5d , group 2 has a generally better agreement with the observations than group 1, for which UI total is clearly outside the range of the observed UI total. Thus, models in group 2 are able to fully capture the estimation of the upwelling transport by realistically representing all the dynamical indices in such a way that the simulated upwelling in this subregion tends to be systematically more tightly clustered.
Quantitative measure of the model skill In this section, we evaluate quantitatively the performance of the models in simulating the CUS using the arcsine Mielke M score (see  Sect. 2. 4). M is computed between the observed and the simulated upwelling indices as a function of the nominal ocean resolution. The reference data sets are OISST-v2 for the thermal index (SST-based) and ERA5 for the dynamical (wind-based) indices. Note that the skill score values may be sensitive to the choice of the observational data sets to measure the model performance in some cases (Fig. C1  in Appendix C). For instance, for most subdomains and indices, differences in skill scores computed with HadISST or OISST-v2 (ERA5 for dynamical indices) for UI sst are as large as 250 points between the M skill scores computed with ERSST-v5 (NOAA-20CR-v3), but the slopes of the lines that connect the skill scores at different resolutions remain unchanged.
Observational consistency is quantitatively assessed using the average of the M scores computed from each possible combination of pairs of observational data sets. This consistency is represented by the horizontal lines in each panel of Fig. 6. It is moderately high (above 300 points) for the thermal index (top panels) and Ekman transport (central panels) in most of the subregions analyzed. However this value is very low for the Ekman pumping (Fig. 6 , bottom panels) in the case of the IP and nMoUS. This feature indicates, for these subdomains, a weaker similarity among the validation data sets. These results illustrate the challenge that exists in providing an accurate characterization of the upwelling systems in observations and reanalysis. For UI sst (top panels), the slopes of the lines that connect group 1 and group 2 in the MoUS and SMUS (panels b, c, and d) subregions are negative, indicating a higher skill for higher resolution (group 2).
However in the southern part of Morocco and in the Senegalo-Mauritanian areas, upwelling indices derived from the validation data sets at lower resolution (ERSST-v5 and NOAA-20CR-v3) show greater magnitudes than those derived from the higher-resolution data sets. The average of the M skill scores used to quantify the consistency among observational and reanalysis data sets at different resolutions is not very high. This highlights the challenge that exists for choosing a proper observational data set to evaluate global climate models' performance. The impact of increasing model resolution is not the same in the different subdomains of the CUS. In the northern part, within the IP domain, the high-resolution models do not seem to better simulate the structure of the climatological SSTs and the winds linked to the upwelling.
Acknowledgements. This work is carried out as part of the  TRI-ATLAS project (Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management ). The authors thank the colleagues within the climate group of CERFACS (https://cerfacs. fr/en/climate-variability-and-predictability/, last access: 12 September 2022) for their interesting discussions and feedback. Financial support. This work was mainly funded by  European Union Horizon  ( H2020) research and innovation program  (grant no. 817578 ), as part of the  TRIATLAS  project. Review statement. This paper was edited by  Riccardo Farneti  and reviewed by three anonymous referees.
Data 1(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) corresponds to the Ekman process computed from CCMP, T geo from the AVISO SSH product, and MLD from de Boyer Montégut (2004). Data 2 and data 3 represent the Ekman process from ERA5 and NOAA-20CR-v3 over 1985-2014 respectively, T geo from GODAS, and the same MLD used in data 1. The discontinuous horizontal lines highlighted the observational range.
org/10. 5281/zenodo. 7309969; Sylla, 2022a) 1995-2005 0. 25 • de Boyer Montegut (2004) Climatology 2 • × 2 • • GODAS reanalysis (https://doi. org/10. 5281/zenodo. 7309969; Sylla, 2022a) 1980-2020 1 • × 1 •.
The histogram data provided in Fig. 1    (%∆) for SOBP plan measurement versus prediction. Fitting the data distribution with a normal function yields µ(± σ) values of -0. 42(± 0. 79) and -0. 52(± 1. 06)% for RS-MC and FRoG, respectively. Physical validations of FRoG in clinical-like scenarios performed using an anthropomorphic head phantom setup yielded good agreement between FRoG and RS-MC. Figure  2  presents an overview of results for investigating performance in heterogenous regions. In summary, deviations in all DVH metrics (CTV and OAR) between FRoG and RS-MC were within <0. 5% and the 3D-Γ passing rate for 3%/1. 5 mm with DT10 (local) was 95. 6%.
The relative IDDs acquired via the StingRay were converted to absolute dose by scaling each curve by the dose calibration measurements acquired with the PPC05 at 3 cm depth in water for a 10. 4 × 10. 4 cm2 field, with a 2 mm spot distance, for each IEL. Measurements were conducted for 28 proton beam energies ranging from 98. 4 MeV to 226 MeV, corresponding to Bragg peak positions (R 80 ) of ~7. 5 to ~32 cm. Due to the large RaShi thickness used at the NPTC (~ 6. 5 cm Lexan, with waterequivalent thickness of ~ 7. 4 cm), a higher order Gaussian beam model was parameterized using a triple Gaussian to describe the lateral dose evolution in water and the low-dose envelope (secondary beam profile from scattering in the nozzle and beam modifiers). FLUKA MC simulations were conducted to generate the corresponding IDDs and lateral beam evolution data used for parameterization.
TG parameterizations are obtained by fitting radially scored FLUKA dose distributions as a function of depth in water for all commissioned energies. To use facility specific IDD measurements as FRoG base input, universal Proteus ® ONE beam corrections were devised to adjust StingRay measurements accounting for lost dose contribution outside of the detector. IDD simulation in FLUKA MC scored dose radially with radii of 6 cm (StingRay integration size) and 25 cm. Subsequent IDDs ratio between the 6 cm and 25 cm simulation sets was used as depth-wise scaling factors for the measured profiles to use as FRoG's input. Integral depth LET d for Z = 1 particles was additionally scored radially using the same 25 cm radius condition. For eventual evaluation of LET d distributions and proper implementation of the McNamara variable RBE model, LET was specifically defined by only scoring Z = 1 particles (primary and secondary protons) in the FRoG database generation of LET d.
In sum, for the commissioned energies at NPTC, a database containing IDD, LET d (Fig. 5 ) and TG parameters (sigma (σ) and weight [w]) were generated for beams without modifiers. For beams with RaShi, specific TG databases were generated for RaShi to skin distances between 1. 5 and 43 cm with step-size of 1. 5 cm. During the calculation process in FRoG, a database is interpolated online for the planned energies (and if present, at a specified RaShito-skin distance) using the reference databases. Dose in FRoG is scored within a circle around every pencil with a radius of 3 × σ(x), where σ(x) is the maximum Gaussian standard deviation of the TG beam shape approximation at depth x in water.
Absolute location from BCSF-Rénass catalog Seismic activity has been recorded since March 2018 in the northern sector of Strasbourg in real time using SeisComP3 software  [Hanka et al. , 2000, Helmholtz-Centre Potsdam-GFZ German Research Centre for Geosciences and gempa GmbH, 2008]. Real-time automatic events localizations by BCSF-Rénass were obtained using a Seiscomp pipeline consisting first of automatic picking using the scautopick module (with the STA/LTA method, and a detection threshold lowered to four stations) and an automatic localization module scautoloc (grid search) using the 1D Haslach velocity model described in Table  1. All automatic events were manually relocated, still using Haslach velocity model, by refining the automatic picks, adding additional manual picks on available stations and adding polarity when possible.
GEORG geological model and the Robertsau fault The public database GEORG (http://www. geopotenziale. org) provides a regional geological model for extracting geological sections such as those shown in Figures  7b  and c. Figure  7c  shows a section extending over a little more than 12 km from Souffelweyersheim to Auenheim crossing the area of the southern seismicity cluster of November 12, 2019. This figure also shows the surface location of the main regional faults, which are oriented generally N10 and dip westwards by around 70°. Figure  7b  shows a section oriented parallelly but further north intercepting the northern swarm near the Geoven wells. A spatial geological link between the two clusters over the significant distance (4. 6 km) exists owing to the presence and the orientation of the Robertsau fault that connects the two swarms.
This latter limitation is important in 2018 when a very limited number of stations were operational in the study area. Several frequency ranges have been explored and lead to variability in the final number of newly detected earthquakes. We have chosen here the 5-24 Hz frequency range which gives the most robust results. The search for events similar to those in the catalog by template matching was carried out over the entire period from March 14, 2018, to January 31, 2020. It can be seen from Figure  8 , that significantly more earthquakes are identified than in the initial catalog (approximately two times more, that is, more than 1,115 events in the final catalog). However, the final number of events may vary: depending on the initial database, the choice of stations or the frequency range used for detection.
8 (Byerlee criterion for a normal stress lower than 200 MPa), and that the cohesion is null (C 0 = 0), we obtain that the equilibrium conditions resulting from the Coulomb criterion are Both background images are extracted from GEORG database (http://maps. geopotenziale. eu); at the back is the geological cross-section at the Geoven wells, at the bottom is the geological map at 4000 m depth. CS = +0. 7 MPa, that is, CS > 0, that is, an unstable fault (Figure  17 ). The value of the Coulomb stress (CS) is actually weakly positive showing that the fault is marginally unstable. However, if we assume a cohesion of C 0 = 1 MPa as estimated at Soultz under comparable conditions, we obtain CS = -0. 3 MPa at 5620 m, that is, a marginally stable fault. This estimate also shows that the fault is very sensitive to fluid overpressure.
Acknowledgments The authors are grateful to  Christophe Sira ,  Antoine Schlupp ,  Marc Schaming ,  Jérôme Van der Woerd ,  Pascal Bernard ,  Jean-Robert Grasso ,  Bertrand Delouis ,  Florent Brenguier ,  Philippe Gueguen ,  Stéphane Guillot ,  Nicolas Arnaud ,  Christophe Maisons ,  Thomas Kohl  and  Beata Orlecka-Sikora  for their fruitful discussions. This manuscript is dedicated to the memory of François Cornet who suddenly passed away on May 23, 2020. This work has been performed under the framework of the  Laboratory of Excellence LABEX   ANR-11-LABX-0050-G-EAU-THERMIE-PROFONDE  and the  Interdisciplinary Thematic Institute GeoT , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS , and  Inserm , supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US project  ( ANR ANR-20-SFRI-001 ) under the framework of the  French Investments for the Future Program.
R E S E A R C H A R T I C L E JCI Insight 2022;7(13):e159254 https://doi. org/10. 1172/jci. insight. 159254 and patients with NASH (Figure  7A ). Moreover, expression analyses in HCC patients revealed that ACE expression is increased in HCC induced by chronic HCV and HBV infection (Figure  7, B  and C ). Collectively these data indicate a potential functional role of the RAS also in viral hepatocarcinogenesis. To obtain insights on whether captopril may have therapeutic efficacy in patients, we assessed the effects of captopril on the expression of the clinical PLS associated with HCC risk and survival in different patient-derived models. First, we applied a 3D multicellular spheroid model from patient tissues (including hepatocytes and nonparenchymal cells, NPCs) for NASH in which the 186 patient-derived PLS can be robustly induced by FFA exposure (Figure  7D )  (13).
Interestingly, captopril was the ACE inhibitor with the highest efficacy to revert the poor-prognosis status of the PLS in our cell-based system  (13). Our mechanistic data show that crosstalk between the local RAS in the liver and the EGFR pathway in hepatocytes is most likely responsible for the HCC chemopreventive properties of captopril. Our study reveals that inhibition of the EGFR pathway by captopril treatment in vivo constitutes a potentially novel mechanism of action by which ACE inhibitors prevent HCC. Interestingly, our previous studies have shown that inhibition of EGFR by erlotinib effectively inhibits stellate cell activation, hepatic fibrosis, and development of HCC in animal models  (18). In contrast to erlotinib, which is currently in clinical investigation for HCC chemoprevention (phase 2 clinical trial, NCT04172779), captopril has a superior safety profile.
R E S E A R C H A R T I C L E JCI Insight 2022;7(13):e159254 https://doi. org/10. 1172/jci. insight. 159254 of the medical treatment of the patient. Informed consent is provided according to the Declaration of Helsinki. Detailed patient information and informed consent procedures are implemented by the Strasbourg University Hospital Biological Resources Center (HUS CRB). While there were clinical descriptive data available, the identity of the patients was protected by internal coding. A brief summary of patient characteristics (diagnosis and treatments) is provided in Supplemental Table  4. The following public databases were used in the study are available on https://www. ncbi. nlm. nih. gov/ geo/query (Figure  7, A  and B ): GSE48452, NASH patient cohort; GSE20140, HCV-HCC patient cohort; and GSE94660, HBV-HCC patient cohort.
RNA-Seq on rat liver tissues. Total RNA was isolated from snap-frozen liver tissues of PBS-treated control rats, DEN-treated cirrhotic rats, and DEN-treated rats with captopril treatment (n = 3 for each experimental group) using RNeasy kit (Qiagen). After quality assessment (RNA integrity score > 9), 200 ng total RNA samples were used for library preparation using Tru-Seq kit (Illumina) and sequenced on NextSeq 550 genome sequencer (Illumina) according to manufacturer's instruction to generate 100 nt single-end RNA-Seq reads. Raw reads were aligned to the reference genome (rattus norvegicus, Rnor_6. 0) using the spliced gap aligner STAR (61), and count-based quantitation was carried out by the Subread package featureCounts at the gene level based on ENSEMBL annotation build (Rnor_6. 0. 101).
The whole-genome transcriptome read count data were normalized and modeled with overdispersed Poisson data as trimmed mean of M values (TMM) using a negative binomial model in the Bioconductor package edgeR  (62). For subsequent data analysis, genes with no expression in more than 50% of the samples and low variance across the samples (coefficient of variance < 0. 01) were excluded. The rat genes were mapped onto human orthologues based on NCBI Homologene database (build 68, https://www. ncbi. nlm. nih. gov/ homologene), and expression levels of multiple rat genes mapped to a human gene were summarized with their median value. Dysregulation and modulation of molecular pathways were assessed by GSEA (56) using the Hallmark gene sets (63) from Molecular Signature Database (MSigDB) v. 7. 0  (64).
Molecular pathways dysregulated in comparison between the DEN rats and the control rats were selected at a significance cutoff of FDR < 0. 005 and visualized as GSEI, defined as -log 10 (GSEA P value based on 1000 gene permutations), with a sign of GSEA normalized enrichment score (NES) as well as a bar plot of NES. We similarly analyzed human cirrhosis coregulatory gene modules defined in our previous study  (10)  and EGFR transcriptional target gene signatures from the MSigDB database. The transcriptome data set is available via NCBI Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/; accession no. GSE157919). All bioinformatics data analyses were performed by using GenePattern genomic analysis toolkit (www. genepattern. org) or R statistical package (www. r-project. org). Culture of organotypic ex vivo patient liver slice, patient-derived spheroids, and tumorspheroids.
For spheroids and tumorspheroids, patient information is summarized in Supplemental Table  4. Statistics. In vitro experiments were reproduced 2 (PLS) or 3 times in an independent manner with similar results. The precise number (n) of biologically independent samples used to derive statistics is indicated in the figure legends. The data are presented as the mean ± SD (unless otherwise stated) and were analyzed by the unpaired 2-tailed Student's t test or the 2-tailed Mann-Whitney U test, as indicated in figure legends, after determination of distribution by the Shapiro-Wilk normality test. P < 0. 05 was considered statistically significant. Significant P values are indicated by asterisks in the individual figures. Statistical analyses were performed with GraphPad Prism 8 software. No statistical analyses were performed if n < 4.
For in vivo experiments, the sample size estimate was based on a P value of 0. 01 at 90% power assuming a 50% difference in means in tumor burden with 33% SD between control and drug-treated animals. The Kruskal-Wallis test, followed by Dunn's multiple comparisons test, was used to compare the 3 groups or unpaired Student's t test to compare 2 groups. P < 0. 05 was considered statistically significant. For the PLS assay, variation of the poor-and the good-prognosis genes was determined by a NES obtained using GSEA. Significance of the data was determined by the FDR values. According to GSEA (https://www. gsea-msigdb. org/gsea/index. jsp), results are significant if FDR < 0. 25 for discovery in cell culture. Results are expressed as a heatmap (log 10 of FDR). For RNA-Seq on liver tissues, the threshold was adjusted at FDR < 0.
05. For clinical data presented in Figure  7 , data were extracted from a publicly available database (https://www. ncbi. nlm. nih. gov/geo/). GEO number is indicated in each figure panel. For patient-derived spheroids, due to the rarity and the limited quantity of patient liver tissues, the experiments were performed only 1 time in quadruplicate but on several patients. For cell culture/in vitro data, poor or insufficient technical quality of experiment or data analysis resulted in exclusion of samples (also known as the empirical method, in which values are excluded if X < or > to μ ± σ).
Study approval. The protocols for experiments with human tissues were approved by the local Ethics Committee of the University of Strasbourg Hospitals and Mount Sinai Hospital, respectively (Center for Digestive and Liver Disease of the Strasbourg University Hospitals University of Strasbourg, France: DC-2016-2616 and RIPH2 LivMod IDRCB 2019-A00738-49, ClinicalTrials. gov NCT04690972; Mount Sinai Hospital, New York City, NY: HS13-00159). All animals were housed in accordance with the guidelines of the Massachusetts General Hospital IACUC (protocol approval nos. 2007N000113 and 2009N000207) and received humane care according to the criteria outlined in the Guide for the Care and Use of Laboratory Animals (National Academies Press, 2011).
Acknowledgments We thank  R. Bartenschlager  ( Heidelberg University, Heidelberg, Germany ) for providing plasmids for production of HCVcc Jc1 strains,  F. Chisari  ( Professor Emeritus Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, San Diego, California, USA ) for the gift of Huh7. 5. 1 cells. We acknowledge the  Centre de Ressources Biologiques-Biological Resource Centre, Strasbourg, France , for the management of patient-derived liver tissues. Finally, we thank  NanoString Technologies Inc , for technical advice. This work was supported by  ARC, Paris and IHU, Strasbourg  ( TheraHCC2. 0IHU201901299  to TFB), the  European Union  ( EU H2020-HEPCAR #667273 ,  ERC  PoC-2019-HEPCAN # 862551 ,  ERC Adg ERC-AdG -2020-FIBCAN # 101021417  to TFB, and  ERC-AdG -2014-671231-HEPCIR to TFB and YH),.