dataesr/scientific-paragraphs-categorization huggingface dataset:
Study-Specific Modeling Approximation One asset of VACS is its ability to take into account the effects related to the history (power variations, control-rod insertions, etc. ) of each reactor  [6]. This capability is important, because it was shown in  [8]  that not taking into account the history of the reactor (i. e. , modeling a reactor operating continuously at nominal power) can induce a significant deviation in the estimates of RPV aging. However, in order to accurately model the reactor core power variations, it is mandatory to have a detailed description of the control-rod composition, the insertion map of the various rod banks in the reactor and their insertion sequences. In the case of the HBR-2 reactor, the authors could not find any public data about the insertion sequence of the control-rod banks as a function of the reactor power level.
Therefore, the reactor was modeled as operating at full power with all control-rod banks fully extracted. Considering the study detailed in  [8] , this approximation could bias the dosimeter activity estimates by up to about 8%. However, this value must be considered with caution, as it was obtained with control-rod management that was probably very different from that used in the HBR-2 reactor. Indeed, the control-rod type and the insertion map of the different banks given in  [8]  (which correspond to those used in several French 900 MW e PWRs) significantly differ from the data found for the HBR-2 reactor in  [17]. It is therefore only possible to say here that this assumption could lead to a non-negligible bias in the evaluations of the specific activities of the dosimeters. Based on this assumption, the end of the cycle was automatically determined by SIMULATE5 by seeking for the point in time at which the critical boron concentration equaled 0 ppm.
Modeling Fission Neutron Attenuation from the Core to Sites of Interest The second step of the calculation deals with the transport of fission neutrons from the core to sites of interest (i. e. , the dosimeters located at the monitoring program capsules or in the cavity), using the fission neutron distribution determined in the previous step. To perform this step, a MCNP6 fixed source calculation  [27]  was performed using a model developed on the basis of the "HBRHE81" MCNP5 input file, available on the SINBAD database, which was initially provided by P. Ortego (Shielding Engineering and Analysis, Spain). The following sections describe the reactor model as provided in the "HBRHE81" input file and highlight the modifications made by the authors. The model developed in this process was used to perform fixed source calculations using the fission neutron source terms generated using the α and β approaches.
The dosimeter composition is not explicitly given in the SINBAD database. However, a fictitious material (the term "fictitious material" refers here to a material which is not used in the definition of the geometry) representative of the dosimeter composition was defined in the "HBRHE81" input. This material can therefore be associated with the "FM" cards in order to calculate the isotopic reaction rates. The composition of this material is shown in Table  3. However, it is important to note that this dosimeter composition could not be found elsewhere in the benchmark data. Regarding the dosimetry cross sections, the following libraries were tested to calculate the reaction rates:.
The factor 1/8 is added to account for the fact that only one eighth of the reactor is modeled in the MCNP6 attenuation calculation. On the one hand, for the α approach (Section 3. 1. 2), the C f coefficient value was taken from the study performed by I. Remec and F. B. K Kam  [11] , in which a value of 8. 175 • 10 16 neutrons • s -1 • MW -1 is specified. As explained before, this value was evaluated using the DOTSOR code  [22]  for a burn-up equal to 28. 596 GWj/t, which corresponds to the average exposure of the S08, S09 and S10 assemblies in cycle no. 9. On the other hand, for the β approach (Section 3. 1. 3), the C f coefficient was evaluated according to Equation (  9 ), using the cycle-averaged data calculated by SIMULATE5. C f = ∑ a ∑ i R f ,i,a • ν i P th,S5 (9) where, for an isotope i and an assembly a, R f ,i,a • ν i is the cycle-averaged fission neutron production (neutrons • s -1 ) and P th,S5 is the average thermal power of the reactor over the cycle modeled with SIMULATE5 (2300 MWth = reactor nominal power).
Dosimeter Reaction Rates Reaction rates were calculated using the average fission neutron distribution in cycle no. 9, evaluated using the α or β approaches. Post-processing of the MCNP6 tally outputs was performed as described in Section 3. 3. The obtained results are presented in Table  5  for the capsules and in Table  6  for the cavity. In order to have a point of comparison, these tables also summarize the reaction rates calculated in 1997 by I. Remec and F. B. K Kam  [11] , using the two-dimensional transport code DORT  [35]  and the nuclear data library BUGLE-96  [36]. Putting Tables  5  and 6  side by side, it can be seen that the standard deviation of the MCNP6 tallies are systematically lower in the cavity than in the capsule. This behavior is due to the parameterization of the weight windows generated by the ADVANTG-3.
Dosimeter Specific Activities The specific activities of dosimeters and the associated C/E ratios were calculated from the reaction rates, as described in Section 3. 3. Figure  8  presents the results obtained using the α and β approaches with the ENDF/B-VII. 1 nuclear data library. In the same way as before, the results obtained by I. Remec    In Figure  8  (and in those following), we chose not to represent the statistical uncertainties of the C/E values, as these were not significant. Only the standard deviation of the average C/E is shown, which is significantly greater than the statistical uncertainty of the Monte Carlo calculations. Nevertheless, it is still worth mentioning that the uncertainty propagation work done by R. Pittarello et al. in  [13]  showed that, in the case of the HBR-2 benchmark, the ±1σ uncertainty in the specific activity assessments can be of the order of 15%.
1 library. In contrast, the C/E ratios evaluated with the ENDF/B-VIII. 0 library are largely degraded in comparison with the other tested nuclear data libraries. It is also possible to note that the C/E ratio obtained with ENDF/B-VIII. 0 for the 63 Cu(n,α) specific activity is very different from those evaluated for the other specific activities. This observation suggests that there is also an anomaly in the ENDF/B-VIII. 0 63 Cu(n,α) cross section. In order to confirm this last statement, Figure  10  presents the obtained C/E values using the ENDF/B-VIII. 0 library to model neutron transport and the IRDFF-II library for dosimetry. This figure shows that using the IRDFF-II dosimeter cross sections does not significantly improve the C/E ratio obtained, when using the ENDF/B-VIII. 0 library to model neutron attenuation from the core to the sites of interest.
The relative qualities of several nuclear data libraries (ENDF-B/VII. 1, ENDF-B/VIII. 0 and JEFF-3. 3) for modeling the neutron attenuation from the core to the capsules and cavity were also studied. The H. B. Robinson-2 dosimetry benchmark was intended to be used to validate the attenuation of the neutron flux only, which corresponds to the second step of the VACS approach. Nevertheless, information was gathered to use this benchmark to validate both steps, including the fission distribution inside the core. Thus, this work provides some recommendations regarding information that is still missing for performing a precise validation of the core calculation. The authors expect that with some additional information on the core management, the worth of this benchmark will be extended. The results obtained in the frame of the current study indicated a satisfactory accuracy for the specific activity predictions obtained with VACS and the ENDF-B/VII.
5% Triton X-100 just before fixation. The following secondary antibodies were used: goat antimouse or goat antirabbit IgG (H + L) conjugated with Alexa Fluor 488 or Alexa Fluor 633 (Molecular Probes, ThermoFisher). Imaging was performed using a Leica SP5 confocal system attached to a DMI6000 stand using a 63/1. 4 objective. A minimum of 100 nuclei in at least 10 different fields distributed randomly on each sample were imaged. Each assay was at least duplicated. Images were processed using a macro written to automate the foci recognition into ImageJ software complemented with the LOCI bioformat plugin (http://www. loci. wisc. edu/ome/ formats. html, accessed on 15 December 2021). The focus quantification was statistically analyzed using the Mann-Whitney test with StatEl software (ad Science, Orsay, France) implemented in Excel.
To mainly generate double-or singlestrand breaks, we used the multiphoton laser at 810 nm (set to 30% output for 500 ms) and 405 nm, respectively, and focused onto a spot of 176 nm diameter. For XRCC4 recruitment, the cells were pretreated with Hoechst 33342. The laser was set to a maximal output of 100 ms for XRCC1 recruitment, and 500 ms for PARP1 recruitment. Records were produced using the appropriate argon laser line (514 nm for YFP or 488 nm for GFP) at 2 s intervals for the following 132 s. The recruitment quantification was obtained using ImageJ software and a macro to automate data extraction by calculating the mean photodamage spot intensity relative to the mean nucleus intensity, excluding the spot corrected by the background and photobleaching. The maximum of the recruitment was estimated with the kinetics.
We propose that the inappropriate modification of the chromatin impaired the repair of ionizing radiation-induced DNA double-strand breaks. Collectively, these data suggest that, upon AsiDNA treatment, part of the nuclear DNA-PK complex is loaded onto AsiDNA, resulting in the activation of DNA-PKcs kinase activity, and subsequently to the uniform widespread nuclear phosphorylation of histone H2AX. We propose that the inappropriate modification of the chromatin impaired the repair of ionizing radiation-induced DNA double-strand breaks. Table  1. Inhibition of repair enzymes recruitment at DNA damage. Cells treated for different times with AsiDNA at 150 µg/mL were nonirradiated or irradiated at 2Gy for P-ATM, or at 10Gy for the other enzymes. Focus formation was analyzed by immunofluorescence at 10 min (for P-ATM) or 2 h (for the other proteins) after irradiation.
3°S to 59. 8°S, and the resolution is reduced to 1/4° in longitude, that is, a quasi-isotropic resolution ranging from 14 km at the northern boundary to 6. 5 km in the southernmost part of the domain. Bathymetry, as well as surface and lateral boundary conditions also differ from  Jourdain et al. (2019 ) and cover the period 1958 -2018 in this study. The period 1958 -1968  is left for spin-up and discarded in our analyses. The bathymetry and ice-shelf draft interpolated on the model grid are from the second version of the BedMachine Antarctica dataset  (Morlighem et al. , 2020). This recent dataset represents Thwaites Ice Shelf after its partial collapse. The B22A iceberg as well as other very large tabular icebergs, absent from BedMachine Antarctica, are represented as static flat ice shelves.
Their horizontal shape and location are derived from a MODIS-visible image (provided by the US National Snow and Ice Data Center) taken on September 5, 2003. The depth of the B22A iceberg base was calculated as 𝐴𝐴 𝐴𝐴= 𝐻𝐻𝐻𝐻𝑤𝑤∕(𝐻𝐻𝑤𝑤 -𝐻𝐻𝑖𝑖) , where ρ w is the seawater density (1,027 kg m -3 ), ρ i the iceberg density (917 kg m -3 ), and H the freeboard that is 40. 8 m on average for B22A in the altimetry dataset of  Tournadre et al. (2015). The resulting estimated depth is approximately 380 m, which we apply to the smaller tabular icebergs that are absent from the altimetry dataset. We then assume that these icebergs behave like flat ice shelves if the bathymetry is deeper than 380 m plus two ocean model vertical levels (the minimum water column thickness in NEMO) and like an island (i. e. , no water column) if the bathymetry is shallower.
The huge B22A iceberg calved from the Thwaites ice tongue in 2002 and has drifted very slowly since then (Antarctic Iceberg Tracking Database,  Budge & Long, 2018). A similar calving event occurred in the late 1960s  (Lindsey, 1995). The resulting iceberg was eventually designated B10 in 1992 when it started a 15-year drift across the Amundsen Sea before breaking up and drifting further away  (Budge & Long, 2018). Numerous smaller icebergs regularly drift westward in the Amundsen Sea and ground on the eastern flank of bathymetric ridges shallower than approximately 400 m  (Mazur et al. , 2017). We therefore artificially place a wall along the 380 m isobath on the eastern flank of Bear Ridge (in a similar way as  Bett et al. , 2020) , north of Siple Island, and on the main ridge in between.
These permanent lines of grounded icebergs were shown to favor the formation of polynyas with impact on ice-shelf melting  (Bett et al. , 2020; Nakayama et al. , 2014). The conditions at the lateral ocean and sea-ice boundaries are derived from the 5-day mean outputs of a global simulation very similar to the one described in  Merino et al. (2018)  except that it is spun up from 1958 and that the imposed ice-shelf melt flux increases linearly from 1990 to 2005 and is constant before and after that, with values corresponding to the FRESH+ and FRESH-reconstructions of  Merino et al. (2018). Here, the temperature and salinity boundary conditions are corrected by the difference between the seasonal climatology of the World Ocean Atlas 2018 (WOA18) database  (Garcia et al. , 2019)  and the seasonal climatology of the global simulation.
Used regional configuration of the Amundsen Sea. Bathymetry and ice-shelf draft are from the second version of the BedMachine Antarctica dataset  (Morlighem et al. , 2020). Grounded ice is shaded in white, ice shelves are colored in blue, and main tabular icebergs in light cyan. The general view is drawn from the geospatial data package Quantarctica  (Matsuoka et al. , 2021). extent variability (Section S3 in Supporting Information S1), temperature and salinity stratification (Section S4 in Supporting Information S1), and ice shelf basal melt rates (Section S5 in Supporting Information S1). The atmospheric forcing fields are perturbed (Section 2. 2) to investigate ocean tipping points.
Acknowledgments This study was funded by the  European Union 's  Horizon 2020 research and innovation programme  under Grant agreement  820575  (TiPACCs) and by the  French National Research Agency  under Grant  ANR-19-CE01-0015  (EIS). This work was granted access to the HPC resources of CINES under the allocation  A0100106035  attributed by GENCI. The authors would like to acknowledge  Clara Burgard  for her valuable advice on figures and presentations as well as  Hélène Seroussi  for her constructive feedback as a member of the PhD committee.
Data Availability Statement The model version and set of parameters used to run our experiments are provided in https://doi. org/10. 5281/ zenodo. 7432159. The python scripts used to build the figures are provided in https://doi. org/10. 5281/ zenodo. 7436689 and are mainly based on the Xarray  (Hoyer & Hamman, 2017) , Numpy  (Harris et al. , 2020) , and Matplotlib  (Hunter, 2007)  packages.
Methods Data used for this study were obtained from the Center for Neurodegeneration and Translational Neuroscience (CNTN) database (www. nevadacntn. org). The current study was approved by the institutional review board of Cleveland Clinic and all subjects provided informed written consent. The inclusion criteria for patients with PD in this study was a diagnosis of PD based on the United Kingdom Parkinson's Disease Society Brain Bank Diagnostic Criteria. 36  PD participants were evaluated for FOG with a self-report measure (FOGQ) and a comprehensive battery of clinical tests (FOG score  37  including Timed Up and Go and Movement Disorders Society-Uni ed Parkinson's Disease Rating Scale (MDS-UPDRS III)). These assessments were recorded and reviewed by three members of the research team (two movement disorders specialists and one physical therapist with expertise in FOG) to con rm the presence or absence of FOG.
All subjects underwent rs-fMRI scans on a 3T Siemens Skyra scanner. A 32-channel head coil was used for data acquisition, and all PD participants were scanned in the clinically de ned 'ON' state. The rs-fMRI involved gradient-echo T2*-weighted echo-planar imaging (EPI) acquisition with 850 dynamics [Repetition Time (TR)=700ms, Echo Time (TE)=28. 4ms, ip angle=42°, resolution=2. 3x2. 3x2. 3mm 3 , 64 axial slices, multiband=8, and phase encoding= posterior (P) >> Anterior (A)]. We acquired spin-echo EPI images with the same parameters in the same and opposite phase encoding (P>>A and A>>P respectively) for distortion correction. A high resolution (1x1x1mm 3 ) T1-weighted structural image was acquired for each subject using a T1-weighted gradient echo 3D with magnetization-prepared 180 degrees radio-frequency pulses and rapid gradient-echo sequence (TR=2300ms, TE=2.
96ms, ip angle =9°). The total time of acquisition was approximately 18 minutes. The rst 15-time frames (10 seconds) were removed to allow the MR signal to achieve T1 equilibrium. Time frames were distortion corrected, slice-time corrected, and realigned to the mean image using the statistical parametric mapping (http://www. l. ion. ucl. ac. uk/spm/), further co-registered to the subject T1space, and then normalized to the standard MNI-152 2mm-template using advanced normalization tools (http://stnava. github. io/ANTs/). 246 different cortical regions of interest (ROIs) were identi ed based on the Brainnetome functional atlas. 38  The rst principal component resulting from the singular value decomposition of all the time series within the ROI mask was de ned as the time series for the ROI.
Nanostring gene expression arrays Total RNA was extracted from Trizol-stabilized cell pellets using NucleoSpin 96 miRNA kit (Macherey-Nagel). RNA concentrations were measured using Quantifluor RNA system kit (Promega) and RNA integrity numbers were determined using the Agilent RNA 6000 Nano kit (Agilent Technologies). Total RNA samples were analyzed using the Human Host Response panel profiling 800 immunology and host response related human genes (Nanostring). Gene expression data were normalized as previously described  28.
Statistical Analysis GraphPad Prism and R were used for statistical analysis. We applied multinomial logistic regression models between patient groups using IFN response phenotypes and accounting for impacts of age and sex (known factors associated with COVID-19 severity) using the "nnet" (version 7. 3) R package. For comparison of patient subgroups with smaller sample sizes, nonparametric Kruskal-Wallis tests, followed by Dunn's post-test for multiple group comparisons were performed. Significance cut offs are denoted as follows *P < 0. 05; **P < 0. 01; ***P < 0. 001. Correlations between the different assays were calculated using Spearman test. UMAP plots were performed with "M3C" R package (v1. 10. 0). Gene set enrichment analysis (GSEA) was performed as previously described 2 using a pathway data set built from the Nanostring Host response panel annotation file.
3c ). To assess whether differences in receptor expression could explain these cytokine differences, we examined the relevant TLR gene expression data from whole blood. Expression of TLR7 was similar in all groups, while expression of TLR3 was significantly lower, and of TLR4 and TLR8 was significantly higher, in hospitalized patients. Expression of IFIH1 was higher in both patient groups, although the effects were modest and the differences are unlikely to explain the differences in cytokine responses (Fig. 3d ). To examine whether these immune differences were restricted to IFN-I responses, we measured an additional 42 cytokines in the whole blood stimulations by Luminex (Fig. S3 ). This revealed 8 additional cytokines with significant differences in hospitalized patients, mostly after TLR3 stimulation.
) from Hopital Cochin and Hopital Bichat, Paris under clinical study protocols in the setting of the local RADIPEM biological samples collection, derived from samples collected in routine care as previously described 2 , or from the INSERM-sponsored French COVID-19 clinical study (NCT04262921). Biological collection and informed consent were approved by the Direction de la Récherche Clinique et Innovation and the French Ministry of Research (no. 2019-3677, 2020-A00256-33). The studies conformed to the principles outlined in the Declaration of Helsinki, and received approval by the appropriate Institutional Review Boards (Cochin-Port Royal Hospital, Paris; no AAA-2020-08018 and Comité de protection des personnes Ile de France VI; no 2020-A00256-33). Plasma samples were obtained from COVID-19 patients (n=342) for cytokine analysis and for autoantibody analysis (n=212), and whole blood for immune stimulations (n=77) and cellular phenotyping (n=31) from subgroups.
Acknowledgments This study was supported by the " URGENCE COVID- 19 " fundraising campaign the  Institut Pasteur (CoVarImm and Steroid Response ), from the  Agence Nationale de la Recherche  ( ANRflash COVID-19 ), by the  Laboratoire d'Excellence 'Milieu Intérieur ' (grant no. ANR-10-LABX-69-01 ), the  Fonds IMMUNOV for Innovation in Immunopathology , and  Science Foundation Ireland. We thank the  STTAR-Bioresource of TCD-SJH-TUH COVID-19 bioresource which supported collection of patient samples. NS is a recipient of the  Pasteur-Roux-Cantarini Fellowship. COF, NC and CNC are part-funded by a  Science Foundation Ireland (SFI)  grant, Grant  Code 20/SPP/3685. LT is supported by the  Irish Clinical Academic Training (ICAT) Programme , supported by the  Wellcome Trust  and the  Health Research Board  (Grant Number  203930/B/16/Z ), the  Health Service Executive , National Doctors Training and Planning and the  Health and Social Care, Research and Development Division, Northern Ireland.
N. B. is funded under the  Science Foundation Ireland Phase 2   COVID-19 Rapid Response Call  ( 20/COV/8487 ) and the  Health Research Board   COVID-19 Rapid Response Call  ( COV19e2020e053 ). The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  National Institutes of Health (NIH)  ( R01AI088364  and  R01AI163029 ), the  National Center for Advancing Translational Sciences (NCATS) ,  NIH Clinical and Translational Science Award (CTSA) program  ( UL1 TR001866 ), a  Fast Grant  from  Emergent Ventures ,  Mercatus Center at George Mason University , the  Fisher Center for Alzheimer's Research Foundation , the  Meyer Foundation , the  JPB 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  ANRS -COV05, ANR GENVIR  ( ANR-20-CE93-003 ),  ANR AABIFNCOV  ( ANR-20-CO11-0001 ) and  ANR GenMISC  ( ANR-21-COVR-0039 ) projects, the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824110  ( EASI-genomics ), the  Square Foundation, Grandir -Fonds de solidarité pour l'enfance , the  Fondation du Souffle , the  SCOR Corporate Foundation for Science, The French Ministry of Higher Education, Research, and Innovation  ( MESRI-COVID-19 ),  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  REACTing-INSERM  and the  University of Paris.
Finally, the plate was dipped in a revelation reagent (10 mg primuline, 160 mL acetone, 40 mL dH 2 O), which was then visualized, derivatized, and scanned using a TLC scanner 3  (Breil et al. , 2016; Ravi et al. , 2019; Gharby et al. , 2020)  equipped with WinCATs software (CAMAG).