dataesr/scientific-paragraphs-categorization huggingface dataset:
Turbulence models The LES implementation in Code Saturne  [22]  is based on the dynamic Smagorinsky model  [23]  with Lilly's minimization  [24]  in which the Smagorinsky constant C S is clipped below zero and above 0. 065. This model has been successfully used for flows around bluff bodies and in turbo-machinery; please see the effect of clipping of C S in  [25]. A turbulent Prandtl number of 0. 5 was used when solving for the temperature with LES, see  [26]. In the current study three low Reynolds number URANS models were chosen based on their availability and relative popularity in the heat transfer community: the k-ω-SST model  [27] , the φ-model  [28]  and the EB-RSM model  [29]. The k-ω-SST model uses a blending between the standard k -ω and k -ε models. The φmodel uses the elliptic relaxation concept introduced by  [30].
Grid Convergence study for URANS A separate grid convergence study was performed for all the three URANS models at the two higher Reynolds numbers. Here only the study performed for Re D = 10, 000 with the EB-RSM model is presented for the grid convergence study as conclusions for all other models were similar. Three levels of refinement were used for all the models as described before. Figure  4a  and b show the mean stream-wise velocity and the pressure coefficient along the line B (pin mid-line) for the Re D = 10, 000 case for row 3, respectively. As the computations were unsteady, the r. m. s. values, or more generally the Reynolds stresses were combined as the sum of the resolved and modelled parts: R ij = R res ij + R mod ij. These modelled and resolved parts are not shown in the current paper but can be downloaded from the ERCOFTAC-KB-Wiki database.
It is observed from Fig. 4  that the coarse mesh results are very far from the experiments. However, the two finest meshes only show slight difference in profiles. Thus, the discussions in Sections 4 and 5 are based on the results obtained with the finest mesh (called here "very fine mesh"). In order to gain confidence in the finest mesh results, a further sensitivity study on the numerical discretization schemes with and without slope test was also performed. Based on the results it was concluded that the use of a centered scheme for the turbulent quantities did not affect the results. Other tests were also carried out such as introducing outer iterations for pressure/velocity coupling but none of them exhibited a noticeable effect on the results and are hence not reported here (please see the Sensitivity study to convection scheme on the ERCOFTAC-KB-Wiki database for detailed plots).
It can be anticipated that the main difference between the EB-RSM and the eddy-viscosity models lies in the tendency of the latter to provide a steady solution because of their strongly diffusive character. It can also be argued that eddy-viscosity models tend to overestimate turbulent production close to stagnation points and thus overestimate the turbulent energy in the boundary layers around the cylinders and, consequently, in their wake. This hypothesis will be investigated below. database). One observes from Fig. 7b  that the EB-RSM model and LES predictions are very close to each other. In fact, for row 3, the EB-RSM model captures the near wall behavior even better than LES, as the peak value by LES is slightly overpredicted. For row 5, both LES and the EB-RSM model slightly overpredict the mean velocity near the pin.
2. 17 of Viollet et al. [47] ). The lack of consideration of this stress-strain lag prevents the large-scale unsteadiness to be captured in cylinder wakes  [46] , a problem that can be avoided by using a Reynolds-stress model  [43]. Figure  10  shows the r. m. s. profiles of the stream-wise velocity along line B. Here again only the profiles along row 3 are shown and the remaining profiles can be accessed via the ERCOFTAC-KB-Wiki database. The quantity plotted in the figure is u = u 2 1 + R 11 , where u 1 is the resolved streamwise velocity fluctuation and R 11 the time-averaged value of the stream-wise modelled Reynolds normal stress R 11. LES and EB-RSM models perform very well compared to the experimental data. As mentioned earlier, it can be seen in Fig. 11  that for the stream-wise stresses, a major part of the total fluctuations of the EB-RSM model comes from the resolved part, which again shows that this model easily reproduces the large-scale unsteadiness appearing in the wake of the pins.
Mean pressure coefficient Figure  12  shows the profiles of the mean pressure coefficient along the midline of pins 3 and 5 for the three Reynolds numbers. Profiles along other pins can be accessed via the ERCOFTAC-KB-Wiki database. Note that the experimental results have an uncertainty of ±0. 075 at Re D = 3, 000 and of ±0. 025 at Re D = 10, 000 and Re D = 30, 000. LES results are in relatively good agreement with the experimental data. EB-RSM exhibits overall satisfactory results as well. In fact for Re D = 10, 000 for Row 3, the EB-RSM prediction is even better than LES. This result is surprising at first glance, but it is important to emphasize that the LES does not have any fluctuating content at the inlet. Therefore, it takes some rows for the resolved turbulent energy to reach a level compatible with the experiments, in contrast to the EB-RSM for which the turbulence intensity at the inlet is 5%.
Sperm morphology and morphometric analysis Sperm morphology and morphometry were performed using the CASMA Sperm Class Analyser system (SCA® ; Microptic SL) version 5. 4 software and using methods previously described  (Van der Horst et al. , 2018). Analysis used bRLJKT dHMG PQTLFS HNQMPYLOJ D. * X objective, i. e('. ** X NDJOLdFDTLPO' DOG EMUH dMTHR on a Nikon E200 microscope. One hundred sperm/animal were analyzed (N= 10 / per group). All spermatozoa were measured randomly, to exclude any bias in favour of a specidc morphological form. Images were captured digitally using a Basler 780^75 gigaethernet camera (Basler Microscopy Camera, Germany)( BKH A42 SYSTHNbS morphology module RatTox (Microptic S. L. , Barcelona, Spain) automatically analyzes morphometric dimensions of the sperm head and middle-piece (MP).
Statistical Analysis Results are expressed as mean ± standard deviation (SD), ATUGHOTbS T-test was routinely performed for statistical analysis of data, and was replaced by Mann-Whitney Rank Sum Test when the equal variance test failed (determined by Sigmaplot Stat software). The Fisher exact test was used to compare fertility index and a quasi-poisson regression was conducted for the anomaly rates modeling. Since the epididymis data consist of a paired observation within the same rat, a reasonable assumption is that these observations are correlated and therefore, statistical methods that recognize and account for the correlation of observations within a subject are appropriate. Thus, a generalized estimating equations (GEE) regression  (Zeger and Liang, 1986 ) was conducted using using the Zelig Package of the R software (R Software, 2016).
, 2014). Although these data suggest a role for the W-hole in the interface, this has not been demonstrated, and there is no direct structural data on any P-CTD complex. In this study we use NMR spectroscopy to elucidate the STAT1-binding interface on P-CTD. Results indicate that the P-CTD/STAT1 complex comprises a discrete intermolecular interaction, by which P-CTD disrupts pY-STAT1/DNA binding. However, the interface did not include the W-hole and rather is extended, comprising several distinct regions in P-CTD that contact different STAT1 domains, indicating that IFN antagonist/ STAT complexes can involve extensive interfaces. Consistent with this, ablation of the interaction and antagonism of STAT1/ DNA binding/transcriptional activation required multiple mutations of different regions of P-CTD.
With GB1-STAT1, intensity for H203, I205, A206, E207, and D235 reduces to <0. 5 (unbroken line) and for I201, Q204, F209, D236, I237, L276, and L277 reduces to within 0. 6-0. 5 (dashed line). Residues contributing to interactions lie within helices 1, 2, and 5 of P-CTD. W265 and M287 are indicated. (B and C) Examples of intensities of cross-peaks of P-CTD in the presence of GB1-STAT1: (B) off-resonance and (C) on-resonance saturation. Intensities reduced >0. 5 (red boxes) or within 0. 6-0. 5 (yellow boxes) are indicated. Insets in (B) and (C) show that N ε H of W265 is not attenuated. (D) Surface representations of residues significantly attenuated with GB1-STAT1 (<0. 5 in red, 0. 6-0. 5 in yellow) and residues of the W-hole (purple) mapped onto CVS P-CTD structure (PDB: 1VYI), viewing the round (left panel) and flat faces (right panel).
Data from each individual assay are the mean of triplicates; where multiple assays were performed, n values (in parentheses) indicate the number of biological replicates used to calculate mean ± SD. ND, not done. a Solubility of P-CTD expressed from E. coli. Cell  Reports 29, 1934 -1945 , November 12, 2019 1937  intensity, 83 peaks) (Figure  2B ). In contrast, F209A/D235A P-CTD produced little broadening (average 7% loss of intensity, 85 peaks) (Figure  2C ). Thus, although W265G/M287V reduces STAT1 binding, F209A/D235A lacks detectable binding, consistent with the data indicating that regions A and B are critical (Table  1 ). To confirm that effects on isolated protein interactions correlate with interactions in cells, we used coIP and confocal laser scanning microscopy (CLSM) analysis. The original identification of P-protein/STAT1 interaction  (Vidy et al.
Secondary structure analysis was conducted in DichroWeb  (Whitmore and Wallace, 2004, 2008)  using the program CDSSTR. Thermal unfolding was measured by raising sample temperature from 20 to 90 C at a rate of 1 C/min (Figures  S6B-S6D ). Thermal unfolding transitions and mid-point melting temperature (T m ) were calculated by plotting normalized ellipticity values at 222 nm as a function of temperature and fitted to a two-state transition (Equation  1 ), assuming no change to heat capacity for folded and unfolded, and correcting for pre-and post-transition changes  (Martin and Schilstra, 2008) : Y = ðY N + b N TÞ + ðY D + b D TÞe À DH Tm 1À T Tm =RT 1 + e ÀðDH Tm À 1À À T Tm ÁÁ =RT (1) where Y is the observed ellipticity at a given temperature, Y N (Y D ) and b N (b D ) are the slopes and intercepts of the pre-and post-transition slopes; T is temperature ( C), T m the mid-point melting temperature, and DH Tm is the enthalpy at T m.
Samples were centrifuged at 50,000 rpm (20 C) and monitored continuously. Data were fitted in SEDFIT  (Schuck, 2003)  using 100 sedimentation coefficient increments from 0 to 15 S, with regularization parameter of p = 0. 95. Frictional ratios were fitted, and for the fluorescence-detected experiments, meniscus positions were fitted. Nuclear Magnetic Resonance spectroscopy P-CTD and STAT1 samples were dialyzed in 50 mM Na 2 HPO 4 , 100 mM NaCl, and 1 mM DTT, pH 6. 8 (NMR buffer) prior to making final samples in 10% D 2 O/90% H 2 O. NMR data were acquired at 25 C on a Bruker Avance IIIHD 700 MHz spectrometer equipped with a triple resonance cryoprobe. The near-complete assignment of the 1 H, 13 C, 15 N resonances of WT P-CTD are reported elsewhere  (Zhan et al. , 2019)  (Biological Magnetic Resonance Bank, accession code 27498).
To assign backbone (HN, 15 N, 13 Ca, 13 Cb, 13 C') resonances of P-CTD mutants, data were collected for the 3D experiments (HNCO, HN(CA)CO, HNCACB, HNCOCACB) using uniformly 13 C-15 N labeled protein. 2D 15 N, 1 H Heteronuclear Single Quantum Coherence ( 15 N, 1 H HSQC) spectra were collected using traditional approaches whereas 3D spectra were recorded using 10% non-uniform sampling (NUS) and Poisson gap sampler  (Hyberts et al. , 2010). Spectra were reconstructed with the compressed sensing algorithm using qMDD  (Kazimierczuk and Orekhov, 2011) , processed using NMRPipe  (Delaglio et al. , 1995) , and analyzed with NMRFAM-SPARKY  (Lee et al. , 2015). Secondary structure of the P-CTD mutants was assessed using SSP with 13 Ca and 13 Cb chemical shift data  (Marsh et al. , 2006). Interactions between 30 mM 15 N-labeled P-CTDs and GB1-STAT1 were monitored by acquiring 15 N, 1 H HSQC spectra with and without 30 mM GB1-STAT1.
For transferred cross-saturation experiments  (Impagliazzo and Ubbink, 2004; Nakanishi et al. , 2002; Nishida et al. , 2003)  500 mM uniformly labeled 2 H, 15 N P-CTD was mixed with 50 mM GB1-STAT1 protein in NMR buffer in 90% 2 H 2 O/10% H 2 O. Prior to mixing, 2 H, 15 N P-CTD was kept in the same buffer for 8 h at room temperature and then 2 days at 4 C to allow amide exchange to reach equilibrium. The transferred cross-saturation experiments used a 2D 15 N, 1 H TROSY-HSQC pulse scheme and WURST 1 H-saturation pulse (15 ms, 2800 Hz band-width)  (Takahashi et al. , 2000). Data were acquired with 25% NUS with interleaved rows for on-and off-resonance saturation; spectral widths of 12 ppm in 1 H (2048 data points), and 27 ppm in 15 N (512 data points). The WURST saturation of the aliphatic protons of P-CTD was 2 s with a saturation frequency set at 0.
Data were fitted to a non-linear curve assuming a two-state exchange (xcrvfit 4. 0. 12; Boyko and Sykes, University of Alberta, www. bionmr. ualberta. ca). Hydrogen-deuterium exchange was monitored via acquisition of 2D 15 N, 1 H HSQC spectra (25 C, pH 6. 8) on a 600 MHz Bruker Avance III spectrometer. Exchange was initiated by passing a 250 mM sample over an Illustra NAP-5 column pre-equilibrated in NMR buffer, 100% D 2 O. Data were acquired with spectral widths of 13 ppm in 1 H (2048 data points) and 26 ppm in 15 N (256 data points). NUS was used for acquisition with 25% sampling. For each spectrum 16 scans were acquired per 15 N data point resulting in acquisition times of 20 min. Acquisition of the first spectrum occurred after 8 mins following initial exchange. Exchange rates (k a ) (Table  S1 ) were determined by fitting to a single exponential, I = e Àkat ( 3 ) where I is the peak intensity, k a the exchange rate and t is time.
Coverslips mounted onto glass slides using Mowiol were imaged using a Leica SP5 microscope with 60 3 oil immersion objective and images processed with FIJI software  (Schindelin et al. , 2012). Images are shown in Figure  S5. Protein Complementation Assay HEK293-T cells seeded into 96-well plates were transfected 18 h later with 100 ng of Glu1 and Glu2  (Brunel et al. , 2014)  chimeric constructs, and 5 ng of pGL4. 50 (Promega), which constitutively expresses firefly luciferase. Cells were treated without or with IFNa (1000 U/mL) 24 h post-transfection and Gaussia and firefly luciferase activities measured after a further 24 h using the Renilla and Firefly Luciferase Assay Systems (Promega), respectively. Gaussia-luciferase activity was normalized to firefly luciferase activity. Protein-protein interaction levels are expressed as normalized luminescence ratios (NLRs), according to the following formula  (Cassonnet et al.
4 for 1 h at room temperature with recombinant protein (pY-STAT1, WT or mutant P-CTD, or pY-STAT1 pre-incubated with WT or mutant P-CTD at room temperature for 20 min). 3 mL of DNA loading dye (0. 25% Orange-G and 50% glycerol in milli-Q water) was then added to each sample prior to electrophoresis on a 1. 2% agarose gel. 2-log DNA ladder (NEB) was used to indicate the size of the bands. Gels were run in 1x TAE buffer (40 mM Tris, 1 mM EDTA, 20 mM glacial acetic acid) and 0. 5x SYBR-safe (Invitrogen TM , catalog #S33102) (140 V, 25 min) before imaging on a ChemiDoc TM MP Imaging system with Image Lab TM software.
ACKNOWLEDGMENTS We thank  Cassandra David  for assistance with tissue culture, the  Monash Micro Imaging facility  ( Monash University ), and the nuclear magnetic resonance facility ( University of Melbourne ;  Australian Research Council  equipment grant  LE120100022 ). This work was supported by  National Health and Medical Research Council  grant  1125704  to G. W. M. , P. R. G. , and H. B. and grants  1160838 ,  1079211 , and  1003244  to G. W. M. , and  Australian Research Council  grant  DP150102569  to G. W. M. ; a  Grimwade Fellowship  provided by the  Meigunyah Fund  to G. W. M. ; and  Japanese Society  for the Promotion of Science fellowships  S11102  and  L14563  to G. W. M.
By considering the nuclei ratio or isotopic activity ratio of measured radionuclides, it is possible to evaluate their time of release (age of release)  (Nir-El, 2004 , 2006). It has also been shown that the age determination using isotopic activity ratios is very sensitive to some decay parameters like decay constants  (Yamba et al. , 2016b). In that work, decay data used come from nuclear databases such as the French NUCLEIDE from Laboratoire National Henry Becquerel (LNHB -CEA) (LNHB, 2017), the US Evaluated Nuclear Data structure File (ENSDF) from National Nuclear Data Center  (NNDC, 2017). The nuclear database NUCLEIDE has updated some CTBT-relevant radioxenon decay data Within the framework of the DDEP (Decay Data Evaluation Project) project  (Galan, 2017; Galan et al. , 2018). When IMS facilities of CTBTO detect a CTBT-relevant radionuclide it is crucial to determine its source characteristics including the time origin of the fission reaction and of the release of the observed radionuclides.
Xenon isotopic activity ratio at zero time One of the most important information in the activity ratio formula that is used to calculate the time of a radionuclide event is the initial isotopic activity ratio (activity ratio at zero time or at the time of release). That initial activity ratio is easily understandable in the nuclear explosion case, by using independent and cumulative fission yields (according to the situation) of the isotope of interest. Values of these fission yields can be found in a nuclear data bank like ENSDF (Evaluated Nuclear Structure Data File). The initial activity ratios that is to be considered for Nuclear Power Plants (NPPs) release and Medical Isotopes Production Facilities (MIPFs) are not fixed values. In considering a nuclear reactor running in steady state during a long time, the equilibrium level can be calculated for each activity ratio of interest.
Measurements made and reported by operators of Nuclear Power Plants on the gaseous effluents are the basis of a statistical study of radioxenon releases from NPPs  (Kalinowski and Tuma, 2009). These data are used to determine the distribution of radioxenon isotopic ratios resulting from the batch mode with a median value, an upper and a lower limit. The same approach was taken with regard to the CTBT-relevant radioxenon isotopes that are determined by Kalinowski/Grosch/Hebel (2014) to be typically released from medical isotopes production facilities. These values are very important in characterizing the source time of a radionuclide-monitoring event because they are based on the real observations. Figure  1  shows the distribution of the xenon isotopic activity ratios measured at the stack of nuclear power plants and expected at medical isotopes production facilities.
Using Fukushima debris data Fukushima nuclear accident occurred in Japan took place over several days following the Great East Japan Earthquake off the Pacific coast of Japan on 11 May 2011. The earthquake generated a huge tsunami and its waves overwhelmed the tsunami barriers of the Fukushima Daiichi nuclear power plant (NPP) site. They flooded the primary and backup power systems and equipment, as well as the ultimate heat sink systems and structures, of all six units on the site. Following the off-site power loss that occurred before the tsunami due to the earthquake damage to the transmission system, the flooding caused also the loss of on-site power sources and on-site power distribution systems. Units 1-5 of the Fukushima Daiichi NPP experienced extended station blackout (SBO) events, which exceeded nine days in Units 1 and 2, and 14 days in Units 3 and 4.
The ages of releases calculated using xenon isotopic activity ratios are divided by the age with to 12 March 2011, 15:30 as the assumed date of the release to get the age precision. Figure  7  show the distribution of age precision calculated using data from Fukushima accident debris, where -except for the ratio of Xe-133/Xe-133m -the NPPs release case gives the closest age precision to 1, unlike the nuclear explosion scenarios or MIPFs release cases which have the age precisions relatively far from 1. It is remarkable that determining the ages of Fukushima debris provides the best precision for the range of NPP releases (between minimum and maximum) without making use of the knowledge of the estimated isotopic activity ratios that were released from the Fukushima Daiichi NPPs. With time progressing, the quality of the age precision deteriorates.
For this purpose, two data sets were used and evaluated ages were proposed according to the kind of radioactivity release: a nuclear explosion and a nuclear reactor accident release, both characterized by a sudden release. The first data set (reported data from Nevada underground nuclear explosions) is studied with reported times of origin for each xenon isotope recorded. The second case of Fukushima debris data is used with the assumption that a known first part of the release is dominating. This assumption is justified by the fact that the first batch release of radionuclides from Fukushima accident that started on 12 Mars 2011, around 15:00 (INTERNATIONAL ATOMIC ENERGY AGENCY, 2015) contained a large part of the cumulative release from all Fukushima Daiichi reactors. The ages of radioactivity releases calculated using xenon isotopic activity ratios are used with known dates of release to evaluate the age precision assuming three kinds of radioactivity sources: nuclear explosion, NPPs and MIPFs.
Discussion & conclusion This work focused on the usability of event zero time determinations using xenon isotopic activity ratios given the real atmospheric background observations. Two data sets from real observations were used to calculate the age precisions of radioxenon release: radioxenon activity data from Nevada test site and radioxenon concentration data from Fukushima accident debris. For the purpose of this investigation, three kinds of radionuclide events are distinguished: the nuclear explosions scenarios (full fractionation and under in-growth conditions), the nuclear power plants (NPPs) releases (NPP-median, NPP-lower limit and NPP-upper limit values), the medical isotopes production facilities (MIPFs) releases (MIPFsmean, MIPFs -min and MIPFs -max values). The radionuclide event screening parameter proposed in this work is the age precision differences.
This parameter is interpreted such that the most probable radionuclide event is the one that has the lowest value. The first data set is from Nevada nuclear test site where United States performed underground nuclear explosions from 15/09/1961 to 23/09/1992. The interpretation of the age precision difference shows that a nuclear explosion under in-growth condition assumption is found to be the most likely source of the observed radionuclide events. This can be considered a validation of this screening approach since it confirms the known kind of the radionuclide event. The second data set is from Fukushima nuclear accident in May 2011 in Japan, where the radioactivity release started by the explosion of the reactor 1 on 12 March 2011, at 15:30. From our study, is it found that the nuclear power plant release gives the most accurate results because the age precision difference values are the lowest for that type of event.