dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). For proteins, the curvature has a limited range with θ varying between 80 °and 160 °, whereas c can take nearly any value between -180 °and +180 °. The SSEs correspond statistically to specific areas in the coarse-grained (c, θ) maps. Supplementary Figure  S1  shows the distribution of (c, θ) angles computed by the DSSP algorithm for the 14,652 experimental structures extracted from the ASTRAL database for helix (H + G + I) and β-sheet (E) (in brackets the one-letter SSE codes in DSSP). The areas corresponding to helix and β-sheet identified by DSSP in the (c, θ) map are nicely reproduced by the CUTABI algorithm, as shown in Supplementary Figure  S1 , using an identification of the SSE based on the coordinates of the C α atoms as follows. In CUTABI, the minimal size of a helix is set to 4 residues.
In CUTABI, the minimal size of a β-strand is set to 2 residues, i. e. , a β-sheet cannot be smaller than 4 residues. The β-strands of 1 residue forming β-bridges (B code in DSSP) are thus not considered. To define parallel and anti-parallel β-sheets (Figure  1 ), both local curvature and torsion parameters as well as distances between the two β-strands forming the sheet are necessary. If the four residues k, k + 1, i, and i + 1 pertain to a parallel β-sheet (Figure  1 ), then the angles θ k , θ k+1 , θ i , and θ i+1 must be between 100 °and 155 °and the angles c k and c i must be smaller than -80 °or larger than 80 °. In addition, the distances between k and i and k + 1 and i + 1 (Figure  1 , red arrows) must be smaller than 5. 5 Å, and the distance between k + 1 and i + 2 (Figure  1 , light blue arrows) must be smaller than 6.
As shown in Figure  2  (right panel), 5,739 structures examined (40% of the database) have less than 5% of residues pertaining to an SSE different in CUTABI compared to DSSP. This number is only 3,084 (20% of the database) in P-SEA. In total, assignments of SSE by CUTABI and DSSP agree for 84% of the structures studied (5,739 + 6,628 12,367 structures) with a difference compared to DSSP of maximum 15% of the sequence length. The average of the percentage of agreement over all the structures of the database (14,652) for all SSEs between CUTABI and DSSP is 93% for a computational time at least 10 times smaller.
Performance of As shown in Figure  2 , one observes a large difference of secondary structure assignments between CUTABI and DSSP for a very small number of proteins. Most structures in this category were measured by X-ray diffraction but with a low resolution (>3Å). In addition, as hydrogen atom positions are not detected in the X-ray (except for ultra-high resolution), the application of DSSP to these structures may be less accurate as DSSP is based on the calculation of hydrogen bond energy. To illustrate the precise origin of this finding, we examined the structures and selected two representative structures with 35% of difference in SSE: one for the helix (Figure  3 , left blue panel) and another for the β-sheet (Figure  3 , right red panel). Figure  3  (left panel) represents the fragment (from residue 19 to 98) of a calcium-gated potasium channel (PDB ID: 1lnq and ASTRAL ID: d1lnqa2).
In this example, CUTABI detects a much larger number of residues in the helix than DSSP. However, the 3D representation shows that although these extra residues missed by DSSP are not in a canonical helix, the overall shape of the main chain is indeed helical. Figure  3  (right panel) represents the fragment (from residue 1 to 90) of a glycosyltrehalose trehalohydrolase (PDB ID: 1eh9 and ASTRAL ID: d1eh9a1). The number of residues in the β-sheet is much larger in CUTABI than in DSSP. As in the case of the helix, the 3D representation indicates that the additional residues in the β-sheet in CUTABI are part of a main chain segment with the overall shape of the β-sheet, although not canonical, due probably to the low experimental resolution. The algorithm CUTABI was applied to the α-syn conformations to compute the number of residues in the α-helix (α) and β-sheet (β) of each structure for WT and mutants.
FUNDING The calculations were performed using HPC resources from  DSI-CCuB (Centre de Calcul de l'Université de Bourgogne ). This work is part of the project  NANO-NEURO-MED  ( 2019-2022 ) supported by the  EIPHI Graduate School  (contract  ANR-17-EUR-0002 ), the  Conseil Régional de Bourgogne Franche-Comté , and the  European Union  through the  PO FEDER-FSE  Bourgogne  2014/2020  programs. This work was supported by a grant from the  National Institutes of Health  grant no. R01GM14312.
Until the antenna gain is sufficient to overcome the path loss in EHF bands, the curves show an increasing trend. When propagation properties cause high path loss, the curves start to fall and reach zero on the y-axis when the signal cannot be received. Furthermore, in Fig. 5 , we validate the behavior of the analytical model as per Eq. (  19 ) by comparing achieved results to simulation when using (i) an isotropic antenna with no tapering generated by Matlab antenna array analyzer ('Sim (M)'), and (ii) a linear function of the beamwidth, as proposed in  [37]  ('Sim (L)'). As one may notice from Fig. 5 , the analytical model exhibits a trend close to the simulation results but with a shift toward the right side. This displacement is small for low values of r, whereas it rises when the distance from the NR AP increases, especially for small values of HPBW.
The experimental procedure is described in  Rosener and Géraud (2007). Only the first injection step, allowing the description of the porosity distribution vs the pore threshold diameter is discussed in this study. A Visual Basic script was developed to detect the pore throat diameter where a significant porosity volume is injected. First, the incremental Hg injection volume is calculated for each pressure step. Then the maximum injected volume on a single step is detected. The pore size diameter associated with a significant porosity volume are then defined as where the incremental injection reached more than 40% of the maximum injected pressure at each pressure point. With empirical tests we notice that this procedure allows to separate one, two or three pore throat families in a single injection curve.
Conclusions With the three areas of interest, our study offers a first order overview of the reservoir properties of fault zones in the basement rocks of the WER grabens. As these structures are the targets of several drilling programs, namely with high temperature geothermal prospects, estimations of these properties are essential for pre-drilling resource evaluations. The weathered layer at the top of the basement rock can also be a significant reservoir, therefore samples of this layer are also included to the dataset. The extensive data collection in the three locations studied shows three groups with similar petrophysical property evolution in the FZ despite the high initial petrographic variability. Thus, the granite and granodiorite facies (group I) show mainly mineral alteration in the DZ, with only substantial microcracks in the FC and local fracture corridors.
Background Genetic association tests for continuous or binary phenotypes have uncovered many susceptible genes or variants related to diseases. Various methods and efficient software have been developed and used for continuous and binary traits. For family samples, due to the correlation between relatives and violation of the independence assumption of ordinary linear regression, some alternative approaches were proposed. For example, Therneau and colleagues developed an R package (coxme) implementing linear mixed effects model to evaluate the association between a genetic variant and a continuous trait or survival outcome accounting for correlation present in family samples. Similar extensions to account for familial correlation using mixed effects models have been proposed for gene-based association tests  1.
Ocean biogeochemistry component The biogeochemical module (BIOGEM) represents the transformation and spatial redistribution of biogeochemical tracers  (Ridgwell et al. , 2007). The state variables are inorganic nutrients and organic matter. Organic matter is partitioned into dissolved and particulate organic matter (DOM and POM). The model includes iron (Fe) and phosphate (PO 4 ) as limiting nutrients but similar to  Asselot et al. (2021a) , we do not consider nitrate (NO - 3 ) here. Furthermore, BIOGEM calculates the air-sea CO 2 and O 2 exchange.
The detailed nature of type Ia supernovae (SNe Ia) remains uncertain, and as survey statistics increase, the question of astrophysical systematic uncertainties arises, notably that of the evolution of SN Ia populations. We study the dependence on redshift of the SN Ia SALT2. 4 light-curve stretch, which is a purely intrinsic SN property, to probe its potential redshift drift. The SN stretch has been shown to be strongly correlated with the SN environment, notably with stellar age tracers. We modeled the underlying stretch distribution as a function of redshift, using the evolution of the fraction of young and old SNe Ia as predicted using the SNfactory dataset, and assuming a constant underlying stretch distribution for each age population consisting of Gaussian mixtures. We tested our prediction against published samples that were cut to have marginal magnitude selection effects, so that any observed change is indeed astrophysical and not observational in origin.
g. ,  Neill et al. 2009; Lampeitl et al. 2010; Gupta et al. 2011; D'Andrea et al. 2011; Pan et al. 2014). Following the A+B model and the connection between SN stretch and host galaxy properties,  Howell et al. (2007)  first discussed the potential redshift drift of the SN stretch distribution. In this paper we revisit this question using the most recent SNe Ia datasets. We here step aside from the cosmological analyses to probe the validity of our modeling of the SN population, which we claim is constituted of two age populations  (Rigault et al. 2013 (Rigault et al. , 2015 (Rigault et al. , 2020)) : an older and a younger population, the former having lower light-curve stretches on average and being brighter after standardization. We use the correlation between the SN age as probed by the LsSFR and the SN stretch to model the expected evolution of the underlying SN stretch distribution as a function of redshift.
This modeling relies on three assumptions: (1) There are two distinct populations of SNe Ia, (2) the relative fraction of each of these populations as a function of redshift follows the model presented in  Rigault et al. (2020) , and (3) the underlying distribution of stretch for each age sample is constant. This paper tests this specific model with datasets from the literature. We present the sample we used for this analysis in Section 2. It was derived from the Pantheon catalog  (Scolnic et al. 2018). We discuss the importance of obtaining a "complete" sample, that is, a sample that is representative of the true underlying SNe Ia distribution, and how we built one from the Pantheon sam-ple. We then present our modeling of the distribution of stretch in Section 3 and our results in Section 4.
If the redshift drift is still significant in the conservative sample, it would be even more meaningful in a carefully tailored selection-free sample. These samples are adequate for the goal of this study, which is to develop a first implementation of a model for drift in SN Ia properties. If fruitful, the sample selection can later be refined as required with a more detailed model of the observational selection, for instance, using the SNANA package  (Kessler et al. 2009). The SNLS typically acquired SNe Ia in the redshift range 0. 4 < z < 0. 8; at these redshifts, the rest-frame Bessel B band roughly corresponds to the SNLS i filter, which has a 5σ depth of 24. 8 mag 1. This converts into a z lim = 0. 60, in agreement with  Neill et al. (2006) ,  Perrett et al. (2010), and Bazin et al. (2011).
Fig. 14  of  Perrett et al. (2010,  see their Section 5), however, suggests a lower limit of z lim = 0. 55. We therefore used z = 0. 60 and z = 0. 55 as redshift limits for the fiducial and conservative samples, respectively, for the SNLS. Similarly, PS1 observed SNe Ia in the range 0. 2 < z < 0. 4, their g-band 5σ depth is 23. 1 mag  (Rest et al. 2014) , which yields z lim = 0. 31, in agreement with Fig. 6  of  Scolnic et al. (2018) , for example. If we were to be conservative, this figure would also suggest of a more stringent z lim = 0. 27 cut; we therefore used 0. 31 and 0. 27 for our fiducial and conservative samples, respectively, for PS1. 1 CFHT final release website. Table  1. Composition of the SNe Ia dataset used in this analysis. Conservative cuts are indicated in parentheses. The SNf limit is set by  Rigault et al.
2. 2 ; see  Aldering et al. 2020). For this dataset, spectroscopic screening was performed for candidates with r 19. 5; redshift cuts were then applied when selecting which SN Ia to follow, resulting in a redshift range of 0. 02 < z < 0. 09, further reduced to < 0. 08 in  Rigault et al. (2020)  to extract local host properties. These 114 SNfactory SNe Ia are thus in the volume-limited part of the survey  (Aldering et al. , in prep. ) , and are therefore assumed to be a random sampling of the underlying SN population. The SNfactory sample is particularly useful for studying SN property drift because it enables us to have a large complete SN Ia sample at z < 0. 1. Finally, we include the HST sample from Pantheon  (Strolger et al. 2004 ). These high-redshift SNe are of great interest as they provide the greatest leverage for testing evolution.
While at these redshifts the supernovae typing is challenging, the target classification was robust enough to include them in the cosmological analysis  (Scolnic et al. 2018 ), and we did not impose further cuts. Section 4 highlights that while compatible with it, our results are not dependent on the inclusion of this dataset. We present the stretch distribution and redshift histogram of these five surveys up to their respective z lim in Fig. 3. We observe here that the fraction of low-stretch SNe (typically x 1 < -1) appears to decrease as a function of redshift; this is confirmed in Fig. 6 , in which the evolution of the mean stretch is shown, with the data split in redshift bins of regular sample size. SNe Ia at higher redshift have a larger stretch (0. 34 ± 0. 10 at z ∼ 0. 65) on average than those at lower redshift (-0.
Testing the construction of a volume-limited sample In section 2. 1 we have built volume-limited samples from a set of magnitude-limited ones using simple redshift cuts. This simplified approach is statistically suboptimal, but should suffice to test our key question whether redshift evolution of stretch is compatible with the model of  Rigault et al. (2020). However, the possibility remains that a complex observational selection function related to spectroscopic follow-up efficiencies below our fiducial (or even conservative) redshift cuts might still affect our sample, making it not fully volume limited; this would in turn bias our conclusion about the astrophysical drift of the SNe Ia population. We now examine this possibility. To test for the existence of potential leftover observational selection biases in our sample, we compared the stretch and color distributions of the SNe Ia originating from different datasets having overlapping redshift ranges: these distributions should be similar if they reflect the underlying parent population.
4. We performed a similar analysis for PS1 and SNLS over the redshift range 0. 20 < z < 0. 31 (Fig. 4 , lower panels), where the same conclusion can be drawn: There is no substantial sign of discrepancy in the stretch and color distributions between the low and high end of our fiducial SNLS and PS1 samples, respectively. Nonetheless, the small size of the SNLS dataset at z < 0. 31 (12 SNe Ia vs. 90 for PS1) limits the sensitivity of this test, and only a strong deviation would be noticeable. Extending the redshift range to 0. 20 < z < 0. 40 (although we have no PS1 data above 0. 3) allows increasing the SNLS subsample to 31, but the stretch p-value remains high (34%), showing no sign of inconsistency. We finally highlight that the SNe Ia color is more prone to observational selection effects than stretch, as illustrated in Fig.
Without LsSFR measurements When direct LsSFR measurements are lacking (i. e. , p i y ), we can extend the analysis to non-SNfactory samples by using the redshift evolution of the fraction δ(z) of young SNe Ia (Eq. 1) as a proxy for the probability of a SN to be young. This still corresponds to minimizing Eq. 4 with respect to the parameters θ ≡ (µ 1 , µ 2 , σ 1 , σ 2 , a) of the stretch distribution X 1 (Eq. 2), but this time, assuming y i = δ(z i ) for any given SN i. For the remaining analysis, we therefore minimized Eq. 4 using p i y , the probability for the SN i to be young, when available (i. e. , for SNfactory dataset), and δ(z i ), the expected fraction of young SNe Ia at the SN redshift z i , otherwise. Results of fitting this model to all the 569 (resp. 422) SNe from the fiducial (conservative) sample are given Table  2 , and the predicted redshift evolution of mean stretch (expected x 1 given the distribution of Eq.
Results We adjusted each of the models described above on both the fiducial and conservative samples (cf. Section 2). The results are gathered in Table  3  and are illustrated in Fig. 7. Because the various models have different degrees of freedom, we used the Akaike information criterion (AIC, e. g. ,  Burnham & Anderson 2004)  to compare their ability to properly describe the observations. This estimator penalizes additional degrees of freedom to avoid overfitting the data and is defined as follows: AIC = -2 ln(L) + 2k, ( 5 ) where -2 ln(L) is derived by minimizing Eq. (  4 ), and k is the number of free parameters to be adjusted. The reference model has the smallest AIC; in comparison to this model, the models with ∆AIC < 5 are coined acceptable, those with 5 < ∆AIC < 20 are not favored, and those with ∆AIC > 20 are deemed excluded.
This roughly corresponds to 2, 3, and 5 σ limits for a Gaussian probability distribution. The best model (with the smallest AIC) is the so-called base model and thus is our reference model; this is true for the fiducial and conservative samples. The base model also has the smallest -2 ln(L), making it the most likely model even when the overfitting issue that is accounted for by the AIC formalism is ignored. Furthermore, we find that redshift-independent stretch distributions are all excluded as suitable descriptions of the data rel-   44 ) 95 (  57 ) 94 (  70 ) SNf (  114 ) Reference(  569 ) Conservative (  422 ) Mean stretch Fig. 6. Evolution of the mean SN SALT2. 4 stretch (x 1 ) as a function of redshift. Markers show the stretch plain mean (the error is estimated from the scatter) measured in redshift bins of equal sample size, indicated in light gray at the bottom of each redshift bin.
Full and light markers are used when the fiducial or conservative samples are considered, respectively. The horizontal orange line represents the mean stretch for the nonevolving Gaussian model (last line of Table  3 ) on the fiducial sample. Best fits of our base drifting model are shown as blue, dashed blue, and gray when fitted on the fiducial sample, the conservative sample, or the SNfactory dataset only, respectively; all are compatible. The light blue band illustrates the amplitude of the error (including covariance) of the best-fit model when considering the fiducial dataset. Table  3. Comparison of the relative ability of each model to describe the data. For each considered model, we report whether the model is drifting, its number of free parameters, and for both the fiducial and the conservative cuts, -2 ln(L) (see Eq.
Hence, its ∆AIC < -20, which could be interpreted as a probability p = 2 × 10 -7 of being as good a representation of the data as the base model. We note that when models are compared that were adjusted on individual subsamples rather than globally, the Bayesian information criterion (BIC = -2 ln(L) + k ln(n), with n the number of data points) might be better suited than AIC because it explic-itly accounts for the fact that each subsample is fitted separately: the sample-based model BIC is rightfully the sum of the BIC for each sample. We find ∆BIC = -48, again refuting the samplebased asymmetric Gaussian model as being as pertinent as the base model. In order to ensure that our results are not driven by the incompletely modeled HST subsample, we recomputed ∆AIC for each model excluding this dataset; this did not change ∆AIC by more than few tenths.
Light markers show the results when the analysis is performed on the conservative sample rather than the fiducial one. Color bands illustrate the validity of the models, from acceptable (∆AIC > -5) to excluded (∆AIC < -20), see text. According to the AIC, all nondrifting models (open symbols) are excluded as a poorer representation of the data than the base (drifting) model. sians on the supposedly selection-free part of the samples and the results derived using the BBC formalism supports our approach to constructing a sample with negligible observational selection effects. If we were to use the  Scolnic & Kessler (2016)  and  Scolnic et al. (2018)  best-fit values of the µ 0 , σ ± asymmetric parameters for the SNLS, SDSS and PS1, respectively, the ∆AIC between our base drifting model and the BBC modeling would go even deeper from -32 to -47.
An analysis dedicated to this question will be presented in  Briday et al. in prep. Finally, ignoring the LsSFR measurements, which are only available for the SNfactory dataset (see Section 3), reduces the significance of the results presented in this section, as expected. Even so, nondrifting models remain strongly disfavored. For instance, the best-fitting sample-based Gaussian asymmetric model is still ∆AIC < -10, which is less representative of the data than our base drifting model.
Conclusion We have presented an initial study of the drift of the underlying SNe Ia stretch distribution as a function of redshift. We built effectively volume-limited SN Ia subsamples from the Pantheon dataset  (Scolnic et al. 2018, SDSS, PS1, and SNLS) , to which we added HST and SNfactory data from  Rigault et al. (2020)  for the high-and low-redshift bins. We only considered the SNe that have been discovered in the redshift range of each survey in which observational selection effects are negligible, so that the observed SNe Ia stretches are a random sampling of the true underlying distribution. This resulted in a fiducial sample of 569 SNe Ia (422 SNe when more conservative cuts were applied). Following predictions made in  Rigault et al. (2020) , we introduced a redshift drift model that depends on the expected fraction of young and old SNe Ia as a function of redshift, with each age population having its own underlying stretch distribution.
4. Given the current dataset, we suggest the use of the following stretch population model as a function of redshift: X 1 (z) = δ(z) × N(µ 1 , σ 1 2 ) + (1 -δ(z)) × a × N(µ 1 , σ 1 2 ) + (1 -a) × N(µ 2 , σ 2 2 ) , (2) with a = 0. 51, µ 1 = 0. 37, µ 2 = -1. 22, σ 1 = 0. 61, and σ 2 = 0. 56 (see Table  2 ), and using the age-population drift model, δ(z) = K -1 × (1 + z) -2. 8 + 1 -1 (1) with K = 0. 87. We considered a simple two-population Gaussian mixture modeling. Additional data free from significant Malmquist bias would enable us to refine it as required. We note that samples at the low-and high-redshift ends of the Hubble diagram would be particularly helpful for this drifting analysis; fortunately, this will soon be provided by the Zwicky Transient Facility (lowz,  Bellm et al. 2019; Graham et al.
Acknowledgements. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation program  (grant agreement no  759194 -USNAC ). This work was supported in part by the Director,  Office of Science, Office of High Energy Physics of the U. S. Department of Energy  under Contract No. DE-AC025CH11231. This project is partly financially supported by  Région Rhône-Alpes-Auvergne.