dataesr/scientific-paragraphs-categorization huggingface dataset:
Traction Force Calculation The stresses the cells transmit to the matrix were calculated using fast Fourier transform (FFT), following Butler et al  [41]. Before calculating the stresses, displacements were filtered using the wiener2 function in MATLAB. Sabass et al. [42]  have shown that such filtering was comparable with using a regularization technique. The FFT method was adapted to the non-uniform Young's modulus of the hydrogel for the specific case where the Young's modulus had no in-depth variations (which is the case here, see Figure  S1 ). In the absence of volumetric external stresses, the displacement field u in a semi infinite elastic medium results from the derivation of the local mechanical equilibrium: ∇ • ∇ u + (1 -2ν)∆ u = 0 (1) Only the Poisson's ratio ν of the material comes in Equation  (1) , not the Young's modulus Y.
Conceptualization, D. G. -D. and A. N. ; methodology, A. M. , C. M. , D. G. -D, H. D. -A. and A. N. ; software, H. D. -A. ; validation, A. N. , D. G. -D. , and H. D. -A. ; formal analysis, N. B. and P. D. ; investigation, A. M. , C. M. , N. B. , P. D. , E. L. -S. , C. T. ; resources, M. C. , E. L. -S. and D. F. ; writing-original draft preparation, A. N. , H. D. -A. and D. G. -D. ; writing-review and editing: A. M. ,C. M. ,C. T. , D. G. -D. , H. D. -A. ; supervision, A. N. ; project administration, A. N. and D. G. -D. ; funding acquisition, A. N. and D. G. -D. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Agence Nationale de la Recherche, grant number ANR-12-JSVE5-0008. A. M. acknowledges the support of CEA Nanoscience and CEA TechnoSanté programs.
Author Contributions: Conceptualization, D. G. -D. and A. N. ; methodology, A. M. , C. M. , D. G. -D, H. D. -A. and A. N. ; software, H. D. -A. ; validation, A. N. , D. G. -D. , and H. D. -A. ; formal analysis, N. B. and P. D. ; investigation, A. M. , C. M. , N. B. , P. D. , E. L. -S. , C. T. ; resources, M. C. , E. L. -S. and D. F. ; writing-original draft preparation, A. N. , H. D. -A. and D. G. -D. ; writing-review and editing: A. M. ,C. M. ,C. T. , D. G. -D. , H. D. -A. ; supervision, A. N. ; project administration, A. N. and D. G. -D. ; funding acquisition, A. N. and D. G. -D. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Agence Nationale de la Recherche, grant number ANR-12-JSVE5-0008. A. M. acknowledges the support of CEA Nanoscience and CEA TechnoSanté programs.
Mantella aurantiaca -The golden mantella frog is distributed in central-Eastern Madagascar (region of Moramanga; Fig. 1 ). We obtained 131 occurrence data from  Piludu et al. (2015). Those included compiled published data from surveys conducted between 2001 and 2007  (Bora et al. , 2008; Randrianavelona et al. , 2010)  and new locations from additional surveys conducted between 2008 and 2013. More recent surveys conducted between 2014 and 2019 enabled to add 39 occurrences. The region has been extensively surveyed and it is very likely that most occupied habitats were identified. After aggregation to match the resolution of environmental data, sample size resulted in 101 occurrence points. Photo (M. aurantiaca): © Frank Vassen Golden Mantelle, Torotorofotsy marshes, Madagascar (https://en.
Distribution modelling We modelled and projected species distributions with the biomod2 R package  (Thuiller et al. 2009 ), using 10 modelling techniques: generalised linear model (GLM), generalised additive model (GAM), classification tree analysis (CTA), artificial neural network (ANN), surface range envelop (SRE, as known as BIOCLIM), flexible discriminant analysis (FDA) and random forest (RF), Multiple Adaptive Regression Splines (MARS), Generalised Boosting Model (GBM) and Maximum Entropy (MaxEnt).
We therefore used a random partitioning for M. aurantiaca and a jackknife for P. inexpectata. The jackknife procedure is appropriate for small sample sizes  (Pearson et al. , 2007; Galante et al. , 2018). This approach consists in running n iterations corresponding to the number of occurrence data, removing one occurrence for each run of calibration. Models are evaluated with the withheld occurrence. We did not used it for M. aurantiaca given that the number of occurrence data was sufficient for a standard procedure, and used an 80% calibration subset (random partitioning, five runs). We assessed model performance using the Area Under the Operative Curve (AUC), the True Skill Statistics (TSS) and the Boyce index (the latter was not computed for the jackknife procedure, because this index is based on Spearman's coefficients and cannot be estimated on the basis of a single location).
Madagascar -Mantella aurantiaca is exclusively found in swampy forested areas  (Randrianavelona et al. , 2010). We filtered our suitability maps to include only cells corresponding to rainforests. We obtained forest cover data for two periods (1990 and 2017) from a study that combined global tree cover loss data with historical national forest (resolution 30 m; Vieilledent et al. , 2018; Fig. S6 ). We used forest cover of the year 1990 for current distribution models because it corresponds approximately to the period of the oldest occurrence data (some areas were deforested since then). For future projections, we used the latest forest cover data available, i. e. 2017.
, 2019) , as assumed in  Fieldsend et al. (2021). This might be the case for both species, especially P. inexpectata since the island is smaller and mostly occupied by a closely related species (P. borbonica;  Dubos et al. , 2021a). The restricted background corresponds to a hypothesis where M. aurantiaca dispersal is limited by current forest cover, and where P. inexpectata is limited to the driest part of Reunion Island. This procedure enabled to encompass the widest range of possibilities with respect to two extreme hypotheses. We included the 19 bioclimatic variables for the selection process, which is not recommended in most cases. However, we reduced the number of variables by removing the intercorrelated ones, and then selected the most biologically meaningful variables. We used the finest resolution available for climate data (i.
It is hard to envision a radical change in the field if institutions (universities, journals, funding bodies) do not commit to research that seeks to systematically explore, generalize, and falsify our models of human cognition by exploring non-English-speaking peoples and societies. While institutional change is slow, there are some mitigating strategies scholars can embrace to move research away from an English-blinkered cognitive science. First, there is a growing number of open databases with information about linguistic diversity, including, for example, lists of vocabularies (Lexibank,  [131] ) and grammatical structures (WALS,  [8] ), as well as their cultural and ecological correlates (D-PLACE,  [132] ) that can be consulted to ground claims of universality and diversity. While not necessarily unbiased representations of the cultural and linguistic diversity around the world  [133, 134] , they are nevertheless valuable resources that cover swathes of human societies that research in psychology and cognitive science typically does not  [2].
Acknowledgments The authors thank  Felix Ameka ,  H. Clark Barrett ,  Vera Kempe ,  Gary Lupyan ,  Tanya Nikitina ,  Cole Robertson ,  Arturs Semenuks ,  Mariya Toneva ,  Jyotsna Vaid ,  Qi Wang , and three anonymous reviewers for comments on an earlier version of this paper, and  Mona Xue  for her assistance on formatting the first draft. D. E. B. acknowledges funding from  Harvard's Data Science Initiative and the Branco Weiss Fellowship. E. A. acknowledges funding from the  French National Research Agency  ( ANR-10-LABX-0083 ). A. M. was supported in part by the  Radcliffe Institute for Advanced Study at Harvard University.
(PL phospholipids, ROS reactive oxygen species, AOP adverse outcome pathways, MIE molecular initiating event, KE key event, AO adverse outcome). protein expression alterations due to toxics exposure  [135]. This generic term refers to the combination of several approaches including transcriptomics, proteomics, metabolomics, and epigenomics. Taking advantage of oligonucleotide microarrays or nextgeneration sequencing, transcriptomics quantitatively detects simultaneous modifications affecting the expression of large sets of genes (i. e. number of mRNA copies). Interestingly, an open-access database has been developed thanks to the FP7-NANOMMUNE European project dedicated to nanotoxicology. This database (NanoMiner) offers a repository for transcriptomics data, this contributing to facilitate data analysis.
In Section 3, we present the details of SALT observations and data analysis. These details for the NOT component of the survey are available in  Krogager et al. (2018). The details of emission line identification, redshift measurement, and AGN classifications based on the SALT-NOT spectra are provided in Section 4. Section 5 presents selected results based on radio continuum and optical emission line properties. For AGN at 1. 9 < z < 3. 5, we also estimate BH masses, accretion rates, and the occurrence of broad-absorption lines (BALs), and revisit the issue of their dependence on radio loudness. We also discuss emission line less AGN with highest optical photometric variability and the targets with no optical counterparts. Finally, in Section 6, we summarize the results and complement the SALT-NOT catalog with AGN from the literature to assemble a target list of ∼650 AGN defining the MALS footprint to search for cold atomic and molecular gas at z < 2.
We thank the anonymous referee for useful comments and suggestions. P. P. J. was supported in part by the  French National Research Agency (ANR)  under contract No. ANR-16-CE310021. P. P. J. thanks  Camille Nóus  ( Laboratoire ORCID  iDs N. Gupta https:/ /orcid. org/ 0000-0001-7547-4241R. Srianand https:/ /orcid. org/ 0000-0002-9062-1921J. -K. Krogager https:/ /orcid. org/ 0000-0002-4912-9388P. Noterdaeme https:/ /orcid. org/ 0000-0002-5777-1629  A. J. Baker https:/ /orcid. org/ 0000-0002-7892-396XF. Combes https:/ /orcid. org/ 0000-0003-2658-7893J. P. U. Fynbo https:/ /orcid. org/ 0000-0002-8149-8298E. Momjian https:/ /orcid. org/ 0000-0003-3168-5922M. Hilton https:/ /orcid. org/ 0000-0002-8490-8117K. Moodley https:/ /orcid. org/ 0000-0001-6606-7142J. Jose https:/ /orcid. org/ 0000-0003-0608-6258K.
The same resolution has been reached by the Mars Express/PFS instrument studying the northern seasonal cap  (Giuranna et al. , 2007). On the other hand, an analysis with even higher spectral resolution (resolving power ∼10 4 ) from space, has not yet been performed. ExoMars Trace Gas Orbiter (TGO)/Nadir and Occultation for MArs Discovery (NOMAD) is a suite of three spectrometers conceived to study the Martian atmosphere at high spectral resolution (Section 2). The first analyses of its data were devoted to methane, other trace gases, and suspended aerosols abundances and climatology  (Aoki et al. , 2019; Gérard et al. , 2020; Korablev et al. , 2019; Liuzzi et al. , 2019 Liuzzi et al. , , 2020;; López-Valverde et al. , 2018; Vandaele et al. , 2019 , to cite a few). In this work we start exploring the NOMAD data set information content in order to assess the feasibility of surface ices studies.
Since the instrumental characteristics are not optimal for surface spectroscopy, we adopt a semi-qualitative approach aimed to assess the sensitivity of the data set to the presence of CO 2 ice exposed on the surface and its discrimination from surface water ice and clouds. Moreover, we discuss the potential of the CO 2 ice 2. 35 μm absorption band for the retrieval of the ice grains size and optical depth. This kind of investigation represents a useful framework for more quantitative and extended retrievals of CO 2 ice properties, which will be discussed in follow-up papers (Section 6). We include data spanning the second half of Martian Year (MY) 34 and the first half of MY35. In particular, MY34 data cover the 2018 global dust storm  (Guzewich et al. , 2019; Kass et al. , 2019; Smith & Guzewich, 2019; Viúdez-Moreiras et al.
Data Calibration and Uncertainties The shape of the raw NOMAD-LNO spectra is strongly modulated by the AOTF spectral transmission curve and by the spectral response of the grating (the so-called Blaze function). Both curves are characterized by strong peaks, whose exact position on the detector is determined differently and changes from one order to the other. The AOTF peak position is tunable through an internal radio frequency generator, whereas the peak width, which defines the nominal spectral range assigned to a given order, is nearly constant (∼22 cm -1 ). On the other hand, while the Blaze function is well approximated by a Gaussian curve, the AOTF transmission curve includes several secondary peaks (side-lobes, i. e. , relative maxima at both sides of the main lobe) and is better modeled as a combination of a sinus-cardinal with a Gaussian  (Liuzzi et al.
It lies in the solar reflected part of the spectrum where the detector sensitivity is higher, thus allowing a higher SNR (70 at max) with respect to longer wavelengths. Moreover, since this band is relatively weak, it is affected to a lesser degree by atmospheric particulate scattering (Section 4. 3). However, the moderate SNR of the LNO nadir data set makes its measurement rather noisy and, therefore, we take advantage of a spectral classification method for describing the overall behavior of this band in the data set (see Section 4. 2). For the above reasons, we focus this investigation of the LNO information content about CO 2 ice on the analysis of the data of orders 169, 189, and 190 (Table  2 ), to which we apply the Minimum Noise Fraction algorithm  (Boardman & Kruse, 1994; Green et al.
Airborne Dust and CO 2 Ice Clouds Reflectance Factor Spectra The reflectance factor of Martian dust layers and CO 2 ice clouds is obtained through the MITRA radiative transfer tool  (Adriani et al. , 2015; D'Aversa et al. , 2022; Oliva et al. , 2016 Oliva et al. , , 2018;; Sindoni et al. , 2017) , already applied to the atmospheres of Saturn, Jupiter and Mars. Dust layers are simulated using the optical constants from  Wolff et al. (2009 Wolff et al. ( , 2010) )  while for CO 2 ice clouds we adopt the refractive index defined by  Hansen (2005) , as in the case of surface ice modeling. Dust layers and ice clouds are described adopting lognormal particle size distributions with 0. 5 effective variances (parameters taken from Mars Climate Database, MCD,  Millour et al. , 2019). Regarding dust opacity, at the wavelengths considered in this work we are more sensitive to abundance than grain size variations and, hence, the latter parameter is assumed constant (1 μm effective radius) in all the simulations.
7 μm band. We define the Ice Index as the ratio between the reflectances measured in orders 190 and 169 (Section 2. 3): Ice Index = 𝑅𝑅190 𝑅𝑅169 (2) This ratio is of course applicable also to other datasets, for example, for OMEGA, that we use here as a benchmark to test its effectiveness in detecting ice. As we can clearly see in Figure  3 , the Ice Index calculated for OMEGA spectra (for OMEGA, Equation 2 reduces to the simple ratio of reflectance factors at 2. 62 and 2. 33 μm) correlates with CO 2 ice abundance, measured through the depth of the 2. 35 μm band (panel c in the figure). This test also confirms the Ice Index to be sensitive to H 2 O ice, measured through the depth of the 1. 5 μm band (panel b in the same figure). As expected, the Ice Index has a value of about 1 on non-icy terrains while it becomes larger for stronger band depths.
However, this approach cannot be extrapolated to the NOMAD data set, since the differences in data acquisition and processing affect the threshold values. With NOMAD data we are interested in longitudinally averaging the Ice Index, in order to perform a seasonal study of the ice coverage. To achieve this, we group the reflectance factors taken at the central wavelengths of the selected orders (R 169 and R 190 , taken at 2. 62 and 2. 33 μm respectively) in bins of latitude and Ls, by averaging the data at all available longitudes. As a result, we obtain a latitudinal-seasonal map for each order. As mentioned in Section 2. 3, a different longitudinal coverage pertains to orders 169 and 190, making their direct ratio biased by spatial albedo inhomogeneities. In order to mitigate this issue we resort to MGS/TES data on Mars bolometric albedo  (Christensen et al.
, 2001). By keeping track of the longitudes falling in each bin of the two orders' seasonal maps, we average TES data the same way as NOMAD ones, obtaining a corresponding map for each order. We can then use these maps as weighting factors to correct for the different spatial coverage of the two orders. We define the zonally averaged index <Ice Index> that is sensitive to the presence of ices, namely: <Ice Index> = <190> <169> • TES<169> TES<190> (3) where R <X> and TES <X> are the zonally averaged NOMAD and TES seasonal maps respectively, and <X> denotes the average along the longitudes inside the same latitude/L s bin of order X. The ratio of the reflectance in the two orders, weighted by TES albedo data, correlates with the amount of ice in the footprint. It is evident how the approach in the computation of the Ice Index with OMEGA and NOMAD datasets is quite different.
Significant differences between the two datasets come from the relatively high LNO noise level, its spectral resolution, the related penetration depth in the ice layers, and the TES data uncertainty. Regarding this last issue, we remind the reader here that TES albedo is bolometric, and therefore carries information related to a wider spectral range with respect to the single wavelengths we are considering with NOMAD data. Moreover, despite the TES map being filtered to minimize the effect of atmospheric dust and clouds, it still contains surface ice  (Christensen et al. , 2001) , increasing the uncertainty in transitional regions. Another difference between OMEGA and NOMAD datasets is that OMEGA spectral resolution is lower (resolving powers of 10 2 and 10 4 for OMEGA and NOMAD respectively).
This implies that the OMEGA steep short wavelength shoulder of the 2. 7 μm band is probably probing different depths of penetration in the ice with respect to NOMAD, due to the wider wavelength range convolved in a single spectral point. All these sources of uncertainty are particularly significant in ice/no-ice transition regions, and, overall, prevent the definition of an absolute threshold value for ice detection. For this reason, we focus on the global distribution of the Ice Index and its qualitative correlation with regions of interest on the planet. As in the case of OMEGA, in the NOMAD data set Ice index values as low as 1 are found in regions where no large concentrations of ices are expected, for example, at the equator and mid-latitudes. On the other hand, an Ice Index larger than 2 is found on the polar caps where both H 2 O and CO 2 ices are known to be abundant (Figure  4a ).
Conclusions and Future Work In this work we explored NOMAD LNO information content of orders 169, 189, and 190 (Table  2 ) demonstrating that, although its main focus is on atmospheric gases, this data set can be exploited for the investigation of surface ice. Taking advantage of orders 169 and 190, partially covering the 2. 7 μm band, we defined an index for the identification of abundant CO 2 and H 2 O ices deposits (Ice Index, Section 4. 1). We produced seasonal maps of the ice coverage that are in general in good agreement with MCD global climate model and with OMEGA data (Section 5), during both the peak and the sublimation phases of the polar caps. A seasonal map of the exposed CO 2 ice was obtained using the 2. 35 μm absorption band, which is well covered by LNO order 189 (Section 4.
35 μm absorption band analysis. This investigation demonstrates how LNO order 189 has large potential to be exploited to retrieve CO 2 ice microphysical properties. Moreover, this sets the basis for an in-depth investigation of the band also in other datasets, like OMEGA and CRISM ones, even if characterized by a lower spectral resolution. The analysis presented in this paper sheds some light on the possibility of conducting surface and aerosol science using a data set that is nominally conceived for the study of Martian trace gases, such that of NOMAD LNO, and opens the way for several follow up studies. An analysis of NOMAD UVIS channel information content, focused again on surface studies, is currently being performed as a continuation for the study presented in this paper. A dedicated quantitative radiative transfer analysis for the retrieval of surface ice microphysical and geometrical properties such as grains size, mass density and layers thickness, is already in progress and is the subject for a future paper.
Acknowledgments ExoMars is a space mission of the  European Space Agency (ESA)  and  Roscosmos. The NOMAD experiment is led by the  Royal Belgian Institute for Space Aeronomy (IASB-BIRA ), assisted by Co-PI teams from  Spain (IAA-CSIC), Italy (INAF-IAPS) , and the  United Kingdom (The Open University ). This project acknowledges funding by the  Belgian Science Policy Office (BELSPO) , with the financial and contractual coordination by the  ESA Prodex Office  ( PEA 4000103401 ,  4000121493 ), by  Spanish Ministry of Science and Innovation (MCIU)  and by European funds under grants  PGC2018-101836-BI00  and  ESP2017-87143-R (MINECO/FEDER) , as well as by  UK Space Agency  through grants  ST/V002295/1 ,  ST/V005332/1 , and  ST/S00145X/1  and  Italian Space Agency  through Grant  2018-2-HH. 0.
This work was supported by the  Belgian Fonds de la Recherche Scientifique-FNRS  under Grant No. 30442502  (ET_HOME). The  IAA/CSIC  team acknowledges financial support from the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ). US investigators were supported by the  National Aeronautics and Space Administration. Canadian investigators were supported by the  Canadian Space Agency. SR thanks  BELSPO  for the FED-tWIN funding (Prf-2019-077-RT-MOLEXO). Open Access Funding provided by  Istituto nazionale di astrofisica  within the  CRUI-CARE Agreement.
Data Availability Statement The OMEGA data used in this study are available from ESA's Planetary Science Archive at https://archives. esac. esa. int/psa/#!Table%20View/OMEGA=instrument. The Ice Index and SAM Similarity Index seasonal maps, the longitude-latitude map of NOMAD LNO order 189 observations in Figure  2 , and LNO order 189 spectra from Figure  5  (upper panel, blue line) and Figure  8  (panel a, black line) are made available through the Mendeley Data Repository at https://data. mendeley. com/datasets/cs72xnsz7k/2 (Oliva, 2022).
Input physics: Red giants We computed stellar evolution models of red giants, starting from the zero-age main sequence (ZAMS), with the Geneva stellar evolution code (GENEC;  Eggenberger et al. 2008). We refer to the mentioned work for details on the input physics and numerical treatment. We briefly explain the most relevant parameters we used to compute the models presented in this work, and in particular for the modelling of AM transport in stellar interiors. Rotation is treated assuming shellular rotation  (Zahn 1992). The equation to follow the evolution of the AM is treated in an advecto-diffusive way (see Eq. (  1 )). In convective zones we assumed solid-body rotation. For radiative zones, the In all of our models we adopt solar metallicity with the solar chemical mixture given by  Asplund et al.