dataesr/scientific-paragraphs-categorization huggingface dataset:
The STAGGER code In this work, we used a standard version of the radiativemagnetohydrodynamic (R-MHD) STAGGER code  (Nordlund & Galsgaard 1995 ; Nordlund et al. 2009 ; Magic et al. 2013 ) , which generates 3D hydrodynamical model atmospheres that allow us to study stellar granulation across the HR diagram. The code solves the equations for the conservation of mass, momentum, and energy, as well as the radiative transfer equation under the assumption of local thermodynamic equilibrium (LTE). This equation is solved along a set of inclined rays specified by the user, which provides the heating and cooling rates needed for the energy equation. To account for the wavelength dependence of the radiative transfer we used the opacity binning method  (Nordlund 1982 ; Skartlien 2000 ; Ludwig & Steffen 2013 ; Collet et al.
2018 )  to generate bins with an averaged opacity strength o v er specific wav elength ranges. The line opacities are taken from  Gustafsson et al. ( 2008 ) , while a sample of continuum absorption and scattering coefficients are adopted from  Hayek et al. ( 2010 ). Furthermore, a realistic treatment of microphysics is included in the code, which accounts for ionization and molecule formation. This is done by using an impro v ed v ersion of the equation of state (EOS) by  Mihalas, Dappen & Hummer ( 1988 )  obtained by  Trampedach et al. ( 2013 ,    Every 3D simulation created with the STAGGER code is defined by three stellar parameters: the entropy at the bottom that defines the ef fecti ve temperature ( T eff ), surface gravity in logarithmic scale ( log g), and metallicity ([Fe/H]). T eff is determined by the entropy -defined by both energy per unit volume and density -at the bottom boundary of every model, while the metallicity is defined by the abundance of chemical elements.
7. Regarding the stellar radii, they did not provide enough information about how the values were determined. Ho we ver, we kno w that those v alues depend on the atmospheric parameters of the 3D models, and the method and underlying physical assumptions used to calculate the radii. If the proper conditions are not chosen, the code might provide an incorrect stellar radius. By comparing the radii values from  Samadi et al. ( 2013 )  and the ones determined in this work with BASTA for the models with similar or equal T eff , log g, and [Fe/H], we found that the values reported by  Samadi et al. ( 2013 )  are o v erestimated, especially for models with log g ≤ 3. 0. F or e xample, for the t40g15m00 models, the radius difference is of 12. 18 R , while for the t50g25m00 models the difference is 2.
2013 ) 1 -we refer to  Lund et al. ( 2017 )  for details on the optimization. One of the three components in equation (  10 ) is included to account for any low-frequency activity signal, while the other two (with the lowest time-scales, τ ) are treated as components related to the granulation. 1 https://github. com/dfm/emcee Based on the background fit we compute from the MCMC chains the distribution from the combined σ signal of the two granulationrelated components, by adding the respective σ i values in quadrature. From the resulting distribution, we determine the median and uncertainties from the limits of the 68 per cent highest probability density interval. The resulting total σ values are provided in Table  4. Similar to the procedure adopted for the 3D simulations, we also compute a distribution for the ef fecti ve e-folding time-scale, τ ACF , of the combination of the two granulation components by sampling from the MCMC chains of the fit.
A3 Effect of grid resolution and box modes The 3D stellar models computed with the STAGGER code have typically the following grid resolutions: low resolution, where every direction has 120 points; and high resolution, where each has 240 points. Furthermore, the simulations also include box modes  (Nordlund & Stein 2001 ) , which are acoustic modes generated by both the stochastic excitation and the work done by the bottom boundary. These modes have, ho we ver, excessi vely large amplitudes, due to the shallow nature of the simulation boxes and, consequently, due to their very low inertia. In order to assess the possible impacts of both the resolution and box modes, four solar models were created as follows: (i) damping of box modes and low resolution (120 3 ); (ii) damping of box modes and high resolution (240 3 ); (iii) no damping of box modes and low resolution (120 3 ); (iv) no damping of box modes and high resolution (240 3 ).
AC K N OW L E D G E M E N T S Funding for the  Stellar Astrophysics Centre  is provided by The  Danish National Research Foundation  (grant agreement no. DNRF106 ). LFRD and VAB-K acknowledge support from the  Independent Research Fund Denmark  (research grant  7027-00096B ) and the  Carlsberg Foundation  (grant agreement  CF19-0649 ). The numerical results presented in this work were obtained at the  Centre for Scientific Computing , Aarhus: http://phys. au. dk /forsk ning/cscaa/. Parts of the calculations have also been performed using the OCA/SIGAMM mesocentre. This research was undertaken with the assistance of resources provided at the  NCI National Facility  systems at the  Australian National University  through the  National Computational Merit Allocation Scheme  supported by the  Australian Go v ernment.
The different arthropods groups play key ecological roles in forest ecosystems (e. g. , herbivores, detritivores, predators) and their diversity, taxonomy and ecology are well known in the Azores  (Borges et al. , 2010). Arthropod species were classified into three biogeographical categories according to their colonization status following the Azorean biodiversity checklist  (Borges et al. , 2010). We considered a species as endemic when its distribution is restricted to the Azores, native non-endemic those species that arrived naturally to the archipelago but are also present elsewhere, and as introduced species those that have been introduced (accidentally or deliberately) by humans in the Azores. The complete dataset is available in GBIF  (Borges and Lamelas-López, 2022)  and described in detail in  Borges et al.
, 2014). We calculated the Hill numbers with the iNEXT function in the R package iNEXT  (Hsieh et al. , 2016). We used linear mixed-effects models to test the effect of habitat and sampling period (month) on arthropods abundance and diversity. Analysis of Variance (ANOVA) was used to assess significance of habitat effect followed by a pairwise comparison Tukey test to evaluate the difference between habitat pairs. Habitat was set as a fixed factor and the sites inside habitats as random factors in the ANOVA model. We also used generalized linear mixed models (GLMMs) to assess the effects of sampling period (month), habitat and their interaction on species abundance and diversity indices. We used function lme in R package nlme for linear mixed-effect models; functions anova and glht in R package multcomp respectively for ANOVA and for Tukey tests; function glmmTMB in glmmTMB R package  (Brooks et al.
Edge effect on species composition Models derived from partial dbRDAs (i. e. those with sampling year and sampling month partialled out) were highly significant (p ≤ 0. 001) when all species were considered in the dataset as well as when biogeographical categories were analyzed separately. Similarly, the effect of habitat was also highly significant in all cases (Table  A. 6 ). These models explained a lower amount of variance and their AICs varied amongst the four groups (Table  1 ). The model with all species had the best R 2 explaining up to 7% of the variance and the model with native non-endemic species explained only 2% of the total variance (Table  1 ). Arthropod assemblages were well-separated between the three habitats when all species and only endemics were considered (Figure  7A-B ), but the similarity of species composition between habitats was high for native non-endemic species and even higher for introduced species (Figure  7C-D ).
However, this strategy by definition involves fragmentation and therefore it is not suitable to conserve native and endemic forest species. In order to prevent the impoverishment of native arthropod assemblages, stakeholders should rather focus on minimizing these impacts and increase forest patch size and connectivity. Funding: N. T. and M. T. F. are supported by Secretaria Regional do Ambiente e Alterações Climáticas-LIFE-BETTLES (LIFE18 NAT_PT_000864) (2020). M. B. was supported by FCT -DL57/2016/CP1375/CT0001. Field work funded by Portuguese FCT-NETBIOME -ISLANDBIODIV grant  0003/2011 (between 2012 and 2015). The manuscript was also partly financed by FCT-Fundação para aCiência e a Tecnologia, within the project UID/BIA/00329/2013-2020. The Natural Park of Terceira (Azores) provided the necessary authorization for sampling.
0885, 1. 0965, 1. 1040, and 1. 1145 g/ml, respectively) and a denser fraction in or below the densest Percoll layer (with a density ≥ 1. 122 g/ml; Figure  2A ). The five fractions were harvested from the gradient using a Pasteur pipette, from top to bottom, and then processed for storage: three consecutive washing steps (two in PBS and a third one in SAGM) followed by final resuspension in SAGM. The RBC density fractions were stored as described above. The relative content in RBCs for each density layer was estimated from pictures of the centrifugation tubes after sorting (Figure  2A ). The images were acquired with a 1 Mpixels digital color camera and processed using Matlab. Only the red channel of the RGB images was kept and converted to grayscale; then, the layers were delimitated manually, based on intensity.
Data Analysis For each sample (L2 and L4 density layers from blood samples) RBCs were tracked at typically 3 to 5 shear rate values between 1 and 20 s -1. In healthy and SCD blood samples, at least 14 RBCs were tracked in each layer while only 12 RBCs were tracked in L2/L4 layers of the Sickle Cell-β-thalassemia blood sample. For a given shear rate, the median value within a sample was calculated from orbital angles measured for all tracked RBCs that were still in the flip-flopping regime. As the number of RBCs still in the flip-flopping regime gradually decreased when the shear rate increased, we arbitrarily defined a minimum threshold of 4 RBCs per shear rate per sample to calculate the ϕ median value. The ϕ-standard deviation per sample was also calculated at the shear rate of 0. 04 Pa (between 4 and 18 flip-flopping RBCs per sample).
, 1983; Embury et al. , 1984; Fabry et al. , 1984; Mohandas et al. , 1989; Kumar et al. , 2014). The RBC distribution of the HbβS sample displayed no L5 layer and spread toward lighter fractions. Consistently, the MCHC measured on the HbβS sample is the lowest among the seven blood samples (see Table  2 ), thus disclosing an Hb deficit within individual Sickle Cell-β-thalassemia RBCs. We estimated the mean density of each blood sample by averaging the percentages of RBCs present in each layer weighted by the average density of the layer. This estimated mean density correlated well with the MCHC measured on each blood sample, as displayed in Supplementary Figure  2  together with data collected from the literature, thus validating our approach to estimate the RBC amount in the density layers.
Microscopy and time-lapse recordings For standard and superresolution confocal microscopy, embryos were mounted on 2% agarose pads in a drop of M9 medium. Standard confocal images were acquired with a SP8 confocal microscope (Leica) equipped with a HC Plan-Apo 63×, 1. 4 NA objective and the LAS AF software. Z-stacks were acquired with 400 nm steps. Superresolution images were acquired with a LSM 880 microscope (Zeiss), equipped with a Plan-Apo 63×, 1. 4 NA objective and the Zen Black software. The Airy Scan module was used to obtain superresolution images. Z-stacks were acquired with 180 nm steps (optimal step size). For lineage experiments, adult hermaphrodites were dissected in M9 medium. Embryos were then transferred to a 4 µL drop of M9 medium on a 35 mm glass bottom dish (poly-D-lysine coated, 14 mm microwell, No 1.
Image analysis and quantifications Images were assembled for illustration using the Fiji and Inkscape 1. 1 softwares. All embryos are oriented with the anterior pole to the left. Images obtained with the Airy Scan module were processed to enhance resolution with the ZenBlack software. Max intensity z-projections, orthogonal views and 3D reconstructions were obtained with Fiji. In lineage experiments, there are very strong differences in signal intensities between the end-1p::GFP::H2B and elt-2p::NLS::GFP::LacZ reporters, with the elt-2 signal being much stronger than the end-1 signal. The signal intensities on images from lineage movies were thus adjusted to enable proper visualization of intestinal nuclei at all the embryonic stages observed. TEM micrographs were analyzed using Fiji. Microvilli length was measured from the tip to the point where their base intersected with the apical pole.
Statistical analysis Statistical analysis and graphic representations were performed with the Graphpad Prism 8. 0. 1 software. Details of the statistical tests used are indicated in each figure legend. ) V This study Fig. 1A  KK1248  par-6(it319[par-6::gfp] ) I Kemphues lab, via CGC Fig. 1B  FL249  par-6(it319[par-6::gfp] ) I; par-4(it47) V This study Fig. 1B  FL626  ) V.
Acknowledgments We thank  M. Boxem ,  B. Grant ,  M. Soto  and the  Caenorhabitis Genetics Center  (funded by the  National Institute of Health Office of Research Infrastructure Programs ,  P40 OD010440 ) for providing worm strains. We also thank the  Biology of Caenorhabditis elegans Facility  ( Université Lyon 1, UMS3421 ) for generating CRISPR strains. We thank  Anaëlle Raoul  who helped with the characterization of intestinal cell number in par-4 mutants as well as the  Michaux and Pecreaux labs  for helpful discussions. Imaging was performed at the  Microscopy Rennes imaging Center (MRiC Photonics  and  TEM, Biosit, Rennes, France ), a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
Identification by mass spectrometry of storage proteins in P. nigra seeds. The protein assignments and accession numbers were retrieved from the P. trichocarpa database (NCBI, P. trichocarpa (taxid:3694)). All descriptors were obtained from analyses of P. trichocarpa material. % cov. : The percentage of the sequence covered by sequences identified in the included searches. MW [kDa]: Molecular weight of the protein. # AAs: Length of the protein sequence. Abund. : abundance (arbitrary unit).
Acknowledgements We gratefully acknowledge the valuable contributions of  Jérome Armand  and  Jean-Michel Faton  ( National Natural Park "Ramières du Val de Drôme" ),  Michel Chantereau  and  Damien Hémeray  ( National Natural Park "Saint Mesmin" ),  Vincent Lejeune  and  Rémy Gobin , the help in data collection and/or experiments provided by  Christophe Borel  and the  BioForA and GBFor INRAE units , and the valuable assistance of  Marie Pégard  and  Clément Cuello  in statistical analysis and production of macrophotos, respectively. We also gratefully acknowledged the helpful comments provided by  Drs Françoise Corbineau ,  Marie-Anne Lelu-Walter  and  Luc Paques. A preprint version of this article has been peer-reviewed and recommended by  Peer Community In Forest and Wood Sciences  (https://doi.
[13] igure  2  presents the evolution of the number of publications including the keyword "4D print*" in the title (from the Web of Science database on the 5th of September 2022  [14]  ). A total of 593 publications have been found to date, with increasing numbers of papers published every year from 2014. The graph also presents key publications in the domain. The first two key papers defining the term 4D printing were published in 2014 by Tibbits et al. [15]  and by Ge et al. [16]  Bakarich et al. [17]  measured the mechanical properties and the deformation properties of hydrogels and proposed them as an efficient material for 4D printing. This paper paved the way for use of hydrogels in 4D-printed actuators. The latter hydrogel route has been taken by Gladman et al. [18]  who published the most cited study of the field by cellulose-reinforced hydrogelbased actuators.
Image data processing can be used for the measurement of the dimensions in most materials  [56]  systems. Moreover, those image data processing techniques are to be preferred if the material measured is soft, as the contact with the caliper deforms the specimen. Such type of measurements was performed in the case of hydrogels, for instance, by Lv et al. [271]  On the other hand, the caliper could be used for stiffer materials. [58] ables  12  and 13  describe the hygro-expansion measurements performed on materials with the potential to be used as 4D-printed hygromorphs. The moisture expansion of fiber and matrix can be measured separately in the case of fiber reinforced hygromorph composites. Atomic Force Microscopy has been implemented to measure the change of dimensions in cellulose aggregate fibrils with moisture absorption.
For instance, most studies consider squares specimens of material with parallel printing paths. [8, 58, 87, 278] ther studies however consider dog bone specimens,  [277, 279]  or the specific shape of the coupon for which the material is developed for [271]. Finally, a precise evaluation of the range of variation of humidity should be part of a standardization of the measurement techniques. The knowledge of this range of variations would enable a better comparison between datasets present in the open literature. More data are needed to perform the same level of comparison than the one conducted for the stiffness of the materials used in 3D printing. The standardization should be also specific for 3D-printed materials, as the printing path considered affects the expansion obtained along the different directions.
If the validation is satisfying, then it could be used to make predictions. Processing, structural and conditioning parameters were ranked in this study in terms of relative importance. From a design perspective, it is needed in order to select the most appropriate parameters of control and the quickest way to define printing parameters. The gradient boosting algorithm was highlighted as the most accu-rate way to predict the actuation. When basing the machine learning algorithm on experimental data, obtaining experimental data is in some complex cases. Su et al. [304]  have proposed a Gaussian-based data augmentation technique to create additional training sets of data. Finally, this research group also proposed a deformation forecasting technique based on three different algorithms: exponential smoothing, long short term memory and echo state network.
In this study, the surface of the wood pieces assembled to form the hygromorph are scanned. From these scans, the wood grain orientations are obtained. In parallel the curvature deformation of the hygromorph is measured while the specimen deforms from 26% to 95% RH. The grain orientation and the curvature values are used as training data for the model. For the structures considered in the present review, a similar methodology could be applied by replacing the grain orientation by the fiber orientations of each 3D-printed layer. One potential axis of development for the use of neural networks in the field of 4D printing consists in using them for more complex actuation types such as twisting or combination of twisting and bending deformations. They also present the potential to be used to program the deformed shape of more complex structures, such as mesh structure.
Optimized Geometry to Printing Parameters The derivation of the printing parameters from optimized geometry of bi-material structures is mostly a straightforward process, because one can use conventional slicers and CAD models (i. e. , Ultimaker Cura, Slic3r, PrusaSlicer). For instance, the g-code of voxel-based models obtained with Rhinoceros3D  [54, 96, 124, 316]  can be obtained from the "voxel2Gcode" github library. [54] Fernandez et al. [351]  proposed an optimization technique to minimize the printing costs. The resin sometimes used to develop multi-materials configurations can lead to the presence of anisotropic properties within the printed filaments (e. i. , PLA or ABS based-filament  [352]  ). The filling patterns of the printed block of materials printed must be therefore carefully chosen.
Crystal structure determination The X-ray diffraction data of 2 were collected using synchrotron radiation (λ = 0. 7380 Å) through the BM16 beamline at the ESRF (Grenoble, France). The data were indexed, integrated and scaled using the HKL2000 program  [8]. All calculations for data reduction, structure solution, and refinement were done by standard procedures (WINGX)  [9]. The structure of 2 was solved by direct methods and refined with the full-matrix least-squares technique on F 2 using the SHELXS-97 and SHELXL-2013 programs  [10]. All the non-hydrogen atoms of the main molecule were refined anisotropically, but some of the oxygen atoms from the solvent water molecules and perchlorate anions were kept isotropic due to severe disorder. The values of the residual electron density after the final refinement are somewhat high, but despite this situation, we preferred to keep the model with the solvent molecules and avoid the use of a squeeze routine.
Photomagnetic measurements Powdered samples of 2 were placed in a sealed Pyrex tube saturated with Ar during the irradiation experiments at room temperature. The magnetic properties of the irradiated samples were immediately measured after different irradiation times. Variable-temperature (2. 0-300 K) magnetic susceptibility measurements were carried out with a SQUID magnetometer. The experimental data were corrected for the diamagnetic contributions of the constituent atoms and the sample holder as well as for the temperature-independent paramagnetism (TIP) of the copper(II) ion (60×10 -6 cm 3 mol -1 ). β ( • ) 102. 52(3) V (Å 3 ) 9469(3) Z 4 ρ calc (g cm -3 ) 1. 541 µ (mm -1 ) 1. 622 T (K) 100. 0(  2.
Acknowledgements This work was supported by the  Spanish MICIU  (Projects  PID2019-109735GB-I00  and  PID2020-115004RB-I00 ) and by the  Gobierno de Canarias  (Project ProID2020010089). R. R. and W. P. B. acknowledge the financial support from the  Generalitat Valenciana  ( GRISOLIAP/ 2017/057 ) and the  Brazilian Agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  for doctoral and post-doctoral grants, respectively. J. P. thanks the  Fundación Caixa and Fundación CajaCanarias  for the Excellence Research Contract at ULL. We also thank  Scheme 3. (a) Spin state structure for a trinuclear Cu II.
At least four fields were exploited for an accurate mean grain size measurement and evaluating the grain growth kinetics. EBSD measurements were performed using a FEG-SEM SUPRA 55 VP field emission gun scanning electron microscope working at 20 kV in the mid-thickness of the RD-ND plane of the samples (RD and ND mean the rolling and normal directions, respectively). A standard clean-up procedure was used to analyze the orientation imaging microscopy (OIM) data  [11, 12, 23, 39]. The scanned areas were 400 × 400 μm 2 with a 1 μm step size for deformed and annealed samples at 250 and 350 °C, respectively and were 200 × 200 μm 2 with a 1 μm step size for annealed samples at 150 °C. In addition, smaller areas of 100 × 100 μm 2 and a step size of 0. 5 μm were scanned to examine twins and shear bands.