dataesr/scientific-paragraphs-categorization huggingface dataset:
The exposure time and rotation speed were adapted accordingly to collect a full dataset from a crystal of Seikagaku lysozyme plunged in viscous Parabar 10312 (Hampton Research), mounted in a cryoloop and protected from dehydration using the MicroRT room temperature kit from MiTeGen. 720 images were collected with a rotation of 0. 25 • and an exposure time of 2 s per frame. Data were processed with the XDS package  [22].
Structure Determination The structures of PhaCCA and HEWL were refined in PHENIX  [23]  using PDB entries 6QY6 and 6F2I (cleared of solvent molecules and ligands), respectively, as starting models for initial rigid body adjustment. Several rounds of refinement and manual inspection in COOT  [24]  were performed until convergence of R free (calculated using 5% of reflections). The model of PhaCCA was refined using one TLS group and includes 365 residues (the N-terminal expression tag and the flexible loop encompassing residues 83-94 are not visible), two phosphate ions, two acetate ions, and three glycerol molecules present in the mother liquor and the cryoprotection solution. The model of HEWL was refined using anisotropic atomic displacement parameters (ADPs) and includes the full enzyme sequence, one sodium, two chloride ions, a full Tb-Xo4 complex and two additional Tb 3+ positions clearly identified in the anomalous density map but for which the ligand was not observed because of low occupancy.
(A) the DLS monitoring of Sigma lysozyme in the XtalController upon Tb-Xo4 injection (green line) shows same features as in Figure  4A , yet amplified by the higher enzyme concentration. (B) The phenomenon was followed on the same batch in a Nanostar DLS instrument. The top distribution detects almost exclusively the monomeric lysozyme before the injection, whereas populations with a Rh of ~100 nm and >1 µm appear in the bottom distribution already 2. 5 min after Tb-Xo4 injection. Crystallization assays performed with both batches were transferred from the XtalController to a Linbro plate for incubation. (C) Images showing resulting crystals after 9 months before harvesting for data collection. The crystal marked by an arrow was used to collect the dataset presented in Table  1. 4  was applied to Sigma and Seikagaku lysozyme samples at a higher concentration (50 and 71 mg/mL, respectively) to promote the growth of larger crystals.
(A) the DLS monitoring of Sigma lysozyme in the XtalController upon Tb-Xo4 injection (green line) shows same features as in Figure  4A , yet amplified by the higher enzyme concentration. (B) The phenomenon was followed on the same batch in a Nanostar DLS instrument. The top distribution detects almost exclusively the monomeric lysozyme before the injection, whereas populations with a R h of ~100 nm and >1 µm appear in the bottom distribution already 2. 5 min after Tb-Xo4 injection. Crystallization assays performed with both batches were transferred from the XtalController to a Linbro plate for incubation. (C) Images showing resulting crystals after 9 months before harvesting for data collection. The crystal marked by an arrow was used to collect the dataset presented in Table  1.
Characterization of Crystals Grown in the XtalController The quality of PhaCCA and HEWL crystals grown using the XtalController was assessed by X-ray diffraction. Crystals of PhaCCA were analyzed under cryogenic conditions at the FIP-BM30A synchrotron beamline (ESRF, Grenoble, France). Table  1  shows excellent statistics for such a crystal leading to a complete diffraction dataset at 2. 28 Å resolution. The signal extended isotropically up to 1. 8 Å (not shown) as for our best data collected on a much stronger beamline installed on an insertion device (PROXIMA2A, SOLEIL, Saint-Aubin, France). Here, data were truncated because of the presence of two diffraction rings at resolutions of about 1. 9 and 2. 15 Å. They were probably due to suboptimal cryocooling of the solvent and significantly altered the statistics in the high resolution shell (<2.
2 Å). However, the resulting electron density maps are extremely clear and allowed us to observe two phosphate ions (Figure  6A ) of the crystallant bound to the surface of this RNA binding enzyme. Lysozyme crystals were analyzed at room temperature using the X-ray lab source from the INSTRUCT-FRISBI platform at the IGBMC (Illkirch, France). A fast data collection was applied using an EIGER pixel detector to collect complete anomalous data before crystal decay. The Tb 3+ ions gave a strong anomalous signal at 1. 5418 Å (f" of 9 electrons at the Cu Kα wavelength). The dataset collected at 1. 51 Å resolution (Table  1 ) allowed the identification of three anomalous peaks corresponding to Tb 3+ sites, a major site with an occupancy of q = 0. 8 for which the ligand could be built, and a minor site with two alternate positions (Figure  6C ).
Acknowledgments: The authors thank the team of FIP beamline at the  European Synchrotron Radiation Facility  ( ESRF, Grenoble, France ) for beamtime allocation;  Alexandra Bluhm  and  Léna Coudray  for their contribution to crystallization experiments and structure refinement;  Robin Schubert  and  Christian Betzel  ( University of Hamburg ), as well as  Karsten Dierks ,  Arne Mayer  and  Annette Eckhardt  ( Xtal-Concepts GmbH, Hamburg ) for fruitful discussions and exchanges about the XtalController. The authors acknowledge the support and use of the resources of the  French Infrastructure for Integrated Structural Biology (FRISBI ,  ANR-10-INBS-05 ) and of Instruct-ERIC.
Both GA and MLP are already explored in the literature. However, utilization of MLP in the evaluation process of the GA has two advantages: first, it can increase the speed of GA algorithm significantly which is a known bottleneck of the GA algorithm in the literature. The second advantage is that MLP can have very high accuracy in calculation of the natural frequencies if the training data is sufficient. These advantages will be explained in details in the text. To validate the analytical modelling and proposed optimization method of this paper, commercial finite element software COMSOL Multiphysics (COMSOL, Inc. , Burlington, NJ, USA) is used. The performance of optimization method in matching the natural frequency of the structure with the excitation frequency is also assessed. The rest of the paper is organized as follows-in Section 2, vibrational analysis is performed and the piezoelectric voltage due to external excitation is found.
Multi Layer Perceptron Neural Network In this section, neural network fitting algorithm in MATLAB software is utilized for the purpose of finding approximate functions for the natural frequencies based on the optimization variables. The algorithm is based on the multi-layer perceptron (MLP) which is a class of artificial neural network. MLP consists three layers of artificial neurons-input layer, output layer and hidden layers. There is just one input layer and one output layer in the MLP network and the number of neurons in input and output layers are equal to the number of input and output variables. However, there can be different numbers of hidden layers in MLP and the main method to increase the performance of the MLP is to define the adequate number of hidden layers which can be done with the trial and error approach.
2015). Essential variables can also be used to identify large-scale indicators available at high resolution enabling for site-specific assessment. Their identification can allow the harmonisation of monitoring efforts across sites, coordination facilitation, and data collection prioritisation, which in turn benefits decision-making  (Turak et al. 2017). Following an essential variable (EV) framework,  Turak et al. (2017)  identified 22 priority activities for freshwater biodiversity conservation to be tackled by 2020 to allow its global mapping. In our study, we put those recommendations into practice by testing different variables for wetland monitoring. We used harmonised and standardised site-based data (species, climatic, and habitat-extent) at high spatial resolution across the Mediterranean.
Wetland sites We considered 236 wetland sites located within a region defined by the limits of coastal watersheds that drain into the Mediterranean Sea. The wetland site compilation involved the collection of data through different channels, such as the Mediterranean Wetland Outlook (MWO) that identified wetland sites from the north of the Mediterranean Sea, mainly based on national, European, and global databases on protected areas, as well as the national Ramsar authorities that referenced the Maghreb and the Near East sites (Table  S7 ). We classified each wetland site according to the protection coverage, relative to the surface area of a site considered under a protection programme or convention such as Natura 2000, National Park, or Ramsar (i. e. 'high protection coverage' [> 50% of the total surface area of a site], 'low protection coverage' [≤ 50%], and 'no protection coverage' [0%]).
Data processing We identified 15 variables that describe climatic, land cover, and bird species patterns closely related to wetland ecosystem structure. Those variables were partitioned into four EV classes (i. e. species abundance, ecosystem structure, temperature, precipitation  (Pettorelli et al. 2016; Gill et al. 2017) , Table  S6 ) and used to produce indicators (e. g. percentage of wetland change). The variables were selected according to their availability and their accordance with common criteria: (i) cover the wetland sites uniformly, (ii) overlap in time-all variables were representative of two periods  (1990 and 2005) , and (iii) have high spatial resolution capable of describing local processes. To capture the climatic part of wetlands' hydrological cycle, we selected climate and precipitation variables which encompass both average and extreme climatic conditions (e.
g. leading to aridity). The climate variables were downloaded from CHELSA  (Karger et al. 2017 ) such as monthly mean, maximum, and minimum temperature (°C) from which four variables were derived: (1) annual temperature mean (Tmean), (2) temperature seasonality (Tseas), (3) maximum temperature of the warmest month (Tmax), (4) minimum temperature of the coldest month (Tmin), and monthly precipitation (mm) data from which another four variables were processed: (1) annual precipitation (Pmean), (2) precipitation of the wettest month (Pwet), (3) precipitation of the driest month (Pdry), and (4) precipitation seasonality (Pseas). We computed yearly climatic variables using monthly data from 1986 to 1995 as a representation of 1990's climate, and from 2001 to 2010 to represent 2005's climate. We also transformed each climatic and land cover variable to produce indicators that account for changes.
We then transformed the initial km 2 unit of the land cover categories in percentage of cover per site (further used for the partial triadic analysis), and in percentage of change (PoC) per site via the following equation: variable PoC = (data2005 -data1990) / data1990. We considered the percentage of change of natural wetlands representative of the wetland state. For the bird species data (wintering waterbirds only), two variables were produced for each site: Simpson diversity (alpha diversity) and Sorensen beta diversity. Both fit the population abundance EBV  (Kissling et al. 2018 ). The population abundance was obtained from regular surveys occurring between 1991 and 2010. We represented the 1990 period by averaging the data from 1991 to 1995, and the 2005 period by averaging from 2006 to 2010.
International labels like Important Birds Areas (IBA) that are not part of a convention and do not impose any legal constraint were not considered as a form of protection per se if no any other kind of protection overlapped. As labels often overlapped within a site without any clear management objective defined, we considered the total percentage of protection cover per site to implement a protection grade. We considered the percentage of protection covered per site above and below 50% and without protection as three protection levels: highly protected, low protection level, and not protected. The protected areas were designated from 1934 to 2016. Not all protected areas had information about their year of designation (n = 103, 44% of sites). For LULC-derived variables, we used data from 236 wetland sites monitored in 1990 and 2005 at 30 m of spatial resolution and with a minimum mapping unit (MMU) = 1 ha (see the 'Data processing' section for more details).
The used LULC database for the southern and eastern countries of the Mediterranean basin was developed in the framework of the GlobWetland-II project (GW-II 2010-2014, funded by the European Space Agency) and completed by the Mediterranean Wetlands Observatory (Tour du Valat) for the northern shore. All LULC  (for 1990 and 2005)  were derived from Landsat time series (see  Perennou et al. (2018)  and  Beltrame et al. (2015)  for methods). The LULC classes were defined using a hybrid nomenclature that combines both CORINE Land Cover (CLC) and Ramsar definitions (see S1). Out of the 305 sites covered by the GW-II/MWO database, 164 were also monitored for mid-winter waterbird populations between 1991 and 2010 following a specific guideline  (Delany 2005). Concerning climate data, all following calculations were done according to  Karger et al.
This allowed us to assess which variables were changing the most within the Mediterranean basin within each climate or land cover change indicator. We made a site characterisation of the wetlands using the compromise between 1990 and 2005 (Figure  S1 ). We also assessed the conversion of the land cover extent between the 2 years by using the remote sensing software 'GEOclassifier' (see https://www. swosservice. eu/documents_mapping-software/) to produce a conversion matrix (Table  S2 ). We identified the factors that can affect natural wetland habitat loss or waterbird diversity (beta-Sorensen and Simpson diversity) by the use of generalised linear models and beta-regression, respectively. Each climate variable's change (e. g. Tmean diff), land cover variable PoC (e. g. Tmean PoC), climate, and land cover VoC were tested as explanatory variables.
Acknowledgements We would like to acknowledge the work of the thousands of volunteers and professionals involved in waterbird counts, national coordinators from the countries included in the study, and the  Mediterranean Waterbird Network: Wetlands International, Tour du Valat , the  French National Office for Hunting and Wildlife , and national  IWC North African coordinators GREPOM/BirdLife Morocco, Directrion Générale des Forêts (Algeria) ,  AAO/BirdLife Tunisia, Libyan Society for Birds  and the Egyptian Environmental Affairs agency, as well as  Anne Mimet  for her valuable advice for the statistical analysis. Funding information Open Access funding provided by Projekt DEAL. CG and IG were funded by ECOPOTENTIAL, a project funded by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement no.
X-Ray computed tomography was used to measure density. • Results Large differences were observed between components. Basic density of components was little influenced by tree size and stand density. Overall, bark, knot and branch biomasses were highly underestimated by using basic density measured at 1. 30 m. • Conclusion Using available wood density databases mainly based on breast height measurements would lead to important biases (up to more than 40%) on biomass estimates for some tree components. Further work is necessary to complete available databases.
In this study, all images were calibrated by using the method described in  Freyburger et al. (2009)  that was implemented in a specific plug-in called "CalDenQB" for the image analysis software ImageJ  (Schneider et al. 2012). We assumed that this calibration works for bark. The stem discs (A) were scanned twice (green and oven-dried states). The CT images were analysed by using the "sectorization" method described by  Longuetaud et al. (2016)  to compute both D 0 and BD for each disc. The disc sectors including knots, despite the care taken in the field to avoid knots in these samples (A), were removed Fig. 1  Description of disc sampling within the tree (on the left). The stem discs (15 stem discs per tree) are in green. The short-logs containing knots (seven per tree: two below the crown base and five above) are in yellow.
Mean basic density of the tree components On average for all three species, the density values are in the following decreasing order: knots, branch insertions, branches without insertion, bark and knot-free stem wood (Fig. 3  and Appendix B to compare with the values reported in public databases in Appendix D). Except for Ps. menziesii, the mean BD of knots, branch insertions, branches without insertion and bark were all larger than the breast height BD that was of 407, 356 and 453 kg • m -3 for A. alba, P. abies and Ps. menziesii, respectively.
Difference of BD between tree components Our values of BD at breast height were slightly higher than values found in available databases for A. alba, lower for P. abies and similar for Ps. menziesii. The mean knot-free stem BD was lower than the BD at breast height for all the species and the difference was significant for A. alba and Ps. menziesii. This is a commonly found result as in  Cown (1967)  for Ps. menziesii or  Wassenberg et al. (2015)  for various species. And it perfectly confirms the results obtained by  Longuetaud et al. (2017)  on other trees from the same species. Bark, knot and branch components were all found denser than the knot-free stem wood, although it was less pronounced for Douglas fir. This confirms what we have seen in the literature. Miles and Smith (2009) also found bark density higher than stem wood density for several North American Abies species 1 (360 kg • m -3 for stem wood and 490 kg • m -3 for bark in average) and P.
Conclusion Very large differences in BD were observed between the studied tree components (stem wood, bark, knots and branches). These differences should be taken into account to estimate correctly the biomass of each tree component but also the total aboveground biomass of tree in avoiding biases. In particular, the biomass of knots and bark was strongly underestimated when using a BD measured at breast height or provided by available databases. These two tree components are targeted to be used as sources of extractives in the development of an industry based on wood chemistry and the assessment of their availability in terms of biomass at the resource scale must be accurate. A lighter sampling protocol has to be proposed in order to increase the number of trees and environmental contexts.
The last two columns give the percentage of volume and biomass of the component in relation to the total volume/biomass of the tree. The average density of the tree is calculated from the averages of each component, weighted by their relative percentage of volume. that delivered a database for the main French species, TROPIX  (Paradis et al. 2015 ) is a large database constituted by the Centre de coopération internationale en recherche agronomique pour le développement (CIRAD) including many tropical species and that is often used in industry, and the Global Wood Density Database (GWDD)  (Zanne et al. 2009a ) is an international database compiling a huge number of literature references. The values from TROPIX, that were given for a moisture content of 12%, were converted to basic density (using the usual conversion equation given by  Vieilledent et al.
Acknowledgments We would like to thank  Christine Deleuze  ( ONF ) for her help in achieving this study,  Vincent Rousselet ,  Frédéric Bordat ,  Loc Dailly  and  Adrien Contini  for the sampling in the forest and measurements at the laboratory, and  Charline Mola  for the CT scanner acquisitions. We are grateful to  Adeline Motz  and  Daniel Rittié  for the ring width measures. Also, we thank  Chloé Agro ,  Lucie Heim  and  Leopold Bergé  who worked during their Master theses on basic density and volume of knots, and  Anaélle Rajaonarivo ,  Léa Bissieux  and  Bernard Poirot  for the manual segmentation of knots.
Structure The driving motivation for THEMIS is to produce a modular and easily extensible framework for unifying existing and developing future analyses of EHT and auxiliary data. Modularity reduces the practical bars to significant contribution substantially: would-be developers need only understand the relevant elements of the interfaces between modules. In the presence of rapidly evolving data sets and model types, this is critical to ensuring the longevity of prior development efforts. THEMIS consists of five distinct collections of components, each of which is designed to be interchangeable: Data Structures: Management and standardization of observational data throughout THEMIS. These facilitate the rapid introduction of new data products, expand the capability of existing data products, and define the objects for which predictions are ultimately made.
Of practical importance, as seen in Figure  1 , are the various classes of predictions enabled by a particular model class propagated to subsequent child classes (in this case, image-based models, see Section 4. 1); for more details, see Section 4. THEMIS is under version control provided by git with a modern, state-of-the-art branching strategy including master, development, and feature branches. Users are encouraged to generate new code branches, develop, and contribute to the code in the form of a pull request that will be reviewed by the THEMIS core development team. A suite of tests is run regularly via a script in an effort to identify bugs or regressions as early as possible. The script performs these tests and sends a report to the THEMIS core development team. These include short tests using EHT data as well as full-scale parameter estimation validation tests similar to the ones presented in Section 8.
The code is written in C++, making it maximally portable, and has been tested on a variety of systems. THEMIS is designed with minimal dependency on external libraries to avoid installation conflicts; currently, the only required external libraries are FFTW  (Frigo & Johnson 2005)  and the Message Passing Interface (MPI). 93 Up-to-date documentation is critical in a rapid development environment. To meet this challenge, THEMIS has integrated documentation comments which may be optionally rendered via Doxygen 94  to produce a comprehensive, cross-linked HTML and/or PDF document. We now turn to describing each component collection independently.
2009; Mościbrodzka et al. 2009; Pu et al. 2016a; Gold et al. 2017; Bronzwaer et al. 2018; Chan et al. 2018; Mościbrodzka & Gammie 2018; Jeter et al. 2020). Thus, in principle, modeling the brightness distribution offers additional probes of gravity  (Broderick & Loeb 2006a; Johannsen 2013; Broderick et al. 2014; Johannsen et al. 2016; Mizuno et al. 2018 ). Third, it provides direct information about the high-energy astrophysical processes responsible for the growth of black holes and the launching of jets  (Broderick & Loeb 2009b; Dexter et al. 2009; Levinson & Rieger 2011; Mościbrodzka et al. 2014; Broderick & Tchekhovskoy 2015; Hirotani & Pu 2016; Gold et al. 2017). Within THEMIS, two general relativistic ray-tracing and radiative transfer packages are provided. The first of these is the vacuum ray-tracing and radiative transfer package Vacuum Ray Tracing and Radiative Transfer (VRT 2 ).