dataesr/scientific-paragraphs-categorization huggingface dataset:
The barley (Hordeum vulgare L. ) β-D-glucan glucohydrolase HvExoI is the only plant family 3 glycoside hydrolase (GH3) for which the atomic structure is available  [1] [2] [3]  , although the Carbohydrate-Active Enzymes CAZy database  4  lists nearly 48,400 GH3 entries (August 2022) from archaea, bacteria, fungi, and eukaryotes, including economically important plants Triticum, Oryza, Glycine, Zea, Brassica, Medicago, Phaseolus, and Solanum. HvExoI folds into a two-domain structure consisting of an (α/β) 8 barrel (domain 1) and an (α/β) 6 sandwich (domain 2) with a 13 Å-deep active site pocket embracing the subsites -1 and +1, positioned at the interface between the two domains. At the -1 subsite, the pocket contains the catalytic nucleophile Asp285 and the acid/base catalyst Glu491, while the +1 subsite is shaped in a clamp with a coin slot formed by the Trp286 and Trp434 residues that hold β-D-glucosides entering the active site in a productive mode for hydrolysis  [1] [2] [3] 5  (Fig.
From this structure, two more systems representing the product of the hydrolytic reaction of G4OG3OG were modelled. The Glc product at the -1 subsite was removed in both systems and for one system the WT structure was retained, while for the other, Trp434 was mutated to Ala. These three systems were parametrised using the tleap program of AmberTools21 79 using the ff19SB protein force field 80 , Glycam06  76  for saccharides, and the TIP3P force field 77 for water molecules. Amber20 79 was used to minimise and equilibrate all systems using the protocol described above, and applying restraints when necessary until the RMSD values of protein backbones stabilised. Simulations were extended to 150 ns each. QM/MM MD metadynamics simulations of G3OG and G4OG3OG substrates in WT For these studies or conformational Free Energy Landscape (FEL) simulations of Glc moieties of G3OG and G4OG3OG positioned in active sites of HvExoI, we used the CPMD code (parallelised plane wave/pseudopotential implementation of Density Functional Theory, Copyright IBM Corp 1990-2008)  81  patched with the open-source community-developed PLUMED2 library  82, 83.
Analyses of cMD and QM/MM MD metadynamics simulations Data analyses were performed in VMD1. 9. 3 92 using home-made Python3 scripts, the matplotlib library  93  , and the previously described script  94. The open-source, community-developed PLUMED library 83 version 2. 7. 2  82  was used for block analyses. The cpptraj tool from Ambertools21 was used to obtain representative structures of the most populated clusters in simulations. cMD simulations of laminaritriose or laminarihexaose hydrolysis products by WT and the W434H and W434A mutants For each enzyme form, a model was built with bound hydrolysed products from laminaritriose (i. e. Glc at the -1 subsite and G3OG at the +1 and +2 subsites) or laminarihexaose (Glc at the -1 subsite and laminaripentaose at the +1 and +2 subsites and pointing to bulk solvent).
For a comparison, models for the apo and laminaritriose substrate-bound forms of HvExoI were also generated. All models were built based on the crystal structures of WT (in complex with 4NP-G3SG; PDB 6JG2), W434A (in complex with 4NP-G3SG3OG; PDB 6L1J), and W434H (in complex with 4NP-G3SG; PDB 6JGN). The starting structures for cMD simulations were prepared using USFC Chimera  95. Ion, water, GOL, and PEG molecules present in crystal structures were removed and proteins were protonated at pH 7, and with the catalytic acid Glu491 in a neutral form as it corresponds to this step of the catalytic cycle  1, 2. The (1,3)-linked β-Dglucosides (laminaribiose, laminaritriose, and laminaripentaose) used in simulations were generated in the carbohydrate builder GLYCAM, using the legacy version of the software (https://glycam.
For comparison, GaMD simulations of bound laminaritriose and the apo form were also examined using 400 ns production runs. GaMD inputs were generated following the recommendations from the developers (Y. Miao and R. Walker, Gaussian accelerated molecular dynamics tutorial; https:// miaolab. ku. edu/GaMD/lib/GaMD_Amber-Tutorial. pdf). After the simulations were completed, mdcrd files were converted into a dcd format using the Amber Cpptraj tool and visualised in USCF Chimera. All systems were checked for convergence  [100] [101] [102]  by calculating RMSD, allto-all RMSD, and principal component analyses values for each set of simulations with a Python script. GpathFinder simulations to explore Glc displacement routes in WT, and the W434H and W434A HvExoI mutants This method uses the multi-objective genetic algorithm of the Gau-diMM engine  103  , wherein genes are structural operators (e.
Structural images were prepared in PyMOL Molecular Graphics System v2. 3. 3 (Schrödinger LLC, Portland, OR, USA). The summary of 25 structures with thio-analogues and Glc and PDB accession numbers, data collection, and refinement statistics are reported in Supplementary Table 6. The (2 m|F o |-D|F c |) difference electron density maps were calculated using Phenix 67 with the mtz and PDB files after CCP4 REFMAC5 refinements. The maps were contoured at the 1. 0 σ level. Ligands bound in active sites (Figs. 2-4; Supplementary Figs. 2-4) were visualised in PyMol Molecular Graphics System v2. 3. 3.
Acknowledgements Funding was supported by the  Australian Research Council  ( DP120100900 ) to M. H. , and the  Australian Synchrotron  to M. H. and V. A. S. J. G. S. and M. H. thank the  University of Adelaide  and M. H. acknowledges the  Huaiyin Normal University  for support. J. R. K. C. and S. L. acknowledge funding from the  Suranaree University of Technology , the  Synchrotron Light Research Institute , and the  Thailand Research Fund  ( RSA6280073 ). A. N. H. and C. R. acknowledge funding from the  Spanish Ministry of Science, Innovation , through  MICINN/AEI/FEDER ,  UE ,  PID2020-118893GB-100 , the  Spanish Structures of Excellence María de Maeztu  ( MDM-2017-0767 ), and the  CARBOCENTRE  project, which is funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program ERC-2020-SyG-95123.
C. R. and A. N. H. also thank the  Barcelona Supercomputing Center  and  Red Nacional de Supercomputación RES  for computational resources at  Marenostrum IV  and  CTE-Power  for technical support. S. A. -G thanks the  Spanish Ministry of Economy and Competitiveness  for a  Predoctoral Scholarship  ( BES-2012-051782 ). S. P. and S. F. recognise  Glyco@Alps  via  ANR-15-IDEX-02 ,  Carnot Institut PolyNat ,  Labex ARCANE , and CBH-EUR-GS through NR- 17-EURE-0003 , and thank  NanoBio ICMG  ( UAR 2607 ) for providing expertise for mass spectrometry and NMR analyses. J. -D. M. , X. F. L. , and L. M. acknowledge the  Spanish Ministerio de Ciencia, Innovación y Universidades  via the  PRE2019-088412  fellowship, and the  PID-2020-116861GB-I00  and  PGC2018-098592-B-100  grants, and L. M. acknowledges the  Universitat Autònoma de Barcelona Talent Program.
Data availability The data that support this study are available from the corresponding author upon request. The atomic coordinates and structure factors have been deposited in the Protein Data Bank (www. pdb. org) with the following PDB accessions: WT HvExoI, and in complex with 4NP-G3SG, and (G4SG4O) 3 are 6JG2, and 6JG1, respectively. The PDB accession of W286A in complex with G6SG-OMe is 6JG6, and W286F in complex with G2OG-OMe, 4NP-G3SG, and G6SG-OMe are 6JG7, 6JGA, and 6JGB. The PDB accession of W286Y in complex with Glc, and G6SG-OMe are 6JGC and 6JGD. The PDB accession of W434A in complex with Glc, G2SG-OMe, 4NP-G3SG3OG, (G4SG4O) 3 , and G6SG-OMe are 6KUF, 6JGE, 6LIJ, 6LBB, and 6K6V. The PDB accession of W434F in complex with G2SG-OMe, 4NP-G3SG, (G4SG4O) 3 , and G6SG-OMe are 6JGG, 6LC5, 6JGK, and 6LBV.
The PDB accession of W434H in complex with G2SG-OMe, 4NP-G3SG, G4SG-OMe, and G6SG-OMe are 6JGL, 6JGN, 6JGO, and 6JGP. The PDB accession of W434Y in complex with G2sG-OMe, 4NP-G3SG, G4SG-OMe, and G6SG-OMe are 6JGQ, 6JGR, 6JGS, and 6JGT. Source data are provided with this paper.
Structural images were prepared in PyMOL Molecular Graphics System v2. 3. 3 (Schrödinger LLC, Portland, OR, USA). The summary of 25 structures with thio-analogues and Glc and PDB accession numbers, data collection, and refinement statistics are reported in Supplementary Table 6. The (2 m|F o |-D|F c |) difference electron density maps were calculated using Phenix 67 with the mtz and PDB files after CCP4 REFMAC5 refinements. The maps were contoured at the 1. 0 σ level. Ligands bound in active sites (Figs. 2-4; Supplementary Figs. 2-4) were visualised in PyMol Molecular Graphics System v2. 3. 3. cMD simulations of Michaelis complexes derived from thio- ligand complexes.
We consider the DM density distribution in our Galaxy according to the Navarro-Frenk-White profile  [62]  (Fig. 3 top) , where a schematic of the 3 He production from cosmic-ray interaction with the interstellar gas or DM annihilations is also shown. The propagation of charged particles within galaxies is driven by magnetic fields. The propagation is commonly described by a transport equation that includes the following terms: (1) a source function, (2) diffusion, (3) convection, (4) momentum variations due to Coulomb scattering, diffusion and ionization processes, (5) fragmentation, decays and inelastic interactions. This equation, discussed in more details in Methods, can be solved numerically employing several propagation models  [63] [64] [65] [66]. In this work the publicly available GALPROP code  [66]  is employed.
Propagation modelling The possible sources of antinuclei in our Galaxy are either cosmic-ray interactions with nuclei in the interstellar gas or more exotic sources such as DM annihilations or decays. Cosmic rays consist mainly of protons and originate from supernovae remnants, whereas DM has so far escaped direct or indirect detection but its density profile can be modelled  [88]. The propagation in the Galaxy can be carried out using the publicly available propagation models  [63] [64] [65] [66]. We choose the GALPROP code (version 56 available at https://galprop. stanford. edu) for the implementation of 3 He cosmic-ray propagation, which is discussed in details elsewhere  [89]. GALPROP numerically solves a general transport equation for all included particle species  [66]. This transport equation reads as ∂ ψ ∂t = q(r, p) + ∇ • (D xx gradψ -Vψ) + ∂ ∂ p p 2 D pp ∂ ∂ p ψ p 2 - ∂ ∂ p ψ dp dt - p 3 (∇ • V)ψ - ψ τ.
Acknowledgements We thank  A. Strong  for his guidance in the implementation of the antinuclei propagation within the GALPROP code,  A. Shukla  and  P. von Doetinchem  for providing the 3 He production cross sections in cosmic-ray collisions with interstellar medium, and  J. Herms  and  A. Ibarra  for model calculations of the 3 He spectra stemming from DM annihilation. The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A.
2. 14 However, during the model validation phase, we measured the predictive accuracy of our model comparing its outcomes with empirical data and results already reported in the literature. First, we validated the aerosol transmission in our model compared to that proposed in  Gkantonas et al. (2021)  reproducing the same settings in our ABM. Indeed, Figure  3  compares the number of infected individuals after two hours lessons in a room with 20 participants and with only the teacher being infected obtained by our model compared to that reported by  Gkantonas et al. (2021). More specifically, the red line and histogram represent the mean and empirical distribution of such a measure derived by 1000 simulation traces of our model, while the dashed blue line represents the mean computed in  Gkantonas et al.
Acknowledgements DB was supported by a grant from " Ripresa delle attività socio-economiche e delle scuole: modelli per la progettazione e supporto di linee guida per la convivenza con il Covid-19 " (Cod. ROL 73459 , 2020) funded by  CRT foundation  ( PI Matteo Sereno ). SP was supported by a grant from " Creation of a computational framework to model and study West Nile Disease " (Cod. ROL:  67410 ), funded by  CRT foundation  ( PI Marco Beccuti).
(2015). For the initial guess of fertilizer application rate, we used an annual fertilizer application rate for 2013 from  Lu et al. (2017) , which is the most up-to-date gridded data. Only a negligible increase (<4. 0%) was found in total N-fertilizer consumption over western and central Europe from 2013 to 2016 (https://www. ifastat. org/databases/plant-nutrition), with a small shift from 21. 4% to 24. 4% found in the portion of urea which has a relatively high NH 3 emission factor compared to other N fertilizers  (EEA, 2016). This annual fertilizer application rate was further scaled to daily values using day-to-day variation derived from MODIS Enhanced Vegetation Index (EVI) product  (Hudman et al. , 2012; Zhu, Henze, Bash, Jeong, et al. , 2015). To compare with those from uni-di, NH 3 emissions (F emis ) and deposition (F dep ) from bi-di were calculated using Equation 1 and Equation  2 , respectively, following  Zhu, Henze, Bash, Jeong, et al.
To drive uni-di simulations with the same prior monthly emissions from bi-di, we first ran bi-di simulations without optimization, saved the monthly NH 3 emissions, and then used these NH 3 emissions as the prior NH 3 emissions for uni-di simulations. The bi-di NH 3 flux scheme  (Zhu, Henze, Bash, Jeong, et al. , 2015)  is explicitly applied to fertilizer application. We calculated the NH 3 emission potential in fertilized soil using soil pH and soil  NH + 4 concentration. We updated the soil pH from an older version of the World Soil Information dataset used in  Zhu, Henze, Bash, Jeong, et al. (2015)  to a more recent dataset  (Hengl et al. , 2017) , which has been constrained using long-term soil profile measurements  (Batjes et al. , 2020). In contrast, livestock NH 3 emissions are implicitly involved in the bi-di process via their impact on simulated surface NH 3 concentrations and deposition to soil, with the latter serving as a  NH 4 + reservoir for bi-di NH 3 flux  (Zhu, Henze, Bash, Jeong, et al.
Data Availability Statement The surface NH 3 measurements and NH x wet deposition measurements used in this study are available at https://ebas. wp2. nilu. no/data-access/ [Dataset], https://uk-air. defra. gov. uk/ [Dataset], https://zenodo. org/ record/4513855#. YRt41edBphE [Dataset], https://data. rivm. nl/data/luchtmeetnet/ [Dataset], https://man. rivm. nl/ [Dataset], https://www. bafu. admin. ch/bafu/en/home/topics/air/publications-studies/studies. html [Dataset], and https://ebas. wp2. nilu. no/data-access/ [Dataset]. The CrIS CPFR Version 1. 5 ammonia data is publicly available at https://hpfx. collab. science. gc. ca/∼mas001/satellite_ext/cris/snpp/nh3/ [Dataset]. GEOS-Chem adjoint v35m source code is available online (http://wiki. seas. harvard. edu/geos-chem/index. php/GEOS-Chem_Adjoint) [Software].
We developed a set of web-based tools to meet the demand for spatial planning and help to determine the available space suitable for marine aquaculture activity. These tools were derived from AkvaVis concept, which was initially designed for the management of Norwegian aquaculture. The AkvaVis concept was adapted to different national aquaculture contexts and two other tools were developed in France and China. Besides using GIS maps and thematic layers, interactive functions were added to enable the user to select spatial parameters, build indicators for aquaculture siting and instantly display the requested information. For each tool, we describe the main technical features, input data, data geoprocessing, output products, tool strengths and limits, and applicability to other case studies.
The tool uses GIS maps and thematic layers with the addition of an interactive function where choices relating to spatial parameters can be made by the user and the tool instantly displays the requested information. Tool versions for demonstration included a dynamic interface between the user and an integrated system of data compilation, model simulations, coastal zone development plans, regulatory framework analysis and assessments  [ 2 , 3 ]. The AkvaVis concept was adapted and developed for use within four case studies which deal with very different scales of aquaculture, management frameworks and issues related to aquaculture in four different countries  [4]. These cases applied specific methods and technical features to adapt the concept, resulting in the developments of the Spatial Information System for AQUAculture (SISAQUA) in France, the Aquaculture Planning Decision Support System (APDSS) in China, while AkvaVis was readily applied in Norway and Northern-Ireland.
AkvaVis was developed in collaboration between Institute of Marine Research and Hordaland County Council for conceptualizing related to management and providing models and data, and Christian Michelsen Research developing the technical features. Based on the Web Map Service (WMS) technology, AkvaVis implements several functionalities, e. g. , visualization of environmental data (current, wave, depth, etc. ), existing aquaculture facilities, source farm locations and dispersion of parasites (salmon lice), and virtual siting of aquaculture farms. AkvaVis contains two main parts: "client" for interface to attain user requests and displaying results upon requests, and "server" for implementing respective functionalities. AkvaVis client was developed by HTML techniques (JavaScript, CSS) and Adobe Flash (Action script), and the server was developed by Java following the standard MVC (Model, View, Controller) structure ( Fig.
1  ). The main development has been focused on the server part. Different modules are implemented, including WMS (CRS: coordinate reference system, WFS: web feature service, map manipulation), farm module for controlling a virtual farm, aquaculture models for running relevant computational models for different species, and data management module to handle the relevant environmental data. The AkvaVis software package was packed into a WAR file, which is then deployed in Tomcat. Certain functionalities can be accessed via APIs. AkvaVis codes were stored in an internal repository, and advanced developed tools, e. g. , Intellij IDEA Ultimate as IDE, Gradle for project management, Subvision for version control, have been used when developing the tool by a development team. AkvaVis is running on an internal server with irregular checks of running logs.
Input data AkvaVis mainly uses raster data in the Netcdf format. AkvaVis layer presentation is based on locally stored data. It can also receive and display external data as additional layers, given that the external data sources follow the common Web Map Service (WMS) protocol. In principle, the data should follow standardized protocols defined by the Open Geospatial Consortium (OGC), e. g. , WMS for layer visualization, map manipulation, and Web Feature Service (WFS) for data/layer meta information display. In addition, AkvaVis does not produce data, its main functionality is to display data on an electronic map and make decision support. The nature of data integrated to each tool in relation with the different case studies are extensively described in the Appendix 3 of the companion paper  [1]  ( https://ars.
Geoprocessing and output products WMS and WFS techniques were employed to convert the Netcdf data to layers with geographical coordinates, meta information about individual data can also be displayed on the map. AkvaVis generates geographical layers based on the supplied data, and a spatial suitability report is also generated based on the suitability analysis of a chosen location of aquaculture site. Panel screenshots showing output examples for each tool and case study are shown in Figs. 2 to 5 in the companion paper  [1].
Tool benefits and limits A limitation experienced in AkvaVis is episodic instability under high requests, and the background map of AkvaVis is dependent on external WMS services. On the other hand, AkvaVis is a daughter product from a family of software packages under the same WMS module. A common module has been developed for WMS serving, and individual clients can be easily and agilely implemented. AkvaVis has provided a simple user manual.
Reproducibility and application in other cases AkvaVis is developed following the protocol of WMS, and many fundamental modules have been implemented. Therefore, these modules can be easily reused in many other map applications. There are several other tools developed by Christian Michelsen Research that share the common WMS modules, e. g. , a tool for AIS (Automatic Identification System) signal processing. On the other hand, the architecture of AkvaVis tool has been elaborately designed such that the tool can be directly extended to other aquaculture areas. For example, if only the functionality of spatial planning for new aquaculture farms is needed, AkvaVis can directly be used in other areas given that the relevant environmental data is provided. This was also the main reason why the tools SISAQUA and APDSS have heavily adapted the concept of AkvaVis, with application in four different areas, environments, and regulatory frameworks.
Tool technical features SISAQUA is a web-based tool relying on the Spatial Data Infrastructure Sextant (SDI, https://sextant. ifremer. fr/ ). This SDI has been developed by Ifremer over 20 years evolving with technology: norms updating, new standards, and new software. The tool is based on a set of free software to which Sextant also contributes. Main architecture relies on a metadata catalogue describing the data through the Geonetwork software (version 3. 10; Fig. 2  ). The language used is XML. Metadata really constitute the core of the tool and guarantees that all data access services (viewing, downloading, processing or even filters) are associated to metadata. In addition SISAQUA offers a map viewer relying on the Openlayers solution. Data are displayed through Web Map Services (WMS), which are implemented using the software Qgis Server (version 3.
16), Mapserver (version 7. 6) and Thredds (version 4. 6). Data downloading is achieved by direct access (e. g. ftp) or through a Geospatial Data Abstraction (GDAL/OGR) library. This library allows choosing of the format and the projection when downloading the data. An overlayer is added to these softwares to customize the display of the tool. The tool is finally deployed as an Application Programming Interface (API) that can be completely customized. Javascript is the language of this API. SISAQUA is managed with respect to Sextant architecture, which relies on three pillars and specific Ifremer services: administration, development, and maintenance under operational condition. Global funding of Sextant is based on a perennial own funding warranting the durability of the tool as well as the assimilation of the continuous evolution of computer science technology.