dataesr/scientific-paragraphs-categorization huggingface dataset:
An unrooted phylogenetic tree of those 454 TaGNAC sequences was designed using the maximum-likelihood method available in MEGA-X software  [58] , based on the alignment of the protein sequences using the ClustaW method. Then, the sequences anchored in the clade containing the homeologs of our four TaGNACs were retrieved. On the MEME Suite platform (http://meme-suite. org/tools/meme, accessed on 15 January 2022), we used the MEME tool to search for the presence of conserved motifs among these protein sequences with parameters as follows: motif number = 20, width = 10-150 amino acids. Subsequently, we imported these motifs into the FIMO tool to check for their presence in the 64 orthologous sequences of bread wheat TaGNACs (the results were filtered using a p-value < 1 × 10 -10 ).
We searched for conserved motifs in promoter sequences, using MEME Suite (https: //meme-suite. org, accessed on 26 February 2022) with parameters as follows: motif number = 10, width = 10-150 bp. On these motifs, the TOMTOM analysis of MEME Suite was performed using the default parameters  [60] , the PBM motifs database from Arabidopsis thaliana  [61]  and an e-value < 10. An unrooted phylogenetic tree was designed using the maximum-likelihood method available in MEGA-X software, based on the alignment of the GNAC protein sequences using the ClustalW method. The presence of the NAC domain (IPR003441, PS51005 and PF02365) on the protein sequences of TaGNAC, TmGNAC and 13 other cereal species was verified by InterProScan and the presence of intracellular localization signals was studied using the Phobius software.
OneArgo is a major expansion of the Argo program, which has provided two decades of transformative physical data for the upper 2 km of the global ocean. The present Argo array will be expanded in three ways: (1) Global Core: the existing upper ocean measurements will be extended to high latitudes and marginal seas and with enhanced coverage in the tropics and western boundaries of the major ocean basins; (2) Deep: deep ocean measurements will be obtained for the 50% of the global oceans that are below 2,000-m depth; and (3) Biogeochemical: dissolved oxygen, pH, nitrate, chlorophyll, optical backscatter, and irradiance data will be collected to investigate biogeochemical variability of the upper ocean and the processes by which these cycles respond to a changing climate. The technology and infrastructure necessary for this expansion is now being developed through large-scale regional pilots to further refine the floats and sensors and to demonstrate the utility of these measurements.
Introduction W hen it was originally conceived in 1998, the goal of the Argo program was to provide profiles of temperature and salinity over the upper 2 km of the ice-free global ocean at a nominal spacing of 300 km on a 10-day interval to match the observations of ocean sea level obtained from Jason satellite altimeters  (Argo Steering Team, 1998). The Argo program is now a major component of both the Global Ocean Observing System and the Global Climate Observing System. Roemmich et al. (2019)  proposed at OceanObs '19 to expand the original Argo Program to OneArgo. OneArgo will (a) improve Argo's global coverage and value beyond the original design; (b) extend Argo to span the full ocean depth; (c) add biogeochemical (BGC) sensors for improved understanding of oceanic cycles of carbon and nutrients, and ecosystems; and (d) consider experimental sensors that might be included in the future.
Progress toward full implementation of OneArgo has begun but will take 5-10 years to complete and necessitate a tripling of funding. The Argo program established a revolutionary new framework and infrastructure for collecting, managing, and freely sharing in situ oceanographic measurements on a global scale for use in operational, climate assessment, and research communities  (Johnson et al. , 2022). Intergovern-mental Oceanographic Commission (IOC) resolution XX-6 set up a process to notify Coastal States to protect their rights when Argo floats drifted into their territorial waters. Another resolution, IOC Resolution IOC/EC-LI, was adopted in 2018 to extend these regulations to BGC Argo floats and their six BGC parameters. OneArgo will build on Argo to provide data expanded geographically, both horizontally and vertically, with new multidisciplinary parameters while maintaining the rights of coastal states.
In collaboration with commercial vendors, OneArgo will develop and improve upon the existing Argo float design by expanding their lifetimes to maintain the global array, incorporating higher accuracy conductivity/ temperature/depth (CTD) sensors to sample the deep ocean (4,000-6,000 m for Deep Argo), and adding new BGC and optical sensors to collect BGC data. The data system has already been developed to assemble and disseminate the expanded data files, and procedures to quality control and calibrate the new sensors are being refined and evaluated. The goal of OneArgo is to integrate the Deep and BGC floats seamlessly, avoiding degrading the spatial and temporal coverage that the present Core array delivers for realtime, seasonal, and climate analyses. While the work to implement OneArgo is well underway, there is still a large resourcing challenge to meet and many developments that must be completed before it will provide the global data set needed for the expanded scientific objectives proposed under the auspices of the UN Ocean Decade.
The Argo Steering Team was established with representation from several agencies and countries and the Argo Data Management Team was set up to de-velop, oversee, and facilitate data mana g e m e n t a n d q u a l i t y c o n t r o l procedures. National and regional Data Assembly Centers were established to receive, decode, and quality control Argo data according to agreed-upon real-time tests and then share it with central repositories (Global Data Acquisition Centers, GDACs) and send it within 12 h onto the Global Telecommunication System data stream to support real-time ocean analyses and forecasting at operational centers. Quality controlled delayed-mode data, produced by Argo data experts following agreed-upon procedures, are available within 12 months for climate research and assessments.
As of 2022, the Argo program has collected nearly 3 million profiles. Uncertainty of the Earth's Energy Imbalance estimate has decreased by a factor of 4 due to Argo  (Johnson et al. , 2016; Wijffels et al. , 2016) , and over 5,000 peer-reviewed papers using Argo data have been published. FIGURE 1 The original array was limited spatially, excluding marginal seas and the polar oceans, and significantly undersampled in dynamically important regions, such as the tropical oceans and western boundary currents. OneArgo proposes to address these limitations (Figure  1b ). A challenge for OneArgo will be to implement the array shown in Figure  1b , while maintaining the spatial and temporal density of Core Argo, which is crucial for real-time forecasts and minimizing uncertainties in climate and seasonal analyses.
Improvements in the buoyancy systems have also increased float lifetimes. New CTDs are being evaluated that have the potential to decrease the CTD energy cost to 10% of that used at present. Methods to detect sea ice have been developed so that floats can operate in polar seas with minimal losses. In addition to float technological advancements, improved quality control procedures are being developed to efficiently identify errors in the data and adjust for any sensor drift. That is, Argo floats and data management procedures have continued to evolve and improve to maintain the Core Argo array as effectively as possible.
To enable measurements within 3 m of the ocean floor without contaminating the CTD and while keeping the float from grounding, a mechanical bottom detection (wire) system has been mounted on the bottom of 6,000-m floats and bottom detection algorithms have been implemented on 4,000-m float models. Procedures to evaluate and quality control the deep ocean data have extended those for Core Argo. The Deep Argo mission team will operate as an international consortium that reports to the Argo Steering Team (https://argo. ucsd. edu/expansion/ deep-argo-mission/). Regional pilot arrays have been deployed in the South Pacific and the North and South Atlantic (Figure  2 ). These arrays have resolved (a) abyssal warming in the Southwest Pacific and Brazil basins  (Johnson et al. , 2019 (Johnson et al.
The observations of complete seasonal cycling of chemistry and biology in the upper 2 km of the ocean are essential to understanding ocean BGC variability and the processes that govern how these cycles respond in a changing climate. Profiling floats with one or more of these BGC sensors have been deployed throughout the global ocean over the past 20 years, and over 300 peer-reviewed publications have used BGC sensor data from BGC-Argo floats (bibliography is maintained at https://biogeochemical-argo. org). Research utilizing BGC-Argo data has revolutionized our understanding of oceanic chemical and biological cycling (summaries available in  Chai et al. , 2020; Claustre et al. , 2020; Johnson, 2017). The success of BGC-Argo measurements and scientific advancements achieved stimulated extensive community planning efforts for a global BGC-Argo array.
Following an international meeting in 2016, a plan for BGC-Argo, "The Scientific rationale, design and Implementation Plan for a Biogeochemical-Argo float array" (Biogeochemical-Argo Planning Group, 2016) was completed. The goal for BGC-Argo is to deploy and then sustain an array of 1,000 floats equipped with the full suite of BGC sensors. These floats must also contribute to Core Argo and will meet the Argo program protocols, including real-time, public data access. The array size requirement was determined from a variety of studies, The background colors indicate ocean bottom depth: < 2,000 m (white), 2,000-3,000 m (light gray), 3,000-4,000 m (light blue), 4,000-5,000 m (blue), and > 5,000 m (dark gray). Data courtesy of OceanOPS. including observing system simulation experiments, correlation length scales for BGC variables, and the subsampling of high-resolution satellite observations.
Conclusions While the original Argo program fundamentally changed our ability to describe the seasonal to interannual variability and rate of ocean change of the upper 2 km of the global ocean, OneArgo will extend this revolutionary set of global observations to higher latitudes and marginal seas with enhanced resolution in specific regions including the tropics and western boundaries currents, provide data over the full water column, and collect BGC data to describe essential biological and geochemical variability and its response to a changing climate. The requisite technology and data management infrastructure has been developed but requires sustained funding support and continued improvements to meet OneArgo objectives. Pilot studies have demonstrated the ability to operate OneArgo's missions, and full global implementation is now underway.
Input data with noise A more stringent test is model discovery from noisy data, as this points to experimental application. A problem, however, is that sparse regression is known to be potentially sensitive to noise, and it is therefore necessary to adapt techniques such as SINDy  [49] [50] [51]. To this end, one approach has been to apply the SINDy algorithm separately to random subsets ("bootstraps") of a given sequence of input data, thus yielding a number of distinct returned "models, " each associated with its own terms and coefficients  50. The statistical analysis of these different coefficients then yields an estimation of mean values and uncertainties of the different contributing terms of the system. In our analysis of noisy FWM data, we use a similar approach. However, instead of analysing bootstrapped samples from a single data series  50  , we consider an ensemble of input data based on scanning over different initial conditions; this would apply more typically to experiments in optics where it is straightforward to measure large data sets  31.
We assume equal initial sideband amplitudes A 1 (0) = A -1 (0) throughout the paper. The FWM relative amplitude and phase data for input to the SINDy algorithm is generated from the numerical integration of the coupled equations in Eq. (1) using standard numerical methods  53, 54. The input X to SINDy consists of vectors η(ξ m ) and φ(ξ m ) sampled at discrete ξ m , and generated for multiple initial conditions to fully sample the phase space on both sides of the separatrix (see the black squares in Fig. 3c ). The integration relative and absolute tolerance were both ∼10 -8. To generate the input datasets with added noise, we first integrated Eq. (1) as above, and then added Gaussian noise to the noise-free data. 22  The choice of the dimensionality of the state vector, the number of sampling points, and the sample spacing in the data set is linked to the physical problem under study.
2(d ) and includes a total of 32 different functions: polynomial functions (up to 3rd order), trigonometric (periodic) functions of both variables, as well as their combinations: �(η, φ) = [1, η, φ, η 2 , ηφ,. , sin η, sin φ, cos η,. , η sin η, η sin φ, φ cos η,. , η 2 sin η, φ 2 sin φ,. ]. Based on data, SINDy will attempt to estimate the contributing dynamical terms that underlie the evolution of the sampled η(ξ m ) and φ(ξ m ). Figure  2d  highlights the "target" terms associated with the ideal FWM system by red squares. Figure  2e  illustrates the algorithm results for noise-free data which are output in terms of a matrix with both zero and non-zero coefficients. Our implementation of SINDy used the publically-available open-source code  24, 55. For the main result of this paper (the Hamiltonian system) computation times on a standard Windows PC with 3.
Funding Academy of Finland  ( 318082 ,  320165 Flagship PREIN, 333949 );  Centre National de la Recherche Scientifique  ( MITI Evènements Rares 2022 );  Agence Nationale de la Recherche  ( ANR-15-IDEX-0003 ,  ANR-17-EURE-0002 ,  ANR-20-CE30-0004 ). We thank  Daniel Brunner  and  Pierre Colman  for stimulating discussions.
, 2019). The experimental sequence is schematically described in Figure  1. Coesite was first synthesized in-situ in the DAC by compressing the sintered quartz-Pt sample to 4. 3 GPa at room temperature and then laser-heating for a few minutes to 1650 K. The progress of the quartz-coesite transformation was monitored by X-ray diffraction and the Dioptas software  (Prescher & Prakapenka, 2015)  to follow the powder diffraction reflections. The laser-heating continued until the vanishing of reflections from quartz in the diffraction pattern confirmed the complete transformation of the sample into coesite. The sample was quenched to room temperature within seconds by turning off the power of the lasers. In the next step, the sample was heated to 1700 K and compressed beyond the theoretical coesite-stishovite transformation pressure of ∼10 GPa  (Ono et al.
, 2020; Shen & Lazor, 1995). MGC analyses were conducted after the quartz-coesite transformation at 4. 3 GPa (MGC4) and after the coesite-stishovite transformation at 14. 7 GPa (MGC5), at ambient temperature with the laser switched off (Figure  1 ). The diamond-anvil cell was rotated over Δω = 54° and diffraction patterns were acquired during every 0. 5° increment of rotation δω, with collection times of 1 s per image. This results in a series of 108 images per MGC analysis. We provide, here, a short description of the MGC data analysis procedure. Details can be found in  Langrand et al. (2017). We used the open-source software package FABLE-3DXRD (https://github. com/FABLE-3DXRD) complemented with our custom scripts from the TIMEleSS-MGC-tools (https://github. com/FABLE-3DXRD/ TIMEleSS). After a thorough quality check of the images and subtraction of the background of each image, we extract a list of the locations of the observed single-grain reflections using the Peaksearch algorithm  (Sørensen et al.
, 2012). In parallel, we compute an average of the image series, from which we obtained the lattice param eters of the relevant phases using the software MAUD  (Lutterotti et al. , 1997 (Lutterotti et al. , , 2014)). The ImageD11 software  (Wright, 2005)  is then used to generate a list of potential G-vectors using the list of observed single-grain reflections and the determined lattice parameters. Each G-vector is assigned to its observed intensity and location in orientation space (2θ, η and ω angles) and potential h, k, and l Miller indices. The assignment of the G-vectors to sample grains is then performed using the GrainSpotter algorithm  (Schmidt, 2014). Lastly, a secondary refinement is performed using FitAllB  (Oddershede et al. , 2010)  to provide a list of better-defined sample grains.
Finally, we use MTEX  (Mainprice, Bachmann, Hielscher, & Schaeben, 2015; Mainprice, Bachmann, Hielscher, Schaeben, & Lloyd, 2015)  in order to fit an orientation distribution function (ODF) to the list of single-grain orientations and plot the results as pole figures. Estimated errors in the grain orientations are typically below 0. 4° in simulated datasets  (Langrand et al. , 2017)  and they are not expected to exceed 1-2° for real data here.
We first extrapolated data for the density and the elastic moduli of isotropic aggregates of coesite, garnet and clinopyroxene to 10 GPa and 1700 K using the finite strain formalism  (Chantel et al. , 2016; Davies & Dziewonski, 1975 ; Table  S1  in Supporting Information S1). In addition, we also account for effects of texture on the elastic properties of stishovite (Figure  1 ). We use the single-crystal elastic tensor C ij of stishovite at 10 GPa and 1700 K based on high-pressure Brillouin scattering data  (Jiang et al. , 2009)  and a temperature correction derived from molecular dynamics simulations of  Yang and Wu (2014). The effect of texture is modeled using our experimental ODF and the MTEX software  (Mainprice, Bachmann, Hielscher, & Schaeben, 2015; Mainprice, Bachmann, Hielscher, Schaeben, & Lloyd, 2015).
A. Systems preparation The protease dimer structure (PDB code: 7L11) was used for all the MD simulations and it was prepared at physiological pH (pH=7). This structure has a higher resolution (1. 80 Å) than the PDB structure (PDB code: 6LU7) used in our previous work  12  (resolution of 2. 16 Å). Both structures are of the holo state in complex with covalent inhibitors, and the rotamers of the key residues at the catalytic site (Cys145, His41, His162, His163, His172) are virtually identical. The protonation states of His residues were assigned based on previous work  38  , where His41 and His80 are protonated at the delta carbon atom and all other His residues are epsilon-protonated, which is favorable for the substrate binding  38. This is different from our previous work where His64 and His80 are protonated at the delta carbon atom and all other histidines are epsilon-protonated.
B. Simulations protocols All-atom simulations were performed using Qubit Pharmaceuticals' Atlas platform which enables the use of any type of High-Performance Computing (HPC) systems including cloud supercomputing infrastructures. Among its possibilities, Atlas has the ability to efficiently handle polarizable force field molecular dynamics simulations using a custom version of the multi-GPU module  27  of the Tinker-HP molecular dynamics package  28, 39  , to perform docking runs using either Autodock-Vina  40  or Autodock-GPU  41  , and to enable machine learning predictions of molecular properties.
B. 2. Molecular Docking protocol The protonation states of the ligands were calculated at a neutral pH and the hydrogen atoms were added using Chimera. Next, we docked the ligands QUB-00006-Int-01(R) and QUB-00006-Int-01(S) into the M pro dimer structure using Autodock Vina 1. 1. 2  40. AutoDock Vina requires pdbqt format for the input files of the receptor and the ligand. Therefore, using the scripts 'prepare_receptor4. py' (v 1. 13) and 'prepare_ligand4. py' (v 1. 10) provided by Autodock Tools  47  , we generated pdbqt files corresponding to the receptor and the ligands, respectively. We set the exhaustiveness search to 100 and the num_mode option to 50. Since molecular docking could suggest reasonable potential binding modes, but does not always rank the most likely binding mode as the best docked pose  14, 48  , we visually inspected the generated docked poses and chose an ensemble of binding poses with different binding orientations that we used to run MD and ABFE calculations in order to explore the binding mode of QUB-00006-Int-01, as described in the Results and discussion section.
To build a water solubility QSPR predictor, AqSolDB dataset  52  was used as a training set. To predict octanol/water partition coefficients (logP), the dataset from EPA's OPERA  53  was used as a training set. Selected datasets were preprocessed and standardized to some extent by authors of the corresponding publications. However, the need for additional processing was identified when doing exploratory data analysis. We discarded compounds with less than two carbon atoms and kept molecules with molecular weight between 50 and 750 daltons. Additional rules of fragments standardization developed at Qubit Pharmaceuticals were applied. Similarity analysis. Tanimoto similarity  54  to the x0195 compound was calculated for each molecule using the MAACS fingerprint from the RDKit Open-Source Cheminformatics Software (https://www.
Each compound at the final concentration of 100 µM was incubated with M pro for 20 minutes at room temperature. After incubation, the peptide substrate (5 µM final) was added to initiate the reaction which was monitored for 50 min at 37°C. The final DMSO amount was 3,75%. Two controls were prepared for each experiment: the peptide substrate in the absence of M pro (0% M pro activity, hence minimal fluorescence intensity detected) and the reaction mixture in the absence of compound (100% M pro activity, therefore maximal fluorescence intensity detected). Following the preliminary screening, the most active compounds (hits) were tested at increasing concentrations (0. 25, 0. 5, 1, 5, 25, 50, 100, 150 µM) to determine the dose-response curves and calculate IC50 values fitted by using GraphPad Prism 5 software.
E. Binding studies by Mass Spectrometry Samples were prepared by mixing appropriate volumes of M pro (10 µM final) with each compound in the reaction buffer (20 mM Tris-HCl, 100 mM NaCl, 1 mM EDTA and 1 mM DTT, pH 7. 3). The final mixtures contained 1:1 or 10:1 of compound:protein molar ratio. Samples were incubated at room temperature for 20 min before analysis. Control experiments were performed on 10 µM solutions of M pro in the absence of compound. Mass spectrometric analyses were carried out in positive ion mode by ESI-MS under denaturing conditions i. e. water/acetonitrile 50:50 with 0. 1% formic acid on a Q-Tof Xevo G2S (Waters, Manchester, UK). Data were processed by using MassLynx V4. 1 software.
Results and Discussion Several diverse fragments binding the viral M pro have been identified by high-throughput crystallographic screening of this protease. Among the screened fragments, x0195 (PDB ID: 5R81 61 -Figure  2A ) shows one of the highest binding affinities  62  and therefore provides a reasonable starting point for fragment-based design of novel M pro inhibitors. The crystal structure shows that x0195 is located within the M pro substrate binding pocket, at the interface of the two subpockets S2 and S4 as described by Cannalire, et al. 63. S4 is a solvent exposed subpocket that is partially composed by a flexible loop delimited by Gln189 and Gln192, while S2 is defined by the side chain residues of Phe140, Asn142, His163, Glu166, and His172, and the backbone atoms of Phe140 and Leu141.