dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge the  W. M. Keck Metabolomics Research Laboratory ( Office of Biotechnology, Iowa State University, Ames, IA ) for providing analytical instrumentation, and we thank  Ann M Perera ,  Lucas J Showman , and  Matthew W Breitzman  for their assistance and support. We thank  Sarah Pottinger  from the  Innes laboratory  for constructing the miniTurbo Gateway entry plasmid pBSDONR P4r-P2:miniTurbo. We also thank  David Wright  and  Amber Testroet  for their assistance with the transgenic soybean lines. Iowa State University  is located on the ancestral territory of the Baxoje, or Ioway Nation; we are grateful to live, work, and play on these lands.
The Global Soil Wetness Project (GSWP; phase 1), a modeling activity of ISLSCP, also formed at the same time, but with a more global, rather than local, focus on LSM assessment E134  (Dirmeyer et al. 1999). A pilot phase of GSWP created a 2-yr global dataset of soil moisture, temperature, runoff, and surface fluxes by integrating uncoupled land surface schemes using externally specified surface forcings from observations and standardized soil and vegetation distributions  (Dirmeyer et al. 1999). A far-reaching modeling initiative of phase I that laid the foundation for developments to come, including those anticipated of the current decade ("From local to global cloud-resolving modeling-A GEWEX legacy" sidebar), was an initiative that developed around the concerted use of higher-resolution models to advance the parameterization of clouds in global models.
g. , NAS 2018). Strategies for sustained monitoring of the essential variables of the Earth system also remains a work in progress. Morel and Readings also identified soil moisture as an important but missing global measurement. This gap was subsequently addressed by both the Soil Moisture and Ocean Salinity (SMOS) mission of ESA  (Kerr et al. 2010 ) and the Soil Moisture Active Passive (SMAP) mission of NASA  (Entekhabi et al. 2010 ) launched in 2009 and 2015, respectively. GEWEX played important roles in these missions forming the International Soil Moisture Working Group in 2005 and later the development of the International Soil Moisture Network  (Dorigo et al. 2011)  funded by ESA to serve as a calibration source for these missions. Phase II-A period of consolidation  Phase II was intended to utilize GEWEX "prediction capabilities, datasets and tools for assessing the consequences of global change," particularly as they relate to water resources and the related applications communities.
These ranged from the evaluation and analysis of the lengthening observational data records with emphasis on uncertainty quantification, assessment of the degree to which water and energy budgets could be "closed" notably on a continental scale, and assessments of models of varying complexity. Evaluation of Earth's energy budget and water cycle datasets. This objective sought to produce consistent research-quality datasets complete with error descriptions of Earth's energy budget and water cycle necessary for understanding the context of variability and trends on interannual to decadal time scales, for use in climate system analysis and for model development and validation. Consequently, the growing emphasis on assessment of data records during this period brought a sharper focus on understanding and quantifying uncertainties attached to the different GEWEX products.
Notable were the cloud assessment of  Stubenrauch et al. (2013)  which continues today in a second phase and the assessment and validation of a 20-plus-yr record of surface radiation balance (SRB;  Zhang et al. 2009). The latter depended heavily on the continued oversight, stewardship, and procedures of the BSRN  (Ohmura et al. 1998 ) that has been a flagship data effort of GEWEX  (Driemel et al. 2018 ). The SeaFlux project was also initiated within the GRP with the aim to produce a high-resolution satellite-based dataset of surface turbulent fluxes over the global oceans to complement existing global surface radiation fluxes and precipitation products  (Curry et al. 2004 ). SeaFlux and the SRB assessment were part of a larger concerted effort that revolved around both addressing gaps and quantifying the errors of individual energy and water cycle components that, from the energy balance perspective, were summarized for the first time in  Stephens et al.
Earth's energy budget Quantifying the various ways energy flows through the Earth system has been a foundational activity of GEWEX from the outset and the latest version of the annual global mean depiction is presented in Fig. SB3  based on the most up-to-date GEWEX data records. A number of sustained GEWEX activities, like the surface radiation budget project, land and ocean heat flux activities, maintenance of the GPCP precipitation climatology precipitation, TOA radiation budget assessments evolved over time with a focus on defining the uncertainties of the energy components of the budget, which are reflected in Fig. SB3. The NASA NEWS project produced a synthesis of a vast amount of these global data and provided, for the first time, a careful and more detailed assessment of the joint uncertainties attached to both global energy and water budgets.
The NAME field experiment highlighted the E141 important processes associated with lower-atmospheric circulations and their modulations via interactions with the land surface, the diurnal cycle, the influence of synoptic conditions, and the important role of atmospheric boundary layer all affecting the onset of the North American monsoon. The second phase of the Global Soil Wetness Project (GSWP-2) produced the first global gridded multimodel land surface analysis  (Dirmeyer et al. 2006 ) developed from multimodel simulations forced by common "hybrid" observational and reanalysis forcing datasets. This forcing included observed precipitation, radiation and near-surface meteorology interpolated using model fields on finer space-time resolutions not available in the observations but required to force the models.
The analysis was presented on a regular 1° × 1° grid and reported for the same 10-yr core period of  ISLSCP (1986-95). Figure  4  is a highlight of this analysis showing a multimodel analysis of the hydrological cycle over global land presenting global land means of the water fluxes and soil water stores (box values), as well as the range of interannual variability of these global values for the 10-yr period. The horizontal black bars and values represent the ranges of these global mean annual hydrological cycle components and are an indicator of model uncertainty. The fact that there existed such wide variability among LSMs driven by the same forcing data suggests there is still much room for improvement in the modeling of this part of the Earth system. Phase III-The quantitative understanding of water and energy coupling (2013-22) Building both upon the results and experience from phases I and II, GEWEX reorganized its panels splitting GMPP into two panels, the GLASS Panel and the GEWEX Atmospheric Systems Study Panel (GASS) and renamed GRP as the GEWEX Data Assessments Panel (GDAP) to reflect more appropriately the activities of that panel.
Abstraction points for each demand will also have to be predicted  (Zhou et al. 2021). GLASS and GHP continue to lead the community in this direction. Changes in extremes: How does a warming world affect climate extremes, especially droughts, floods, and heat waves, and how do land area processes, in particular, contribute? INTENSE was the first major international effort to focus on global subdaily rainfall extremes, enabling progress in quantifying observed historical changes and providing some physical understanding of processes necessary for improved regional prediction of change. It delivered a rain gauge-based data record to study short duration precipitation and its changes. The data have been used in a number of studies, and  Fowler et al. (2021)  summarize the main findings so far as well as provide suggestions for future directions of research.
SB3  are constructed primarily using Earth's energy imbalance as a global constraint. While we have not yet established ways to define constraints more regionally, progress is occurring. Regional constraints on energy budgets over ocean basins, for example, were introduced in the study of  Thomas et al. (2020)  in the form of the additional horizontal transports in oceans derived from reanalyses. Furthermore, our lengthening data records on the TOA balance are also now adding new insights about how these budgets change overtime. With the development of advanced tools to diagnose these changes and link them to correlative properties of the Earth system, we are able to identify those processes that shape these changes (e. g. ,  Loeb et al. 2021; Kramer et al. 2021; Stephens et al. 2022)  hinting at important feedbacks within the Earth system.
Remaining challenges emerging from the phase III era. HydrOlOgy Of HigH terrain. The Intergovernmental Panel on Climate Change Working Group II (IPCC WG II) Report (  2014 ) notes that the changing nature of precipitation, and changes to the degree of snow and ice melt, are altering hydrological systems and affecting water resources both in terms of quantity and quality. Understanding the sensitivity of hydrological processes to the warming being experienced in high-elevation snowy and glacierized headwater catchments is of paramount importance for improving our ability to understand and predict the climate, ecology, and water system changes not only within those regions, but also for large portions of the world population dependent on snowmelt  (Immerzeel et al. 2020). The development of reliable alpine datasets for advancing such understanding combined with developing and testing models continues to define INARCH's goals going forward.
The continuous support from NASA of the International GEWEX Project Office has been foundational to the long-term success of GEWEX, as were the GEWEX data services provided by UCAR in the early stages of the project and support of various GEWEX activities by ESA. The research grants from various funding agencies to support GEWEX scientists over the years has also contributed to the long-term success of GEWEX. We also want to acknowledge the valuable comments and insights of one of the anonymous reviewers of the original manuscript as well as the important contributions of Benoit Meyssignac, who led the updated analysis that produced Fig.
Data availability statement. GEWEX provides the stewardship of many global and regional datasets and data producing networks. The data are publicly available, and an overview of these data is provided at www. gewex. org/panels/gewex-data-and-analysis-panel/gdap-matured-datasets/. Specific links to important datasets that have been maintained over the many years of GEWEX include the following: Matured datasets International Satellite Cloud Climatology Project (ISCCP): https://isccp. giss. nasa. gov www. ncei. noaa. gov/products/international-satellite-cloud-climatology Global Precipitation Climatology Project (GPCP): https://disc. gsfc. nasa. gov/datasets/GPCPDAY_3. 2/summary?keywords=GPCPDAY_3. 2 (latest) www. ncei. noaa. gov/products/global-precipitation-climatology-project (historical) Surface radiation budget (SRB): https://asdc.
Establishment of recommendations The recommendations were based on the data collected (scientific literature review, non-scientific review, interviews with health professionals) on CRCI management during summer 2021 by MD for the iPAAC project  [28]  and on the clinical experience of the authors (GB, ML, JLF, IHL, CC, FJ). The scientific literature search was made on the Web of Science, Pubmed (MEDLINE), University of Montreal, Wiley, and ScienceDirect databases (for the narrative review see  [27] ). The primary focus (articles used to develop the recommendations) was interventions to manage CRCI in adults with non-CNS cancers (e. g. , cognitive training, rehabilitation, physical activity, meditation, yoga, cognitive behavioral therapy). For details on data collection, see iPAAC guide  [28].
The non-scientific review with a search engine (Google) and interviews with stakeholders during the iPAAC project covered patient management more broadly in five topics  [28] : -informing about CRCI; -screening CRCI and evaluating cognitive functioning; -organizing CRCI management (orienting, coordinating, offering interventions); -managing CRCI to allow the return to work; -identifying health professionals to be involved in CRCI management and training them. Recommendations about screening and the evaluation of cognitive difficulties were based on the scientific literature review, complemented by the authors' personal digital libraries on CRCI and data of health professionals' iPAAC interviews. Recommendations on interventions were based on the literature review (updated in 2022) and health professionals' interviews in the iPAAC survey.
Survey development After validation by the ethics committee of the University Hospital of Caen, the software LimeSurvey, provided by the University of Caen Normandy, was used to create the questionnaires. Each recommendation or group of recommendations was preceded by a short state of the art and a short definition of the intervention (see the section 'Establishment of recommendations'). A 9-point Likert scale was used to rate the clinical relevance and feasibility, including "1-Not agree at all", "5-Undecided", and "9-Totally agree". Participants had to specify to what extent they thought the recommendation was clinically relevant (i. e. , useful and necessary for patients) and to what extent the recommendation could be feasible in their institution or another establishment (to be specified in the comment box).
Results AA9-X282s form a distinct clade among other AA9s. In the study by Hage et al. , the AA9-X282s were found among the most up-regulated LPMOs after 3 days of growth of the six different Polyporales on cellulose (28-500-fold change in transcript read counts compared to the maltose control condition), and all clustered with cellulose-responsive genes (Fig. S1 )  22. Interestingly, only a single AA9-X282-encoding gene was identified for most Polyporales. Seeking for hints as to the potential activity of these AA9-X282s, we first performed a phylogenetic analysis to determine their evolutionary distance to other characterized AA9s (Fig. 1 ). To this end, we gathered the AA9 domain only of all the AA9s from the six Polyporales analyzed in Hage et al. 22  (104 sequences) to which we added the 39 sequences of all the hitherto experimentally-characterized AA9s available in the CAZy database, and 13 other AA9-X282 sequences selected to fully cover the 3 different C-terminal modularities (see clade X1 in Fig.
After investigating the phylogenetic relationship of AA9-X282s to other AA9 family members, we carried out a deeper analysis of AA9s containing the X282 extension (174 sequences in the CAZy database). We identified three distinct C-term architectures based on the primary sequences: (i) AA9 harboring only a C-terminal X282 extension [AA9-X282]; (ii) AA9 possessing a CBM1 after the X282 extension [AA9-X282-Linker-CBM1]; and (iii) AA9 holding a conserved motif of 9 residues of unknown function (henceforth called "9res"), directly following the X282 extension [AA9-X282-9res] (Fig. 1A ). Structural predictions (AlphaFold2)  30  of a representative of each modularity are shown in Fig. 1A. A phylogenetic analysis of the catalytic domain of the 174 AA9-X282 sequences available in the CAZy database allowed to identify five clades (Fig.
1B  and Fig. S4 ). This analysis revealed that the three AA9-X282 modularities can be found in every clade and that AA9-X282-CBM1 populates mainly clade A and E and is the most   22. The tree was inferred using IQTree 28 (1000 bootstraps; value displayed on tree as percentages) and visualized with Interactive Tree Of Life (ITOL) software  29. Clades are colored in yellow when populated with AA9s from at least 5 out of the 6 Trametes species studied in Hage et al. 22. Clade X1 contains AA9-X282s from Basidiomycetes and clade X2 contains AA9 orthologs (devoid of X282) found in Ascomycetes. The sequences from Ascomycetes are marked with an orange circle. The repartition of the AA9-X282 modularity is shown with colored triangles (see corresponding homology models of the three types of AA9-X282s at the bottom of panel A).
In the structure predictions (AlphaFold2 30 ), shown as surface, the catalytic domain is colored in light grey, the X282 extension in cyan, the 9res motif in orange, the linker in dark grey and the CBM1 in red. (B) Phylogenetic analysis of 174 AA9-X282 catalytic domains, showing 5 clades (A to E). Each clade is colored according to its most prevalent modularity (> 50% of sequences) using the same color code as in panel A (see Fig. S4  for more details). The tree was generated with IQ-Tree (1000 Bootstraps). www. nature. com/scientificreports/ represented modularity (56% of sequences), followed by the AA9-X282-9res (23%) and AA9-X282 (21%) (see Fig. S4  for more details). The presence and frequent occurrence of the CBM1 modularity is indicative of activity on PCW polysaccharides. The AA9-X282-9res and AA9-X282 modularities mainly cluster in clades B and D, accounting respectively for 77% and 66% of the sequences.
This contrasts with the site finder simulations in a reference ATPase model (PDB id 1XU4), where, as expected, we observed a clear minimum in proximity to the lysine of the Walker A motif when simulated with ATP (Fig. S10C ). However, it may be worth noting that, in the case of NADPH, albeit interaction energies remain low, a cluster with a relatively low distance between PcoAA9-X282-9res and NADPH was observed (Fig. S11C ). It is plausible that the variations observed in the 9res motif of PcoAA9-X282-9res, compared to canonical Walker A and Rossman fold, may alter hypothetical binding to substrates and, in such   47. In the canonical sequence "GXXXXGK(S/T)", the lysine residue is crucial for the coordination of the nucleotide β-and γ-phosphates while the serine/threonine residue ensure the coordination a Mg 2+ -ATP complex required for its hydrolysis  41, 48, 49.
ATP is colored in blue and the Mg 2+ cation is colored in green. (B) PcoAA9-X282-9res structure prediction (AlphaFold2) and zoom-in view of the 9res motif, colored in orange. (C) Zoom-in of the NADPH binding site of a canonical NADPH oxidase crystal structure (PDB id 5FF9)  50. Key amino acids from the P-loop involved in phosphate binding are colored in orange. NADPH is colored in red.
Materials. Most chemicals, including Avicel, lignin-derived compounds and horseradish peroxidase were purchased from Sigma-Aldrich (Saint-Louis, Missouri, United-states). TNP-ATP was purchased from Jena Bioscience (Jena, Germany). Oligo-and polysaccharides substrates were purchased from Megazyme (Wicklow, Ireland). Both α-and β-chitin were purchased from Mahtani Chitosan (Gujarat, India). PASC was prepared as described in Wood  62. Enzymes cloning and production. The proteins were produced using the in-house 3PE platform (Pichia pastoris Protein Express; www. platf orm3pe. com) as described in Haon et al. 63. The nucleotide sequence of the 6 AA9-X282 from Coprinopsis cinerea (CciAA9-X282-CBM1, Uniprot ID: A8NCG7), Irpex lacteus (IlaAA9-X282-CBM1; GenBank: KAI0763186. 1), Pycnoporus coccineus (PcoAA9-X282-9res; GenBank: OSD05161.
1), Trametes ljubarskyi (TljAA9-X282-9res; GenBank: KAI0367364. 1), Trametes elegans (TelAA9-X282-9res; Gen-Bank: KAI0761258. 1) and Schizophyllum commune (ScoAA9-X282; GenBank: KAI4528925. 1. ) were codon optimized for expression in Pichia pastoris. Gene synthesis was performed by Genewiz (South Plainfield, New-Jersey, USA), the region corresponding to the native signal sequence was kept and the genes were inserted into the expression vector pPICZαA (Invitrogen, Cergy-Pontoise, France) in frame with a C-terminal poly-histidine tag, except for PcoAA9-X282-9res NT where the native C-terminal sequence remained unmodified. PmeI-linearized pPICZαA recombinant plasmids were used to transform competent P. pastoris cells by electroporation. Zeocinresistant transformants were then screened for protein production.
Phylogenetic analysis. To build the phylogenetic tree of AA9s, we used 156 AA9 sequences including 39 sequences of all the biochemically and/or structurally characterized AA9s available in the CAZy database at the time of this study, 13 AA9-X282 sequences covering the diversity of their C-terminal modularity and 104 AA9s sequences regrouping all the AA9s from the six Polyporales studied by Hage et al. 22. Of note, the variable C-terminal regions of all AA9 sequences were cut using BioEdit 64 in order to keep the catalytic domain only. The 156 AA9 sequences batches were aligned using MAFFT-DASH (L-INS-i method)  65  , which include structural data input. The resulting multiple sequence alignments were used to infer the phylogenetic trees via the IQ-tree webserver. To infer the tree, the Whelan and Goldman (WAG) amino acid substitution model was selected with a maximum Likelihood optimized (ML-optimized) state frequency.
The new conformation is then accepted or rejected based on the Metropolis criterion  70. Many protocols have been implemented using this algorithm to tackle different problems of biological interest. One of these protocols is Site Finder  71, 72  which performs a global exploration around the entire surface of the protein to find a suitable binding site for a given ligand. This is achieved by creating N copies of the structure, as many as the number of processors, where the ligand is randomly placed around the protein and allowed to run for a predefined number of PELE steps. The conformations with the lowest interaction energies are then refined with PELE by constraining the ligand to a local exploration in order to generate the most optimal binding mode. The models for the simulation were generated with AlphaFold2 or downloaded from the PDB databank and prepared with the Protein Preparation Wizard, Schrödinger, LLC, New York  73.
22. Clade X1 contains AA9-X282s from Basidiomycetes and clade X2 contains AA9 orthologs (devoid of X282) found in Ascomycetes. The sequences from Ascomycetes are marked with an orange circle. The repartition of the AA9-X282 modularity is shown with colored triangles (see corresponding homology models of the three types of AA9-X282s at the bottom of panel A). In the structure predictions (AlphaFold2 30 ), shown as surface, the catalytic domain is colored in light grey, the X282 extension in cyan, the 9res motif in orange, the linker in dark grey and the CBM1 in red. (B) Phylogenetic analysis of 174 AA9-X282 catalytic domains, showing 5 clades (A to E). Each clade is colored according to its most prevalent modularity (> 50% of sequences) using the same color code as in panel A (see Fig. S4for more details).
Acknowledgements We would like to thank the following scientists for very insightful discussions:  Dr. Amel Latifi  ( Aix-Marseille University ),  Dr. Gabriel Paës  ( INRAE, Reims ),  Dr. Solange Morera  ( CNRS, Gif sur Yvette ), and  Prof. Pedro M. Coutinho  ( Aix-Marseille University ). We also thank the  CAZy team (AFMB)  for providing us with AA9-X282 sequences and  Dr. Marie-Noëlle Rosso  for providing us with the transcriptomic data used in this study. Part of the work described was performed using services provided by the 3PE platform, a member of IBISBA-FR (https:// doi. org/ 10. 15454/ 08BX-VJ91 ; www. ibisba. fr), the  French  node of the European research infrastructure,  EU-IBISBA  (www. ibisba. eu).
12 13 n October 3, 2023 by guest. Protected by copyright. http://bmjmedicine. bmj. com/ bmjmed: first published as 10. 1136/bmjmed-2022-000229 on 28 February 2023. Downloaded from Randomised controlled trials to investigate vaccines as a treatment for long covid are no longer logistically feasible in most western countries because most people have already been vaccinated. Therefore, we used data from a large ongoing prospective cohort of patients with long covid to emulate a target trial evaluating the effect of vaccinationon the symptoms and on the impact of long covid.