dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, TRENDY simulations showed that productivity continued to have stronger control on net CO 2 uptake in the LGS. All data showed that EGS productivity had the strongest influence on LGS respiration, followed by temperature. %P had a negative influence on LGS respiration, suggesting warmer conditions result in higher LGS respiration. Based on the observationallyconstrained data, net CO 2 uptake in the LGS was primarily regulated by respiration, which was mainly controlled by increased labile carbon from enhanced early season productivity and temperature. Therefore, larger increases in productivity as tree cover increases (Fig. S10 ), together with warming, explain the differences in late growing season respiration along the tree cover gradient (Fig. S13 ). However, given the strong influence of temperature on EGS productivity (Fig.
Productivity dataset Direct observations of vegetation productivity do not exist at a circumpolar scale. We therefore used two long-term gridded satellitebased estimates of vegetation productivity, including gross primary production (GPP) derived using a light use efficiency (LUE) approach (LUE GPP, 1982-1985)  21, 66  and satellite observations of Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS NDVI, 1982-1985)  67. LUE GPP (monthly, 0. 5°spatial resolution, 1982-2015) is calculated from satellite observations of NDVI from the Advanced Very High-Resolution Radiometer (AVHRR; 1982 to 2015) combined with meteorological data, using the MOD17 LUE approach. LUE GPP has been extensively validated with a global array of eddy-flux tower sites  [68] [69] [70]  and tends to provide better estimates in ecosystems with greater seasonal variability at high latitudes.
Eddy Covariance (EC) measurements of bottom-up CO 2 fluxes A total of 48 sites with at least three years of data representing the major NHL ecosystems were obtained from the FLUXNET2015 database (Table  S1  and Fig. S1 ). EC measurements provide direct observations of net ecosystem CO 2 exchange (NEE) and estimate the GPP and TER flux components of NEE using other climate variables. Daily GPP and TER were estimated as the mean value from both the nighttime partitioning method  73  and the light response curve method  74. More details on the flux partitioning and gap-filling methods used are provided by  75. Daily fluxes were summed into seasonal and annual values and used to compare with trends from ACIs (Fig. S7 ), to estimate the climate and environmental controls on the CO 2 cycle in the pathway analysis (Fig.
S14 ). CO 2 fluxes in this analysis are defined with respect to the biosphere so that a positive value indicates the biosphere is a net sink of CO 2 absorbed from the atmosphere. The different data products described above use different terminology (e. g. , NEE, NBP) with slightly different meanings; however, they all provide estimates of net land-atmosphere CO 2 exchange  78. Climate, tree cover, permafrost, and soil moisture data Monthly gridded air temperatures at 0. 5-degree spatial resolution from 1980 to 2017 were obtained from the Climate Research Unit (CRU TS v4. 02) at the University of East Anglia  79. Air temperature was summarized at seasonal and annual scales to calculate temperature sensitivities of net CO 2 uptake and to investigate the mechanism underlying the seasonal CO 2 dynamics.
For LGS, we tested the relative influences of Air T, PAR, SM, %TC, %P, and labile carbon carried over from the early growing season (PreGPP) on vegetation photosynthetic activity and respiration, and the resultant influence on the net CO 2 uptake results  4, 10, 26, 52  (Fig. 5 ). SEMs were fit using the sem function of the lavaan package in R  83. The performances of the SEMs were evaluated using a combination of the chi-square statistic (where χ2 ≤ 2 and p > 0. 05 indicate a good fitting model), Bentler's comparative fit index (CFI, where CFI ≈ 1 indicates a good fitting model), and the root mean square error of approximation (RMSEA; where RMSEA ≤ 0. 05 and p > 0. 1 indicate a good fitting model). The standardized regression coefficient can be interpreted as the relative influences of exogenous (independent) variables.
The slope of the regression line was interpreted as the spring temperature sensitivity of the CO 2 cycle. Simple linear regression was used here mainly due to the strong influence of spring temperature on the seasonal and annual CO 2 cycle in NHL ecosystems  30. Temperature sensitivity (γ: g C m -2 day -1 K -1 ) is the change in net CO 2 flux (g C m -2 day -1 ) in response to a 1-degree temperature change. The sensitivity of net CO 2 uptake to warm spring anomalies was calculated for different seasons (EGS, LGS, and annual) and regions (i. e. , permafrost and non-permafrost), and the T-test was used to test for the difference in γ among different regions, seasons, and datasets. Similarly, direct effects of temperature on net CO 2 uptake were calculated using the same season data (Fig. S14 ).
Acknowledgements Z. L. acknowledges support from the  NSFC (41922006) ,  CAS Project for Young Scientists in Basic Research  ( YSBR-037 ), and  Major Program of Institute of Applied Ecology, Chinese Academy of Sciences  ( IAEMP202201 ); B. M. R. acknowledges support from the  National Aeronautics and Space Administration (NASA) Arctic-Boreal Vulnerability Experiment (ABoVE)  and  Carbon Cycle Science programs  ( NNX17AE13G ); J. S. K. acknowledges support from  NASA   Terrestrial Ecology and MEaSUREs programs  ( 80NSSC19M0114 ,  80NSSC18K0980 ); B. M. R. and S. M. N. acknowledge  NASA   ABoVE support  ( NNX15AT81A ); J. D. W. and A. -M. V. acknowledge support from the  Gordon and Betty Moore Foundation. J. D. W. acknowledges support from the  NASA   New Investigator Program  ( NNH17ZDA001N ).
Data availability All data analyzed in this study are publicly available. Carbon tracker is obtained from NOAA Earth System Research Laboratory (https://www. esrl. noaa. gov/gmd/ccgg/carbontracker/), Carbon Tracker Europe from Wageningen University (http://www. carbontracker. eu/), Jena CarboScope is from MPG (http://www. bgc-jena. mpg. de/CarboScope/), and CAMS from ECMWF (http://apps. ecmwf. int/datasets/data/camsghg-inversions/). SMAP L4C data are from the National Snow and Ice Data Center (https://nsidc. org/). TRENDY simulation is obtained from http://dgvm. ceh. ac. uk/index. html. Climate data are from Climate Research Unit (https://crudata. uea. ac. uk/cru/data/hrg/). EC data is from FLUXNET2015 Dataset (https://fluxnet. org/data/fluxnet2015dataset/).
All these three compounds as well as most of those under pre-clinical or clinical evaluation have been discovered using loss/gain-of-function studies, phenotypic screening, or animal models. Computer-aided drug design (CADD) has the potential to guide the rational development of small molecules targeting RNA  [27, 28]. To date, this strategy is hampered by our limited understanding of RNA structural and dynamic properties as well as of the mechanisms of RNA-small molecule (RNA-SM) recognition  [14, 18]. Previous efforts to characterize the physico-chemical properties of RNA binders and their intersection with the chemical space of drugs targeting proteins provided insights into their molecular interaction with RNA  [16, [29] [30] [31]. Databases that collect all the known compounds binding RNA can be exploited for ligand-  [32, 33]  and 2D structure-based  [34] [35] [36]  virtual screening.
DrugPred_RNA is, to the best of our knowledge, the only tool that has been trained and tuned using 3D structure data in order to characterize RNA binding sites  [37]. In addition, most of current CADD pipelines have been developed for protein targets and might not be directly applicable to RNA. A first step in the development of structure-based approaches therefore requires an extensive assessment of the existing tools for pocket detection and ligand docking and possibly the development of new tools that exploit all the available structural information on RNA-SM complexes. While there have been previous efforts to collect such data  [38] [39] [40] [41] , there is currently no comprehensive, curated, and regularly updated repository available to the scientific community. Here, we present <Harnessing RIBOnucleic acid 3 Small molecule Structures= (HARIBOSS), a curated online database of RNA-SM structures.
Each entry in HARIBOSS corresponds to a structure deposited in the RCSB Protein Data Bank (PDB) database  [42]  containing at least one chemical compound matching a list of basic drug-like criteria and interacting with at least one RNA chain. Ligands are annotated by their physico-chemical properties and by the number and composition of interacting RNA molecules. RNA pockets occupied by ligands are characterized in terms of geometric properties, such as volume, hydrophobicity and hydrophilicity, and overall propensity to bind small molecules and druglike compounds. HARIBOSS is freely accessible via a web interface (http://hariboss. pasteur. cloud) and will facilitate understanding the nature of the interactions that drive RNA-SM recognition and benchmarking existing tools for in silico drug design with RNA targets.
Materials and Methods Construction of the database. The HARIBOSS database was built in 3 steps (Fig. 1A ), which were implemented in a series of python scripts. The operations described below are performed on a monthly basis to update HARIBOSS with the new structures deposited in the PDB. I. Initial fetching from the PDB. The first step consisted of querying the PDB and collecting all the structures that contained at least one RNA molecule and one ligand. This operation was performed using a solr-based search API developed by the European Molecular Biology Laboratory of the European Bioinformatics Institut (EMBL-EBI)  [43]. At this stage, we collected structures in which small molecules interact with RNA, DNA, proteins or a combination of these biological entities. II. Identifying structures with drug-like compounds bound to RNA.
RNA chains with less than 10 atoms in the radius of 6 Å around the ligand were not considered as interacting (Table  S1 ). For the first release of HARIBOSS, we retained in the database only the structures in which ligands interact exclusively with RNA chains. At this stage, we annotated each entry with the following information: total mass of the system, molecular composition (RNA, RNA/DNA, RNA/protein, RNA/DNA/protein), experimental method used to determine the structure (experiment type, resolution and deposition year), properties of bound ligands (PDB name, residue number and chain id, molecular mass, identity of the interacting RNA chains). The RNA-SM complexes obtained at the end of this filtering step constitute the redundant version of the HARIBOSS database. III. Clustering of RNA/ligand complexes.
We defined a clustering procedure to select representative RNA/ligand complexes and build a non-redundant version of HARIBOSS. Since the database contained ligands interacting with more than one RNA chain, we adopted the following procedure: 1. we created a FASTA file with all the sequences of the individual RNA chains, each annotated with its interacting ligand, which we processed with CD-HIT-EST  [47]  to cluster them at 90% sequence identity; 2. in case of ligands interacting with multiple RNA chains, we defined two structures to belong to the same cluster if the individual chains from the two entries were clustered together at step 1; 3. to obtain a variety of different RNA/ligand interactions, RNA chains belonging to the same cluster but bound to different ligands were classified in separate subclusters; 4.
these subclusters were further classified based on the structural similarity of the pocket atoms by performing a hierarchical clustering using the RMSD of the aligned pocket atoms of the nucleic backbone as metrics and a cutoff of 1. 5 Å; 5. for each cluster, we selected as representative the entry closest to the cluster center with highest experimental resolution, which we consider an appropriate choice for the use of HARIBOSS in computational structural studies. Analysis of the database. The HARIBOSS entries were analyzed based on general information about the structure, physico-chemical properties of the ligands and of the RNA pockets.
Exploration of the chemical space of RNA binders. To present an overview of the chemical space of the RNA binders included in our database, the TMAP visualization method was used  [48]. In TMAP the molecules are represented by their fingerprints and indexed in an LSH forest structure. Based on the distances calculated during this step, an undirected weighted c-approximate k-nearest neighbor graph (c-k-NNG) is used to construct a minimum spanning tree. This tree is then projected onto the Euclidean plane. For the creation of the spanning tree, the MHFP6 fingerprints and a point size of 20 were used. For more information about the method, we refer the readers to the original publication  [48]. TMAP was chosen over simple clustering or other chemical space visualization tools for the informative nature of its tree-based layout, which enables to locally as well as globally locate specific clusters and their relative position compared to other clusters.
Cavity volume analysis with mkgridXf. To identify the cavities occupied by ligands in the structures of our HARIBOSS database and measure their volume, we followed a 3-steps procedure. 1. Cavity identification. We started by using mkgridXf  [49]  to identify potential cavities in each structure. Ligands, ions, and water molecules were first removed from the corresponding mmCIF file with MDAnalysis  [44]. The system was then processed by mkgridXf with inner and outer radii spheres equal to 1 Å and 8 Å, respectively. The cavities found by mkgridXf were extremely large, often extending throughout the entire RNA structure. We therefore decided to further classify them into sub-cavities. 2. Sub-cavity classification. We segmented each cavity found by mkgridXf using watershed, an image processing algorithm that can be used to detect contours and separate adjacent objects in a 3D volume  [50].
The minimum distance between the centers of two sub-cavities was set at 6 Å based on the dimension of the smallest ligands in our database. 3. Identification of sub-cavities occupied by ligands and volume calculation. We used Delaunay triangulation  [51]  to identify all the sub-cavities occupied by each ligand. To avoid including sub-cavities populated by a few ligand atoms, we considered a sub-cavity to be occupied only when at least 20% of the ligand atoms were found inside its volume. Finally, for each ligand we merged all the occupied sub-cavities and calculated the total volume. The use of different cutoffs for the sub-cavity occupation in the range 10%-30% did not significantly alter the final volume distribution (Fig. S1 ). Pocket analysis with Schrodinger suite. We analyzed each pocket in the HARIBOSS database using the tools available in Schrodinger Maestro suite v.
2021-3. This analysis is articulated in 4 steps: 1. We extracted the region within 12 Å of the ligand in order to optimize the computational cost of the analysis, as our database contained large systems up to 10 4 kDa; 2. We used Maestro  [52]  to prepare each substructure by adding missing hydrogens, optimizing their assignment at pH of 7. 4 with PROPKA  [53] , and minimizing the resulting model using steepest descent in combination with OPLS3  [54] ; 3. We used SiteMap  [55, 56]  to first define pockets using the coordinates of the bound ligand and a sitebox equal to 6 Å. In cases in which multiple pockets were identified for the same ligand, we used Delaunay triangulation to select the pockets occupied by at least 20% of the ligand atoms (Table  S2 ).
HARIBOSS website. The HARIBOSS database is available online through a web application (https://hariboss. pasteur. cloud). The online version of HARIBOSS is enriched with additional crosslinks and properties, either fetched from RCSB PDB or computed using the RDKit library 1. The web application allows to query, visualize and download the data using either a compound-centric or a complex-centric perspective. The compound-centric perspective (https://hariboss. pasteur. cloud/compounds/) allows to access the list of compounds identified as RNA binders in HARIBOSS. Multiple options allow to filter these compounds based on their properties (e. g. molecular weight) or on the properties of the PDB complexes where they have been identified (e. g. experimental resolution). It is possible to visualize the compounds either as thumbnails (the default representation), as a list or as a table.
The details of each of these compounds include different identifiers (SMILES, IUPAC, InChi, InChiKey), as well as links to external databases, compliance with some of the drug-likeness criteria, and the list of RNA-SM complexes in which each compound has been identified. Multiple elements of this section of the web application were heavily inspired by the iPPI-DB database  [57]. The graphical representation of the compounds uses the SmilesDrawer component  [58]. The complex-centric perspective (https://hariboss. pasteur. cloud/complexes/) provides an access to the list of RNA-SM complexes. It also includes filtering options based on the properties of the complexes, as well as of the associated ligands. For each complex, a detail page provides a graphical representation of the complex, using the NGL library  [59] , and its main properties.
This page also lists the identified pockets, color-coded according to their SiteScore ligandability score. Selecting the pockets allows to highlight them in the graphical representation of the complex. Both the compound-and complex-centric perspectives provide similar download features to retrieve either the entire query represented on the screen or a set of items that can be selected using the corresponding checkboxes. 1 https://doi. org/10. 5281/zenodo. 591637.
Results and discussions HARIBOSS is constructed in 3 steps (Fig. 1A , Materials and Methods): initial fetching of RNA structures from the PDB, filtering of the database to identify systems with at least one small molecule bound to RNA (redundant HARIBOSS), and clustering based on RNA sequence identity and pocket structural similarity (non-redundant HARIBOSS). As of May 2022, the redundant version of HARIBOSS contains 716 PDB structures of RNA-SM complexes, for a total of 1226 pockets occupied by 267 unique ligands. After clustering, the non-redundant version of HARIBOSS contains 484 PDB structures, with a total of 676 pockets occupied by 267 unique ligands. In the following paragraphs, we present an overview of the general properties of the structures included in the HARIBOSS database, the physico-chemical properties of the ligands bound to RNA, and of the pockets and cavities.
General properties of the HARIBOSS structures. The molecular composition of the structures changed at different stages of the construction of the database. Initially, HARIBOSS contains systems composed of RNA/protein complexes (59. 3%), RNA molecules only (23. 1%), RNA/protein/DNA (13. 4%) and RNA/DNA complexes (4. 2%) (Fig. 1A, cyan ). All the RNA/protein/DNA complexes along with several RNA/protein complexes were filtered out because molecules other than RNA were involved in ligand binding and therefore were beyond the scope of the present study (Fig. 1A, red ). In the non-redundant version of HARIBOSS, the majority of structures (62. 0%) contained only RNA molecules, while the remaining part consisted of either RNA/protein (37. 8%) or RNA/DNA (0. 2%) complexes. (Fig. 1A , yellow). The systems initially included in our database significantly vary in size, with a total mass ranging from 0.
5 to 10 4 kDa regardless of the presence of a small molecule (Fig. 1B, cyan ). Interestingly, all the structures above 10 2 kDa are ribosomal RNA/protein complexes. Furthermore, the filtering of the initial database reduced the number of structures with mass below 5 kDa from 23% to 9% (Fig. 1B, red ). This significant reduction supports the idea that RNA must have sufficient structural complexity to bind small molecules  [14, 18, 40]. The majority of structures in the non-redundant version of HARIBOSS (~83%) were determined by Xray crystallography with a typical resolution of 2. 8 Å. However, over the past 5 years the number of cryo-electron microscopy (cryo-EM) structures, in particular of large ribosomal RNA/protein complexes, has steadily been increasing (Fig. 1C ) and currently represents 11% of our database with a typical resolution of 3.
0 Å. Finally, structures determined by Nuclear Magnetic Resonance (NMR) spectroscopy constitutes the remaining 6% of the database. Cryo-EM and NMR structures are particularly interesting from the point of view of rational drug design as they provide dynamic information about the RNA molecules that can be exploited in virtual screenings  [60]. Overall, the number of RNA-SM structures deposited in the PDB every year is constantly increasing as a reflection of the growing interest of the community in studying RNA-SM interactions (Fig. 1C ).
Properties of the HARIBOSS ligands. To explore the chemical space of RNA binders, we first created a dataset of unique ligands, based on their PDB identifier. As of May 2022, the total number of unique ligands in the HARIBOSS database is 267. Among these ligands, there are 11 that appear in more than 10 structures of the non-redundant HARIBOSS (Table  S3 ). To provide an overview of the chemical matter present in our dataset, we used a minimum spanning tree representation (Materials and Methods) 2A). Diverse ligand scaffolds are present, with some of them belonging to known classes of therapeutic agents. There is a significant number of structures of known antibiotics, like Linezolid and other oxazolidinones, Tiamulin, Eravacycline and other tetracyclines (Fig. S2 ). It is not surprising that GTP and nucleoside analogs as well as long polar molecules, like PEG and spermidine-derived polyamines, also appeared as common RNA binders.
The high polarity of RNA binders is depicted in their TPSA distribution, with less than half of the molecules (44%) being beyond the Veber drug-likeness threshold (140 • 2 ) (Fig. 2B ). This high polarity is in line with the overall good solubility of the ligands in our HARIBOSS dataset (Fig. 2C ). The compounds in our database significantly vary in size with a molecular mass spanning a range from 162 Da to 972 Da. The vast majority of these compounds (82%) have a mass lower than 600 Da (Fig. 2D ). Approximately 30% of the RNA binders had a number of either donors and/or acceptors above the druglikeness threshold (Fig. 2E  and Fig. S3 ). This is again consistent with the high polar nature of these small molecules as well as the RNA targets. Moreover, RNA structurally forms Òwarm-likeÓ long cavities and therefore long and flexible small molecules are expected to be among its binders.
S4 ). On the contrary, weaker correlations were found between MW and both FOSA (Fig. S5 ) and FISA (Fig. S6 ), and between FISA and FOSA (Fig. S7 ). Interestingly, 25. 2% of our ligand database is above the FISA drug-like threshold, while only 3% shows high FOSA, in agreement with TPSA (Fig. 2B ). Among the set of unique RNA binders 55% do not show potential hERG liabilities (Fig. S8 ). Approximately half of the ligands (52%) are predicted to have poor Caco-2 cell permeability (Fig. S9 ) while more 70% of the molecules have a good predicted brain/blood partition coefficient (Fig. S10 ). Properties of the RNA pockets and cavities. In the non-redundant version of HARIBOSS, the majority of pockets occupied by ligands and identified by SiteMap and mkgridXf (Materials and Methods) are formed by a single RNA chain (67%), while the remaining are at the interface of two (29%) or more (4%) RNA chains (Fig.