dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation and analysis of simulated fluvial C fluxes We first evaluated DOC concentrations and pCO 2 at several locations along the Congo main stem (Fig. 1 ) and on the Oubangui River against the data of Borges at al. (2015b) and  Bouillon et al. (2012 Bouillon et al. ( , 2014)). We also compared the various simulated components of the net C balance (e. g. NPP) of the Congo against values described in the literature  (Williams et al. , 2007; Lewis et al. , 2009; Verhegghen et al. , 2012; Valentini et al. , 2014; Yin et al. , 2017). In addition, we assessed the relationship between the interannual variation in presentday  C fluxes of the Congo Basin and variation in temperature and rainfall. This was done through linear regression using STATISTICA TM. We found trends in several of the fluxes over the 30-year period  and thus detrended the time series with the "Detrend" function, part of the "SpecsVerification" package in R (R Core Team, 2013), before undertaking the statistical analysis focused on the climate drivers of interannual variability.
200 000 km 2 ; Fig. 1 ) part of the Cuvette Centrale wetlands against the GIEMS inundation dataset  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) , maximum inundation minus minimum or permanent water bodies;  Prigent et al. , 2007; Becker et al. , 2018). While our model is able to represent the seasonality in flooded area relatively well (R 2 = 0. 75; Fig. 4c ), it considerably overestimates the magnitude of flooded area relative to GIEMS (Fig. 4c , Table  2 ). However, the dataset that we used to define the MFF and MFS forcing files  (Gumbricht et al. , 2017 ) is produced at a higher resolution than GIEMS and will capture smaller wetlands than the GIEMS dataset; thus, the greater flooded area is to be expected. GIEMS is also known to underestimate inundation under vegetated areas  (Prigent et al.
, 2007; Papa et al. , 2010)  and has difficulties capturing small inundated areas  (Prigent et al. , 2007; Lauerwald et al. , 2017). Indeed, with the GIEMS data we produce an overall flooded area for the Congo Basin of just 3 %, less than one-third of that produced with the Gumbricht dataset  (Gumbricht et al. , 2017)  or the GLWD  (Lehner and Döll, 2004). As such, it is to be expected that there is a large RMSE (272 %; Table  2 ) between simulated flooded area and GIEMS; more importantly, the seasonality of the two is highly correlated (R 2 = 0. 67; Table  2 ). In Fig. 5 , we compare simulated DOC concentrations at six locations (Fig. 1 ) along the Congo River and Oubangui tributary against the observations of Borges at al. (2015b). We show that we can recreate the spatial variation in the DOC concentration within the Congo Basin relatively closely with an R 2 of 0.
, 2012 (Bouillon et al. , , 2014)) , which as far as we are aware is the longest time series of pCO 2 published (and accessible) from the Congo Basin, spanning March 2010 to March 2012 (with only the single month of June 2010 missing). Again, while the model fails to correctly predict the precise timing of the peak as with the Kinshasa and Kisangani datasets, the broad seasonal variation in pCO 2 is captured, with the observed and modelled times series ranging from 227-4040 and 415-2928 ppm, respectively (Fig. 6 ).
Our estimate of the integrated present-day aquatic CO 2 evasion from the river surface of the Congo Basin (32 Tg C yr -1 ) is the same as that estimated by  Raymond et al. (2013)  (also 32 Tg C yr -1 ), downscaled over the same basin area, but smaller than the 59. 7 Tg C yr -1 calculated by  Lauerwald et al. (2015)  and far smaller than that of  Borges et al. (2015a)  at 133-177 Tg C yr -1 or  Borges et al. (2019)  at 251 ± 46 Tg C yr -1. The recent study by  Borges et al. (2019)  is based on the most extensive dataset of Congo Basin pCO 2 measurements to date and thus suggests that we substantially underestimate total riverine CO 2 evasion. As previously discussed, we simulate the broad spatial and temporal variation in observed DOC and pCO 2  (Borges et al. , 2015a, b; Fig. 5 , Table  3 ) relatively well.
Therefore, the lack of a bespoke macrophyte PFT is indeed likely to be one reason for the discrepancy between our results and those of Borges but largely due to their particularly high export efficiency to the river-floodplain network as opposed to differences in NPP. While being a significant limitation, creating and incorporating a macrophyte PFT would be a substantial undertaking given that the authors are unaware of any published dataset which has systematically mapped their distribution and abundance. It is important to note that while ORCHILEAK does not include the export of C from aquatic macrophytes, it also neglects their NPP. Moreover, most aquatic macrophytes described in the literature have short (< 1 year) life cycles  (Mitchel and Rogers, 1985). As such, while this model limitation is likely one of the causes for our relatively low estimate of riverine CO 2 evasion, it will only have a limited net effect on our estimate of the overall annual C balance (NBP, NEP) of the Congo Basin.
Trends in terrestrial and aquatic carbon fluxes There are relatively sparse observed data available on the long-term trends of terrestrial C fluxes in the Congo. Yin et al. (2017)  used MODIS data to estimate NPP between 2001 and 2013 across central Africa. They found that NPP increased on average by 10 g C m -2 per year, while we simulate an average annual increase of 4 g C m -2 yr -1 over the same period across the Congo Basin. The two values are not directly comparable as they do not cover precisely the same geographic area, but it is encouraging that our simulations exhibit a similar trend as remote sensing data. As previously noted, MODIS-derived estimates of NPP do not fully include the effect of CO 2 fertilization (de  Kauwe et al. , 2016) , whereas ORCHILEAK does. Thus, the MODIS NPP product may underestimate the increasing trend in NPP, which would bring our modelled trend further away from this dataset.
On the other hand, forest degradation effects and recent droughts have been associated with a decrease in greenness  (Zhou et al. , 2014)  and above-ground biomass loss  (Qie et al. , 2019)  in tropical forests. Up to a point, our results also concur with estimates based on the upscaling of biomass observations  (Lewis et al. , 2009; Hubau et al. , 2020). Lewis et al. (2009)  upscaled forest plot measurements to determine that intact tropical African forests represented a net uptake of approximately 300 Tg C yr -1 between 1968 and 2007, and this is consistent with our NEP estimate of 275 Tg C yr -1 over the same period. However, more recently an analysis based on an extension of the same dataset found that the above-ground C sink in tropical Africa was relatively stable from 1985 to 2015  (Hubau et al.
Furthermore, ORCHILEAK uses a "poor soils" mask forcing file (Fig. 2j ) based on the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009), which prescribes reduced decomposition rates in low-nutrient and low-pH soils (e. g. Podzols and Arenosols). The effect of the "poor soils" forcing can clearly be seen in the spatial distribution of the soil C stock in Fig. A3 , where the highest C storage coincides with the highest proportion of poor soils. Interestingly, this does not include the Cuvette Centrale wetlands (Fig. 1 ), an area which was recently identified as containing the world's largest intact tropical peatland and a stock of around 30 Pg C  (Dargie at al. , 2017). One potential improvement that could be made to ORCHILEAK would be the development of a new tailored "poor soils" forcing file for the Congo Basin which explicitly includes Histosols, perhaps informed by the Soil Grids database  (Hengl et al.
, 2014) , to better represent the Cuvette Centrale. This could, in turn, be validated and/or calibrated against the observations of  Dargie et al. (2017). A more long-term aim could be the integration or coupling of the ORCHIDEE-PEAT module with ORCHILEAK. ORCHIDEE-PEAT  (Qiu et al. , 2019)  represents peat as an independent sub-grid hydrological soil unit in which peatland soils are characterized by peat-specific hydrological properties and multi-layered transport of C and water. Thus far, it has only been applied to northern peatlands, and calibrating it to tropical peatlands, along with integrating it within ORCHILEAK, would require considerable further model development but would certainly be a valuable longer-term aspiration. This could also be applied across the tropical region and would allow us to comprehensively explore the implications of climate change and land use change for tropical peatlands.
Acknowledgements. We would like to thank  John Melack  and the other anonymous reviewer for the time and effort they took to carefully review and improve the paper. Financial support. Financial support was received from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Skłodowska-Curie  grant agreement no. 643052  ( C-CASCADES  project). Pierre Regnier acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  776810  (project  VERIFY ). RL acknowledges funding from the  ANR  ISIPEDIA  ERA4CS  project and from  French  state aid managed by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-16-CONV-0003. Review statement. This paper was edited by  Rui A. P. Perdigão  and reviewed by  John Melack  and one anonymous referee.
Code availability. A description of the general ORCHIDEE code can be found here: http://forge. ipsl. jussieu. fr/orchidee/browser# tags/ORCHIDEE_1_9_6/ORCHIDEE (last access: 1 July 2018) (ORCHIDEE, 2018a). The main part of the ORCHIDEE code was written by  Krinner et al. (2005). See d 'Orgeval et al. (2008)  for a general description of the river-routing scheme. For the updated soil C module, please see Camino-Serrano et al. (2018). For the source code of ORCHILEAK, see Lauerwald et al. (2017)  https://doi. org/10. 5194/gmd-10-3821-2017-supplement. For details on how to install ORCHIDEE and its various branches, please see the user guide: http://forge. ipsl. jussieu. fr/ orchidee/wiki/Documentation/UserGuide (last access: 1 July 2018) (ORCHIDEE, 2018b).
The weekly snow cover extent maps are digitized to a 88×88 grid following a 190 km polar stereographic projection and contain binary snow cover information. The retrieval of snow cover information for this product is not interfered by clouds due to the weekly aggregation prior to June 1999 and the inclusion of passive microwave data posterior. The NOAA CDR has been widely used in climate-snow studies over the NH (e. g. ,  Brown and Robinson, 2011   , 2015; Hori et al. , 2017; Santolaria-Otín and Zolina, 2020)  and more specifically over HMA (e. g. ,  Xu et al. , 2016). This dataset is adapted for continental-scale studies but shows limitations over mountainous regions  (Déry and Brown, 2007) , even if the inclusion of Meteosat-5 data in 2001 significantly improved its quality over the Asian continent  (Helfrich et al.
The multimodel mean (Fig. 1h ) shows globally higher values of precipitation over HMA in comparison with the observations. Precipitation tends to spread more over the TP in the model compared to the observations, which might be partly due to the smoothing of the topography in the models. However, precipitation rates are also generally underestimated in observational datasets because of snowfall undercatch issues, which could lend credence in the stronger precipitation rates modeled at high elevation. https://doi. org/10. 5194/esd-12-1061-2021 Earth Syst. Dynam. , 12, 1061-1098, 2021.
2f ). The ESA CCI product shows lower snow cover values compared to NOAA CDR and ERA-Interim, with values around 30 % during the winter in HMA, suggesting that model biases may be even larger. The large difference in spatial resolution with the latter product may also play a role in this discrepancy, as valleys and other aspects of fine-scale topography are not being well resolved in the other products and thus lack a good representation of the spatial heterogeneity of snow cover compared to the ESA CCI product. This suggests a general snow cover overestimation in both model and reanalysis data based on coarse resolution, which is especially pronounced over high mountain areas (Fig. 2f, g ). Nevertheless, the positive bias of snow cover fraction simulated by the models over HMA is mainly related to the overestimation of the snow cover over TP, where the snow cover varies around 20 % for the observations, while values above 60 % are found in the multimodel mean, despite a wide dispersion among the models (from 20 % to 90 %).
The strongest precipitation rates occur during the Asian summer monsoon over the HM region (> 2500 m), with precipitation rates reaching a monthly mean of 10 mm d -1 (∼ 300 mm month -1 ) on average in the multimodel mean, while precipitation rates reach lower values than 2 mm d -1 during the winter (Fig. 2k ). Other regions exhibit smaller precipitation amounts below 5 mm d -1 most of the year. In the HK region, larger precipitation rates are found in late winter and spring (Fig. 2j ) mostly due to WDs, as explained in Sect. 1. While the model spread generally encompasses the observations for temperature and snow cover, APHRODITE precipitation data are most of the time below the minimum of the model values. This difference might be explained by snow undercatch issues typically obtained with rain gauge measurements (e.
The recently published ESA CCI snow cover product at a better temporal and spatial resolution, even if not exactly on the same period, suggests that snow cover biases in models can even be higher over HMA. Generally, the complex and highly variable snow cover behavior calls for better spatiotemporal resolution than currently available from presentday snow cover products based on both observations, reanalyses or models and thus hampers reliable trend detection. Precipitation is particularly uncertain over HMA. It is well known that most of the observational datasets underestimate solid precipitation  (Palazzi et al. , 2013; Sanjay et al. , 2017; Sun et al. , 2018)  due to gauge undercatch. An additional issue is the scarce observational network in this region. Using glacier mass balances to infer the high-altitude precipitation in the upper Indus Basin,  Immerzeel et al.
(2015)  suggest an underestimation of precipitation reaching a factor of 2 to 10 in observational datasets. Most studies comparing models and reanalyses with observational datasets also account for large differences from about 100 % to 200 % (e. g. ,  Palazzi et al. , 2013; Su et al. , 2013; Salunke et al. , 2019). GPCP is in closer agreement with CMIP6 models, which could be explained by a better representation of solid precipitation. Overall, it is challenging to estimate model biases, even if the coarse GCM resolutions might underestimate the orographic barrier effect of high mountains leading to a potential excess https://doi. org/10. 5194/esd-12-1061-2021 Earth Syst. Dynam. , 12, 1061-1098, 2021 of water vapor transport toward the TP  (Lin et al. , 2018). Hence, all models and analyses regarding precipitation need to be considered with caution.
We thank the CLIMERI-France infrastructure (http://climeri-france. fr/, last access: 5 October 2021) for making the CMIP6 data available. The authors thank the two anonymous reviewers for their constructive comments and their contribution to the improvement of this paper. Financial support. This research has been supported by the European Space Agency (ESA) Snow Climate Change Initiative (CCI+) project (grant no. 4000124098/18/I-NB). Review statement. This paper was edited by Govindasamy Bala and reviewed by two anonymous referees.
The overlap between the two datasets is small (309 pairs), and a comparison with two large scale compendiums of structural models (  (Mosca et al, 2012) , Methods) indicates that 62,019 of the combined pairs do not have experimental models or can be modelled easily by homology, suggesting a significant potential gain in structural knowledge. We predicted the structure of 65,484 non-redundant pairs using the FoldDock pipeline  (Bryant et al, 2021) , based on AlphaFold2  (Jumper et al, 2021). We have previously shown that larger interface size and higher lDDT (plDDT) scores from AlphaFold2 in the interfaces of the predicted complexes are associated with more reliable predictions  (Bryant et al, 2021). As in the FoldDock pipeline, we combined these two metrics into a single score, which can be used to predict the DockQ score of a complex, dubbed pDockQ (Methods) that can rank models by confidence.
Discussion We have attempted here to generate predicted complex structures for pairs of human proteins known to physically interact from two different datasets based on different experimental approaches. We noted that the source of data used for the protein interactions is important and impacts the fraction of models that can be confidently predicted. Our analysis suggests the protein interactions supported by a combination of affinity-based and complementation based methods results in higher confidence models. We believe these interactions tend to correspond to high-affinity interactions that are very likely to share a direct physical permanent interaction. We show that it is possible to use metrics from the models (e. g. pDockQ score) to rank higher confidence models, providing an additional accuracy level to the large scale protein-protein interaction studies.
Protein interaction data and annotations Human protein pairs known to physically interact were obtained from the Hu. MAP dataset, retaining pairwise interactions with >=0. 5 confidence, and most interactions from the HuRI dataset. These interactions were further enriched by obtaining annotations on cross-linking peptides matched across pairs of interactions, disease related mutations and protein phosphorylation sites in the selected proteins. Crosslink data was collected from  (Yugandhar et al, 2020; Schweppe et al, 2016; Klykov et al, 2020; Steigenberger et al, 2019; Klykov et al, 2018; Fasci et al, 2018; Eliseev et al, 2018; Gestaut et al, 2019; Klatt et al, 2020; Sabath et al, 2020; Mohamed et al, 2021) , filtered for peptides assigned to only one sequence. Clinical missense variants associated with disease were collected from ClinVar.
Protein complex prediction To predict protein complexes of pairwise interactions, we utilise the FoldDock pipeline  (Bryant et al, 2021)  based on AlphaFold2  (Jumper et al, 2021). We use the option of fused+paired multiple sequence alignments (MSAs) and run the model configuration m1-10-1 as this provides the highest success rate accompanied by a 20-fold speed-up. Both the fused and paired MSAs are constructed from running HHblits on every single chain against Uniclust30. The fused MSA is generated by simply concatenating the output of each of the single-chain HHblits runs for two interacting chains. The paired MSA is constructed by combining the top hit for each matching OX identifier between two interacting chains, using the output from the single-chain HHblits runs.
pDockQ confidence score To score models, we use features from the predicted complexes to calculate the predicted DockQ score, pDockQ. This score is defined with the following sigmoidal equation: The parameters were optimised to predict the DockQ score using the dataset from  (Green et al, 2021). The number of interface contacts is defined as elsewhere in this paper (any residues with an interface atom within 10Å to the other chain) , and the plDDT is the predicted lDDT score from AlphaFold2 taken over the interface residues as defined by the interface contacts.
Analysis of phosphosites in the protein-protein interfaces Phosphosite residues in interfaces were identified from a previously published comprehensive list of known human phosphosites  (Ochoa et al, 2020). Kinases associated with phosphorylation of interface residues were obtained from the PhosphositePlus database and over-representation analysis of kinases was performed using a hyper-geometric test. Highly regulated interface phosphosites were defined as those with more than two-fold change in phosphorylation in more than two perturbation conditions across a collated phosphoproteomics dataset comprising a range of physiological conditions and drug treatments  (Ochoa et al, 2016). Pearson correlation was calculated amongst these regulated phosphosites and clusters of co-regulated phosphosites were identified using hierarchical clustering ('ward' method) of euclidean distances of the correlation matrix.
Phosphosite clusters were created by cutting the dendrogram at the appropriate level using cutree (h=17) function in R. Phosphosite clusters that were significantly regulated in each perturbation condition were identified by z-test from the comparison of fold changes in phosphosite measurements of all phosphosites in a cluster against the overall distribution of phosphorylation fold changes across the condition. Gene ontology over-representation of each cluster was performed separately using a hypergeometric test in R. The gene ontology terms were obtained from the c5 category of Molecular Signature Database (MSigDBv7. 1) (Subramaniam et al, 2005). All over-representation analysis (ORA) were performed using the enricher function of clusterProfiler package (version 3. 12. 0) (Wang et al, 2012)  in R.
Acknowledgements R. A. S. acknowledges funding through the  European Union   Horizon 2020 program  INFRAIA project  Epic-XS (Project 823839 ) and the research programme  NWO TA  with project number  741. 018. 201 , which is partly financed by the  Dutch Research Council (NWO). AE was funded by the  Vetenskapsrådet  Grant No. 2016-03798  and  Knut  and  Alice Wallenberg  foundation. Computational resources were provided by  SNIC , grant No:  SNIC 2021/5-297  and  Berzelius-2021-29.
2017; Priolo et al. 2019) , calibration of strong-motion records for realistic ground shaking estimates  (Cauzzi et al. 2014; Michelini et al. 2020  and references therein), assessment of site-specific hazard for critical infrastructures  (Bazzurro and Cornell 2004a, b; Rathje et al. 2015; Pecker et al. 2017; Aristizabal et al. 2018) , estimation of ground motion models  (Bozorgnia et al. 2014; Douglas 2016; Bindi et al. 2019; Kotha et al. 2020) , soil classification following the building code prescriptions  (NEHRP, BSSC 2015; Eurocode 8, EC8 2004; NTC18 Italian code, NTC 2018). Most existing strong motion databases actually include some information on the V S30 proxy, which was the first one to be proposed in the nineties as a continuous, quantitative alternative to the binary (or ternary) classification soil/rock or soft soil/stiff soil/rock  (Borcherdt 1992 (Borcherdt , 1994;; Boore et al.
1994 ). In the last decade, the number of stations of permanent and temporary seismic networks worldwide has largely increased  (Margheriti et al. 2011; Mazza et al. 2012; Michelini et al. 2016; Moretti et al. 2016; Hetényi et al. 2018; Cara et al. 2019; Chen et al. 2019) , rising the amount of recorded data  (McNamara and Buland 2004; Pintore et al. 2012; Lanzano et al. 2019) , without paying much attention on site characterization information. Recently some efforts have been carried out at national level to perform extensive site characterization at seismic stations (e. g. Sandikkaya et al. 2010; Michel et al. 2014; Stewart et al. 2014; Albarello et al. 2017; Felicetta et al. 2017; Hollender et al. 2018). Currently only a few seismological national networks expose site condition characteristics with very detailed information and reports on site topography, morphology, geology and on seismic surveys used to derive V S profiles  (Cornou and Bard 2019) : examples of national database are provided by Switzerland through Site Characterization Database for Seismic Stations (Swiss Seismological Service (SED) at ETH Zürich 2015, http:// stati ons.