dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The PROFOUND Database (https://doi. org/10. 5880/PIK. 2020. 006/, Reyer et al. , 2020)  is available under the Creative Commons Attribution-NonCommercial 4. 0 International license (CC BY-NC 4. 0). The PROFOUND R Package (ProfoundData, https://CRAN. R-project. org/package=ProfoundData, last access: 5 June 2020, Silveyra Gonzalez et al. , 2020) is available via a GLP3 license. An earlier version of the database, including an outdated reconstruction of the Sorø tree data has been published as  Reyer et al. (2019).
Conclusions A wide range of data are needed to properly evaluate complex process-based vegetation models. The PROFOUND database compiles data from soil, climate, stand and flux measurements with data from remote sensing, atmospheric nitrogen modelling and climate modelling. Moreover, by providing data at 0. 5 • × 0. 5 • grid level plus locally biascorrected climate data, the datasets can be used to compare local forest models to global vegetation models. The PRO-FOUND database thus facilitates model evaluation, calibration, uncertainty analysis and model intercomparisons, highlighting the immense value of long-term environmental monitoring data for robust inferences about causal processes and future dynamics of forests.
June 2020) that were bias-corrected and interpolated to a common grid resolution of 0. 5 • × 0. 5 • according toHempel et al. (2013). The climate variables for each site available were extracted from the grid cell of the downscaled climate forcing dataset in which the forest plot is located. The data can be used in very different ways by the vegetation modelling community. (https://www. wcrp-climate. org/wgcm-cmip/wgcm-cmip5; improvement of the method of Hempel et al. (2013) last accessed: 5 -The "ISIMIP Fast Track" scenarios (ISIMIPFT) con-sist of daily climate data available from five different as described in Frieler et al. (2017) and Lange (2017) and the local observed climatologies presented in Sect. 3. 2. 3. The ISIMIP2bLBC data are hence best suited for scenario studies that require climatic data to be as consistent as possible with the observational data (Fig.
Effect of GEXP15 on the activity of PP1 Based on the ability of recombinant GEXP15 to interact directly with PP1c, its effect on the phosphatase activity was assessed. As depicted in Fig 1D  and 1E , using a quantity of PfPP1c generating a linear release of phosphate from pNPP substrate, the addition of either PbGEXP15 4-590 or a shorter PbGEXP15 4-178 protein strongly increased the dephosphorylation activity of PfPP1c in a concentration-dependent manner. A two-fold increase was observed at 100 pmol/well with both versions of PbGEXP15 proteins, suggesting that the main activating region of PfPP1c is carried by the N-terminal moiety of GEXP15. The use of the PbGEXP15 4-178 KKKKKAQA mutant abolished the regulatory effect on PP1 activity (Fig  1F ). These data exclude any non-specific activation of PfPP1c and support the major role of RVxF in the function of GEXP15.
Earlier studies mainly focused on phenotypic analyses have shown similar data obtained with targeted disruption of hmgb2 described as a pro-inflammatory protein  [53] , MSP7 involved in invasion of erythrocytes  [54]  or plasmepsin-4 contributing to hemoglobin digestion  [55]. Although, the parasite derived molecules triggering host responses are still largely unknown, the use of Δgexp15 parasites might contribute for a better understanding of the protective mechanisms against ECM. Of note, the essentiality of GEXP15 in the blood stages was further supported by a recent study using a piggyBac transposon inserted randomly in P. falciparum genome  [56]  in which they did not obtain viable parasites with a disrupted pfgexp15 gene despite the presence of 35 potential insertion sites. For an in-depth dissection of the biological functions of GEXP15, we performed quantitative proteomic and phosphoproteomic analyses.
Western blots were carried out as described above and probed with anti-RFP pAb (1:1000, MBL) followed by goat anti-rabbit IgG-HRP (1:20000, Sigma-Aldrich). Then, the membrane was stripped and probed with mouse sera anti-GEXP15 (1:100) followed by Mouse TrueBlot 1 Ultra: Anti-Mouse Ig HRP (1:2000, eBioscience). For the Mass-spectrometry analysis, electrophoretic migration, tryptic digestion and nanoLC-MSMS analysis were performed as previously described  [74]. Raw data collected during nanoLC-MS/MS analyses were processed and converted into �. mgf peak list format with Proteome Discoverer 1. 4 (Thermo Fisher Scientific). MS/MS data were interpreted using the search engine Mascot (version 2. 4. 0, Matrix Science, London, UK) installed on a local server. Searches were performed with a tolerance on mass measurement of 0.
2 Da for precursor and 0. 2 Da for fragment ions, against a composite target decoy database (2 � 22,202 total entries) built with Mus musculus Uniprot database (10090-17,008 entries), Plasmodium berghei PlasmoDB database (Release 41. 0-5 December 2018-5,076 entries) fused with the sequences of PbPP1-HA or PbGEXP15-mCherry, recombinant trypsin and a list of classical contaminants (118 entries). Cysteine carbamidomethylation, methionine oxidation, protein N-terminal acetylation, and cysteine propionamidation were searched as variable modifications. Up to one trypsin missed cleavage was allowed. For each sample, peptides were filtered out according to the cut-off set for proteins hits with one or more peptides longer than nine residues. Ion and identity score were fixed to obtain a 1% false positive rate.
At 127 min, the gradient returned to 5% to re-equilibrate the column for 20 min before the next injection. One blank was run between biological replicates to prevent sample carryover. Samples eluting from the column were analyzed by data dependent MS/MS, using the top-10 acquisition method. Peptides and phosphopeptides were fragmented using higher-energy collisional dissociation (HCD). Briefly, the instrument settings were as follows: resolution was set to 70,000 for MS scans and 17,500 for the data dependent MS/MS scans in order to increase speed. The MS AGC target was set to 3. 10 6 counts with maximum injection time set to 200 ms, while MS/MS AGC target was set to 1. 10 5 with maximum injection time set to 120 ms. The MS scan range was from 400 to 2000 m/z. Dynamic exclusion was set to 30 sec duration.
The MS files were processed with the MaxQuant software version 1. 5. 8. 3 and searched with Andromeda search engine against the database of Mus musculus from swissprot 07/ 2017 and Plasmodium berghei ANKA from PlasmoDB (v37)  [20]. To search parent mass and fragment ions, we set a mass deviation of 4. 5 ppm and 20 ppm respectively. Strict specificity for trypsin/P cleavage was required, allowing up to two missed cleavage sites. Carbamidomethylation (Cys) was set as a fixed modification, whereas oxidation (Met) and N-term acetylation were set as variable modifications. For the analysis of MS files issuing from of TiO2 enrichment, the variable modification of phosphorylation on S, T and Y were also added. The false discovery rates (FDRs) at the protein and peptide level were set to 1%. Scores were calculated in MaxQuant as described previously  [77].
Match between runs was allowed. The reverse hits were removed from MaxQuant output. Proteins were quantified according to the MaxQuant label-free algorithm  [77, 78]  using LFQ intensities and phosphopeptides according to intensity. Protein quantification was obtained using at least 2 peptides per protein. Statistical and bioinformatic analysis, including volcano plots and clustering, were performed with Perseus software (version 1. 6. 0. 7) freely available at Perseus  [79]. For statistical comparison we set two groups: P. berghei parental and Δgexp15. Each group contained four and three biological replicates for schizonts and gametocytes respectively and each sample was run in technical triplicates. For the total proteomes, we analyzed the proteingroups. txt file and the Phospho(STY). txt fil for the phosphoproteomes.
Identified proteins after PbGEXP15-mCherry immunoprecipitation in P. berghei schizonts. The table shows the protein name, accession number and molecular weight of identified proteins and the number of peptides and spectra identified for each experiment are indicated. Three experiments were performed with mCherry antibodies on WT schizonts as negative control and PbGEXP15-mCherry schizonts. Here, we present the protein co-immunoprecipitated with PbGEXP15-mCherry in at least two experiments with peptides � 2 and with peptides and spectra � 2 fold compared with the control strain. Blank cells represent 0 peptide/spectrum identified. Percentage of sequence coverage, peptide sequence, number of shared peptides and other proteins are indicated in reduced columns. In the second sheet, all proteins immunoprecipitated are shown.
(XLSX) S2 Table. Identified proteins after PbGEXP15-mCherry immunoprecipitation in P. berghei gametocytes. The table shows the protein name, accession number and molecular weight of identified proteins and the number of peptides and spectra identified for each experiment are indicated. Two experiments were performed with mCherry antibodies on WT gametocytes as negative control and PbGEXP15-mCherry gametocytes, Here, we present the protein coimmunoprecipitated with PbGEXP15-mCherry in two experiments with peptides � 2 and with peptides and spectra � 2 fold compared with the control strain. Blank cells represent 0 peptide/spectrum identified. Percentage of sequence coverage, peptide sequence, number of shared peptides and other proteins are indicated in reduced columns. In the second sheet, all proteins immunoprecipitated are shown.
(XLS) S3 Table. Identified proteins after PbPP1-HA immunoprecipitation in P. berghei schizonts. The table shows the protein name, accession number and molecular weight of identified proteins and the number of peptides and spectra identified for each experiment are indicated. Three experiments were performed with HA antibodies on WT schizonts as negative control and PbPP1-HA schizonts. Here, we present the protein co-immunoprecipitated with PbPP1-HA in at least two experiments with peptides � 2 and with peptides and spectra � 2 fold compared with the control strain. Blank cells represent 0 peptide/spectrum identified. Percentage of sequence coverage, peptide sequence, number of shared peptides and other proteins are indicated in reduced columns. In the second sheet, all proteins immunoprecipitated are shown.
The atmosphere's chaotic nature limits its short-term predictability. Furthermore, there is little knowledge on how the difficulty of forecasting weather may be affected by anthropogenic climate change. Here, we address this question by employing metrics issued from dynamical systems theory to describe the atmospheric circulation and infer the dynamical properties of the climate system. Specifically, we evaluate the changes in the sub-seasonal predictability of the large-scale atmospheric circulation over the North Atlantic for the historical period and under anthropogenic forcing, using centennial reanalyses and CMIP5 simulations. For the future period, most datasets point to an increase in the atmosphere's predictability. AMIP simulations with 4K warmer oceans and 4 × atmospheric CO 2 concentrations highlight the prominent role of a warmer ocean in driving this increase.
We specifically focus on evaluating changes in the intrinsic predictability of the atmospheric circulation over the North Atlantic under anthropogenic forcing, as represented by the above dynamical systems indicators. In the historical period, there is disagreement between reanalysis datasets. For the future period, most of the projections analysed here point to an increase in the atmosphere's intrinsic predictability. The analysis of Atmospheric Model Intercomparison Project (AMIP) simulations with 4 K warmer oceans and four times atmospheric CO 2 concentrations points to the prominent role of a warmer ocean in driving this increase. We name this robust signal the hammam effect. This occurs through the enhancement of zonal atmospheric patterns, which are more predictable than meridional configurations.
Results Data and predictability metrics. Our analysis is based on two dynamical systems metrics: the local (in phase space) dimension d and the persistence θ -1 17. In simple terms, they describe the recurrences of a system around a state ζ in phase space. In our case, ζ would be a latitude-longitude map of a given variable for a given day and dataset. Values of d and θ -1 are obtained for every time-step (i. e. every state ζ) in the dataset of interest. d provides information on how the system can reach ζ and how it can evolve from ζ, and is a proxy for the system's active number of degrees of freedom. This information is intrinsically linked to the predictability of ζ, but is local in nature. d can change rapidly over timescales of a few days meaning that, when applied to atmospheric fields, it cannot be interpreted in the same way as the error of a medium-range weather forecast initialised from ζ.
θ -1 describes the persistence of ζ in time. A very persistent state is typically highly predictable, while a very unstable state yields low persistence. In this sense, the information provided by θ -1 is more directly linked to that provided by the error of a forecast initialised from ζ  18. d and θ -1 therefore provide complementary information on the intrinsic predictability of an instantaneous state of the atmosphere. Details of how these two metrics are computed are provided in the Methods. We compute the above two dynamical systems metrics for the sea-level pressure field in the North Atlantic region (22. 5°N-70°N and 80°W-50°E) for several datasets. These include the three longest reanalysis ensembles available over our domain, namely: the National Ocean and Atmosphere Administration's (NOAA) atmospheric reanalysis of the twentieth century (20CRv2c)  19  with 56 members covering the period 1851-2014; the European Centre for Medium Range Weather Forecasts' (ECMWF) atmospheric model integrations of the twentieth century (ERA20CM, as ERA20C but with no synoptic meteorological data assimilated and all observational information incorporated in the boundary conditions and forcing  20  ), with 10 members covering the period 1900-2100; and ECMWF's coupled climate reanalysis of the twentieth century (CERA20C)  21   1  for the exact periods used in this study).
This is consistent with the results of ref. 23  that demonstrate that the low-frequency variability in the Northern Hemisphere is spanned by around half-a-score empirical normal modes, with growth rates of around 3-5 days in phase space. We also compute d for the SST fields of some of the above datasets. Hereafter, we specify whether we are referring to d SLP or to d SST. We do not adopt this notation for θ as we compute it for the SLP field only. Finally, we note that the multi-model and ensemble-average d and θ discussed below are not the metrics computed on the mean SLP fields, but rather the mean values of the metrics computed on the SLP fields of each model or ensemble member. This avoids introducing spurious trends in the data  16. Since we are interested in long-term changes in predictability, we apply a 5-year running average to each quantity.
Observed and projected predictability changes. We first comment on the historical period and the reanalysis datasets (Fig. 1  and Table  1 ). The absolute values of d SLP and θ for all datasets are reported in Supplementary Fig. 1. The 20CRv2c ensemble shows a significant decreasing trend in d SLP , whereas the inverse persistence increases only over the early period of the reanalysis, before stabilising. This is possibly an effect of the scarcity of observational data in the 19th century and the consequent large spread of the members  24. The ERA20CM and CERA20C reanalyses broadly agree with 20CRv2c in θ, as they show a weak neutral and positive trend, respectively, but only cover the period where the 20CRv2c θ stabilises. Important discrepancies emerge in d SLP : ERA20CM displays no significant trend, while CERA20C displays a significant increasing trend, opposite in sign to the significant decreasing trend seen in 20CRv2c.
All three datasets also display large interdecadal fluctuations, with ERA20CM showing a lower variability than the other two. It is encouraging to note that, during the common period, CERA20C shows the same interdecadal variability as 20CRv2c; nonetheless, the discrepancy in the long-term d SLP trends between the three reanalyses remains to be explained. The main difference between ERA20CM and CERA20C is the presence, in the latter, of data assimilation and a coupled ocean  21. To save computational time, it is common practice to restart the assimilation process of the reanalyses every 10 years  25. We speculate that this operation may affect the ocean dynamics because it does not give them time to settle on the attractor, as the trajectory is displaced every 10 years. On the contrary, it affects only marginally the atmosphere-only reanalyses without an active ocean component.
This does not, however, explain the differences between ERA20CM and 20CRv2c, which must derive from differences in the numerical models and data assimilation schemes. The historical CMIP5 simulations (Fig. 1  and Table  1 ) display a significant decreasing trend in d SLP , whereas θ shows an increasing, albeit very weak, trend. Unlike the reanalyses, CMIP5 data does not display large interdecadal fluctuations in the two dynamical quantities. This is partly due to the fact that, since we are considering a multi-model mean, the interdecadal fluctuations between individual members average out. However, even when individual historical model runs are analysed, the standard deviations of d SLP and θ are smaller than in 20CRv2c and CERA20C, and comparable to those of the ERA20CM dataset. These values increase to levels comparable to-or even exceeding -those of the two former datasets in the representative concentration pathway (RCP) scenarios.
We next discuss future climates using CMIP5 simulations run under the RCP 4. 5 and 8. 5 scenarios, as well as the Community  Earth System Model (CESM) RCP 8. 5 large ensemble. The CMIP5 models show a significant decreasing trend in d SLP and a weak increasing trend in θ for the RCP 8. 5 scenario (Fig. 1  and Table  1 ). CESM shows similar results for d SLP but no significant change in θ. There is a large spread among the CESM memberslarger in fact than that among the different CMIP5 models. We also note that there is no large difference between the RCP 4. 5 and RCP 8. 5 d SLP trends. With the exception of the two ECMWF reanalyses, all datasets therefore display a decrease in d SLP throughout the period 1850-2100, albeit in some cases modulated by a marked inter-decadal variability. This trend is primarily associated with the summer (June-August, JJA) and autumn (September-November, SON) seasons (see Supplementary Fig.
2  and Supplementary Tables  2  and 3 ). Overall, the datasets with decreasing trends show a relative variation of d SLP of about 5-8%. In order to understand the origin of this trend, we revert to the analysis of the atmospheric configurations prevailing in each dataset. In general, high d are associated with low-predictability meridional configurations, such as blocking or mid-Atlantic ridges. Low d match instead high-predictability zonal configurations  13, 14. A shift of the upper and lower percentiles of the d SLP distributions would therefore correspond to an enhanced zonality or meridionality of the flow. To test this idea, we repeat the analysis shown in Fig. 1  for the top and bottom five percentiles of the d SLP distributions (see Supplementary Fig. 3 ). We find that the trends are consistent with those found for the mean values, implying an enhanced zonality of the flow associated with a decrease in mean d SLP.
2b ). We next verify whether a clear dependence between SST values and d SST is also found in the reanalysis datasets. Above-median values of d SST correspond to widespread cold anomalies across the Atlantic basin, while below-median values display anomalies of the opposite sign (Fig. 3 ). This is not directly comparable to Fig. 2b , as we are looking at SST anomalies here. Nonetheless, it highlights a close coupling between d SST and basin-wide SSTs. For the historical period, all three reanalysis datasets display rapidly increasing SSTs (not shown), yet only one of these datasets (20CRv2c) displays a significant decrease in d SLP , as seen in the models. This points to differences in the ocean-atmosphere coupling as the key to the discrepancies between the three reanalyses. The CMIP5 simulations are coherent with the coupling found in the 20CRv2c reanalysis, since they display a decreasing d SLP trend and a gradual increase in SSTs from the beginning of the historical period to the end of the century  30.
Possible explanations of these phenomena of noise-induced order are investigated in the framework of stochastic dynamical systems  32, 33. Our conclusions come with some caveats. First, the ECMWF reanalyses do not show a decrease in dimensionality, but rather show either an increase of d SLP with time (CERA20C) or no trend (ERA20CM). Although the ERA20CM trend is compatible with those observed in some of the CMIP5 models, the CERA20C case is unique amongst the analysed data. We hypothesise that this may be due to the way the observations are assimilated into the reanalyses. For example, they are reinitialised every ten years, possibly leaving the ocean in an unstable transient state. Differences in ocean-atmosphere coupling between the reanalysis datasets may also play an important role. Moreover, our analysis does not allow us to draw conclusions concerning the global circulation nor small-scale phenomena.
5 W m -2 , respectively, in year 2100, relative to pre-industrial conditions. AMIP simulations cover the 1979-2005 period and are performed by prescribing SSTs and sea ice boundary conditions to an atmosphere-only model. +4 K simulations impose a uniform 4 K warming to observed SSTs, while the 4 × CO 2 simulations impose quadrupled atmospheric CO 2 concentrations relative to the pre-industrial control value of 280 ppm. We additionally analyse a 32-member ensemble of simulations from the CESM  35. This covers the 1950-2100 period, and follows an RCP8. 5 scenario starting from 2006. As the historical part of the simulations is much shorter than for the other datasets, we analyse d and θ trends over the whole CESM period as opposed to performing separate analyses for historical and future trends.
Acknowledgements We thank  A. Jézéquel ,  M. Vrac ,  F. Daviaud ,  Y. Sato ,  V. Lucarini  and  B. Dubrulle  foruseful discussions. We acknowledge  G. Brunet  and  A. Pouquet  for their constructive comments. We also thank  J. Y. Peterschmitt  for the support in downloading the data. D. F. , M. C. A. -C. , G. M. and P. Y. acknowledge the support of the  ERC  Grant No. 338965-A2C2. M. C. A. -C. was further supported by  Swedish Research Council  Grant No. C0629701  and G. M. was supported by a grant from the  Department of Meteorology of Stockholm University  and by the  Swedish Research Council  Grant No. 2016-03724.
Data availability The datasets analysed in this paper are available in the 20CRv2c repository: https://www. esrl. noaa. gov/psd/thredds/catalog/Datasets/20thC_ReanV2c /Dailies/gaussian/ monolevel/catalog. html; in the COBE-SST2 repository: https://www. esrl. noaa. gov/psd/ thredds/catalog/Datasets/COBE/catalog. html; in the ERA20CM repository: https://apps. ecmwf. int/datasets/data/era20cm/; in the CERA20C repository: https://apps. ecmwf. int/ datasets/data/cera20c/; in the CMIP5 repository: https://esgf-node. llnl. gov/projects/ cmip5/; and in the Large Ensemble Community Project repository: http://www. cesm. ucar. edu/projects/community-projects/LENS/data-sets. html.