dataesr/scientific-paragraphs-categorization huggingface dataset:
8 Å resolution, respectively, at beam line 5C at Pohang Accelerator Laboratory (PAL), Korea. Data were processed and scaled using HKL2000  47. The space groups of syntenin PDZ1/KSL-128018 complex was determined as C121 (a= 95. 58 Å, b= 38. 396 Å, c= 56. 777, α=γ=90°, β=92. 523°) and structures were solved using the molecular replacement method with the program PHENIX  48. The crystallographic model was built using the COOT  48  program and refined in PHENIX  49. The statistics for structure refinement are summarized in Table  S7. The coordinates of syntenin PDZ1/KSL-128018 complex has been deposited to the protein data bank with the accession code 5YA7. Pull-down of syntenin from brain lysate. Mouse brain lysate was prepared from adult C57BL/6 animals. Brains were removed after cervical dislocation and rapidly homogenized in 20 mM HEPES, 100 mM KCH3COOH, 40 mM KCl, 5 mM EGTA, 5 mM MgCl2, 5 mM DTT, 1 mM PMSF, 1% Triton X and protease inhibitor Roche complete, pH 7.
In vitro plasma stability. Human plasma was preheated at 37°C for 15 min before spiked with a final concentration of 0. 2 mM parent peptide, KSL-128018 and the six cell-penetrating analogs. Samples were collected after 0, 1, 2, 4, 6 and 24 h or after 0, 2, 5, 10, 30 and 60 min, depending on stability. The compounds were extracted from the plasma by either pre-treatment with 6M urea for 10 min followed by addition of 20% trichloroacetic acid (TCA) or pre-treatment with 26 mg guanidine hydrochloride (GuHCl) followed by addition of 10% TCA in acetone. The samples were centrifuged at 13400 rpm for 10 min and the supernatant was analysed by UPLC (214 nm). The area under the curve (AUC) was determined and normalized to time point 0 h. The half-life (T1/2) was determined by fitting the data to a one-phase decay equation in Prism 6.
The data is represented as the mean of three individual experiments ± SEM. In vitro hepatic clearance. Peptides were incubated in mouse hepatic microsomes supplemented with 1 mM NADPH and 3 mM MgCl2 for up to 60 min. The peptides were extracted from the microsomes and analysed by UPLC. and 5 mM parent peptide, KSL-128018 and the six cell-penetrating analogs at 37°C for 0, 5, 10, 15, 30, 45 and 60 min. The peptides were extracted by pre-treatment with 6 M urea for 15 min followed by addition of 100 µl acetonitrile or by pre-treatment with 6 M GuHCl for 15 min followed by addition of 100 µl 10% TCA in acetone. The samples were centrifuged at 13400 rpm form 15 min before analysed by UPLC (214 nm). The AUC was determined and normalized to time point 0 min. The data was fitted to a onephase decay equation to determine the T1/2.
Cells were cultured at 37 °C in an atmosphere of 5% CO2. Acutely dissociated single cells were plated into a 96-well plate at 3000 cells/well in triplicates. Next day, vehicle or peptide inhibitor was added and cell viability was measured after 72 h using CellTiter-Glo Luminescent Cell Viability Assay (Promega). Results were calculated as relative fold change in ATP with each group internally normalized to the respective vehicle control. The experiments were performed three times in triplicate and the data is presented as mean ± SD. Inhibition of microsomal budding. The inhibition of syntenindependent intraluminal vesicle (ILV) budding was determined as previously described  51. Briefly, MCF-7 cells (ATCC: HTB-22) were grown in DMEM/F12 (1:1) medium supplemented with 10% fetal calf serum. The cells were transfected the day after plating with mCherry-syntenin and Ce-RAB5(Q79L) using Fugene HD (Roche).
Cells were treated for 24 h with vehicle (0. 1% DMSO in sterilised water) or with increasing concentrations of KSL-128018, KSL-128114 or 1-4. The number of mCherry-syntenin filled Ce-RAB5(Q79L) endosomes were analysed with confocal microscopy. The data is presented as the mean of two individual experiments, where at least 40 endosomes were examined in each experiment.
Accession Codes PDB code for PDZ1 domain with bound KSL-128018 is 6AK2. cherche sur le Cancer (PJA 20161204584). Raphael Leblanc is the recipient of postdoctoral fellowship of the Foundation ARC pour la Recherche sur le Cancer (PDF20151203700) and Antonio Luis Egea-Jimenez of la Ligue contre le Cancer (France).
Data is presented as mean ± SEM, n≥3. N. B. indicates a Ki >500 µM. X-ray crystal structure and amide-to-ester mutations reveal a non-canonical binding mode. To obtain molecular insight into the binding mode of peptide KSL-128018, an Xray co-crystal structure of the syntenin PDZ1 domain in complex with KSL-128018 was determined at a resolution of 1. 86 Å (PDB ID: 6AK2). KSL-128018 binds to the canonical PDZ binding site located between the b2 sheet and a2 helix. (Figure 3A,B and Figure S2). In the canonical binding mode.
Funding Sources This work was supported by the  Lundbeck foundation (K. S) , the  Swedish Research Council (LMHK)  and the  National Research Foundation of Korea  (WL,  NRF-2017R1A2B2008483 ). The work in PZ laboratory was supported by the  National Research Agency  ( ANR, Investissements d'Avenir, A* MIDEX  project  ANR-11-IDEX-0001-02 ), the  Fund for Scientific Research-Flanders  ( G. 08646. 15N )and the foundation ARC pour la Re-.
ARTICLE ENSO diversity shows robust decadal variations that must be captured for accurate future projections Bastien Dieppois 1,2 ✉ , Antonietta Capotondi  3, 4  , Benjamin Pohl 5 , Kwok Pan Chun  6  , Paul-Arthur Monerie  7  & Jonathan Eden 1 El Niño-Southern Oscillation (ENSO) shows a large diversity of events that is modulated by climate variability and change. The representation of this diversity in climate models limits our ability to predict their impact on ecosystems and human livelihood. Here, we use multiple observational datasets to provide a probabilistic description of historical variations in event location and intensity, and to benchmark models, before examining future system trajectories. We find robust decadal variations in event intensities and locations in century-long observational datasets, which are associated with perturbations in equatorial wind-stress and thermocline depth, as well as extra-tropical anomalies in the North and South Pacific.
Whatever their nature is, decadal variations in ENSO diversity can be expected to obscure the detection of the climate-change signal, and influence our assessment of 21 st -century projections of ENSO diversity, highlighting the importance of considering the system trajectory, and not only differences between specific future and historical periods, for those assessments. The detection of low-frequency variations in ENSO diversity has however been largely limited by various technical shortcomings. Most observational studies use relatively short records (≤50 years), which are arguably of insufficient length to study ENSO diversity, and to benchmark models  [7] [8] [9] [31] [32] [33]. Long-t rm observational SST datasets are reconstructed based on the more densely sampled recent decades, and may be biased toward the patterns of recent events  34.
Methods). This approach is not new. For example, the "Center of Heat Index" (CHI) provides the basis for a more flexible framework, allowing the longitudinal centre of SSTa to vary as a function of time instead of being geographically fixed  34, 44. A similar concept was recently used to provide a single index capturing the longitudinal shifts of ENSOrelated atmospheric convection  45  , and to identify the emergence of double-peaked El Niño events  46. In addition, the classification of EP-and CP events is dependent on the specific dataset used (Fig. 1b ), suggesting that a probabilistic definition that accounts for the level of agreement among diverse datasets is needed to more robustly assess changes in ENSO diversity. Here, we revisit this classification approach for both El Niño and La Niña events across multiple observational datasets, including century-long reconstructions, ocean and coupled reanalyses, and high-resolution satellite-derived estimates (cf.
Results Observed changes in the likelihood of ENSO location and intensity. The probability distribution of ENSO location and intensity is examined in six observational datasets, using 20-year running periods between 1850 and 2018 (Fig. 2 ). The results obtained using 10-or 30-year running windows are very similar, but the 20-year window is found to optimise the signal-to-noise ratio (Supplementary Fig. 1 ). On average, El Niño events occur over multiple longitudinal locations (Fig. 2a ), but show stronger probability to occur either over the central Pacific (~162-171°W; CP-Niño) or the eastern Pacific (~89-132°W; EP-Niño). We note larger observational uncertainty in the probability of EP-Niño (Fig. 2a ), which is substantially less likely in ERSST. v5, CERA20C, and especially in OISST. v2, which covers a period dominated by CP events.
Comparing the statistical distribution of all observational datasets using 20-year running windows, larger uncertainties in the location of ENSO events are found in the early records (Fig. 2b , grey and black histograms). Nevertheless, looking at the agreement of high probability and the most likely location across all datasets, we identify a coherent and progressive shift to predominant CP-Niño events over the course of the 20 th century (Fig. 2b ). This result confirms findings from previous studies, using different approaches, on the recent increase in CP-El Niño frequency  [13] [14] [15]  , and on the uniqueness of El Niño patterns  36. La Niña events also appear characterised by multiple preferential locations, and show a larger degree of consistency in the probability distribution across the different datasets (Fig.
2c ). CP-Niña events are systematically more likely than EP-Niña events, especially in COBESST. v2 and CERA20C (Fig. 2c ). Looking at temporal changes in the statistical distribution across all datasets, coherent low-frequency variations emerge in the most likely locations of La Niña (Fig. 2d ). CP-Niña events are more frequent in the 1930-40s and after the 1970s than during the 1950-60s (Fig. 2d ). All observational datasets show quasi-normal distributions for event intensity, with an average most probable intensity of +0. 84 °C and -0. 92 °C for El Niño and La Niña events, respectively (Fig. 2e, g ). In all observational datasets, the probability distribution of La Niña intensity shows relatively small variations over time using a 20-year running window. These variations, however, exceed the range of variability that would be obtained in a random field (Fig.
2c , dashed lines and grey shading), and are substantially larger using a 10-year window (Supplementary Fig. 1j ). Decadal variations in intensity are stronger for El Niño events (Fig. 2e ). The most likely El Niño intensities display relative minima in the late 19 th century, the 1920-50s, and 1980-90s, as compared with the early 20 th century, the 1960-70s and post-2000s (Fig. 2f ). The reduced ENSO activity in the 1920-1950s coincides with a period of larger disagreement between observational datasets (Fig. 2f , grey and black histograms), which could explain reduced skill in the prediction of ENSO indices over the same period  49. In addition, we note that the upper tail of the distribution, as characterised by the frequency of events exceeding the 90 th percentile (Fig. 2f , red histogram), is larger at the beginning of the century and after the 1970s, with a peak in the 1980-90s, which is likely associated with the extreme El Niño events of 1982/83 and 1997/98.
In addition, these low-frequency variations in ENSO location and intensity exceed the range of variations that would occur in a random field during some periods, notably the westward shift in the location of El Niño events (Fig. 2b , dashed lines and grey shading), suggesting that such variations are unlikely to occur purely by chance. Evaluation of ENSO diversity in climate models. We compare the simulated probability distributions of ENSO location and intensity from 26 CMIP5 and 28 CMIP6 models to our six observational datasets in Fig. 3. As seen in Fig. 2 , El Niño and La Niña events show a primary location centred around 162-171°W, and a secondary location centred around 89-132°W (Fig. 3a, d ). Yet, the range of simulated event locations varies significantly across models and model generations (Fig.
2 ). In summary, CMIP5 and CMIP6 models simulate some range of pattern diversity for El Niño and La Niña events. However, very few models present a range of event locations in relatively good agreement with observations, and minimal biases in their intensity. Most models hardly depart from an eastward or westward location, favouring either EP-or CP events, and presenting limited ENSO diversity. Notably, larger biases are found for the models that produce erroneous westward extensions of SSTa (cf. MIROC6, Supplementary Figs. 3 4 5 6 ). Robust decadal variations in ENSO-preferred location and intensity. Here, we use spectral analysis to examine whether robust and significant low-frequency variations are found in the most likely location and intensity of ENSO, in both the five longterm observational datasets, CMIP5 and CMIP6 models.
We first examine the timescales on which ENSO behaviour varies using maximum power spectrum (Fig. 4 ), a method that accounts for nonstationarity of ENSO spectral characteristics (cf. Methods), before comparing the observed and simulated magnitude of decadal variations in ENSO-event location and intensity (Fig. 5 ). Despite some discrepancies, all observational datasets show significant variations on interdecadal timescale (14-32 yr) based on the 10-yr most likely location of El Niño and La Niña events (Fig. 4a, d ). Using both historical and piControl simulations, almost all models also produce significant decadal variability in the most likely location of ENSO (Fig. 4b, c, e, f ). In addition, between 54 and 59 (60 and 64)% of historical (piControl) simulations significantly simulate statistically equal decadal variance, as compared with observations, in the locations of El Niño and La Niña events, respectively (Fig.
Methods). Our results also reveal significant decadal modulations in the maximum likelihood of ENSO locations, which are robust and consistent in both observations and climate models. Nevertheless, many models show recurrent biases in simulating realistic magnitudes of decadal variance in ENSO diversity. Large-scale patterns linked to decadal variability in ENSO location and intensity. To identify large-scale patterns of variability associated with spatiotemporal variations in ENSO, separately for El Niño and La Niña years, we compute linear regressions of pan-Pacific SSTa, wind stress, and equatorial 20 °C isotherm depth (Z20) on the location and intensity of events, using five long-term observational datasets and 32 historical simulations from the IPSL-CM6A-LR large ensemble (Fig. 6 ). We focus on the Pacific region, as regressed SSTa is much lower, and often nonsignificant, in the other ocean basins.
7a-f ). Similar diverse behaviour in simulations of future evolution of ENSO event location is found in CNRM-CM6-1, MIROC6 and CESM2 (Supplementary Fig. 8a-f ). In all cases, future trajectories for ENSO location are strongly influenced by the presence of decadal variations, which may themselves be model dependent and not necessarily consistent with observations. These two factors could explain why a recent study of ENSO uniqueness, using future CMIP5 model scenarios, and comparing the multimodel mean over specific decades, could not detect robust changes in ENSO diversity over the 21 st century  36. ). Z20 anomalies are estimated through the median changes between 5°S and 5°N (b, d, l, n, g, j, q, t). Red and blue shades on the Z20 anomalies indicate the spread between the four SST observational datasets and within the IPSL-CM6A-LR large ensemble (light to dark: maximum/minimum, 10/90 th , 30/70 th , and 45/55 th percentiles), for El Niño and La Niña, respectively.
Discussion To overcome existing limitations in analysing ENSO diversity, this study uses multiple observational datasets to provide a Fig. 7  Future scenarios for ENSO diversity in the most realistic models. Twenty-year most likely location of El Niño events in (a) the IPSL-CM6-LR, (b) UKESM1-0-LL and, (c) CNRM-CM5 ensemble mode (black bold lines), in two contrasted individual simulations (light green and yellow lines), and the percentage of agreement of high-probability within the full ensemble (PDF exceeding 0. 01 and 0. 45; colour shades). d-f Same as (a-c) but for La Niña events. g-l Same as (a-f) but for El Niño and La Niña intensity. Grey histograms on the bottom axis of the intensity panels indicate the average number of extreme events (as defined in Fig. 2 ) within the full-model ensemble.
Dashed lines and grey shading indicate the range of variability that could be obtained in a random field at p = 0. 05. SSTa is estimated by removing the 1850-2014 monthly climatology and trend, to allow comparison with observations. The same baseline period was used to estimate the 90 th and 10 th percentiles. probabilistic description of historical variations in event location and intensity, and to benchmark models, before examining future system trajectories. Using multiple century-long observational datasets, CMIP5 and CMIP6 multi-model ensembles, we first identified robust long-term changes and variability in the likelihood of El Niño and La Niña location and intensity. Although the majority of models favour either EP-or CP-ENSO, we found that ENSO diversity is closely linked to significant decadal variations in both observations and climate models.
Methods Observational reference datasets. We use six observational datasets, covering all the different ways to reconstruct long-term variability for SST, as well as different resolutions (Supplementary Table  1 ). This includes three observational reconstructions based on empirical orthogonal functions/teleconnections (EOF/EOTs), spanning the period 1870-2018: (i) the extended reconstructed SST version 5  77  (ERSST. v5); (ii) the Centennial in-situ Observation-Based Estimates  78  (COBESST. v2); (iii) the Hadley Centre SST data set  79 (HadSST1). As the use of EOF/EOTs might lead to underestimate ENSO diversity in the 19 th and early 20 th centuries  34  , observational reconstructions are compared with two reanalysed SST datasets: (i) the eight-member ensemble of ocean reanalysis generated using the Simple Ocean Data Assimilation system with sparse observational input version 3  80  (SODA.
si3) between 1870 and 2015; (ii) the ECMWF ten-member ensemble of coupled climate reanalyses of the 20 th century (CERA-20C  81  ), from 1901 to 2010. Since the use of satellite observations at the end of the 20 th century is known to result in a cold bias in HadSST1 and COBESST. v2  82 , the optimum interpolation SST version 2  83  (OISST. v2) is used for comparison between 1981 and 2018. To examine the potential large-scale patterns associated with changes in the ENSO spatiotemporal variability, surface wind stress was derived from surface zonal and meridional winds for the period 1870-2015, using the NOAA-CIRES-DOE Twentieth Century Reanalysis version 3  84  (NOAA-20CR. v3). The NOAA-20CR. v3 uses SODA. si3 and HadSST1 as boundary forcing, and therefore provides consistent atmospheric circulations for those SST datasets.
Because subsurface potential temperature data are not currently available in SODA. si3, we use SODA. v2. 2. 4, with NOAA-20CR. v2 as boundary forcing, to provide the most consistent estimate of thermocline depth, using the 20 °C isotherm depth (Z20) as a proxy. CMIP5/6 simulations. We use 95 ensemble members of historical simulations from 26 CMIP5 models  47  , and 250 members from 28 CMIP6 48 models, together with longer piControl runs (Supplementary Table  1 ), to evaluate how climate models perform in simulating ENSO diversity. Each individual member of historical simulations allows inferring climate variability from the mid-19 th to the early 21 st century, due to changes in anthropogenic and natural forcings, while accounting for uncertainties associated with internal variability  85.
Similarly, piControl simulations enable assessing the uncertainties associated with the limited length of reliable historical records. In addition, to discuss the implications of our results for future scenarios of ENSO diversity, we use the highest emission scenario or forcing level (8. 5 W. m -2 ), i. e. , the Representative Concentration Pathway RCP8. 5 in CMIP5 models, and the Shared Socio-economical Pathway 5 that updates the highest forcing level, i. e. , 8. 5 W. m -2 (SSP5-85) in CMIP6 models. The number of available realisations is substantially lower in future scenarios than historical runs (Supplementary Table  1 ). Monthly fields of SST, zonal and meridional wind-stress and potential temperature (from which we estimated the thermocline depth from Z20) are used. To ensure consistency with the observational datasets, and to optimise the detection of changing locations and intensity in ENSO, model simulations were all interpolated onto a regular 1.
Examining long-term variability and changes in ENSO location and intensity. To better account for multidimensionally varying properties of ENSO, building on the CHI concept  34, 44  , and other recent studies  37, 45, 46  , we use a flexible framework, which allows for a continuum of patterns, rather relying on a more rigid bimodal view of ENSO, to estimate the location and intensity of El Niño and La Niña events. The location of ENSO events has been defined as the longitudinal location of the absolute maximum of SSTa, within a strip that spans the tropical Pacific from 150°E to 60°W (excluding the warm-pool region), and averaged between 5°S and 5°N over the boreal winter months (December-February). To harmonise the results over variable grid resolutions, and reduce the noise in the signal, the location of the absolute maximum of SSTa has been estimated using a 2°longitudinal smoothing (corresponding to the resolution of the coarser observational SST dataset; cf.
We adopted a probabilistic approach to quantify the likelihood of event location and intensity, and the degree of agreement among diverse observational datasets. This is achieved by estimating the probability-density function (PDF) over every 20-year segments of each observational dataset, and calculating the most likely values (i. e. , the mode in statistical terms), as well as multidataset agreements of high probability (i. e. , probability exceeding 0. 01 and 0. 4 for event location and intensity, respectively). Specifically, the agreement of high probability enables us to examine the temporal evolution of the heads of PDFs across multiple datasets, allowing for detection of multiple heads, while limiting the influence of outliers. Here, focus is given to the results of a 20-year running window, as it is found to optimise the signal-to-noise ratio, but very similar results are found using 10-or 30-yr running windows (Supplementary Fig.
1 ). We also quantify the percentage of disagreement between them, by comparing their respective PDF using a Kolmogorov-Smirnov (KS) test at p = 0. 05 and 0. 1, using the same 20-year running window. Significance of the variations in the most likely location and intensity of events are then tested against the variations that would be occurring in a random field at p = 0. 05. This is achieved by replicating the above procedure using 1000 random fields, generated through autoregressive models of order 1 with the same mean and variance as the observational field. In addition, as the most likely location and intensity might not always be representative of changes affecting the tails of the distribution (or extremes), we also examine the changes in the frequency of extreme El Niño (La Niña) events, by quantifying the 20-year average number of events exceeding (lower than) the 90 th (10 th ) percentile across all datasets.