dataesr/scientific-paragraphs-categorization huggingface dataset:
We therefore used the change in frequency of the 48 MLG (58 individuals in 1987 and 75 in 2009) to build selection gradients for flowering time. Again, we found a gradient with a negative slope (Figure 3b), suggesting that the late flowering MLGs have a reduced realized fitness compared to earlier ones. This confirms the reduced fitness of late flowering genotypes observed in our greenhouse experiment (Figure 2). Yet, the effect of flowering date on the realized fitness was small and only significant when the dataset was restricted to the MLGs present in 1987 and measured in F I G U R E 1 Average flowering time per family for the two sampling years and the two vernalization treatments. Short vernalization is in gray and long vernalization in black. The large dots and the horizontal lines stand for the average flowering date for each vernalization treatment, for the years 1987 (dotted lines) or 2009 (dashed lines.
the short vernalization treatment (n = 12; Figure3b). In addition, the negative slope was mostly supported by the decreasing frequency of the two late flowering MLGs that were prevalent in 1987. The simulation of 22 years of drift with an effective population size of 19 showed that the slope of the observed selection gradient did not deviate significantly from the distribution expected by drift alone (p =. 182). Yet, again, when we restricted the dataset to the MLGs that were present in 1987, the observed selection gradient deviated significantly from the distribution expected by drift alone (p =. 047), which suggests that the drift-alone hypothesis could be rejected.
(b) Selection gradient for flowering time. Each point stands for the average flowering date for a given MLG. The black regression lines are estimated using all points (n = 48; a: slope = 5 × 10 -5 points of frequency per seed p =. 094; b: slope = -0. 0002 95% confidence interval: -0. 0006; 0. 0001 p =. 179). This includes MLGs that were not observed in 1987 (black dots), for which the change in frequency is necessarily always positive. The dotted lines are the regression lines for the analysis restricted to the MLGs present in 1987 (white dots only; n = 12; a: slope = 0. 0002 p =. 024; b: slope = -0. 0009 95% confidence interval: -0. 0017; -0. 0002 p =. 038). Q-Q plots for the selection gradients are provided in Figure S3 F I G U R E 4 Test of selection for increasing values of N e. p-Value, defined as the proportion of simulated datasets where the slope of the selection gradient is steeper than the observed slope, for the simulations of drift alone (a) considering all the homozygous MLGs (n = 48) or (b) considering only the MLGs that were already present in 1987 (n = 12).
DATA AVA I L A B I L I T Y S TAT E M E N T Phenotypic data for the intrapopulation and interpopulation experiments and results from the multilocus genetic structure along with the scripts used for the analyses are available on the INRA data portal https://data. inrae. fr/datas et. xhtml ?persi stent Id=doi:10. 15454/ ZY83BE. The program GENETHAPLO is available at https://github. com/lauga y/Genet Haplo.
2e, f ). This temporal trend in the prominence of diversity hotspots cannot be explained by a secular increase in the maximum lifetime of shelf seas. Geological data from ancient continental margins trapped within orogenic belts  23  and global tectonic reconstructions  24  show no evidence of an increase in the lifespan of passive continental margins or in the maximum ages of the seafloor over the Phanerozoic. Rather, we argue that the temporal proximity between the Ordovician-Silurian (Hirnantian stage), Late Devonian (Frasnian-Famennian stage) and Permian-Triassic mass extinctions, coinciding with a long-lived phase of marine shelves destruction during the assembly of Pangaea, interrupted the development of diversity hotspots during the Palaeozoic. In fact, by deactivating the Late Devonian mass extinction in the model, we found that the development of diversity hotspots before the end of the Permian leads to global diversities that are two to three times greater than those generated by the same calibrated logistic model with all mass extinctions enabled (Extended Data Fig.
6a-c ). By contrast, the comparatively long expanse of time that separated the mass extinctions of the end-Triassic and end-Cretaceous extended the time-for-speciation under conditions of increasing continental fragmentation, giving rise to exceptionally high-diversity regions before the Cretaceous-Palaeogene mass extinction. The extraordinary diversity of Late Cretaceous hotspots ensured the continuity of relatively high diversity levels in the aftermath of the end-Cretaceous mass extinction, facilitating the subsequent development of diversity hotspots during the Cenozoic. To evaluate the performance of the model in reconstructing the spatial distributions of diversity, we compared the results of the present-day calibrated logistic model (that is, 0 Ma) with observations of two of the most relevant groups of marine invertebrates, crustaceans and molluscs, extracted from the Ocean Biodiversity Information System (OBIS)-a global database of occurrence records of marine taxa (Methods).
The regional diversity map generated by the model shows reasonable similarities to the observed diversity distributions along the continental margins (Fig. 2g, h ). The main discrepancies between the model and the OBIS data occur in the surroundings of Australia and New Zealand, where the model underestimates diversity. The model lacks long-distance dispersal, which precludes a more detailed reconstruction of the spatial structuring of diversity in such a highly interconnected ocean region. On the other hand, the OBIS data are not homogeneously distributed over the global ocean and, although our analysis attempts to minimize this bias (Methods), some of the discrepancies between model and observations may be due to database limitations. Despite these discrepancies, both observed and modelled diversity decline from the equator towards the poles (Fig.
Palaeogeographical model We use palaeogeographical reconstructions describing Earth's palaeotopography and palaeobathymetry for a series of time slices from 541 Ma to the present day. The reconstructions merge existing models from two published global reconstruction datasets-those of ref. 32  and ref. 33  (https://doi. org/10. 5281/zenodo. 5348492), which themselves are syntheses of a wealth of previous work. For continental regions, estimates of palaeoelevation and continental flooding rely on a diverse range of geological evidence, such as sedimentary depositional environments and the spatiotemporal distribution of volcanic activity. For a full description, see a recent review  34. Together, these data can be used to define the past locations of mountain ranges and palaeoshorelines  34. For this part of our reconstruction, we used the compilation of ref.
33  with updated palaeoshorelines based on depositional environment information in current fossil databases  35. This compilation comprises 82 palaeotopography maps covering the entire Phanerozoic. Note that each palaeogeographical map is a time slice representing the concatenation of geological data over several million years  36. We quantified the impact of using the original compilation of ref. 33  on our model results and found only small changes with respect to using the reconstructions with updated palaeoshorelines (Extended Data Fig. 3a-c ). Similarly, eustatic sea level is thought to have varied by around 100 m at timescales much shorter than the duration of the time-slices throughout the Phanerozoic  37  , such that the extent of continental flooding could have varied within each time slice by an amount significant for our analysis.
For this reason, and to assess the uncertainty of our results to continental palaeogeography in general, we computed additional maps of continental flooding in which the sea level is raised or lowered by 100 m compared with the original palaeo-digital elevation model grids of ref. 33  (Extended Data Fig. 3d-f ). For deep-ocean regions, the primary control on seafloor depth is the age of the seafloor, so reconstructing palaeobathymetry relies on constructing maps of seafloor age back in time  38. As a consequence, we rely on reconstruction models that incorporate a continuous network of plate boundaries. For this part, we used the reconstruction of ref. 32  and derived maps of seafloor age from the plate tectonic model using the method of ref. 39  for which source code is available at GitHub (https://github.
com/siwill22/agegrid-0. 1). Palaeobathymetry is derived from the seafloor age maps following the steps outlined in ref. 38. It is important to note that seafloor age maps for most of the Phanerozoic (that is, pre-Pangaea times) are not directly constrained by data due to recycling of oceanic crust at subduction zones. Rather, they are model predictions generated by constructing plate motions and plate boundary configurations from the geological and palaeomagnetic record of the continents. Nonetheless, the first-order trends in ocean-basin volume and mean seafloor age are consistent with independent estimates for at least the last 410 million years (Myr)  39. The reconstructions of refs. 32, 33 differ in the precise locations of the continents through time. To resolve this discrepancy, we reverse reconstructed the continental palaeoelevation model of ref.
These points are tracked to subsequent time slices of which the palaeogeography is used to determine whether the point lies within a flooded or emergent region. Points within flooded regions of continents are considered to be seafloor, and the age of this seafloor is accumulated across consecutive time slices where a given point lies within a flooded region. When a point is within an emergent region, the seafloor age is reset to zero. Following this approach, individual points within stable continents may undergo several cycles of seafloor age increasing from zero before being reset. At the continental margins formed during the Pangaea breakup, the age of the seafloor continuously grows from the onset of rifting. Intraoceanic island arcs represent an additional case, which can appear as new tectonic units with the reconstructions at various times.
Palaeoenvironmental conditions under the cGENIE Earth system model We use cGENIE  41  , an Earth system model of intermediate complexity, to simulate palaeoenvironmental conditions of seawater temperature and organic carbon export production (as a surrogate for food supply) throughout the Phanerozoic (from 541 Ma to the present day). cGENIE is based on a three-dimensional (3D) ocean circulation model coupled to a 2D energy-moisture balance atmospheric component and a sea-ice module. We configured the model on a 36 × 36 (latitude, longitude) equal area grid with 17 unevenly spaced vertical levels in depth, down to a maximum ocean depth of 5,900 m. The cycling of carbon and associated tracers in the ocean is based on a size-structured plankton ecosystem model with a single (phosphate) nutrient  42, 43  , and adopts an Arrhenius-type temperature-dependent scheme for the remineralization of organic matter exported to the ocean interior  44.
cGENIE provides a spatially resolved representation of ocean physics and biogeochemistry, which is a prerequisite for the present study to be able to reconstruct the spatial patterns of biodiversity in deep time. However, owing to the computational impracticality of generating a single transient simulation of physics (that is, temperature) and biogeochemistry (that is, export production) over the entire Phanerozoic, we therefore generate 30 model equilibria at regular time intervals throughout the Phanerozoic that are subsequently used as inputs for the regional diversification model (see the 'Model coupling' section). We used 30 Phanerozoic palaeogeographical reconstructions through time (~20 Myr evenly spaced time intervals) to represent key time periods. For each continental configuration corresponding to a given age in Earth history, we generate idealized 2D (but zonally averaged) wind speed and wind stress, and 1D zonally averaged albedo forcing fields  45  required by the cGENIE model using the 'muffingen' open-source software (see the 'Code availability' section).
We quantify the impact of this uncertainty on our model results by conducting additional simulations using half and twice the present-day ocean phosphate concentration (Extended Data Fig. 3g-i ). For each ocean phosphate scenario (that is, 0. 5×, 1× and 2× the present-day value), each of the 30 model simulations is then run for 20,000 years, a duration ensuring that deep-ocean temperature and geochemistry reach equilibrium. For each model simulation, the results of the mean annual values of the last simulated year are used for the analysis. Note that, although cGENIE makes projections of the distribution of dissolved oxygen ([O 2 ]) in the ocean, our diversification model does not currently consider oxygenation to be a limit on diversity. Thus, we assumed a modern atmospheric partial pressure of O 2 in all 30 palaeo simulations and did not use the resulting projected [O 2 ] fields.
When a point arrives in an environment with a carrying capacity lower than the diversity it has accumulated through time, we reset the diversity of the point to the value of the carrying capacity, thereby simulating local extinction. Seawater temperature (T) and food availability (POC flux) are provided by the cGENIE model, which has a spatial and temporal resolution coarser than the palaeogeographical model. The cGENIE model provides average seawater T and POC flux values in a 36 × 36 equal area grid (grid cell area equivalent to 2° latitude by 10° longitude at the equator) and 30 time slices or snapshots (from 541 Ma to the present: each ~20 Myr time intervals). To have environmental inputs for the 82 time slices of the plate tectonic-palaeoelevation model, we first interpolate the cGENIE original model output data on a 0.
5° by 0. 5° grid to match the annotated grids provided by the plate tectonic-palaeoelevation model. As the relatively coarse spatial resolution of the cGENIE model prevents rendering the coast-ocean gradients, we assign surface T and POC flux at the base of the euphotic zone to the flooded continental shelf grid cells, and deep ocean T and POC flux at the bottom of the ocean to the ocean grid cells. As there are time slices without input data of seawater T and POC flux, we interpolate/extrapolate seawater T and POC flux values into the 0. 5° by 0. 5° flooded continental shelf and ocean grids independently. Finally, we interpolate values from these 0. 5° by 0. 5° flooded continental shelf and ocean grids into the exact point locations in each time frame. Thus, each active point is tracked with its associated time-varying T and POC flux values throughout its lifetime.
Fossil data We used three fossil diversity curves of reference  [20] [21] [22]  to (1) extract the patterns of mass extinctions (starting time, duration and magnitude) imposed on the model, and (2) compare the global diversity curves produced by the model with those generated from fossil data. Sepkoski's global diversity curve corresponds to marine invertebrates listed in Sepkoski's published marine genus compendium  20  (data downloaded from Sepkoski's Online Genus Database; http://strata. geology. wisc. edu/jack/). The global diversity curves of refs. 21, 22 are digitized from the original sources, that is, figure  3  of ref. 21 and figure 2a of ref. 22  , respectively, using the free software XYscan. The curve reported by ref. 21 orresponds to genus-richness estimates obtained after correcting for sampling effort using the shareholder quorum subsampling technique.
OBIS data We use the occurrence records of genera belonging to two of the most diverse marine invertebrate groups: subphylum Crustacea and phylum Mollusca, as downloaded from the Ocean Biodiversity Information System (OBIS) on 22 October 2021 (www. obis. org). The list of genera is validated with the genera names in WoRMS (https://www. marinespecies. org) and only the accepted, extant and marine names were selected for the analysis. This corresponds to a total of 10,018,142 records of 9,750 genera (6,540,489 records and 5,533 genera of crustaceans and 3,477,653 records and 4,217 genera of molluscs) collected from 1920 to 2021. The records are gridded into hexagons (800,000 km 2 at the equator) to account for different gamma (regional) diversity across latitudes, otherwise, a bias would occur in the resulting estimates.
To ensure sufficient sampling size, we selected only those hexagons with more than or equal to 10 occurrence records and with more than three genera. Furthermore, we use the frequencies of the genera to estimate the number of unobserved genera per hexagon. We do so by extrapolating the number of genera on the basis of a bias-corrected Chao estimate according to the tail of rare genera (that is, those genera that have only one or two occurrence records in a hexagon)  64, 65. The final number of genera per hexagon is the sum of the observed and unobserved estimates of genera. The analysis is performed with the package vegan 66 in R v. 4. 1. 2. Finally, we spatially overlap the hexagons and 0. 5° × 0. 5° square grid to match the map of the palaeo analysis and extract the value of the diversity index per coastal grid in QGIS v.
Acknowledgements This work was funded by research grant  CGL2017-91489-EXP  from the  Spanish government. A. P. acknowledges funding from the  European Union 's  Horizon research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 838373. S. W. was supported by  NSFC  grant no. 41972237  and  ARC  grant nos DP180102280 and  DP200100966. M. J. B. acknowledges support through  ERC Advanced Grant 'Innovation'  ( ERC 788203 ). P. C. and S. M. V. acknowledge support through grant no. CTM2017-87227-P. A. R. acknowledges  NSF  grant  EAR-2121165 , as well as support from the  Heising-Simons Foundation. The  Institut de Ciències del Mar (CSIC)  received funding from the  Spanish government through the 'Severo Ochoa Centre of Excellence ' accreditation ( CEX2019-000928-S ).
Code availability The coupled palaeogeographical-diversification model presented here uses input data of seafloor age distributions and palaeoenvironmental conditions from the siwill22/agegrid-0. 1 v1-alpha palaeogeographical model and the cGENIE Earth system model, respectively. The code used to reconstruct seafloor age distributions from GPlates full-plate tectonic reconstructions is available at Zenodo (https://doi. org/10. 5281/ zenodo. 3271360). The code for the version of the 'muffin' release of the cGENIE Earth system model used in this paper is tagged as v0. 9. 30, and is available at Zenodo (https://doi. org/10. 5281/zenodo. 6676435). Configuration files for the specific experiments presented in the paper can be found in the directory: genie-userconfigs/PUBS/published/ Cermeno_et_al.
2022. Details of the experiments, plus the command line needed to run each one, are given in the readme. txt file in that directory. The code for the muffingen boundary file generator, is available at Zenodo (https://doi. org/10. 5281/zenodo. 6676451). The code for the coupled palaeogeographical-diversification model is available at Zenodo (https://doi. org/10. 5281/zenodo. 6535496). The code is also available at GitHub (https://github. com/CarmenGarciaComas/ INDITEK); this is the recommended mode of access as it will contain any updates and clarifications.
Objectives and statistical analysis The duration of follow-up was calculated using the inverse method  (17). Patients were excluded from survival analysis if they had less than three months of follow-up. Overall survival (OS) was calculated from the MM diagnosis until death or the last follow-up (whichever occurred first). Time to next treatment was calculated as the time from the start of front-line therapy to an event (start of a new line of treatment, death, or last followup; whichever occurred first). The primary outcome was OS. Categorical variables were compared using a c 2 test or Fisher's exact test, as appropriate. Continuous variables were compared using a Student's t test or a Wilcoxon rank-sum test. Kaplan-Meier curves were plotted for survival, and data for various groups were compared using a log-rank test.
Multiple myeloma treatment and outcomes Most of the patients received triplet association with a backbone of bortezomib and high dose steroids (95%) associated with an immunomodulatory drug like thalidomide (21%), lenalidomide (34%), or alkylating agents like cyclophosphamide (37%) or melphalan (5%) (Supplementary Table  1 ). The anti-plasma cell treatment was started after the end of TPE sessions. If TPE washout criteria was not considered, two-thirds of these patients met the eligibility criteria for the most recent clinical trials for young (MIDAS study, n=16 [NCT04934475]) and older patients (BENEFIT study, n=9 [NCT04751877]). Nevertheless, only two patients (8%) were included in a clinical trial. One patient did not receive MM treatment except TPE and steroids related to multiple septic shocks and died one month after the HVS diagnosis.
Our data show that UbiJ-UbiK 2 interacts closely with the membrane and suggests possible pathways to enable the release of UQ into the membrane. This study highlights the UbiJ-UbiK 2 complex as the likely interface between the membrane and the enzymes of the Ubi metabolon and supports that the heterotrimer is key to the biosynthesis of UQ 8 and its release into the membrane of E. coli.
The strength of interaction with coiled-coil domains and their stability are correlated with the size of the interface area  [16]. To date, the structural characterisation of UbiJ and UbiK remains very limited. Only the N-terminal region (~120 first residues) of UbiJ, corresponding to the SCP2 domain, has been determined by X-ray crystallography  [10]  (PDB codes: 6H6O, 6H6N, 6H6P). Moreover, no 3D template is available for a proper modelling of the rest of UbiJ or for the full UbiK. Given these limitations, the modelling of the UbiJ and UbiK molecular complex remains highly challenging. Therefore, the use of adapted methods, such as ab initio methods, to predict the protein structures is required. The development of this type of methods is crucial in the protein modelling field and has been spotlighted by the game changer AlphaFold2 (AF2)  [17] , outperforming other structure prediction methods in the last CASP challenge  [18].
Of note, we decided in the current study to use the helical conformation predicted by AF2 for subsequent modelling and analyses, without exploring further the conformation of this region. Structural models were superimposed using Pymol. 3D models are displayed in cartoon coloured as a function of plDDT value. (B) Prediction of amphipathic helix profiles using HeliQuest server  [26]. Hydrophobic and polar residues correspond to yellow and red/blue circles, respectively. Residue numbering corresponds to the Uniprot sequence (Uniprot ID: Q46868). The first analysis to evaluate UbiJ models (best ranked shown in Figure  3A ) was to compare the predicted SCP2 domain with the available X-ray crystallographic structures (PDB codes: 6H6O, 6H6N, 6H6P)  [10]. Figure  3B  illustrates one example of structural alignment between the five predicted 3D models and a crystallographic structure (PDB code: 6H6O).