dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey scans were acquired from m/z 375 to 1800 with a resolution of 60,000 at m/z 200 and fragmentation spectra with a resolution of 60,000 at m/z 200 for TMT labeled samples and 15,000 for bioUb samples. Precursors were fragmented by higher energy C-trap dissociation (HCD) with a stepped normalized collision energy of 28, 32 eV for TMT labeled samples and 28 ev for bioUb samples. The maximum injection time was 50 ms for survey and 100 ms for the MS/MS scans, where the AGC target values of 3e6 and 1e5 were used for survey and MS/MS scans, respectively. Repeat sequencing of same peptides was minimized by excluding the selected candidates for 20 sec. Singly charged ions, ions with > 5 charges and ions with unassigned charge state were excluded from MS/MS. Data were acquired using Xcalibur software (Thermo Scienti c).
All raw data les acquired were searched against the UniProt-SwissProt (www. uniprot. org/) mice databases (version 2018_02 with 25153 sequence entries for TMT labeled samples and version 2017_11 with 25146 sequence entries where bioUb, BirA, bioUb precursor and AVID_CHICK sequences were manually included for bioUb samples) with MaxQuant proteomics computational platform versions 1. 6. 5. 0 and 1. 6. 0. 16  [73]. Spectra originated from the different slices corresponding to the same biological sample (bioUb samples) and from different high pH fractionation peptide fractions (TMT samples) were combined. C carbamidomethylation was set as xed modi cation whereas M-oxidation, protein N-terminal acetylation, K-GlyGly (bioUb    Figures.
Species formation is a central topic in biology, and a large body of theoretical work has explored the conditions under which speciation occurs, including whether speciation dynamics are gradual or abrupt. In some cases of abrupt speciation, differentiation slowly builds up until it reaches a threshold, at which point linkage disequilibrium (LD) and divergent selection enter a positive feedback loop that triggers accelerated change. Notably, such abrupt transitions powered by a positive feedback have also been observed in a range of other systems. Efforts to anticipate abrupt transitions have led to the development of "early warning signals" (EWS), that is, specific statistical patterns preceding abrupt transitions. Examples of EWS are rising autocorrelation and variance in time-series data due to the reduction of the ability of the system to recover from disturbances.
In our analysis, neutral data sets served as a control to assess the significance of our results. In the typical EWS literature, significance testing is performed by generating a surrogate (null) time series from a random process  (Dakos et al. , 2012). Here, instead of generating artificially such surrogates, we took advantage of the model itself to generate them and produced neutral data sets. As our focus was on EWS that precede speciation events, we included only data from the beginning of the simulation to the time of speciation and focused downstream analyses on these time windows. Yet, the absolute elapsed time until speciation varied between abrupt, gradual, and neutral data sets. That is, speciation took longer for abrupt than gradual data sets (due to higher levels of homogenizing migration), while neutral data sets continued until the maximum number of generations.
To be able to compare among the different data sets, we obtained equal distributions of lengths between data sets, by trimming the abrupt and neutral data sets to the length of gradual ones. This means that as, on average, the waiting time for speciation is around 160,000 generations for abrupt data sets, and around 12,000 generations for gradual data sets, we kept only the 12,000 generations before speciation for the abrupt data sets and we kept only 12,000 generations from the neutral data sets. Figure  3  shows examples of abrupt and gradual data sets from the beginning of the simulation up to the time of speciation. Supplementary Figure  S2  shows, in the same examples, only the portions of the data sets that were analyzed together with neutral data sets for comparison. Finally, we performed analyses where the metrics of speciation we monitored (see next section for details) were recorded at resolutions of every 300, 100, and 30 generations.
Monitored metrics of speciation The BU2S model outputs time series that provide information on the genetic characteristics of both populations, within each habitat, at each generation. The complete list of parameters is given in Supplementary Table  S1. Here, for each simulation output, we analyzed six metrics that provide information on the genetic differentiation between the two populations. Each of the metrics is given for each population as a time series which constitute our data sets that were used to analyze EWS (abrupt, gradual, neutral data sets  Note the higher number of generations needed for speciation to occur in the abrupt data set when compared with the gradual data set, as expected given the higher migration rate in the abrupt data set. As speciation takes longer for abrupt data sets than gradual data sets, and to obtain data sets of equal length, we trimmed the abrupt data sets to the length of gradual ones by removing their excess data at the beginning of the simulation.
To estimate CSD, we followed methods detailed in  Dakos et al. (2012). We analyzed, for each simulation output, the time series of the six metrics of speciation described in the previous section (effective migration rate, number of selected variable loci, mean fitness of residents, LD, F ST , D abs ). For each data set type, outputs from 50 replicated simulations were analyzed. We report results by data set type. Each time series was analyzed as follows: we first removed any underlying long-term trend to keep only the residual variation. CSD arises when the system starts taking longer to recover from small short-term perturbations from its main trend, and thus it should be detectable by analyzing shortterm deviations from the main trend of a time series (i. e. residuals). We removed trends with a Gaussian filter that calculates a smoothed average trend from our noisy time series.
Receiver operating characteristic curves We assessed the accuracy and sensitivity of our results using the area under curve (AUC) of receiver operating characteristic curves (ROC). Formally, ROC are a way of quantifying the overlap between two distributions, which expresses the trade-off between accuracy (correctly detected EWS or true positives) and sensitivity (incorrectly detected EWS or false positives). We measured AUC from the overlap of Kendall τ distributions from two comparisons: (a) the distributions of Kendall τ from abrupt data sets were compared to those of neutral data sets. For instance, we compared the 50 trends of the AR1 of the F ST of abrupt data sets to 50 AR1 trends computed from neutral data sets. In this comparison, neutral data sets served as control to test if our results were different from those obtained by chance; (b) the distributions of Kendall τ from abrupt data sets were compared with those of gradual data sets to assess whether the results found in abrupt data sets were characteristic of the dynamics of abrupt speciation events or solely of speciation events, regardless of their type.
EWS before abrupt speciation We compared Kendall τ values obtained for abrupt data sets to Kendall τ obtained for neutral data sets. The neutral data sets were used here as a control: the comparison aimed at assessing whether the results obtained for abrupt data sets were different from random. We detected evidence for EWS in abrupt data sets, but their robustness and accuracy varied among differentiation metrics and types of EWS. Robustness referred to the sensitivity of EWS to detrending bandwidths and window sizes we used, whereas accuracy referred to how different EWS were when compared with EWS computed in the control data sets. Abrupt data sets showed robust EWS for F ST and LD for autocorrelation (81. 3% and 87. 4% of positive Kendall τ, respectively, Figure  4A ) and even more strongly for standard deviation (99.
We also observed some unexpected results: We found substantial differences between abrupt and gradual data sets in the standard deviation of D abs (AUC of 0. 24), but this result was partly due to the decreasing trends found for abrupt data sets. In addition, interestingly, positive trends in the standard deviation of the mean fitness and F ST were substantially higher for gradual rather than abrupt speciation (AUC of 0. 21 and 0. 23, respectively, Figure  5B  and D ). For the rest of the metrics, we found modest differences between abrupt and gradual data sets. The number of selected variable loci notably showed no EWS for either data set type. In contrast, trends in the autocorrelation and standard deviation of LD provided clear EWS for both abrupt and gradual speciation, and thus were not specific to either speciation dynamic.
Summary of the observed differences in early warning signals (EWS) between abrupt data sets versus neutral and gradual data sets. Summary of the AUC values computed when comparing abrupt data sets with neutral and gradual data sets. AUC values are a way of quantifying the overlap between two distributions, here Kendall τ characterizing abrupt data sets versus Kendall τ characterizing neutral or gradual data sets. An AUC ranges between 0 and 1, with a value of 0. 5 indicating that the abrupt and gradual/neutral distributions are the same. An AUC close to 1 indicates that the abrupt distribution has higher values compared with the gradual or neutral one. An AUC approaching zero indicates the contrary. When looking for signs of CSD, we expected to obtain higher Kendall τ values on abrupt data sets compared with neutral and gradual data sets, and hence expected AUC values above 0.
Although the reasons for why these particular metrics are the most reliable require further study, their inherent link to reproductive isolation may be part of the explanation, since effective migration, as calculated here, is essentially a measure of reproductive isolation due to selection against first-generation migrants  (Nosil et al. , 2005 ). Yet, other EWS differences between abrupt and gradual data sets contradicted our expectation of being stronger in abrupt data sets: standard deviation of mean fitness of residents, F ST , and D abs showed a stronger rising trend in gradual data sets than in abrupt ones. Nonetheless, these three indicators still show substantially different results when compared with null expectations (Figure  4B , Table  1 ), which implies that they could serve as early warnings of speciation in general, irrespective of whether the speciation events are gradual or abrupt.
In fact, EWS are not specific to tipping points associated only with abrupt transitions, but are more generic and can be identified also prior to gradual transitions that are related to noncatastrophic bifurcation points  (Kéfi et al. 2013; Boettiger et al. 2013). We also observed EWS that were common to both abrupt and gradual data sets, even though speciation in the gradual data sets is not inherently driven by a positive feedback. Moreover, LD, the key driver of the positive feedback, showed equally strong warning signals in both abrupt and gradual data sets: We reported rising autocorrelation and standard deviation approaching speciation for both data set types with similar strengths. This common trend for LD in both abrupt and gradual data sets may not be totally surprising as it reflects the generic behavior of LD as speciation approaches, regardless of the type of speciation (i.
Statistical Analysis Data are expressed as means +/-standard error of the mean (SEM) for bar graphs and median with min. /max. for box plots. Significant differences between groups were determined using one-way or two-way analysis of variance (ANOVA) followed by Sidak post hoc testing with PGraphPad Prism software (PGraphPad Prism 7. 0, La Jolla, CA, USA). A p value < 0. 05 was considered statistically significant.
Nkx2-5 VCS mutant mice present reduced radial strain predominantly affecting the anterior wall region (Figure  4H ). We found a strong correlation between reduced EF and radial strain (Figure  4G ). In addition, high-frequency speckle-tracking echocardiography was used to access subtle changes in diastolic function. Representative curves of longitudinal strain rate showed an important defect in the coordination of myocardial deformation and a reduced peak global strain rate during early diastole (SR E ) in Nkx2-5 VCS mutant compared to control mice (Figure  4I ). The diastolic index E/SR E was increased in Nkx2-5 VCS mutant mice. Taken together, these data suggest a diastolic dysfunction in old mutant mice (Figure  4I ). LV mechanical dyssynchrony was determined as time-to-peak variation, defined as the standard deviation of time to peak over all six segments.
HSF2BP helices α1 and α2 connect BRCA2-bound HSF2BP armadillo tetramers in the HSF2BP-BRCA2 complex We further analyzed our cryo-EM map of the complex between HSF2BP and BRCA2-HBD, in order to describe the 3D structure of HSF2BP N-terminal α-helical region in this complex. First, we modelled a dimer of HSF2BP using AlphaFold  28. This prediction algorithm proposed five similar models for dimeric HSF2BP with high confidence (Extended Data Fig. 3a ). In these models, each HSF2BP monomer is composed of strand β1 (F20-R24), helix α1 (K25-I45) and helix α2 (G48-S136) that overlaps with the armadillo domain (E122-V334). As a validation, we checked that the 3D model of the dimeric armadillo domain is superimposable with the 3D structure observed in the crystal (PDB code: 7BDX  23  , Extended Data Fig.
Building and refinement of the cryo-EM model The density map of the complex was of sufficient quality (between 3 and 8 Å) to fit the crystal structure of the armadillo domain of HSF2BP in complex with BRCA2-HBD (PDB codes: 7BDX) in ChimeraX. The map regions in which the armadillo domains were fitted showed a local resolution of 3-5 Å. In the peripheral region of the complex, corresponding to the N-terminal helices of HSF2BP, the local resolution map was in the range 5-8 Å. In these regions, AlphaFold guided model building. All figures were prepared using UCSF Chimera and UCSF Chimera X.
2 M LiCl, 0. 1 M sodium acetate trihydrate, pH 5, and 20% PEG6000 for 1 and 0. 1 M HEPES sodium salt, pH 7. 5, 40% PEG200 for 1/BRME1-M. Diffraction data were collected on the MASSIF-1 beamline (EMBL Grenoble) of the ESRF synchrotron. Datasets were indexed, integrated and scaled using XDS  45. The three-dimensional structures were solved by molecular replacement using the PHENIX Phaser software  46, 47  and an input coordinate file calculated by AlphaFold2 48 , were iteratively improved by manual reconstruction in COOT  49  , and were refined using the PHENIX Refine and BUSTER (Global Phasing Limited)  50  software. A summary of crystallographic statistics is shown in Supplementary Table  1.
Two-dimensional gel electrophoresis (2DGE) 2DGE was performed as described previously  57. Replication intermediates of pICL at various times were extracted and digested with HincII. Fragments were then separated on a 0. 4% agarose gel in 0. 5× TBE buffer at 0. 86 V/cm for 24 h at room temperature. The lanes of interest were cut out, cast across the top of the second-dimension gel consisting of 1% agarose with 0. 3 μg/ml ethidium bromide, and run in 0. 5x TBE containing 0. 3 μg/ml ethidium bromide with buffer circulation at 3. 5 V/cm for 14. 5 h at room temperature. The gel was dried on Amersham Hybond-XL membrane and exposed to a phosphor screen. DNA was visualized using a Typhoon TRIO+. Co-crystallized regions of BRCA2 (HBD) and HSF2BP (armadillo) are colored in blue and green, respectively; the 3D structure cartoon is shown (PDB code: 7BDX).
No reuse allowed without permission. ). e, Docking of the crystal structure of the complex between HSF2BP armadillo domain and BRCA2-HBD (PDB: 7BDX) into the cryo-EM map. In the top view, a crystal structure is positioned in each of the six globular subvolumes of the map, whereas in the side view, only one crystal structure is displayed. Each crystal structure contains four armadillo domains (in light green, yellow, green and maroon) bound to two BRCA2-HBD peptides (in blue). In the boxed side view, the map is colored as the docked chains of the crystal structure. f, Zoom view on one of the BRCA2 peptides docked into the cryo-EM map. The position of this peptide, represented in blue sticks, readily fits into the cryo-EM density map of the complex between HSF2BP and BRCA2-HBD. The map is colored as the docked chains of the crystal structure.
ACKNOWLEDGEMENTS We warmly thank  Christophe Velours  ( I2BC ) and  Athena Collange  ( Synchrotron SOLEIL ) for the SEC-MALS experiments;  Aurélien Thureau  ( Synchrotron SOLEIL ) for discussions about the SAXS data analysis;  Florine Dupeux  ( IBS ) and all the  HTX Lab  staff for the crystallogenesis and crystallography experiments;  Marie-Hélène Le Du  ( I2BC ) for helpful advice in crystallogenesis;  Matthijn Vos  and the  NanoImaging Core at Institut Pasteur (Paris)  for their support with sample preparation and image acquisition;  Stéphane Bressanelli  ( I2BC ) for his support during the cryo-EM data analyses;  Bertrand Raynal  and the molecular biophysics platform at  Institut Pasteur (Paris)  for their help during the mass photometry analyses; the staff of the  I2BC computing facility  ( I2BC/SICS)  for facilitating data management and accessibility.
We acknowledge SOLEIL ( SWING beamline)  and  ESRF (MASSIF-1 beamline)  for provision of synchrotron radiation and beamlines staff for assistance in collecting data. The research leading to these results has received funding from the  European Community's systèmes complexes dans   les environnements complexes (CACSICE ,  ANR-11-EQPX-008 ) and of I2BC supported by the  French Infrastructure for Integrated Structural Biology  ( FRISBI ,  ANR-10-INSB-05 ) and by the  CEA. This study was supported by the  Oncode Institute , which is partly financed by the  Dutch Cancer Society (KWF). We thank the  Josephine Nefkens Cancer Program  for infrastructure support. P. K. was supported by the  European Research Council (ERC)  through an  ERC Consolidator Grant  ( ERCCOG 101003210-XlinkRepair ) and by the  Oncode Institute , which is partly financed by the  Dutch Cancer Society (KWF).
DATA AVAILABILITY The coordinates and structure factors file corresponding to the crystal structures of HSF2BP helix 1 and the complex between HSF2BP helix 1 and BRME1-M described in the study were deposited in the Protein Data Bank (wwPDB) under the entry codes 8A50 and 8A51, respectively. The cryo-EM map generated in this study was deposited in the wwPDB database under accession code EMD-16432.
However, the data mainly rely on small retrospective case series, conducted before the era of novel chemotherapeutic agents. The aim of this study was to describe the characteristics and outcome of patients treated for SBP, to identify factors associated with progression to MM and death, and to assess whether adjuvant chemotherapy improves outcome. Between 1992 and 2020, patients with histologically confirmed diagnosis of SBP according to the International Myeloma Working Group (IMWG) criteria  (1) , identified through a systematic search in the electronic database, were included. All AP-HP patients are informed that their clinical data can be used for research and give their consent for the use of their data unless they provide an opposition to it. None of the patients in this study objected to the use of their data.
A randomized therapeutic trial (IDRIS Trial, NCT02544308) is on-going to evaluate whether adjuvant lenalidomide can improve progression-free survival compared to radiotherapy alone in patients with high-risk SBP. This study will definitively precise the room of adjuvant therapy in SBP. Our study presents several limitations. Due to the rarity of SBP, inclusion time was very long and the number of patients was relatively small, but in line with existing large series. Thus, the treatment procedures were not standardized. Still, our results suggest a benefit of adjuvant chemotherapy used between 1992 and 2020 and it is likely that with the new drugs now available with even better efficacy, the positive impact of chemotherapy might be even higher. Finally, some data were missing and we could not rigorously use the IMWG response criteria which are however not totally adapted to SBP, particularly in non-secretory SBP.
Data collection The study was carried out at 15 sampling areas in mosaic landscapes of Hungary (Fig. 1 ). We chose the areas in a way to cover a large geographic range in the country, so they were relatively distant from each other, and contained a range of different habitats. The  average distance between the nearest sampling areas was 11. 8 ± 5. 3 km (mean ± SD); the largest distance was 335. 9 km. Each sampling area involved one solar farm and further sampling places in different habitats (control habitats) available around the 5 km radius of the solar farms (Table  A1 ). In each habitat, we determined 1-6 sampling points (3. 6 ± 1. 3 in average) depending on the size of the given habitat surveyed within the given sampling area (Fig. 2 ). Altogether, we collected data from 190 sampling points.
Sound analysis First, we automatically searched the bat calls on the recordings by using the program Bat Detective  (Mac Aodha et al. , 2018)  and then cut the sequences out from the recordings with a self-written script in R (R Core Team, 2019; script available upon request from the authors). We defined a sequence as a series of echolocation calls with inter-pulse interval between calls less than 3 s. The sequences were examined manually using the Kaleidoscope Pro program (Wildlife Acoustics, Inc. ). Species identification was carried out based on frequency and time measurements in the literature  (Obrist et al. , 2004; Papadatou et al. , 2008; Pfalzer and Kusch, 2003; Russo and Jones, 2002; Walters et al. , 2012). We successfully identified the following species: Barbastella barbastellus, Hypsugo savii, Pipistrellus pipistrellus, P.
Statistical analysis We used bat activity as a proxy of bat abundance because it was not possible to differentiate individuals using passive acoustic sampling. Activity was defined as the sum of the sequences recorded over a night. For each sampling point, we calculated the activity by species or species group. Moreover, regardless of the species, we obtained the overall activity. For further statistical evaluations, we excluded the data from B. barbastellus (55 sequences) and feeding buzzes (197 sequences) as few of these sounds were recorded and therefore not sufficient for the statistical modelling. The recording rate of feeding buzzes is typically lower (e. g. :  Russo and Jones, 2003)  because in contrast to the continuously emitted echolocation sounds, bats emit feeding buzzes only when determining the exact location of the prey.
Furthermore feeding buzzes typically consist of low intensity call sequences which makes it difficult to record them. To compare the activity of bats in solar farms versus other habitats, we built Generalized Linear Mixed Models (GLMM) with negative binomial error distributions for each activity variable as a response variable (nine models overall). We included the habitat type and month as fixed factors, and temperature as a continuous fixed effect. We also included the area and the habitat within the area as random effects to control for the spatial dependency of the sampling points both within and between the areas. Statistical analyses were carried out with the glmer. nb function of 'lme4' package  (Bates et al. , 2015)  in R, and the output of the summary function was used to generate the statistical results.