dataesr/scientific-paragraphs-categorization huggingface dataset:
| Geneticstructureatindividuallevel To investigate the genetic structure at individual level, we applied a discriminant analysis of principal components (DAPC), a multivariate statistical approach for which variance in the sample is partitioned into between-group and within-group components to maximize discrimination between groups  (Jombart et al. , 2010). We performed the DAPC for each species in the SNP data set by using the dapc function implemented in the R package 'adegenet'  (Jombart, 2008; Jombart & Ahmed, 2011) , with sampling sites of each individual used as prior information (Supporting Information Figure  S1 ). Individuals' membership probability to each cluster was computed and represented as bar plots. In order to perform a multi-species comparison, we retained the same number of principal components analysis (PCA) axes per species in the DAPC.
| Geneticdiversity We estimated the within-species α-GD with the expected heterozygosity H S (Supporting Information Table  S3 ;  Nei, 1987). For the four sampling sites, H S was calculated using the basic. stats function from the 'hierfstat' R package  (Goudet & Jombart, 2015). Next, we obtained a measure of the ᾱ-GD as a mean of α-GD across the four sites. Similarly, we estimated the within-species γ-GD over all sampled individuals of   S3 ;  Goudet & Jombart, 2015). For each species, both pairwise (among each pairs of sites) and multiple-site (over the four sites) β-GD were quantified with estimates of Hedrick's G" ST , implemented respectively using Gst_Hedrick and pairwise_Gst_Hedrick functions of the 'mmod' R package (Supporting Information Table  S4 ;  Winter, 2012). Nei, 1987) ; it represents the genetic distance between populations, analogous to the common F ST based on biallelic markers but generalized for multiple alleles.
| Speciesdiversity We obtained the fish species occurrence data from Albouy et al. (  2019 ) that merged data extracted from the Ocean Biogeographic Information System (OBIS, http://www. obis. org) with the GASPAR database  (Kulbicki et al. , 2013; Parravicini et al. , 2013; Pellissier et al. , 2014). This database provides information on the occurrences of 14,204 fish species assigned to a spatial location in a grid system covering all oceans at 1° × 1° resolution. From this global database, we extracted the occurrences of species belonging to the 12 families studied, representing 2,446 species worldwide. We restricted the data set to a distance of 1° (111 km at the equator) from the reefs of the four sampling areas by using the function gbuffer from the R package 'rgeos'  (Bivand & Rundel, 2019)  and st_intersects from the package 'sf'  (Pebesma, 2018)  to produce a species presence/absence matrix by site.
| Driversofβ -diversitypatterns To evaluate whether the Monsoon Drift and geographical distance are factors driving both genetic differentiation and species turnover in tropical reef fishes of the WIO region, we used multiple regression on distance matrices (MRM;  Legendre & Legendre, 1998)  implemented in the MRM function from the R package 'ecodist'  (Goslee & Urban, 2007). MRM is similar to linear regression, except that the dependent and independent variables are square distance matrices instead of single vectors  (Legendre & Legendre, 1998). For a given explanatory variable, calculation of the standardized partial regression coefficient enabled us to compare their per-unit effects on compositional similarity, while controlling for the effects of the other variables. To overcome the problem of lack of independence between site pairs, we assessed the significance of the standardized partial regression coefficients and the coefficients of multiple determination (R 2 ) by using a permutation test (n = 999).
Overall, we related both pairwise β-GD and pairwise β-SD to (a) geographical distance between sites and a (b) binary distance metric reflecting whether the studied sites are separated or not from the Maldives by the Monsoon Drift (Table  1 ). (a) Geographical distances between sites were computed as in-water distances, that is, the length of the shortest path within water between two sites with the lc. dist function of the 'marmap' R package  (Pante & Simon-Bouhet, 2013). (b) The binary distance metric consists of assigning a value of 1 for two sites separated by the assumed 'soft' barrier (i. e. , between the Maldives and the three other localities of the WIO), and a value of 0 for two sites that are not separated by this barrier (i. e. , within the three south-west sites). This MRM method was performed considering all of the six pairwise distance values of each of the 20 species, that is, 120 non-independent observations.
phylogenetic generalized least squares (PGLS) methods  (Freckleton et al. , 2002)  implemented in the gls function of the 'nlme' package  (Pinheiro et al. , 2021). PGLS is a modification of generalized least squares that accounts for the non-independence of trait values across related taxa  (Symonds & Blomberg, 2014). For the PGLS, we pruned the 20 species from the most comprehensive available time-calibrated phylogeny of fishes  (Rabosky et al. , 2018). The same approach was applied with the Procrustes analysis instead of the Mantel test (Supporting Information Figure  S6 ). All the analyses were performed in R version 4. 1. 2 (R Core Team, 2021). Figures were made using the 'ggplot2' package  (Wickham, 2016). S2 ). Mayotte Island and Mafia Island (Figure  1d ).
This would require the use of both neutral and non-neutral genetic markers (see  Matala et al. , 2014). In this study, we explored SGDCs not only across communities as initially proposed by  Vellend (2003)  but also across lineages. This approach should be extended to other taxonomic groups in both terrestrial and marine environments in order to test the generality of our findings. This research objective is now possible with the development of large-scale databases of genetic diversity, which is the starting point of the emerging field of macrogenetics  (Leigh et al. , 2021).
Graphs preparation and statistical analysis Statistical analysis was conducted in R (R Core Team, 2023) and custom R scripts are provided at github (https://github. com/cecilelecampion/Perdoux-et-al). Statistical analyses were performed according to normality status. On normally distributed data we performed analysis of variance with post hoc Tukey test and on nonparametric data we performed the Kruskal-Wallis test with post hoc Dunn test from the rstatix package  (Kassambara, 2023). Graphs were produced using the package ggplot2  (Wickham, 2016). Error bars represent standard errors in graphs and histograms. TOR kinase domain structure modeling I-TASSER was employed to predict the kinase domain structures of WT and tor-15 TOR  (Yang & Zhang, 2015)  using the crystal structure of mTORDN-mLST8-ADP-MgF 3 -Mg 2  (Yang et al.
ACKNOWLEDGMENTS This work was supported by a  CEA DRF  doctoral fellowship for R. P. and funding from the  Agence Nationale de la Recherche  funding ( ANR-14-CE19-0007  and  ANR-20-CE20-0027 ). We thank  T. Desnos  for EMS mutagenized seeds,  Fabienne Granier  and  Rapha€ el Mercier  for help with NGS data analysis,  Julia Bailey-Serres  and  Alan Williams  for the anti-RPS6 antibody,  Christian Meyer  for the anti-RPS6 pSer240 antibody,  Ben Field  for help with Fv/Fm measurements and for critical reading of the manuscript,  Muriel Reissolet  and  Patrice Cr et e  for managing the plant growth chambers, and  Lieven De Veylder  for pSMR4::GUS seeds.
Computational methods Molecular docking. The X-ray crystal structure of Eg5-enastron complex (PDB ID: 2X7C) was obtained from the Protein Data Bank,  26  and was prepared with protein preparation wizard in Schrodinger suites. The three-dimensional structures of the derivatives were constructed using Maestro soware, and prepared with Ligprep using OPLS3e force eld. 27 he nal prepared PDB le of the protein and synthesized analogues were submitted in order to run docking process. Docking studies were performed by Glide soware 28 at extra precision. 29 Output les of docked analogues along with Eg5 protein were visualised on Chimera soware. 30 olecular dynamics simulation. Molecular dynamics simulations (MD) were performed using Desmond. 31 The best docking complex for the identied inhibitors from the biological experiments were taken as the initial coordinates for MD simulations.
The model hypothesized that a dynamo could be generated solely from a Venusian basal magma ocean. Much larger versions than our solar system rocky planets exist, super-Earths, most of which lie within 1 to 1. 6 Earth radius, R(E), in the current exoplanet database (Fig. 4 ). For a ~1. 2 R(E) planet with Earth-like core mass fraction and composition, the pressure at the CMB is expected to reach ∼280 GPa, which is the maximum pressure achieved in our experiments (see Materials and Methods). Assuming that LS Fe begins to be the dominant component above 75 to 100 GPa [pressure inferred from the Fe partitioning behavior change found in silicate melt  (9, 10, 20) ], a ~1. 2 R(E) super-Earth could have LS Fe present in the bottom 60% of its silicate magma ocean. Therefore, LS Fe could also be the prevailing electronic configuration of Fe in the deep interiors of super-Earths up to 1.
For the real shot, the target was slightly moved to a fresh spot, and the transit time was measured in the correspondence of the x-ray probed region, to avoid eventual effects linked to the shock curvature. For lower pressures (below melting), the free surface velocity, U f , could be measured at the target rear side through a fringe shift in the interferometry pattern. Examples of VISAR raw data are shown in fig. S1  for runs #84 and #219 corresponding to pressures of 103 ± 20 GPa and 203 ± 30 GPa, respectively. Using the approximation of U f = 2U p , valid at moderate pressures, we could directly infer the fluid velocity, U p. These U p measurements were approached in two ways: (i) as determined from free surface U f (where reflectivity in the sample was still maintained, typically at the lower pressures conditions) and (ii) from the sample/LiF interface velocity and applying the impedance mismatch technique to determine the U p in the sample.
Again, in each case, the uncertainties are determined by SE propagation, which accounts for each variable and SDs, and we have carefully tracked and propagated this through to the pressure values and pressure uncertainties. From the shock velocity and/or the fluid velocity measured from the VISAR, we can infer the density using the known olivine Hugoniot curve. Density values are particularly useful as they are directly related to the Q 1 and the Q 2 peak positions  (24). We can therefore use the VISAR retrieved densities and the measured positions of Q 1 and Q 2 and compare their trends with the data found in literature. For the comparison being pertinent when including silicates with different initial densities, Q 1 and Q 2 are plotted as a function of compression (fig. S3 ). This plot shows a very good agreement with the existing data, both from static and dynamic compressions, supporting our density and pressure estimations.
Independent XRD measurements have been used to cross-check the inferred thermodynamic conditions. In our experiment, density estimations could not be directly extracted from XRD patterns because of the amorphous structure and the limited accessible Q range, insufficient for a pair distribution function analysis. We can instead consider the position of the broad scattering contributions, Q 1 and Q 2 , which are directly related to the density  (16, 24). In fig. S3 , we show Q 1 and Q 2 extracted from our diffraction data as a function of the shock compression, ρ/ρ 0 , with shocked sample density ρ given by the optical diagnostics coupled to the olivine Hugoniot from  (28, 37). In the same plot, we include data from static and dynamic compression experiments on similar silicates (MgSiO 3 glass and Mg 2 SiO 4 forsterite).
For each crystal analyzer, integration along the focusing direction of the signal on the 2D detector results in an emission spectrum. Considering (i) the x-ray attenuation length for the 9-keV incoming photons used in this study, (ii) the 7. 1-keV Fe Kβ XES outcoming photons for the Fe-bearing silicates measured here, and (iii) the geometry of the XES measurement (with the XES outcoming photons reaching the spectrometer at an angle of 60°in the vertical direction from the incoming photons), our calculations show that most of the XES signal is arising from the first 10 μm of the sample. Given the thickness of our samples is in the 40-to 60-μm range, the effect of the x-ray arrival time does not significantly affect the XES spectra as it is purely probing the shocked volume. This calculation was conducted using The Center for X-Ray Optics (CXRO) x-ray interactions with matter databases created by the Lawrence-Berkeley National Laboratory  (42).
Acknowledgements We thank the patients and their families for participating in this study, and  Madeleine Wacker  ( Synofzik Lab ) for preparing some of the samples. We thank  Mr Felix Heindl  ( Munich ) for help with clinical data collection of some of the patients,  Dr Annette Hartmann  ( Medical University of Vienna ) for preparation and handling of samples and related information and  Miss Natalia Dominik  ( University College London ) for technical assistance with RFC1 genotyping. DP holds a Fellowship award from the  Canadian Institutes of Health Research (CIHR).
Funding This work was supported, in part, by the  Clinician Scientist program  '  PRECISE. net' funded by the  Else Kröner-Fresenius-Stiftung  (to CW, AT and MSy), the grant  779257  ' Solve-RD ' from the European's  Union Horizon 2020 research and innovation programme  (to HH and MSy) and the grant  01EO 1401  by the  German Federal Ministry of Education and Research (BMBF)  (to MSt). This project was also supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) No 441409627, as part of the  PROSPAX consortium  under the frame of EJP RD, the  European Joint Programme on Rare Diseases , under the  EJP RD COFUND-EJP N° 825575  (to MSy and BB). This work was also supported by the  Wellcome Trust  and the  UK Medical Research Council (MRC)  (to HH), and by the  Fondation Groupe Monaco  and the grant  PT79418  from the  Montreal General Hospital Foundation  (to BB).
Characterization of calibrated bead diffusion by SPT analysis. To compare the measurements by FCS and SPT, we applied a similar strategy to analyze the diffusion of reference beads with SPT. We increased the diameter of beads to 100 nm to further decrease the range of slow events that could be detected (more details can be found in the material & Methods section). We performed SPT acquisitions in HILO (Highly Inclined and Laminated Optical sheet) microscopy mode  29  , a technique classically used for nucleus imaging because it allows a selection of the plan of observation thanks to a very inclined optical sheet. We analyzed image series with the MTT (Multiple-Target Tracking) algorithm tracking software  30  to detect and reconstruct trajectories of the particles. Since short trajectories strongly penalize the stability of the adjustment function methods  31  , we only selected trajectories with more than 10 detections, which are more reliable.
Biological resources. Cell line culture. Human U2OS osteosarcoma cells (HTB-96 accession number from the American Type Culture Collection, Manassas, VA) were tested for mycoplasma contamination before establishing the RPB1 cell lines. Cells were cultured in DMEM medium (Gibco Laboratories, Gaithersburg, MD) supplemented with 10% FCS (V/V) and penicillin/streptomycin (100 mg/mL). Cells were grown in a humidified incubator at 37 °C/5% CO2. Cells were passaged every 2-4 days before reaching confluency. We generated plasmids encoding for human RPB1 (N792D) α-amanitin-resistant mutated fused to an N-terminal Halo tag, by molecular cloning. We stably transfected U2OS cells with this plasmid using Fugene 6 following the manufacturer's instruction (Promega). α-Amanitin (SIGMA #A2263) was used to select transfected cells, at a concentration of 2 μg/mL and was used thereafter in permanence in the culture of the cells at a concentration of 1 μg/mL to avoid endogenous RPB1 re-expression as described in (Boehning et al.
All devices were driven by NIS software (Nikon Instruments, Japan). D = kT 6πrµ A thermostatic chamber ('on-stage-incubator' , OKOLAB, Italy) was used to keep samples at 37 Celsius degrees. SPT data analysis. Tracks were reconstructed (detection and tracking) by using SLIMfast 32 custom-written MATLAB (MathWorks) with an implementation of the MTT algorithm  30. MTT is well adapted for a concentration of particles up to 10 for a 100 × 100 px field of view). For each condition, we had at least 1000 trajectories lasting more than 10 detections (smaller trajectories were not included in the analysis). Tracks were then analyzed with custom code  32   (τ ) = r 2 (τ ) T = [x(t + τ ) -x(t)] 2 + y(t + τ ) -y(t) 2 T ∝ 4Dτ α where τ = n�t , with n the number of frames considered for the sliding average (0 < n < L traj , L traj the length of the trajectory), t the lag time between two frames (10 ms), D the diffusion coefficient (µm 2 /s), and α is the anomaly coefficient.
Mix diffusion data models. In order to evaluate how our approach could discriminate mixed subpopulations in a sample, we mixed batches of trajectories coming from different conditions (Table  1 ). The conditions and fractions for each mix are indexed in Table  S2. FCS set up. FCS measurements were performed on a Nikon A1 confocal microscope (Nikon Instrument, Tokyo, Japan) with a 40 × objective NA = 1, 25 (Nikon Instrument, Tokyo, Japan). Excitation Argon LASER 488 nm (Coherent, USA) was used to scan samples in confocal illumination. To spectrally select the excitation from the emitted light, we used a bandpass dichroic mirror (405/488 nm). Fluorescence photons were collected on a photodiode and images were reconstructed with NIS software (Nikon Instruments, Tokyo, Japan). To perform FCS, the CLSM was adapted with a TCSPC module (PicoQuant, Germany), and a pulsed laser at 488 nm (LDH-D-C 488, PicoQuant), a TSCPC unit, and a single molecule counting detector (SPCM CD 3516H, Excelitas Technologies Corp.
, USA). To drive this photon counting system, we used SymphoTime software (PicoQuant, Germany) in FCS mode. A high pass filter (BLP-488R, SEMROCK, USA) was set before the detector. FCS analysis. FCS setup was calibrated with ATTO 488 diluted at 10 nM in Milli-Q water to define the focal volume by best practice. In the water at 36°, the temperature of our incubator, the Atto 488 diffusion coefficient is 536 µm 2 /s (source: PicoQuant Application Note Kapusta and GmbH, n. d. )  50. The number N of molecules in this observation volume V was inferred from the autocorrelation function with And the observation volume determined with: N A is the Avogadro number and c is the concentration of the calibration solution. Once calibration was done, we had a good estimate of the focal volume of the microscope and could proceed to the analysis of fluorescence fluctuations.
Measurements displaying a linear regression of the fluorescent signal N = 1 G(0) V eff = 1 G(0). N A. c characteristic of photobleaching were excluded. For data analysis, we use a home-written MATLAB code named EASY FCS  51  which can fit the autocorrelation function (ACF). In the case of normal diffusion the ACFs were adjusted with a Gaussian function (assuming the beam as a Gaussian profile): where N is the number of molecules and τ D is the characteristic residence time. For anomalous diffusion, the mean square displacement is no longer linearly dependent on time, but it evolves proportionally with time to the power of α. In this case, the autocorrelation function becomes: The fitting of the ACFs was done with the Trust-Region-Reflective Least Squares Algorithm. To evaluate the goodness of the fit, we minimized the sum of the squared error.
Since the diffusion coefficient (D) obtained by anomalous model fit is given with time dimensions dependent on exponent α, it is recommended to give the results in transit time  23, 24, 52  to compare data. Standardization of analysis metrics between FCS and SPT. FCS and SPT data are obtained by analytical methods from microscopic approaches with different metrics. In order to compare them and use them together to analyze molecular mobility, it is useful to express them with the same metric and unit. The adjustment of the results by an anomalous model in both techniques also induced a difficulty, because when the anomalous coefficient changed, the time unit was simultaneously modified. To overcome this difficulty, it is usual in FCS to express the ACF results as residence times in the focal volume.
Acknowledgements We thank all members of our team and  GDR Imabio-CNRS , particularly  Dr Hugues Berry ,  Dr Ignacio Izeddin  and  Dr Thorsten Wohland  for their help and fruitful discussion/comments. We are much grateful to  Dr Raoul Torero-Ibad  for his great work and a precious help in providing us with a functional lab environment, and for his enlightened opinion on this study. We thank  Christian Hubert  ( Errol company ) for his valuable input in instrumental advances. We are much grateful to  Dr Jonathan Grimm  and  Dr Luke Lavis  ( Janelia Farm, VA, USA ) for kindly providing us with JF dyes to perform our experiments.
Funding This work was supported by  CNRS  and  Ministerial Funding ;  Agence Nationale de la Recherche  ( Dynam-ic-12-BSV5-0018-02  and  ABC4M-ANR-20-CE45-0023 ); the  LABEX CEMPI  ( ANR-11-LABX-0007 ),  I-SITE ULNE  ( ANR-16-IDEX-0004 ) as well as by the  Ministry of Higher Education and Research, Hauts de France council  and.
Aberrant expression of ncRNAs has been found in cancer  4  and non-cancer disease,  5  and a vast majority of trait or disease-associated variants lie in non-coding regions of the genome. 6  Despite significant progress in describing enhancer transcription,  3, [7] [8] [9] [10]  efforts to fully identify intergenic transcription remain a challenge. This is primarily due to a limited amount of sequencing assays like global run on sequencing (GRO-seq)  11  or its derivatives,  12, 13  impacting the discovery of a broader intergenic transcription landscape. In this study, we compiled each available RNAPII chromatin immunoprecipitation sequencing (ChIP-seq) dataset from the GEO  14  and Encyclopedia of DNA Elements (ENCODE)  1  to construct an atlas of RNAPII-bound intergenic regions in the human genome.
An atlas of intergenic RNAPII occupancy To create an atlas of intergenic RNAPII binding in the human genome, we collected all available ChIP-seq data targeting RNAPII on a wide variety of cells and tissue biosamples from public biological data warehouses  1, 14  (Figure  1A ). The created atlas aggregates 87% of non-ENCODE datasets and 13% of ENCODE datasets (Figure  1A ). This was accomplished through standardized manual curation of sample metadata, uniform biosample annotation, and consistent data processing and quality screening, initiated from the raw sequencing files using the ReMap pipeline (STAR Methods). We conservatively retained 906 RNAPII datasets from diverse cell or tissue types, utilizing various antibodies targeting the POLR2A subunit (Figure  S1A ). These datasets encompassed a wide range of samples, including cancer cell lines (64%) and ''normal'' cell lines/tissues (36%) (Figure  1A ; STAR Methods).