dataesr/scientific-paragraphs-categorization huggingface dataset:
Standardized fMRI acquisition protocol The standardized protocol was determined based on the outcomes of the analysis of MultiRat_rest and used to acquire the StandardRat dataset. Acquisitions were performed chiefly in ~2-month-old free-breathing Wistar rats, mixed sex, and anesthetized using 4% isoflurane and 0. 05 mg kg -1 medetomidine subcutaneous (s. c. ) bolus for induction and 0. 4% isoflurane and 0. 1 mg/kg/h medetomidine s. c. for maintenance. Imaging with a gradient-echo echo-planar imaging technique was conducted 40 minutes after anesthesia induction, with repetition time = 1,000 ms, echo time/flip angle/bandwidth defined as a function of field strength (Supplementary Table  1 ), repetitions = 1,000, matrix size [64 × 64], field of view 25. 6 × 25. 6 mm 2 and 18 interleaved axial slices of 1 mm with 0.
Data pre-processing and confound correction Scans were organized according to the BIDS format  27. Pre-processing was performed on each scan session separately using a reproducible containerized software environment for RABIES 0. 3. 5 (Singularity 3. 7. 3-1. el7, Sylabs)  14. The pre-processing was performed using autobox  28  , N4 inhomogeneity correction  29  , motion correction  29  , a rigid registration between functional and anatomical scans  29  , non-linear registration between anatomical scan and template and a common space resampling to 0. 3 × 0. 3 × 0. 3 mm 3. A volumetric image registration and brain masking workflow was developed to address discrepancies in brain size, image contrast and susceptibility distortions that are found in rodent images. Visual inspection was performed on pre-processing outputs for all scans for quality control.
Acknowledgements This research was enabled, in part, by support provided by  Compute Ontario  (https://www. computeontario. ca/) and Compute Canada (www. computecanada. ca). For the purpose of open access, we have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This research was funded by: the  National Institutes of Health  ( K01EB023983 ,  R03DA042971 ,  R21AG065819 ,  K25DA047458 ,  I015I01CX000642-04 ,  R01NS085200 ,  R01MH098003 ,  RF1MH114224 ,  T32AA007573 ,  R01MH067528 ,  P30NS05219 ,  T32GM007205 ,  R01MH111416 ,  R01NS078095 ,  R01EB029857 ,  F31 MH115656  and  1R21MH116473-01A1 ); the  Wellcome Trust  ( 212934/Z/18/Z ,  109062/Z/15/Z ,  110027/Z/15/Z ,  204814/Z/16/Z  and  203139/Z/16/Z ); the  Dutch Research Council (OCENW.
KLEIN. 334 ,  021. 002. 053 ,  016. 130. 662  and  016. 168. 038 ); the  German Research Foundation  ( SA 1869/15-1 ,  SA 2897/2-1 ,  SFB 1436/B06 ,  SFB874/B3  project no. :  122679504 ,  SFB 1280/A04  project no. :  316803389 ); the  French National Research Agency  ( ANR-15-IDEX-02  and  ANR-11-INBS-0006 );  Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica  ( IN212219 ,  IA202120  and  IA201622 ); the  UK Medical Research Council  ( MR/N013700/1  and  1653552 ); the  Portuguese Foundation for Science and Technology  ( LISBOA-01-0145-FEDER-022170  and  275-FCT-PTDC/BBB/IMG/5132/2014 ); the  Swiss National Science Foundation  ( PCEFP3_203005  and  PCEFP2_194260 );  King's College London, Biotechnology and Biological Sciences Research Council  ( BB/N009088/1 ); the  European Community's Seventh Framework Program  ( FP7/2007-2013 );  TACTICS  ( 278948 ); the  Brain and Behaviour Foundation (NARSAD ,  25861 ); the  Dutch Brain Foundation  ( F2014(1)-06 ); the  National Science Foundation  ( DMR-1644779 ,  DMR-1533260 ,  DMR-2128556 ); the  Human Brain Project  ( 945539 ); the  Canadian Institutes of Health Research  ( PJT-148751 ,  PJT-173442  and  MOP-102599 ); the  Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2020-05917 ,  RGPIN-375457-09  and  RGPIN-2015-05103 ); the  Horizon 2020 Framework Programme of the   European Union  ( 740264  and  802371 ); the  Academy of Finland  ( 298007 ); the  European Research Council  ( 679058  and  802371 );  Innosuisse  ( 18546.
Data availability The raw datasets are available here: unstandardized resting-state fMRI (MultiRat_rest) (https://doi. org/10. 18112/openneuro. ds004114. v1. 0. 0); standardized resting-state fMRI (StandardRat) (https:// doi. org/10. 18112/openneuro. ds004116. v1. 0. 0). The pre-processed volumes, time series and quality control files are available here: https://doi. org/10. 34973/1gp6-gg97. Image pre-processing, confound correction and connectivity analysis were performed using RABIES 0. 3. 5 (https://github. com/CoBrALab/RABIES (ref. 14  ).
Detailed deviations are listed here: https://github. com/grandjeanlab/MultiRat. Animals All acquisitions were performed after approval from the respective local and national ethical authorities. Participating laboratories were instructed to provide n = 10 rat imaging acquisitions consisting of one anatomical and one resting-state functional run. Exclusion criteria were unsuitability for RABIES pre-processing (for example, dedicated image reconstruction needs and restricted field of view). The MultiRat_rest collection consists of n = 65 datasets from 46 research centers and n = 646 rats (141/505 female/male). StandardRat consists of n = 21 data- sets and n = 209 rats (93/116 female/male) from 20 research centers.
This paper provides a summary of the work done within Phase III of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project, under the International Energy Agency Wind Technology Collaboration Programme Task 30. This phase focused on validating the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure. Numerical models of the Technical University of Denmark 10 MW reference wind turbine were validated using measurement data from a 1 : 75 scale test performed during the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project and a follow-on experimental campaign, both performed at the Politecnico di Milano wind tunnel. Validation of the models was performed by comparing the loads for steady (fixed platform) and unsteady (harmonic motion of the platform) wind conditions.
Model definition To validate the accuracy of the rotor loads for an FOWT, measurement data from two wind tunnel experimental campaigns were used. Both campaigns were conducted in the Politecnico di Milano wind tunnel (13. 84 m wide by 3. 84 m high by 35 m long) and used a 1 : 75 scaled version of the DTU 10 MW RWT. The blades were straight, without a cone angle, and rigid. The blade properties can be found in Table 1. The aerodynamic center of the different radial stations is coincident with the blade pitch axis. Its position along the chord, as measured from the leading edge, is given in Table  1. Two-dimensional sectional-model experiments were conducted in the DTU red wind tunnel to characterize the airfoil polars with smooth-and rough-surface conditions  (Fontanella et al. , 2021b). The airfoil polars, with the lift and drag coefficients for the different angles of attack at the 20 radial stations shown in Table  1  for rough-surface conditions, were provided to the participants.
Each airfoil polar contains seven sets of lift and drag coefficients for Reynolds numbers ranging between 5E4 and 2E5  (Robertson et al. , 2023). The rest of the model geometry (e. g. , tower, rotor overhang) was dependent on the testing campaign being studied. The first data set was developed during the UNAFLOW project  (Fontanella et al. , 2021a). The testing was similar to that performed during the LIFES50+ project  (Bayati et al. , 2017). However, for the UNAFLOW test the tower was considered rigid and included a negative tilt angle of 5 • to offset the wind turbine tilt angle, resulting in a rotor perpendicular to the wind tunnel floor. The second data set is from a follow-on testing campaign performed during 2021 in the same wind tunnel. It used the same rotor but a different nacelle and tower length (also rigid) than the one used in the UNAFLOW project.
The participants used modeling approaches of different fidelities to study the system: blade element momentum (BEM) theory, dynamic BEM (DBEM) that accounts for a dynamic inflow effect, generalized dynamic wake (GDW), free-vortex wake (FVW), and blade-resolved or actuatorline-based computational fluid dynamics (CFD). The BEM, DBEM, GDW, some FVW, and the actuatorline-based CFD approaches are based on the lifting-line theory. In these approaches, the airfoil polar data are used as an input for the model. The airfoil polar provides information about the lift and drag coefficients as a function of the angle of attack. Participants can use the airfoil polar information as a look-up table (static polar approach) or to account for unsteady airfoil aerodynamics. The unsteady airfoil aerodynamics accounts for the flow hysteresis in the lift and drag coefficients under unsteady wind and wind turbine operating conditions (e.
g. , blade pitch actuations). The flow hysteresis can occur during attached flow (e. g. , linear region in the airfoil polar) or flow separation, including dynamic stall (e. g. , nonlinear region in the airfoil polar). These unsteady effects are computed by the modeling tools and depend on the underlying theory considered. The lifting surface and threedimensional panel FVW as well as the blade-resolved CFD do not use the airfoil polar data as input. Instead, they use a surface mesh based on the blade geometry. One computeraided design (CAD) file of the blade was provided to the participants. In this case, it may be challenging to reproduce the airfoil polars' behavior due to the relatively small Reynolds numbers during the experiment (mainly below 1E5). Small Reynolds numbers may increase the boundary-layer thickness, resulting in larger drag and smaller lift coefficients.
1 due to a limitation in the 6 DOF robot motion range. Three additional load cases (2. 12, 2. 16, and 2. 17) were included to examine conditions that might create more impactful unsteady aerodynamic responses due to changes in the flow conditions. There are no experimental data available for these conditions; thus, they are used as verification cases only. Load Case 2. 12 includes a platform surge oscillation at the same frequency as Load Case 2. 7 but with an amplitude that is 1 order of magnitude higher. Finally, Load Cases 2. 16 and 2. 17 are based on Load Case 2. 12 but include some rotor speed and blade pitch variations. Some numbers are skipped in the load case numbering sequence because there were more load cases that did not pro-vide additional insight and are therefore left out of the results of Sect.
Most numerical models tend to slightly overpredict the wake expansion. It is possible that the proximity of the blade tip to the ceiling in the experiments tends to inhibit a normal wake expansion  (Soto-Valle et al. , 2020). The CFD participants (e. g. , POLIMI and UNIFI in Fig. 10 ) that included the wind tunnel walls, floor, and ceiling obtained a better agreement with the experiment. FVW participants cannot include this boundary condition in their numerical models without implementing additional features. Interestingly, DTU1 (CFD) did not include the wind tunnel ceiling, and the wake expansion is aligned with the behavior observed by most FVW participants. Cioni et al. (2023)  also studied the hot-wire data and PIV data from Experiment 1 under steady and unsteady wind conditions and provided additional insights.
The dynamic inflow also had a limited impact on the phase of the rotor loads. Thus, this work has shown that while the motion of the turbine itself does not require an unsteady aerody-namic modeling approach to accurately predict the loads in the turbine (at least for the design and forced motion studied in this project), a realistic condition where generator torque control and blade pitch angle control are included will need unsteady aerodynamic models (both airfoil unsteady aerodynamics and dynamic inflow models) for accurate load prediction. di Milano for providing the data to characterize the numerical models and the data recorded during the UNAFLOW project and the follow-on campaigns, as well as for their ongoing support. This work was authored in part by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under grant no.
DE-AC36-08GO28308. Funding was provided by the US Department of Energy's offices of Energy Efficiency and Renewable Energy and Wind Energy Technologies. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes. Financial support. This research has been supported by the office of Energy Efficiency and Renewable Energy (grant no. DE-AC36-08GO28308). Review statement. This paper was edited by Johan Meyers and reviewed by Vasilis A. Riziotis and one anonymous referee.
Data availability. The modeling information, simulation results, and experimental data from this project is available to the public through the US Department of Energy data archive and portal, available at https://a2e. energy. gov/project/oc6 (US Department of Energy, 2023). The data set developed during the UNAFLOW project is available at https://doi. org/10. 5281/zenodo. 4740006 (Fontanella et al. ,  2021a).
The Three Hundred simulation data-set In this work, we rely on dedicated weak lensing simulations of clusters extracted from the re-simulated regions by the Three Hundred Collaboration  (Cui et al. 2018 (Cui et al. , 2022)). The data set consists of 324 regions centred on the most massive clusters (M 200 ⪆ 8 × 10 14 h -1 M ⊙ ) identified at z = 0 in the DM-only MDPL2 MultiDark simulation  (Klypin et al. 2016) , with a boxsize of 1 Gpc on a side. The parent simulation was run adopting the cosmological parameters as derived by the Planck mission (Planck Collaboration 2016): Ω m = 0. 307, Ω b = 0. 048, Ω Λ = 0. 693, h = 0. 678, σ 8 = 0. 823 and n s = 0. 96. For each selected region, initial conditions with multiple levels of mass refinements were generated using the GINNUNGAGAP code 3. Within the highest resolution Lagrangian region -at least five times larger than the cluster virial radius -particles are divided into dark matter (DM) and gas types according to the considered cosmological baryon fraction: m dm = 12.
7 × 10 8 h -1 M ⊙ and m gas = 2. 36 × 10 8 h -1 M ⊙ , respectively. The resolution outside this region is degraded to reduce the computational cost with respect to the parent original simulation. It is worth mentioning that each re-simulated region, with a typical radius of 15 h -1 Mpc from the centre, may contain additional groups and filaments not physically associated and not gravitationally bound to the virialised cluster but important for total projected mass quantity studies. The evolution of the particle distribution from the initialconditions (z = 120) until the present time was followed using GADGET-X, based on the gravity solved GADGET-3 Tree-PM code. The code uses an improved smooth-particle hydrodynamics (SPH) scheme  (Beck et al. 2016)  to follow the evolution of the gas component with artificial thermal diffusion, time-3 https://github.
Acknowledgements. CG, MM, and LM acknowledge support from the grant  PRIN-MIUR 2017   WSCC32 ZOOMING , and the support from the grant  ASI  n. 2018-23-HH. 0. CG acknowledges funding from the  Italian National Institute of Astrophysics  under the grant " Bando PrIN 2019 ", PI:  Viola Allevato  and from the  HPC-Europa3 Transnational Access programme   HPC17VDILO. GC also thanks the support from  INAF  theory Grant 2022:  Illuminating Dark Matter using Weak Lensing by Cluster Satellites , PI:  Carlo   Giocoli. MM acknowledge financial support from  PRIN-MIUR  grant and  2020SKSTHZ ,  INAF  "main-stream"  1. 05. 01. 86. 20 : "Deep and wide  view of galaxy clusters  (P. I. : M. Nonino )" and  INAF  "main-stream"  1. 05. 01. 86. 31  "The deepest view of  highredshift galaxies and globular cluster precursors in the early Universe " (P.
I. : E. Vanzella). SB and MM acknowledge financial support from the  InDark INFN  Grant. GY and AK would like to thank the  Ministerio de Ciencia e Innovación (Spain)  for financial support under research grant  PID2021-122603NB-C21  The THREE HUNDRED simulations used in this work have been performed in the MareNostrum Supercomputer at the  Barcelona Supercomputing Center , thanks to CPU time granted by the  Red Española de Supercomputación. WC is supported by the  STFC   AGP  Grant  ST/V000594/1  and the  Atracción de Talento  Contract no. 2020-T1/TIC-19882  granted by the  Comunidad de Madrid in Spain. He also thanks the  Ministerio de Ciencia e Innovación (Spain)  for financial support under Project grant  PID2021-122603NB-C21. He further acknowledges the science research grants from the  China Manned Space  Project with NO.
scRNA-seq dataset We used the scRNA-seq dataset presented in our previous study available in the Gene Expression Omnibus database under accession code GSE147729  10. This dataset is composed of two pools of young (2/3 months) mouse HSPCs and two pools of aged (18 months) mouse HSPCs. Our previous results (cell cycle phase assignment, cell clustering, pseudotime ordering) were considered in this study to define the HSPC states at the basis of our modeling 10 (Supplementary Table  1 ).
Regulon analysis Identification of regulons with pySCENIC. We used the Single-Cell Regulatory Network Inference and Clustering (SCENIC) approach  25  to identify regulons, which are modules of one TF and its potential targets, and their activities. We ran SCENIC workflow using pySCENIC v1. 10. 0 with its command line implementation  26  as in our previous study regarding gene filtering, TF motifs (motifs-v9-nr. mgi-m0. 001-o0. 0), cis-target (+/-10 kb from TSS mm9-tss-centered-10 kb-7species. mc9nr) databases and command line options  10. In this work, we used as input all 1721 TFs with a motif available in the motif database. We processed with SCENIC workflow all cells together as well as only young or only aged cells. For each cell set, regression per target step with grnboost2 followed by cis-target motif discovery and target pruning were run 50 times using a different seed for the pySCENIC grn command.
Averaged AUCell scores by HSPC states were computed. These scores were standardized in order to hierarchically cluster the regulons using ward. D2 method of the R function hclust with Euclidean distance. The DoHeatmap function from the Seurat v3 package  27  , was used to display the results. Averaged AUCell enrichment scores for young and aged cells by HSPC states were also computed in the same way. Activating regulon markers of HSPC states were identified based on their AUCell scores using FindAllMarkers Seurat function (min. pct=0. 1, logfc. threshold=0) with Wilcoxon rank sum tests. Only regulons with an average AUCell score difference above 0. 001 between one state versus all the others were kept. A p-adjusted value (Bonferroni correction) threshold of 0. 001 was applied to filter out non-significant markers (Supplementary Table  3 ).
Cistrome database analysis Available mouse TF ChIP-seq experiments annotated for bone marrow tissue were analyzed using Cistrome database workflow  30. More specifically, for each bed file of the selected experiments in the databases, the top 10,000 peaks with more than 5-fold signal to background ratio were conserved for downstream analysis. Then, target transcripts were identified with BETA in each TF experiment  31. We considered all TF peaks in an experiment   2  & 5 ). j inside a +/-.
When considering only the reliable transcriptional regulations (found in 90% of the SCENIC runs) and after removing auto-regulations, we obtained a directed graph of 133 nodes (TFs) and 670 edges (regulations) (Figure  1C , Supplementary Table  2 ). We further confirmed these regulatory interactions by analyzing the presence of TF peaks in the regulatory regions of their target genes using ChIP-seq data from the Cistrome database  30. Approximately 60% (302) of the network interactions with an available TF node in the Cistrome database were confirmed by the presence of a peak in the regulatory regions of its targets (Supplementary Table  2 ). We then performed a clustering analysis by weighting the network using a normalized interaction score (NIS) calculated from the SCENIC results and by applying Louvain clustering.
In this work, we developed a new modelling strategy that takes full advantage of the scRNAseq data while incorporating available knowledge from the literature and databases, and that integrates the identified factors playing a role in aging. We obtained a BN explaining the transcriptional mechanisms controlling the priming of HSC toward the different hematopoietic lineages and the impacts of its alterations upon aging. We conducted an original qualitative analysis of the model's trajectories which consists of tracing succession of events leading to the priming of HSCs in the different lineages. We could propose a main path starting from the initial state iHSC and passing through the intermediate state preDiff that was not captured before. From the iHSC, activation of Ikzf1 by Gata2 stabilizes early lymphoid priming of HSCs, whereas activation of Spi1 and Myc together with an inactivation of Gata2 leads to preDiff state.
10 kb window from a Transcriptional Start Site (TSS). TSSs of g for which a regulatory score  $ by  is obtained in experiment  among the  experiments where the regulation is found. This score is weighted by the ,  ratio where  is the number of experiments for the given TF available in the considered cistrome datasets. Only  for Scenic interactions or referenced regulations were retained (Supplementary Tables where, the 𝑇𝑆𝑆 & ' are the 𝑛 ( BETA gave us a regulatory score 𝑠 $ for each 𝑇𝑆𝑆 & ' of potential target genes 𝑔 of a TF 𝑡. Then we defined a global cistrome regulatory score (𝐶𝑅𝑆) for a TF 𝑡 on a potential target gene 𝑔 as follows: 𝐶𝑅𝑆(𝑡, 𝑔) = 𝑚 𝑁 " ! × 4 4 𝑠 $ (𝑡, 𝑇𝑆𝑆 & ( ' ) &%! $%!.
Acknowledgements This work was supported by the  EU  ( ERC AdG Mol-2D 788222 ,  FET OPEN SINFONIA 964396 ), the  Spanish MCIN  (Grants  PID2020-117152RB-100 ,  PID2020-117264GB-I00  and  CEX2019-000919-M  funded by  MCIN/AEI/10. 13039/501100011033 ), the  Generalitat Valenciana  ( PROMETEO Program  and  PO FEDER Program   IDIFEDER/2021/078 ) and the  French National Research Agency ANR  ( ANR-17-CE24-0004 ). A. N. -L. thanks the  Spanish MECD  for a  FPU predoctoral fellowship  ( FPU16/02220 ). R. T-C. thanks the  Generalitat Valenciana  for his  APOSTD Fellowship  ( CIAPOS/2021/269 ). M. M. -G. thanks  MCIN  for a  Margarita Salas Fellowship  ( MS21-041 ) with funding from  European Union NextGenerationEU. N. V. thanks  Generalitat Valenciana  for her predoctoral fellowship  GRISOLIAP/2019/030.
V. R. -G. thanks the  Research Foundation Flanders (FWO Vlaanderen)  for a  Junior Postdoctoral Fellowship  ( 1263622N ). S. T. thanks the  Spanish MINECO  for his  Ramón y Cajal Fellowship  ( RYC-2016-19817 ). Experiments were performed on the DEIMOS and SAMBA beamlines at  SOLEIL Synchrotron, France  (proposal number  20181583  and  20191609 ). The authors are grateful to  Florian Leduc  for assistance in using DEIMOS beamline and to the  SOLEIL  staff for smoothly running the facility. The research leading to this result has been supported by the project  CALIPSOplus  under Grant Agreement  730872  from the  EU Framework Programme for Research and Innovation HORIZON  2020. The authors gratefully acknowledge  DELTA Dortmund  synchrotron for allocation of beamtime at beamline BL9. The authors also thank  Ángel López-Muñoz  and  Isaac Brotons  for their technical support.