dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, there were sex-specific findings within the full models: ventromedial prefrontal cortex grey matter volume was negatively associated with emotional symptoms for males when corrected for whole brain volume, and socioeconomic stress was negatively associated with whole brain volume for females. This study underscores the importance of the peer environment for early adolescent emotional symptoms in both boys and girls, but goes further to suggest distinct gender associations with socioeconomic factors and brain structure which provides a multi-level view of risk and resilience. Future research could exploit existing IMAGEN longitudinal data to strengthen causal claims and to determine the potential longstanding impact of social environment and brain development on adolescent mental health.
There is evidence that males and females IMAGEN coordinator, Jeanne Winterer jeanne. winterer@charite. de) for researchers who meet the criteria for access to confidential data. The data underlying the results presented in the study are available from IMAGEN, with more information presented on the IMAGEN website: https://imagenproject. org/?page_id=547. In order to access the IMAGEN dataset, researchers must submit a study proposal form to the IMAGEN coordinator. This will then be circulated to the IMAGEN Executive Committee, who will decide whether access will be granted to the IMAGEN dataset. If access is granted, IMAGEN will provide information on how to access the data server. We can confirm that others will be able to access the data in the same manner as the authors and the authors did not have any special access privileges that others would not have.
The associations between socioeconomic stress, social relationships, brain structure, and mental health need to be examined for males and females separately, to determine whether there are distinct social and biological profiles for adolescent risk and resilience for males and females. The current study addresses this gap by simultaneously modelling socioeconomic stress, social relationships-family support and peer problems-and brain structure separately for males and females. This was achieved by applying structural equation modelling to a large dataset that contains rich information on adolescent development and mental health-the IMAGEN project  [38]. Cross-sectional data were selected at age 14 due to the importance of early adolescence in development of anxiety and depression symptoms  [14] , and due to the availability of all variables of interest at this time point.
Analysis strategy Out of the 2315 participants with data available for any variable of interest at age 14 years, 1950 were used in the current analysis. The derivation of the sample is depicted in Fig 1. Two participants were removed from the dataset due to data quality problems identified by IMA-GEN. One twin sibling was removed from the dataset; the other twin was retained. The following structural equation modelling (SEM) assumptions were checked: no outliers, no missing data, and relative variances between variables  [55]. Multivariate normality is typically investigated, but the current analysis included ordinal-level variables, thus weighted least squares mean-and variance-adjusted (WLSMV) estimation was used for all analyses instead of maximum likelihood (ML) estimation. WLSMV makes no assumptions about the distribution of the data andit uses diagonally weighted least squares (DWLS) to estimate the model parameters but uses the full weight matrix to calculate standard errors and a mean-and varianceadjusted test statistics  [56, 57].
Univariate outliers-defined by the 'rule of thumb' of three standard deviations from the mean-were identified and removed from all neuroimaging variables to account for scanning inaccuracies and to ensure extreme values did not bias model findings. The number of outliers for each neuroimaging variable were as follows: WBV (n = 25), amygdala (n = 19), vmPFC (n = 21). Univariate and multivariate outliers were as follows: single variable (n = 21), WBV, amygdala and vmPFC (n = 4), amygdala and WBV (n = 3), vmPFC and WBV (n = 12), amygdala and vmPFC (n = 1). Multivariate outliers followed the same direction, i. e. , if one value was three standard deviations below the mean, the other value also followed this. Then, participants with complete data available in all variables used in the model were then retained for the analysis, so that there was no missing data.
Ninety-six cases had data missing in the following measures: missing parent-reported data (e. g. socioeconomic stress and family support), non-completion of the Pubertal Development Scale, missing SDQ items, and missing psychiatric diagnosis information. The final sample consisted of 1950 participants. The reason for using complete data was to allow models to be run on the same data and to allow for model comparison. In terms of relative variances between variables, amygdala GMV, vmPFC GMV and WBV were found to have variances over 1000 times larger than other variables in the model. This may be problematic as variables with large variances also have comparatively larger residual values, which means that more emphasis is placed on the larger-variance variables as the estimator calculates the parameters for the best-fitting model  [58].
To address this, amygdala and vmPFC GMV values were divided by 1000, and WBV was divided by 1000000, so that the values were closer in magnitude to other variables in the model. Next, measurement invariance analysis and structural equation modelling were conducted, with detailed information provided in the respective sections below. Analyses were conducted using the lavaan package (version 0. 6-8)  [59]  in R (version 3. 6. 3) [60]. Measurement invariance analysis also used the measEq. syntax function in the semTools package (version 0. 5-3)  [61]. As mentioned previously, WLSMV estimation was used for all analyses. Model fit was assessed by the robust chi-square (χ 2 ) fit statistic, robust root mean squared error of approximation (RMSEA) with 90% confidence interval and robust comparative fit index (CFI).
Strengths and limitations One of the strengths of the current study is the use of a model with a multidisciplinary perspective that was tested in a large dataset. That allowed the investigation of three frames of reference: socioeconomic conditions, social relationships, and brain structure, providing an integrated view of adolescent mental health  [5]. The large sample size ensured that the study had the statistical power to detect robust effects that are less likely to be spurious  [73]. Another strength is the use of analytic techniques such as measurement invariance and SEM. Establishing measurement invariance allowed us to formally specify that the same latent variables were measured between sex and that differences are not simply due to measurement error  [74]. SEM which allows simultaneously modelling of complex relationships between variables  [75].
Importantly, adolescents who reported poorer parent practices compared to parents were at higher risk of internalising symptoms  [78] ; this discrepancy therefore reveals information about the adult-child relationship that has implications for mental health. Unfortunately, the Family Life Questionnaire in the current study is parent-reported only, and other measures of child-reported family support were not available in the IMAGEN dataset, so this could not be explored in the current study. Future studies should aim to assess discrepancies between parent and child reports of family support in different datasets. IMAGEN is a multi-centre study designed to maximise sample size. Different scanners are used at different sites for the neuroimaging assessment. To minimise variability between sites, a central protocol was used between sites and quality control and pre-processing procedures were implemented, explained in depth elsewhere  [38].
Recruitment centre was included as a covariate in the analysis to further account for potential homogeneity. However, it is acknowledged that variability between sites could have affected the results in the current analysis. The use of cross-sectional data is also limiting because we were unable to investigate developmental trajectories over time. There are significant individual differences in brain development in terms of the intercept and slope of change over time  [2]. Environmental variables have been shown to affect the maturation of the brain across adolescence, such as parental support  [6]  and socioeconomic factors  [7, 37]. Therefore, future research should look at brain development longitudinally, to detect individual differences in the developmental trajectory of the brain, the impact of environmental variables, and how this relates to emotional functioning.
Significance statement Using structural equation modelling in a large dataset (IMAGEN), we investigated the nuanced associations between socioeconomic conditions, social relationships, and regional brain structure in predicting adolescent emotional symptoms, separately for each sex. Using this approach, we found significant associations that were common to both sexes, and associations that were sex specific. Future research should aim to verify the associations using longitudinal data, to assess the directionality of relationships of how both social and biological factors affect mental health in adolescence.
behavior and its brain biomarkers), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfu ¨r Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ;  Forschungsnetz IMAC-Mind   01GL1745B ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-2 ,  SFB 940 ,  TRR 265 ,  NE 1383/14-1 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/ S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ),  NSFC  grant  82150710554  and  European Union  funded project 'environMENTAL', grant no:  101057429. Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Medicale  ( DPA20140629802 ), the  Mission Interministerielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hopitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013 ), the  Federation pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U.
The funders listed above provided support in the form of salaries for authors JS, TB and LM, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. The IMAGEN study was funded by the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313),  Human Brain Project  ( HBP SGA 2 ,  785907 , and  HBP SGA 3, 945539 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-byenvironment interaction analysis of substance use Adolescence;  RO1 MH085772-01A1 ) and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence.
Experimental Section Theoretical: All-electron solid-state DFT simulations were performed using the CRYSTAL23 software package. [48, 49]  All periodic calculations used the GGA PBE density functional  [50]  along with the Grimme DFT-D3-BJ dispersion correction,  [51] [52] [53]  which wa demonstrated to provide excellent results for molecular crystals,  [54]  and the split-valence double-zeta def2-SVP basis set. [55]  Geometric optimization for each studied crystalline material was performed with no constraints other than maintaining the space group symmetry of the solid, allowing all atoms and lattice vectors to fully relax to an energetic minimum, using a convergence criterion of ΔE < 10 -8 hartree. Upon optimization, frequency calculations were performed to produce the vibrational modes, transition frequencies, and IR intensities.
Acknowledgements M. T. R. and P. A. B. thank the  National Science Foundation  ( DMR-2046483 ) for financial support. The authors gratefully acknowledge the ISIS neutron and muon source for beam time (TOSCA instrument). G. S. thanks the  Belgian National Fund for Scientific Research (FNRS)  for financial support through research project  COHERENCE2 N. F. 4536. 23. G. S. is a FNRS Research Associate. G. S. acknowledges financial support from the  Francqui Foundation  ( Francqui Start-Up Grant ). G. D. acknowledges financial support from the  French "Agence Nationale de la Recherche ," project  RAP-TORS  ( ANR-21-CE24-0004-01 ). The authors thank  Samsung Electronics  for supplying the DBTTT material. The authors thank  Samsung Electronics  for their support. H. S. acknowledges financial support from the  Engineering and Physical Sciences Research Council (EPSRC ,  Programme  Grant  EP/W017091/1 ), the  European Research Council  ( 101020872 ) and the  Royal Society  ( RP\R1\201082 ).
Bulk transcriptomics analyzed along with the GSE4170 GEO dataset reveal a significant overlap between ENU-treated cells and primary BC cells. Among overexpressed genes, CD25 was identified, and its relevance was confirmed in a cohort of CML patients. Conclusions: iPSCs are a valuable tool to model CML progression and to identify new targets. Here, we show the relevance of CD25 identified in the iPSC model as a marker of CML progression.
Evaluation of Cytological Characteristics of Cells before and after Mutagenesis Cells collected from the hematopoietic cultures on days 12-14 of differentiation were analyzed after May-Grünwald-Giemsa staining. Approximately 10 5 cells were washed twice with PBS. Cytospins were performed on slides using a Cytospin 4 centrifuge (Ther-moFisher Scientific, Illkirch, France). Thereafter, slides were air-dried for 25 min and stained with RAL Kit 555 (RAL Diagnostics, Martillac, France, 361550-0000). The slides were analyzed on a Nikon Eclipse 90i microscope (Champigny sur Marne, France), and images were taken with a Nikon camera DS-Fi1 and NIS-Elements V. 5. 20 software.
Flow Cytometry Cells collected from day +12 of hematopoietic differentiation were counted in trypan blue to determine their viability and stained with the following antibodies (  Cells were thereafter washed and resuspended in PBS with 1 µg/mL 7-Aminoactinomycin D (7-AAD) (Sigma-Aldrich, Saint-Quentin-Fallavier, France, 7240-37-1). Stained cells were analyzed with a BD LSRFortessa TM (BD Biosciences, San Jose, CA, USA,) flow cytometer and FlowJo analysis software.
Libraries were then sequenced by pair with a HighOutput flowcell using an Illumina Nextseq 500 with the following mode: 26 bp (10× Index + UMI), 8 bp (i7 Index), and 57 bp (Read 2). The sequencing data were processed into transcript count tables with the Cell Ranger Single Cell Software Suite 1. 3. 1 by 10× Genomics (http://10xgenomics. com/, accessed Cells 2023, 12, 598 6 of 20 on 9 February 2023). Raw base call files from the Nextseq 500 were demultiplexed with the cellranger mkfastq pipeline into library-specific FASTQ files. The FASTQ files for each library were then processed independently with the cellranger count pipeline. This pipeline used STAR to align cDNA reads to the Mus musculus transcriptome (sequence: GRCm38, annotation: Gencode v25). Once aligned, barcodes associated with these reads-cell identifiers and Unique Molecular Identifiers (UMIs)-underwent filtering and correction.
Reads associated with retained barcodes were quantified and used to build a transcript count table. The resulting data for each sample were then aggregated using the cellranger aggr pipeline, which performed a between-sample normalization step and concatenated the two transcript count tables. For each of the 6 experimental conditions after sequencing, fastq files were mapped on GRChg38 human genome processed for demultiplexing with cellranger software version 4. 0. 0. h5 filtrated outfiles for each experiment were processed individually in R software environment version 4. 2. 1 (10× Genomics, San Francisco, CA, USA) with Seurat R-package version 4. 1. 1 (Paul Hoffman, Satija Lab and Collaborators, New York, NY, USA)  [29]. Singlecell processing was performed individually by filtrating cells expressing less than one hundred transcripts and also removing transcripts expressed in less than 3 cells.
Before integration, the six experiments were individually log normalized, and integration was based on searching common anchors on the twenty-first mathematical dimensions between experiments. After integration, the combined object composed of 63,462 cells and of 28,182 features was scaled. Successive dimension reductions by principal component analysis and t-distributed stochastic neighbor embedding were performed to identify cell communities within a graph structure; for example, a K-nearest neighbor (KNN) graph approach  [30]. Experiments/cell clusters proportion barplot was drawn with ggplot2 R-package version 3. 3. 6 [31].
Exome Analysis of Mutagenized CML-iPSCs To determine genetic changes induced by ENU-induced mutagenesis and their stability, we have performed, in the PB32 and PB32-ENU cell line, an exome sequencing analysis. Library preparation, exome capture, sequencing, and data analysis have been performed by IntegraGen SA (Evry, France). Genomic DNA was captured using Agilent in-solution enrichment methodology (SureSelect XT Clinical Research Exome, Agilent Technologies, Santa Clara, CA, USA) with their biotinylated oligonucleotides probes library (SureSelect XT Clinical Research Exome-54 Mb, Agilent Technologies), followed by pairedend 75 bases massively parallel sequencing on Illumina HiSeq4000. Sequence capture, enrichment, and elution were performed according to the manufacturer's instructions and protocols (SureSelect, Agilent technologies, Les Ulis, France) without modification except for library preparation performed with NEBNext ® Ultra kit (New England Biolabs ® , Evry-Courcouronnes, France).
Bulk RNA Transcriptomics of CML-iPSC-Derived Hematopoietic Cells before and after Mutagenesis RNA was extracted from the hematopoietic cells derived from the three mutagenized and unmutagenized CML-iPSCs. Samples were processed in duplicate for performing hybridization of Clariom S assay human (ThermoFisher Scientific, USA). Robust microar-ray analysis (RMA) normalization  [32]  was performed in Transcriptome Analysis Console (TAC) software (ThermoFisher Scientific, USA), followed by differential expressed gene analysis with limma algorithm  [33]  between ENU and control conditions of the same individual iPSC. Gene signatures of the three distinct iPSCs were compared by barplot and Venn diagram. Upregulated genes by ENU in PB32 iPSCs were used as a predictive signature of the circulating hematopoietic progenitor of CML patients in blast crisis as compared with ones from patients in chronic phase (Geodataset GSE4170)  [34].
This machine learning analysis was performed with pamr R-package version 1. 56. 1 by leaving one out cross-validation process  [35]. ENU-upregulated signature was also used as a geneset during geneset enrichment analysis (GSEA)  [36]. Unsupervised principal component analysis was performed with FactoMineR R-package version 2. 5  [37]. Functional enrichment was performed with the Toppgene web  [38]  application on Gene Ontology cellular component database  [39]. A functional enrichment network was drawn with Cytoscape standalone application version 3. 6. 0 (Cytoscape Consortium, San Diego, CA, USA)  [40]. Expression heatmap was drawn with pheatmap R-package version 1. 0. 12 (Raivo Kolde, Boston, MA, USA).
LTC-IC assays of PB34 harboring JAK2V617F mutation in addition to BCR::ABL  [28]  showed evidence of increased hematopoietic potential but no significant differences before and after mutagenesis (Figure  4D ). The effect of Ruxolitinib alone or in combination with Imatinib on PB34 and PB34-ENU cells shows that ENU mutation does not affect the effectiveness of these drugs on the JAK2 V617F mutation (Supplementary Figure  S5 ). The transcriptional analysis confirmed that PB32-ENU exhibited the highest number of mutated genes followed by PB34-ENU and PB27-ENU (Figure  5B ), and this was consistent with the potent BC-CML phenotype showed by PB32-ENU when compared to BC datasets (Figure  5B ). PB32-ENU enrichment signatures showed an AML profile, whereas this is not the case for PB32. This shows the effect of the ENU on the in vitro blastic transformation of the disease.
Previous homology models failed to predict the β-cardiac myosin IHM structure In the absence of a high-resolution structure of the cardiac IHM, three homology models of the human Car IHM (PDB code 5TBY; 38 MS03; 26 MA1 27 ) were generated using distinct approaches, including molecular dynamics (MA1 27 and MS03  26  ), and docking in the low-resolution maps of relaxed thick filaments of tarantula striated muscle (5TBY 38 ; MS03  26  ) or human β-cardiac myosin (MA1)  27. However, maps obtained from negative staining of relaxed filaments isolated from striated muscle can be biased due to radial collapse of the heads and misevaluation of the radius of the fibers 39. This bias adds further uncertainty as to the accuracy of published previous fits to negative staining data derived from striated muscle.
Cryo-EM data collection and processing The sample was thawed at 4 °C and centrifugated at top speed (15,000 g) during 15 min before preparation. Note that for each batch, mass spectrometry analysis (MALDI) were performed after thawing in order to be sure that the proteins were not proteolyzed and that the N-terminus extensions were present. The sample was diluted at 0. 3 mg/ ml in 10 mM imidazole pH 7. 5, 2 mM MgCl 2. 0. 5 mM Mg. ATP was added and the mix was incubated 15 min at room temperature. 3 µl was applied to glow discharged UltrAuFoil 1. 2/1. 3 holey gold grid and vitrified in a Mark IV Vitrobot (ThermoFisher). Grids were imaged in ESRF's CM01 facility using a Titan Krios microscope (ThermoFisher) at 300 keV  55  with a K3 direct electron camera at 0. 84 Å per pixel. 5154 movies were collected with 40 frames each and a dose of 0.
Model building and refinement The head/Converter/ELC region was rebuilt based on the bovine β-cardiac myosin PPS complexed to Omecamtiv mecarbil  35  (PDB code 5N69) without solvent or ligand. The coiled-coil S2 region was fitted from the crystal structure  60  (PDB code 2FXM). The first model for the IQ2/RLC region was obtained by homology modeling with the software Swiss-Model 61 using the SmMyo2 IHM  30  (PDB code 7MF3) as a template. Each part was fitted in the density map and the model was built and optimized with the module Quick MD Simulator from CHARMM-GUI  62, 63. Steps of real space refinement were performed against map 1 with Phenix  64. During the procedure, the head region (residues 1-781) which was at higher resolution was subjected to reciprocal space refinement against map 2 with Refmac 65.