dataesr/scientific-paragraphs-categorization huggingface dataset:
by transcriptomic and epigenetic analyses, respectively, including TGFB2 (Fig4E). And only one NTNG1 gene from HMS (N = 75) made part of Class 2 (Dataset EV2). Such low overlap may point to significant differences between steady-state transcriptome and H3K27ac status reflecting differences between chromatin modifications associated with transcriptional activity of the locus and total RNA levels at the equilibrium between RNA synthesis and stability. Several studies propose LSD1 as a factor involved in regulation of promoter activities (Ferrari-Amorotti et al, 2013; Ambrosio et al, 2017; Carnesecchi et al, 2017; Zhang et al, 2018). To address how LSD1 occupancy of promoters is affected in the presence of HOTAIR, we performed CUT&RUN profiling of LSD1 in our system. Our data revealed that HOTAIR expression rather mildly affected LSD1 overall distribution across the genome (FigEV3A and B).
Primary epithelial kidney cells lose epithelial traits and acquire mesenchymal properties with time, through cell division and increase of genomic instability, hence undertaking EMT. When immortalized at different stages before (Epi) and after (Mes) transition, they possess very different transcriptome(Castro-Vega et al, 2013). This was confirmed in our study by RNA-seq and differential expression analysis: 1,274 and 1,423 protein-coding genes were upand downregulated, respectively, in Mes cells, underlying differences in cell phenotypes regarding cell morphology, migration, and expression of EMT markers (Dataset EV4, Appendix FigS5A and B). However, these alterations were not strictly concurrent with those induced by HOTAIR overexpression in Epi cells reinforcing the idea that HOTAIR is not able to fully reprogram Epi cells to mesenchymal identity.
Acknowledgements We deeply thank  Rocco Cipolla ,  Elena Battistello ,  Sandra Carignon ,  Matthieu Lejars , and  Marc Descrimes  for technical contributions,  Karina Jouravleva , and  Luis Castro-Vega  for discussions and expertise. The HOTAIR cDNA containing LZRS plasmid was a kind gift of H. Y. Chang. RNA-seq and CUT&RUN sequencing efforts were supported by the grant from the  ICGex program of Institut Curie  attributed to A. M. and benefited from the facilities and expertise of the  NGS platform of Institut Curie , supported by  Agence Nationale de la Recherche  ( ANR-10-EQPX-03 ,  ANR10-INBS-09-08 ). A. M. , M. P. , and Z. S. were supported by the  European Research Council  ( ERC-consolidator DARK-616180-ERC-2014 ) attributed to A. M. , J. J. was supported by  "La Ligue contre le Cancer.
Data availability The datasets and computer code produced in this study are available in the following links: • Raw RNA-seq and CUT&RUN datasets: Gene Expression Omnibus GSE106517 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE106517). • Computer scripts: GitHub (https://github. com/MorillonLab/HOT AIR_LSD1). Expanded View for this article is available online.
Y433F) or wild-type littermates, and genotyped after weaning. The SERT-KO mice [line B6. 129(Cg)-Slc6atm1Kpl/J] on a C57BL/6 background were purchased from Taconic (Hudson, NY, USA) and bred at the Biomedical Center, University of Freiburg. In every animal, tail biopsies were genotyped with PCR using primers from the Jackson Laboratory database to confirm homozygous knock-out. The SERT-KO mice showed bands at 210 bp, whereas the wild-type mice showed bands at 318 bp, which confirmed the gene disruption at the SERT locus. Finally, for in vivo electrophysiology, male adult Sprague-Dawley rats were used. The present study used males and female mice (18-22 weeks old at the beginning of the experiments), and male Sprague-Dawley rats (15 weeks old at the beginning of the experiments). All animals used in the present study were experimentally naive before the beginning of the described experimental procedures.
This procedure resulted in 2 3 6 3 12 = 144 starting configurations for docking. Rosetta MPdock tool  (Alford et al. , 2015)  was used to generate 960 dimers for each of the 144 starting configurations, yielding a total of 138,240 TM dimer models. These models were sorted by their interface score, and the leading 10% were clustered into five groups using the Calibur clustering application  (Li and Ng, 2010)  based on the models' structural similarity. The structures with the best interface score in each cluster were selected as representative models. The initial model of the human TRKA TM (amino acids 410-443) dimer was adapted from the NMR structure (PDB:2N90). The initial position of the TRKA TM region in the lipid bilayers was determined using the PPM Server  (Lomize et al. , 2017).
The lysates of non-transfected HEK293T cells were used to evaluate background fluorescence, which appeared to be undetectable in current MST set up. Titration series of fluoxetine (0-100mM) were incubated with diluted cell lysates. The measurements were done in premium coated capillaries (NanoTemper Technologies GmbH, MO-K025) using LED source with 470nm and 50% infrared-laser power at 25 C. Hill model was used to evaluate binding affinity, each data point represents mean DFnorm values from four independent experiments. The bound fraction of the GFP-TRKB was determined and the fitting was performed using the Hill model method incorporated by the MO Affinity Analysis v2. 3 software (NanoTemper Technologies). In situ proximity ligation assay (PLA) Proximity ligation assay was done with Duolink In Situ Red Starter Kit Mouse/Goat (#DUO92103, Sigma-Aldrich), following manufacturer's instructions.
For each cell, 35000 frames were acquired, and the localization map of single fluorophores was reconstructed with SoftWoRx 7. 0 software (GE Healthcare). For the localization map, the first 1000 frames approximately were discarded and the reconstruction parameters were maintained identical for all the samples. Reconstruction parameters: For the localization of all the blinking events in the dataset, the point spread function size factor was set at 1. 550 with a local maximum factor of 0. 05. Drift correction was adjusted dividing the frames into 20 groups. Tracking of the fluorophores was performed with a maximum localization precision of 100 nm without fiducial markers. The final localization map of the fluorophores identified was built with a reconstruction pixel size of 10 nm and localization precision set at 5 and 100 nm respectively for the minimum and the maximum, and a fluorophore persistence threshold set at 3 frames.
ACKNOWLEDGMENTS The authors thank  Sulo Kolehmainen  and  Seija La ˚gas (Neuroscience Center-UH) ,  Marc Baumann  and  Rabah Soliymani  ( Department of Biochemistry and Developmental Biology-UH ), and the  Biomedicum Imaging Unit (BIU)  at the  University of Helsinki  for technical help. We also thank  Drs. Todd Gould  ( U. of Maryland ) and  Craig Thomas  ( NIH ) for kindly supplying the R, R-HNK ,  Henri Huttunen  ( UH ) for supplying GLuc-coupled raft-restricted FYN construct, and  Yves-Alain Barde ,  Maija Castren ,  Juan Lima-Ojeda , and  Heikki Ruskoaho  for their insightful comments to the manuscript. The E. C. lab was supported by the  ERC  ( 322742-iPLASTICITY ),  JPND CircProt  (project  301225  and project  643417 ), the  Sigrid Juselius Foundation , the  Jane and Aatos Erkko Foundation , and the  Academy of Finland  ( 294710  and  307416 ).
C. R. A. F. D. received grants from  FAPESP  (project  2018/18500-3  and project  2018/04250-5 ). The C. R. B. lab was supported by the  JPND CircProt  (project  30122 ). The S. E. L. lab was funded by the  Academy of Finland  ( 297211 ). The I. V. lab was supported by the  Academy of Finland (Center of Excellence)  ( 307415 ), the  Sigrid Juselius Foundation , the  Helsinki Institute of Life Science, Human Frontier Science Program  (project  RGP0059/2019 ), and  CSC-IT Center for Science. T. S. was funded by the  German Research Council  ( SE 2666/2-1 ) and the  Forschungskommission of the Medical Faculty of the University of Freiburg  ( SER1149/17 ). The M. S. lab was funded by the  Jane and Aatos Erkko Foundation. None of the funders had a role in the data acquisition and analysis or manuscript preparation.
Structural analysis of mouse Pcdhgb7 and in silico analysis of the mutational effect Crystal structure of the Ca 2+ -bound form of mouse Pcdhgb7 was obtained from the Protein Data Bank (www. wwpdb. org; PDB ID 5v5x). Structural analysis, data visualization, and figure preparation were carried out with the program PyMOL 2. 3 (www. pymol. org; Schrödinger, LLC) and WebLab viewerPro (Molecular Simulations Inc. ).
Identification of biallelic truncating and missense PCDHGC4 variants We performed linkage analysis (family 1) and/or genome/exome sequencing in probands and proband/parent trios. Based on parental consanguinity, autosomal recessive inheritance was considered likely, and we prioritized homozygous, rare exonic, and splice site variants (see Supplemental Information). We identified three different missense variants and five protein truncating variants in the Protocadherin-gamma family member PCDHGC4 (OMIM 606305; NM_018928. 2) in all affected individuals (Fig. 3a , b, S1, Table  2 ). All variants fully cosegregated with the phenotype in the respective families and are absent or very rare in the general human population with minor allele frequencies (MAFs) ranging from 0 to 4*10 -6 , in line with an autosomal recessive pattern of inheritance ( ), that were predicted to lead to an early stop and premature protein truncation, and were absent from the gnomAD database (Fig.
3b , Table  2 ). In family 9, we found the homozygous variant c. 2443-1G>A at the acceptor splice site of intron 1, and by employing an exon-trapping approach we could show that this variant leads to a loss of the acceptor splice-site recognition resulting in severe splicing defects such as whole-exon skipping or usage of a cryptic exonic acceptor splice site, which both are predicted to induce a frameshift and premature protein truncation (Fig. 3b, S3 ). Within the family of γ-PCDHs, PCDHGC4 is the only member that is not only highly conserved across species, but also under strict mutational constraint  [23]. Truncating variants in PCDHGC4 are rarely observed in healthy control individuals. For the canonical transcript of PCDHGC4 (ENST00000306593. 1, NM_018928. 2) only 12 alleles with nonsense variants, all in heterozygous state, were reported in the gnomAD database in contrast to 29.
6 that were expected to be observed in the >240,000 alleles (probability of loss of function intolerance [pLI] = 0. 98). Further, biallelic copy-number variants (CNVs) encompassing PCDGHC4 have not been reported so far in the DECIPHER database, the Database of Genomic Variants (DGV), and the structural variant (SV) data set of gnomAD with only two (DGV) and six (gnomAD) heterozygous alterations enlisted in these data sets that affect PCDHGC4. Interestingly, genetic disruption of the entire γ-Pcdh cluster as well as singular knockout of Pcdhgc4 in mice also cause a severe neurodevelopmental phenotype, both resulting in neurodegeneration in late embryonic stages and leading to early neonatal lethality  [8, [20] [21] [22] [23]. Furthermore, we identified three different homozygous missense variants, c.
ACKNOWLEDGEMENTS We are grateful to all family members that participated in this study. This work was supported by the  Higher Education Commission (HEC) of Pakistan  to M. I. and E. U. H. M. ; the  Center for Molecular Medicine Cologne (CMMC)  (Projects 38-RP and  C12 ;  2635/8029/01  and  2635/8326/01 ) to P. N. and M. S. H. ; the  Koeln Fortune Program  ( Faculty of Medicine, University of Cologne ;  381/2020 ) to M. S. H. ; Intramural Funding from the  Fortune Program (Faculty of Medicine, University of Tübingen ;  2545-1-0 ) and the  Ministry of Science, Research and Art Baden-Württemberg  to B. V. ; the  German Research Foundation (DFG, Deutsche Forschungsgemeinschaft)  under Germany's Excellence Strategy-EXC 2067/1-390729940 to B. W. ; the  Medical Research Council (MRC)  ( MR/S01165X/1 ,  MR/S005021/1 ,  G0601943 ), the  National Institute for Health Research University College London Hospitals Biomedical Research Centre , the  Rosetree Trust, Ataxia UK ,  MSA Trust ,  Brain Research UK ,  Sparks GOSH Charity, Muscular Dystrophy UK (MDUK) ,  Muscular Dystrophy Association (MDA USA)  to H.
H. ; sequencing of family 3 was performed under the  Care4Rare Canada Consortium  funded by  Genome Canada  and the  Ontario Genomics Institute  ( OGI-147 ), the  Canadian Institutes of Health Research, Ontario Research Fund, Genome Alberta ,  Genome British Columbia ,  Genome Quebec , and  Children's Hospital of Eastern Ontario Foundation. Three of the authors of this publication are members of the European Reference Network for Intellectual Disability, Telehealth and Congenital Anomalies (ERN-ITHACA; Project  ID No 739543 ) ( F. Riccardi ;  S. Banka ;  S. Douzgou ). Family 5 was collected as part of the SYNaPS Study Group collaboration funded by The  Wellcome Trust and strategic award (Synaptopathies)  ( WT093205   MA  and  WT104033AIA ) funding and research was conducted as part of the  Queen Square Genomics group at University College London , supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre.
Genomic data  for family 8 was generated by the 100,000  Genomes  Project. The  100 , 000   Genomes  Project is managed by  Genomics England Limited  (a wholly owned company of the  Department of Health and Social Care ). The 100,000  Genomes  Project is funded by the  National Institute for Health Research and NHS England. The  Wellcome Trust ,  Cancer Research UK  and the  Medical Research Council  have also funded research infrastructure. The 100,000  Genomes  Project uses data provided by patients and collected by the National Health Service as part of their care and support. A. J. is supported by  Solve-RD. The  Solve-RD  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  779257.
5 mm (hip joint) and 5. 9 mm (subtalar joint), while the joint axes presented discrepancies between 1°(knee joint) to 11°(subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.
, 2018; Victor and Premanathan, 2013)  and for creating subject-specific musculoskeletal models. Previous studies presented methods to generate subjectspecific models of the entire lower limb  (Marra et al. , 2015; Modenese et al. , 2018)  or individual joints  (Barzan et al. , 2019; Brito da Luz et al. , 2017; Montefiori et al. , 2019a; Nardini et al. , 2020)  and dedicated modelling tools like NMSBuilder  (Valente et al. , 2017a)  or specialized features in the AnyBody software  (Damsgaard et al. , 2006)  are available to implement these workflows. Nevertheless, patient-specific musculoskeletal models are currently employed in small-sized clinical applications  (Falisse et al. , 2020; Montefiori et al. , 2019b; Pitto et al. , 2019; Taddei et al. , 2012; Valente et al. , 2017b) , mostly because the generation of each model is a time-demanding operation requiring manual intervention by specialized operators.
Statistical shape modelling workflows have recently demonstrated high potential for reconstructing bone geometries from sparse anatomical datasets  (Davico et al. , 2019; Nolte et al. , 2016; Suwarganda et al. , 2019)  and landmarks digitized in the gait lab  (Nolte et al. , 2020; Zhang et al. , 2016) , but to the best of the authors' knowledge they do not yet offer methods to generate articulated skeletal models of the complete lower limb. The bone reconstructions are limited to the long bones  (Nolte et al. , 2020; Nolte et al. , 2016)  or omit the talus and foot bones  (Davico et al. , 2019; Suwarganda et al. , 2019; Zhang et al. , 2016) , and in musculoskeletal modelling contexts they have been employed to perform non-linear scaling of pre-existing muscle attachments  (Nolte et al. , 2016)  with scarce focus towards joint modelling.
Workflow to generate automatic skeletal models A set of computational methods proposed in previous literature to define ACSs automatically  (Kai et al. , 2014; Miranda et al. , 2010; Renault et al. , 2018)  were acquired and included in a more extensive modelling workflow. The geometrical methods from  Renault et al. (2018)  were obtained from the public ''GIBOC-KNEE" MATLAB toolbox (https://github. com/renaultJB/GIBOC-Knee-Coordinate-System) and extensively modified and expanded, while the methods described by  Kai et al. (2014)  were independently reimplemented and those of  Miranda et al. (2010)  were obtained through contacting the authors of the publication. Additional algorithms were developed ad hoc for the purposes of this investigation (Table  1 ). The implemented workflow (Fig. 1 ) consisted of the following steps: a) obtaining segmented three-dimensional bone geometries of the pelvis, femur, patella, tibia, fibula, talus, calcaneus and the other foot bones from medical images; b) automatically processing these bone models to extract the geometrical parameters required to define ACSs and appropriate joint coordinate systems (JCSs) for the parent and child bodies of each joint of the lower limb; c) creating an articulated skeletal model of the lower limb in OpenSim format  (Delp et al.
, 2007)  using the identified JCSs and ACSs. In step b), bone geometries were analysed starting with a transformation to the ACSs defined by their principal axes of inertia  (Gonzalez-Ochoa et al. , 1998; Mirtich, 1996) , followed by bonespecific features extraction. The complete list of algorithms available to define each JCS is reported in Table  1  and the details of the methodologies are described in their reference publications, and for the newly developed algorithms for the pelvis, talus and foot, in the supplementary materials. The articulated skeletal models were generated leveraging the MATLAB (The MathWorks, Natick, MA, USA) application programming interface (API) of OpenSim 4. 1  (Seth et al. , 2018) : a rigid body with appropriate inertial properties  (McConville et al. , 1980; Winter, 2009) , was created for each leg segment and joints defined based on the JCSs identified at step c).
Evaluation of the automatic models The models produced using the automatic workflow were compared against musculoskeletal models of the lower limb generated for other purposes using NMSBuilder (manual models) and previously employed in published research  (Montefiori et al. , 2019b)  or contributions at international conferences  (Modenese et al. , 2020; Modenese et al. , 2019). These subject-specific models were built following the codified approach of  Modenese et al. (2018)  and using bone geometries available in public datasets plus an in vivo MRI dataset collected with the approval of the Imperial College Research Ethics Committee. A complete description of the datasets, characterized by quality of the bone geometry meshes ranging from very good (LHDL-CT) to low (JIA-MRI), is provided in Table  3  and Fig.
2. Articulated skeletal models for all datasets were generated using the STAPLE toolbox (automatic models) and their JCSs com-Table  1  Joint coordinate systems and available algorithms to calculate their parameters. Kai-algorithms are described in  Kai et al. (2014) , GIBOC-algorithms are described in  Renault et al. (2018) , Miranda-algorithms are described in  Miranda et al. (2010)  and STAPLE-algorithms are described in the Supplementary Materials of this publication. Please note that the ground coordinate system coincides with that of the medical images in all models and that the algorithms used at the pelvis were always applied to bone geometries excluding the sacrum bone (considered challenging to segment in MRI scans). The algorithms applied to the ''tibia" rigid body processed the geometry of the proximal tibia, full tibia or full tibia and fibula, depending on the algorithm.
Gait simulations using automatic and manual models The sensitivity of joint angles and net joint moments to JCS differences was quantified simulating six gait trials of JIA-MRI  (Montefiori et al. , 2019b)  with the correspondent automatic and manual models, using the OpenSim inverse kinematic and inverse dynamic analyses. The resulting sets of curves were then compared using a Statistical Parametric Mapping (SPM) two-tailed t-test (significance level: a = 0. 05) implemented in the spm1d package  (Pataky, 2012). Correlation coefficients and root mean squared errors (RMSEs) were also calculated to compare individual trials.
Results All the automatic models employed in the study were successfully generated in less than 30 s each using a standard Z640 Dell Workstation (RAM: 64 GB, CPU: 2 Intel Xeon E5-2630 2. 40 GHz). The comparison of automatic and manual models (Table  4 ) resulted in an overall strong similarity of the joint parameters across all considered datasets. The hip and talocrural joint centres were in excellent agreement with the manual estimations (maximum differences hip: 0. 5 mm, talocrural: 1. 2 mm), whereas the maximum difference was 2. 5 mm at the knee joint and 5. 9 mm at the subtalar joint in the JIA-MRI model due to the low quality of bone reconstruction (difference range in the other datasets: 0. 4-3. 2 mm). The cranial-caudal position of the pelvis-ground joint origin differed by up to 4.
9 mm (range: 0. 8-4. 9 mm) causing minor differences in pelvic tilt (range: 1. 8°-3. 6°, Fig. 3-A ) that propagated to the hip-parent JCS. The axes of the knee and talocrural hinge joints (medio-lateral Z axes) were estimated with maximum differences from manual models of 1. 0°degree, while the subtalar joint axes presented maximum differences up to 2. 9°in the datasets with good quality bone geometries but reached 11. 3°in the JIA-MRI model (Fig. 3-B ). Description of the anatomical datasets employed in the current study. Please note that the LHDL-CT and ICL-MRI bone geometries were pre-processed using MeshLab  (Cignoni et al. , 2008). The details of the medical images are presented as reported in the reference publications. Segmentation times are reported for the datasets of which we could contact the curators.
Although not negligible, this offset represents a substantial improvement (almost 15°) compared to the results of  Kai et al. (2014). More recent algorithms, e. g. Fischer et al. (2019) , could be considered for future additional comparisons. The automatic subtalar joint axis in the JIA-MRI dataset was also noticeably different from the equivalent manual model (11. 3°, Fig. 3-B ). This discrepancy was attributed to the low-quality talus bone reconstruction since in all the other models the same axis was estimated within 2. 9°. JCS differences at the hip, knee and talocrural joint were well within the ranges of human inter-operator repeatability reported as standard deviations in previous investigations  (Hannah et al. , 2017; Martelli et al. , 2015; Montefiori et al. , 2019b)  and even the largest difference observed at the subtalar axis was close to the maximum inter-operator variability (9.