dataesr/scientific-paragraphs-categorization huggingface dataset:
Isolated fibers were painted with silicone so that only a short portion of one fiber end remained out of the silicone, in contact with the bathing solution. The culture medium was then replaced by the experimental extracellular solution containing 140 mM TEA-methane-sulfonate, 2. 5 mM CaCl2, 2 mM MgCl2, 1 mM 4-aminopyridine, 10 mM HEPES and 0. 002 mM tetrodotoxin (pH 7. 2). Voltage-clamp was achieved with a glass micropipette connected to a RK-400 patchclamp amplifier (Bio-Logic, Claix, France) in whole-cell configuration, in combination with an analog-digital converter (Digidata 1440A, Axon Instruments, Foster City, CA) controlled by pClamp 9 software (Axon Instruments). The micropipette was filled with a solution containing 120 mM K-glutamate, 5 mM Na2-ATP, 5 mM Na2-phosphocreatine, 5. 5 mM MgCl2, 5 mM glucose, 15 mM EGTA, 6 mM CaCl2, 0.
Acknowledgements We thank  IGBMC  and  ICS  platforms for animal care, histology, photonic and electron imaging,  Shuling Guo  and  Brett Monia  for providing the Dnm2 antisense oligonucleotide,  Quentin Giraud  for his help with mice. and  Doulaye Dembélé  for the statistical analysis assistance. The creation of the Bin1 fl/fl mouse was done with  Phenomin-ICS, Illkirch, France. This study was supported by institute funding from  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique ,  Université de Strasbourg  and by grants from the  Agence Nationale de la Recherche   ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02 ,  Réseau National des Génopoles (RNG) , the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie grant agreement  ( 675392 ),  Muscular Dystrophy Association  ( 576154 ),  Fondation pour la Recherche Médicale  (201903007992), and  AFM-Téléthon  ( 21267 ,  22734 , MyoNeurALP #  5.
Funding information Agence Nationale de la Recherche , Grant/Award Numbers:  ANR-18-CE16-0006 ,  ANR-18-CE37-0020 ;  Fondation pour la Recherche Médicale , Grant/Award Number:  EQU202003010457 K E Y W O R D S : area CA2 , hippocampus small area between  CA1  and  CA3 ,.
6 é/Å 2 ) or ×29,000 (electron dose ~1. 0 é/Å 2 ), providing final pixel sizes of 0. 87 nm and 0. 74 nm, respectively. Pixel sizes were calibrated using TMV as a standard  (Guesdon et al. , 2016). Dual-axis cryo-electron tomography data were acquired as previously described  (Guesdon et al. , 2013). Briefly, a first tilt series of ~40 images was taken in an angular range of ~±60° starting from 0° and using a Saxton scheme. The specimen was turned by an ~90° in plane rotation at low magnification, and a second tilt series was taken on the same area using parameters identical to the first series. Tomograms were reconstructed using the Etomo graphical user interface of the IMOD program  (Kremer et al. , 1996; Mastronarde, 1997). Tilt series were typically filtered after alignment using a low-pass filter at 0.
Cryo-electron microscopy data were acquired on the Microscopy Rennes imaging center platform (Biosit, Rennes, France), member of the national infrastructure France-BioImaging (FBI) supported by the French National Research Agency (ANR-10-INBS-04). Xenopus laevis eggs were obtained from the Centre de Ressources Biologique Xénopes, Université de Rennes 1, Rennes, France. Porcine brains were kindly provided by Y Drillet, Cooperl Arc Altantique, Lamballe France. Tobacco Mosaic Virus was kindly provided by T Candresse, UMR 13332 Biologie du Fruit et Pathologie, INRAE and University of Bordeaux, Villenave d'Ornon, France. Figure 11-video 1 was designed by A Kawska, Illuscienta, Paris, France. This work was supported by two French National Research Agency grants (ANR-16-CE11-0017-01 to DC and MOS, and ANR-18-CE13-0001-01 to DC), a Swiss National Science Foundation grant (310030_192566 to MOS), and a Human Frontier Science Program grant (CDA00019/2019C to RG).
Compounds 2 and 3 were dissolved first at 10 mg/mL in DMSO with good solubility. Further dilutions were prepared in C1 solution. The compounds 2 and 3 were evaluated up to a maximum range of 50 µg/mL, because they elicited non-specific calcium responses in mock-transfected cells at concentration above 100µg/mL. Data were collected from at least three independent experiments carried out in duplicate. The concentration-response curves were obtained after correction of calcium signals for the response of mock transfected cells and normalization to the fluorescence of cells prior to the stimulation. EC 50 values were calculated using a four-parameter logistic nonlinear regression with equation [f(x) = min + (max-min)/(1 + (x/EC 50 ) nH )] with curves fitting of Sigma Plot software.
Conclusions In this review, we have mainly focused on the role of the PC in neuronal progenitors. Many studies provide evidence on why PC are key organelles in cortical development, and notably impact aRG behaviour. Concerning the centrosome, a cell cycle-dependent organelle, it is very hard to attribute the sole role of cilia to the mutant phenotypes observed. However, emerging omics datasets and cilia protein mutations in several cortical malformations do indeed emphasise the importance of the PC. They have been shown to impact aRG proliferation, differentiation and scaffolding during cortical development. Briefly, we can conclude that in aRG, problems in PC formation may be associated with increased proliferation of neuronal progenitors and thus sometimes macrocephaly. Inversely, disassembly defects could lead to aRG premature differentiation and/or cell death and thus often microcephaly.
Cell cycle and apoptosis analysis Experiments were performed as described previously  (Segala et al. , 2017). Briefly, cells were treated with DDA at the indicated concentrations or the solvent vehicle for 24 h. Cell cycle analysis was performed by flow cytometry after treating with RNase and staining with propidium iodide (PI) and the fraction of cells in the different phases of the cycle was calculated using flowJo software (Ashland, Oregon). Cell apoptosis was analysed by flow cytometry using the apoptosis detection kit (Sigma, France, ref APOAF). The percentage of Annexin-V-FITC/ PI-positive cells was determined using CellQuest software (BD Biosciences).
The captured sEV was then incubated with the indicated specific primary antibody for detection: anti-BMP (Millipore, MAB T837 clone 6C4) 1/50 or anti-LC3 (Sigma/Merck, L8918), 1/20 or anti-CD63 (Santa-Cruz, sc5275), 1/20) at 25 • C for 2 h. After two centrifugations for 5 min at 5000 rpm and two washes, the secondary antibody (1/50) was incubated with captured sEV for 1 h at 4 • C. Beads with bound antibody-labelled sEV were diluted in 200 μl of PBS-BSA 4%. 50000 events were analysed by MACSQuant-10 flow cytometer (Miltenyi Biotec) and further analyses were performed using FlowJo Software. Data are shown as percentage of positive beads.
Viral infections and analyses of conditional RARβ knockout mice For viral infections we adapted previously reported protocol  (Krzyzosiak et al. , 2010). To this end, AAV9 vectors were engineered harboring a CAG promoter, the cDNA of interest, woodchuck hepatitis virus posttranscriptional regulatory element (WRPE; nucleotides 1093-1684, GenBank accession number J04514) and simian virus polyadenylation signal sequence. This expression cassette was inserted between the inverted terminal repeats (ITR) of the AAV9 genome. For excision of floxed allele the expression cassette consisted of EGFPCre fusion protein to generate AAV9EGFPCre virus, whereas virus expressing EGFP only (AAV9EGFP) was generated to control of effects of viral infection. Recombinant AAV vectors were produced by transient transfection of human embryonic kidney (HEK293) cells and purified by isolation from two sequential continuous CsCl gradients, as previously described  (Li et al.
Mitochondrial respiration Tissue was placed in an oxygraphic chamber at 37 • C, under stirring with 2 ml MirO5 + creatine solution (EGTA 0. 5 mM, MgCl 2 3 mM, K lactobionate 60 mM, taurine 20 mM, KH 2 PO 4 10 mM, HEPES 20 mM, sucrose 110 mM), creatine 20 mM, BSA 1 g/l). Mitochondrial respiration was performed using a high-resolution oxygraph (Oxygraph-2k; Oroboros Instruments, Innsbruck, Austria). DatLab software 4. 3 was used to measure the oxygen flux (Oroboros Instruments, Innsbruck). After a basal signal, saponin was injected (50 µg/ml) to permeabilize tissue. Then mitochondrial substrates or inhibitors were successively injected in the chamber in order to asses different complexes of the mitochondrial respiratory chain, namely glutamate (10 mM), malate (5 mM) and pyruvate (10 mM), and then ADP (5 mM), followed by succinate (10 mM) were injected to determine the maximal oxygen consumption.
After inactivation of remaining horseradish peroxidase activity, the digoxigenin-labelled probe was revealed using an anti-dig antibody coupled with horseradish peroxidase (Roche), which activity was detected with a TSA-CY3 kit (Perkin Elmer). The sections were rinsed and mounted with Aqua-Poly/Mount for subsequent imaging. Imaging was performed using Nanozoomer digital scanner (Hamamatsu Photonics, France) or Leica M420 or DMLB/DM4000B microscopes equipped with Photometrics digital cameras and the CoolSnap imaging software (Roger Scientific). The cells were counted as previously described  (Rataj-Baniowska et al. , 2015). Briefly, the ROIs were determined in dorsal striatum and nucleus accumbens shell as shown in Fig. 4A  on at least three sections at corresponding brain levels between bregma 1.
34 and 0. 92. Cell counts were performed semi automatically using two approaches by two independent experimenters: (1) ImageJ software (Rasband, http://rsb. info. nih. gov/ij/) using 8-bit image mode and Point Selection with Light Background in "Find Maxima" process (Prominence -noise tolerance -set at 10) and (2) a software developed in the laboratory (M. Baniowski) and used previously  (Rataj-Baniowska et al. , 2015). Counting was manually curated to account for adjacent cells recognized as one object. Both methods gave similar results and mean of these two independent counts was used for analyses to calculate the number of cells per square millimeter.
MitoTracker staining was quantified by Image-J software on the Maximum intenstity projection of the confocal z-stacks. A region of interest (ROI) of a fixed diameter was used to quantify the intensity of signal. At least 3 cells per images and 5 images per group were analyzed. Mitochondria networking was analyzed by using IMARS software (Bitplane, Zurich, Switzerland). Three-dimensional images were elaborated in order to analyze the mean volume of the mitochondria network in the neurites. The software allows the building of an isosurface on a specific section of the 3D image, identifying the mitochondria net. The mean volume of the individual mitochondria was used to quantify the mitochondria network. At least 3 images per coverslip were analyzed. Moreover, in order to study neuronal sensitivity to glutamate toxicity and ROS production, striatal cultures at 7 DIV were treated for 10 min with 42 µM glutamate in Krebs buffer at 37 • C and 5% CO 2.
Funding This study was funded by  France Parkinson Foundation  (M. C. ),  Fondation de France  (M. R. ),  Agence Nationale de la Recherche (ANR)  ( ERA-Net eRARE grant RAinRARE  to W. K. ), and the institutional LabEx  ANR-10-LABX-0030-INRT grant , managed by the  ANR as part of the program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Acknowledgements This work was supported by the  Fonds de la Recherche Scientifique -FNRS  under Grant no. J. 0091. 18. J. W. thanks the '' Fonds pour la Formation ala Recherche dans l'Industrie et dans l'Agriculture '' (FRIA) and the  Erasmus programme  for financial support. This work was partially supported by the  French ' ' Agence National de la Recherche''  ( ANR-16-CE11-0006-01 ) and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) agencies, the region  Auvergne-Rhone-Alpes  and the  Fondation Belge Contre le Cancer  (convention  2018-072 ) agency. The NanoBio-ICMG platforms ( UAR 2607 ) are acknowledged for their support.
In the absence of either RecJ or RecQ, the ssDNA gap is most likely not enlarged, thus impairing the efficiency of HDGR mechanism. Likewise, deletion of the RecA mediators (recF, recO or recR) causes a significant As negative control polB deficient strain was used, while the lexA strain was used as positive control. The asterisk indicates an aspecific band recognized by the anti-Pol II antibodies. B) Graph correlating the Pol II fold change with the percentage of Pol II TLS events measured with our in vivo genetic system. The fold changes, compared to our parental strain, were quantified by Image Lab software (Biorad) and represent the average and the standard deviation of 2-3 independent experiments (see also S2A Fig). NB: For some dots the error bars are not visible because they are too small.
Semi dry transfer in precast Transblot Turbo membrane 0. 2 um nitrocellulose (Biorad) was done. For RecA immunoblot 200ng of total protein extract was loaded and primary antibodies from Abcam (ab63797) were used at a 1:3000 dilution. For Pol II immunoblot 20 μg of total protein extract was loaded and primary antibodies kindly provided by Mark Sutton were used at a 1:10000 dilution. Secondary antibodies anti-rabbit 700 or 800 (Biorad) were used at a 1:10000. A Chemidoc MP imaging system (Biorad) was used for signal detection and Image Lab software (Biorad) was used for quantification.
This work was supported by the  Agence Nationale de la Recherche (ANR)   Grant  [GenoBlock  ANR-14-CE09-0010-01 ] https://anr. fr(VP); and the  Fondation pour la Recherche Medicale  [Equipe FRM-EQU201903007797] https://www. frm. org(VP). Funding for open access charge:  Fondation pour la Recherche Medicale. The funders had no role in study design, data collection and analysis, increase in TLS. On the other hand, inhibition of the post-synaptic phase did not lead to any increase in TLS. We suggest that defects in the presynaptic phase affecting HDGR increase the time window allowed for TLS. decision to publish, or preparation of the manuscript.
1 Fig 3. 13 Fig 3. The presynaptic phase modulates TLS pathway independently from SOS induction. A) Western blot measuring the expression level of Pol II. As negative control polB deficient strain was used, while the lexA strain was used as positive control. The asterisk indicates an aspecific band recognized by the anti-Pol II antibodies. B) Graph correlating the Pol II fold change with the percentage of Pol II TLS events measured with our in vivo genetic system. The fold changes, compared to our parental strain, were quantified by Image Lab software (Biorad) and represent the average and the standard deviation of 2-3 independent experiments (see also S2A Fig). NB: For some dots the error bars are not visible because they are too small. C) The graph represents the percentage of Pol II TLS events in the presence of a G-AAF lesion.
Image analysis Muscle fiber cross sections were analyzed using the open-source software ImageJ  (Schindelin et al. , 2012). Each HE image was converted to 8-bit format with a range of values from 0 to 255, and a threshold of 180 was applied. On each image, 5 squares with 500 μm sides (0. 25 mm 2 ) were drawn in bundles of fibers, and the area of each fiber, and the number of fibers was measured in each square, including incomplete fibers located on the edges of the squares. Since all images were compared, keeping those incomplete fibers was not an issue. To avoid any bias due to small artifacts, areas under 300 μm 2 were excluded. The smallest fiber area visually observed was around 339 μm 2. When fibers were too close to each other, their outlines were delimited manually. The space area between each stained fiber material was calculated and named the extramyofibrillar mass area (EMA).
Pure-Tone Audiogram Pure-tone audiograms were determined by testing the auditory brainstem responses (ABRs) under deep anesthesia (95 mg/kg ketamine, 24 mg/kg xylazine, i. p. ). Mice were placed on a heating blanket to avoid hypothermia. Auditory stimuli (20 ms in duration, risefall time 2 ms) were presented monaurally using an insert earphone (Knowles Electronics) placed in the animal right ear. ABRs were recorded using two subcutaneous electrodes (SC25; Neuroservice, Aix-en-Provence, France) located just above the tympanic bulla and skull dorsal midline, and one ground electrode was placed in the thigh. Insertion of the subcutaneous electrodes was not associated with any sign of discomfort. The signal was digitized (sampling rate 32,000 Hz) and filtered (150-1500 Hz). The software (RTLab, Echodia, Clermont-Ferrand, France) allowed for the averaging of 500 responses during the presentation of five pure-tone frequencies (between 4 and 32 kHz).
, 2011; Faure et al. , 2006). However, the Boolean approach is purely qualitative and does not consider the real time of cellular events (half time of proteins, triggering of apoptosis, etc. ). To cope with this issue, we developed the MaBoSS software to compute continuous Markov Chain simulations on the model state transition graph  (STG) , in which a model state is defined as a vector of nodes that are either active or inactive. In practice, MaBoSS associates transition rates for activation and inhibition of each node of the network, enabling it to account for different time scales of the processes described by the model. Given some initial conditions, MaBoSS applies a Monte-Carlo kinetic algorithm (or Gillespie algorithm) to the STG to produce time trajectories  (Stoll et al. , 2017 (Stoll et al.
ROMA is applied on omics data, either transcriptomics or proteomics. In each pathway, the genes that contribute the most to the overdispersion are selected. ROMA was applied to the TCGA transcriptomics data using gene sets from cancer pathway databases (Appendix 1, Section 1. 1. 3, Appendix figure  1 ). These results were used as guidelines to extend the network to fully cover the alterations found in prostate cancer patients. OmniPath was used to complete our network finding connections between the proteins of interest known to play a role in the prostate and the ones identified with ROMA, and the list of genes already present in the model (Appendix 1, Sections 1. 1. 3 and 1. 1. 4, Appendix figures 2 and 3). The final network includes pathways such as androgen receptor, MAPK, Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH, the cell cycle, the epithelialmesenchymal transition (EMT), apoptosis and DNA damage pathways.
As some of them overlapped, we ended up with 29 nodes: AKT, AR,  ATR, AXIN1, Bak, BIRC5, CDH2, cFLAR, CyclinB, CyclinD, E2F1, eEF2K, eEF2, eEF2K, EGFR, ERK, HSPs, MED12, mTORC1, mTORC2, MYC, MYC_MAX, PHDs, PI3K, PIP3, SPOP, TAK1, TWIST1, and VHL. We used the scores of these nodes to further trim down the list to have 10 final nodes (AKT, AR, cFLAR, EGFR, ERK, HSPs, MYC_MAX, SPOP and PI3K) and added 7 other nodes whose genes are considered relevant in cancer biology, such as AR_ERG fusion, Caspase8, HIF1, GLUT1, MEK1_2, p14ARF, ROS and TERT (Table  1 ). We did not consider the overexpression mutants as they have a very difficult translation to drug uses and clinical practices. To further analyse the mutant effects, we simulated the LNCaP model with increasing node inhibition values to mimic the effect of drugs' dosages using a methodology we specifically developed for these purposes (PROFILE_v2 and available at https://github.
First, a prostate cancer Boolean model that encompasses relevant signalling pathways in cancer was constructed based on already published models, experimental data analyses and pathway databases (Figure  2 ). The influence network and the assignment of logical rules for each node of this network were obtained from known interactions described in the literature (Figure  3 ). This model describes the regulation of invasion, migration, cell cycle, apoptosis, androgen and growth factors signalling in prostate cancer (Appendix File, Section 1). Second, from this generic Boolean model, we constructed personalised models using the different datasets, i. e. 488 patients from TCGA and eight cell lines from GDSC. We obtained Gleason-score-specific behaviours for TCGA's patients when studying their Proliferation and Apoptosis scores, observing that high Proliferation scores are higher in high Gleason grades (Figure  4 ).
We, therefore, based our analysis on clinical grouping defined by the Gleason grades, but some works have emphasised the difficulty to properly assess them  (Chen and Zhou, 2016)  and, as a result, may not be the perfect predictor for the patient subgrouping in this analysis, even though it is the only available one for these datasets. The lack of subgrouping that stratifies patients adequately may undermine the analysis of our results and could explain the Proliferation and Apoptosis scores of high-grade and low-grade Gleason patients. Moreover, the behaviours observed in the simulations of the cell-lines-specific models do not always correspond to what is reported in the literature. The differences between simulation results and biological characteristics could be addressed in further studies by including other pathways, for example, better describing the DNA repair mechanisms, or by tailoring the model with different sets of data, as the data used to personalise these models do not allow for clustering these cell lines according to their different characteristics (Appendix 1, Section 5, Appendix figure  24  and 25 ).
Data acquisition Publicly available data of 489 human prostate cancer patients from TCGA described in  (Hoadley et al. , 2018)  were used in the present work. We gathered mutations, CNA, RNA and clinical data from cBioPortal (https://www. cbioportal. org/study/summary?id=prad_tcga_pan_can_atlas_2018) for all of these samples resulting in 488 with complete omics datasets. Publicly available data of cell lines used in the present work were obtained from the Genomics of Drug Sensitivity in Cancer database (GDSC)  (Iorio et al. , 2016). Mutations, CNA and RNA data, as well as cell lines descriptors, were downloaded from (https://www. cancerrxgene. org/downloads). In this work, we have used 3-and 5-stage Gleason grades. Their correspondence is the following: GG Low is GG 1, GG Intermediate is GG 2 and 3, and GG High is GG 4 and 5.
Prior knowledge network construction Several sources were used in building this prostate Boolean model and, in particular, the model published by  Fumiã and Martins (2013). This model includes several signalling pathways such as the ones involving receptor tyrosine kinase (RTKs), phosphatidylinositol 3kinase (PI3K)/AKT, WNT/b-Catenin, transforming growth factor-b (TGF-b)/Smads, cyclins, retinoblastoma protein (Rb), hypoxia-inducible transcription factor (HIF-1), p53 and ataxiatelangiectasia mutated (ATM)/ataxia-telangiectasia and Rad3-related (ATR) protein kinases. The model includes these pathways as well as the substantial cross-talks among them. For a complete description of the process of construction, see Appendix 1, Section 1. The model also includes several pathways that have a relevant role in our datasets identified by ROMA  (Martignetti et al.
, 2016) , a software that uses the first principal component of a PCA analysis to summarise the coexpression of a group of genes in the gene set, identifying significantly overdispersed pathways with a relevant role in a given set of samples. This software was applied to the TCGA transcriptomics data using the gene sets described in the Atlas of Cancer Signaling Networks, ACSN  (Kuperstein et al. , 2015)  (www. acsn. curie. fr) and in Hallmarks  (Liberzon et al. , 2015)  (Appendix 1, Section 1. 1. 3, Appendix figure  1 ) and highlighted the signalling pathways that show high variance across all samples, suggesting candidate pathways and genes. Additionally, OmniPath  (Türei et al. , 2021)  was used to extend the model and complete it, connecting the nodes from Fumiã and Martins and the ones from ROMA analysis.