dataesr/scientific-paragraphs-categorization huggingface dataset:
AS-impacted genes and change motifs We propose a differential network analysis where the extended core network inferred from the sense only data (S) is compared to the extended core network inferred from the sense and anti-sense data (SAS). We focus our analysis on a sense gene that has no sense neighbour in the SAS extended core network. If a sense node has no sense neighbour in the SAS network, it means that the interactions that exist between this gene and others in the S network are not strong enough to be present in the SAS core network. We say that this gene is an AS-impacted gene. An AS-impacted gene is observed when the mutual information of this gene with an anti-sense transcript is so strong that, even with the accepting rate, the mutual information between this gene and the other sense transcripts are not significant enough.
Given an undirected graph G = (V , E) with a set of vertices V and a set of edges E, and given a subset of vertices S ⊆ V , the minimal Steiner tree problem is to find a subgraph G of G such that S is contained in G , all the vertices are connected by a path, and the number of edges of G is minimal. For a weighted graph, the aim is to minimize the total weight of the edges of G. The Steiner tree problem is used to solve several problems, such as extracting information from a large database of molecular interactions  [44]. For example, for a given set of proteins, the Steiner tree problem may be applied to the interactome graph with these proteins as terminal nodes to compute a minimal set of relations connecting all these proteins. An experimental study used the Steiner tree problem on a large human protein-protein interaction network  [45].
The minimum Steiner tree problem is a NP-complete combinatorial optimization problem  [46]  and several heuristic methods have been designed to use it with large graphs. In our workflow, we use the shortest paths based approximation  [45]  that computes the Steiner tree ST step by step. The first node added to the Steiner tree ST is one of the terminal node. Then, the shortest paths between all the remaining terminal nodes and the nodes of ST are computed, and the closest terminal node is connected to the Steiner tree ST with the shortest path. Once all the terminal nodes are in the Steiner tree ST, the resulting tree is pruned thanks to the minimum spanning tree method. SteinerNet is a R package that allows to compute the minimum Steiner tree using one exact algorithm, or find a solution using four different approximate algorithms  [45].
The shortest paths based approximation is one of the heuristic developed in SteinerNet that can be used on large graphs such as the protein-protein or gene networks. The SteinerNet package was last updated in 2013; then as the dependencies were obsolete, it was removed from the CRAN repository. We updated the package to the R version 3. 2. 0 and we will be pleased to share this updated package upon request. In order to analyse the rewiring of AS-impacted genes, we solve the minimum Steiner tree problem with the subset of vertices composed by a AS-impacted sub-graph. AS-impacted sub-graphs have a particularity: all the genes have a direct connection in S, but because the genes are AS-impacted, all those connections are not represented in the sense and anti-sense network SAS. Thus it is interesting to wonder how these nodes rewired in SAS.
Conclusion The original proposition of our work is to analyse the impact of anti-sense transcription on a large scale. To achieve this goal, sense and anti-sense transcripts are treated in the same way in our gene network inference method. By considering the recent ideas of differential network analysis, our main proposal is to compare network inferred from different sets of data: the sense data in one hand, and both the sense and anti-sense data in an other hand. To compare the inferred networks, we first propose the Extended Core Network inference method. Secondly we define the differential network analysis that we performed with the Extended Core Network method. The comparison of two networks inferred on different sets of actors shows that the of the anti-sense data clearly modifies the network topology.
Acknowledgements We thank  Maryline Bruneau-Cournol  ( IRHS-UMR1345 ) for microarray data production,  Sandra Pelletier  ( IRHS-UMR1345 ) for microarray design, data normalization and differential analyses,  UEH-INRA 0449  for plant production and maintenance, and the ANAN platform of the  SFR QuaSaV  for microarray facilities access.
Availability of data and materials The microarray data have been submitted to the Gene Expression Omnibus data base (https://www. ncbi. nlm. nih. gov/geo/) under the accession numbers GSE59947, GSE64079, and GSE101716 (the last one will be publicly accessible, but now requires the following token: wtobuqokfnepbkj). The design of the microarray chip is IRHS_ARyANE_v1 [110411_Mdom_JPR_exp] identified by the accession number GPL16374. Additional files 4 and 5 are the normalized datasets used in the H and 60DAH experiments, respectively.
Moreover, protein levels of major actomyosin regulators remained unchanged between E13. 5 CCP1 cKO or CCP1 WT MGE extracts (PRIDE: PXD006397). Analysis of globular (G) to filamentous (F) actin ratio suggested that the actin network was not altered in CCP1 cKO cINs  (Figures 2F, 2G, and S2B). Another regulator of MLC phosphorylation is MLC kinase (MLCK), whose expression was similar in cINs of both genotypes (Figures  2I  and S2C ). We thus postulated that the modification of actomyosin contractility in CCP1 cKO cINs might originate from differences in MLCK activity rather than changes in expression.
0068). Therefore, by reducing MLCK activity via enzymatic processing and without affecting its expression in migrating cINs (Figure  2I ; PRIDE: PXD006397), CCP1 might control nuclear movement during the stereotypic two-stroke cycle of migration. To test this hypothesis, we treated cultured MGE explants from CCP1 WT or CCP1 cKO cINs with ML7, a selective inhibitor of MLCK. ML7 treatment rescued pause duration (Figures  2M  and S2F ; Movie S4), amplitude of nucleokinesis (Figures  2N  and 2O ; Movie S4), as well as the maximum nucleus-centrosome distance in CCP1 cKO cINs (Figure  2C ). In the absence of CCP1 activity, several proteins accumulate long glutamate chains, most of them generated by glutamylases, which might modulate their function  (Janke, 2014; van Dijk et al. , 2008). To test if MLCK glutamylation status is a predominant regulator of cINs migration, we rescued general levels of protein glutamylation in CCP1 cKO cINs by expressing inducible small hairpin RNA (shRNA) that target Ttll1 (Figure  S2G ).
The explants were either seeded on the cortical progenitor cultures (co-culture) or in Millicell inserts to avoid direct contact. Proliferation of IPs was measured after a short pulse of EdU (30 min). While proliferation of Sox2 + APs was similar across culture conditions (data not shown), we observed a larger fraction of cycling Tbr2 + IPs in cultures containing cINs explants. Importantly, direct contact was not required to stimulate IPs proliferation (Figures  S6F-S6H ; percentage of change of proliferation in co-cultures: cINs CCP1 WT +62%, cINs CCP1 cKO +66%; in Millicel inserts: cINs CCP1 WT +55%, cINs CCP1 cKO +64%). Interestingly, cINs from CCP1 WT embryos or CCP1 cKO embryos show the same ability to promote IP proliferation. These data suggest that cINs modulate Tbr2 + IPs proliferation paracrine signals.
RNA Sequencing Total RNA was extracted from GFP FACS-sorted cells from CCP1 WT and cKO GEs and purified on columns (Quiagen RNeasy mini kit). ARN quality was controlled with an Agilent 2100 Bioanalyzer. DNA libraries were prepared after depletion of ribosomal RNAs. Paired-end sequencing was done on NextSeq 500 Illumina. Sequences alignment was done on mouse genome using the Tuxedo workflow, using Bowtie2. RNaseq data can be found at GEO: GSE104090.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analysis was performed using Graph Pad Prism 6 or MATLAB software. Parametric or non-parametric t tests were used to compare statistical difference between two experimental groups. The choice between parametric and non-parametric t test was made after verifying normality of distributions on Graph Pad Prism 6. One way-ANOVA was used to compare the statistical difference between at least three experimental groups. Two-way ANOVA was used to compare the statistical difference between at least three experimental groups based on multiple characteristics (e. g. , number of cells per cortical area within different genotypes). Several assumptions were made to use ANOVA: similar variance across experimental distributions, samples were obtained independently and within each sample the observations were sampled independently and randomly.
Proteome analysis Identified peptides were quantified using Mascot Distiller Tool, version 2. 4. 3. 3 (Matrix Science in the precursor mode. All data management was done in ms-lims and data integration was performed using R (http://www. R-project. org) embedded in KNIME. The results of analyses are shown as the peptide ratio of the light (L)-labeled (CCP1cKO mice) versus the heavy (H)-labeled sample (CCP1 WT mice). Robust statistics was applied to the base-2 logarithm values of the peptide ratios accepted as valid by Mascot Distiller. The median of the peptide ratio distributions of both experiments was corrected to zero. Further, peptide ratios being set as FALSE were verified by individual inspection. Protein ratios were then calculated by taking the median of the peptide ratios that identified that protein.
To identify significantly altered proteins, robust statistics was applied, proteins falling within the 95% confidence interval and that were identified by at least two peptides were considered not to be affected by Ccp1 conditional knock-out. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http:// proteomecentral. proteomexchange. org) via the PRIDE partner repository with the dataset identifier PRIDE: PXD006397.
Kinematic simulations of migrating cINs / analysis of cINs migration in MFDs Each surrogate cell moved in one direction following a movement profile of pauses and instantaneous speeds sampled randomly from their distributions in WT and cKO CCP1 cells. The proportion of pause to movement, averaged across surrogates, was kept identical to that observed in each group. Surrogates were simulated for 10 hours and at the end of the simulation time, the percentage of them having crossed 10 different displacement thresholds were calculated. The procedure was repeated for 20 realizations in each of which the pause onsets, durations and the instantaneous speeds for each surrogate was randomized. All simulations were done in MATLAB. Since INs do not move in a straight line in corridors of MFDs, a least-squares line was fitted to each cell's positions over time and its displacements along and perpendicular to this line were found.
Time-lapse recording analysis Parameters such as speed, directionality, nucleokinesis amplitude and frequency were analyzed using MTTrack plugin on ImageJ software. The area of actomyosin contraction was visualized with Fire look-up table and quantified using ImageJ software. Life duration of neuritic branches was manually assessed, by counting the number of frames during which a branch is visible. Maximum centrosome-nucleus distance was manually measured using ImageJ software between the PACT-KO signal and the center of the nucleus on the frame preceding the start of nucleokinesis. See Figures  1, 2 , and S1. Quantification of the TS and TC of Tbr2 + progenitors Ts and Tc were calculated according to  Ponti et al. (2013). See Figure  6  Histological analysis For all histological quantifications at least six rostro to caudal brain sections per brain from at least 3 independent animals were analyzed.
ACKNOWLEDGMENTS We thank  Drs. K. Campbell ,  S. Srivnas ,  A. Pierani ,  O. Marin ,  M. Grigoriou ,  J. Rubenstein ,  S. L. Ang , and  C. Goridis  for sharing mice lines or reagents. We thank  GIGA-Research technology platforms (S. Ormenese ,  J. -J. Goval ,  B. Hennuy ,  W. Coppieters ,  R. Stephan , and P. V. Drion). This study uses data generated by the  DECIPHER community  (we thank  Drs. E. Rosser  and  M. Splitt  for sharing information). A full list of centers who contributed to the generation of data is available from https://decipher. sanger. ac. uk/ and via email from decipher@sanger. ac. uk. C. G. S. , L. N. , and B. M. are postdoctoral researcher, Senior Research Associate, and Research Director of the F. R. S-FNRS, respectively. E. P. is funded by the  Belgian Science Policy (IAP-VII  network  P7/20 ) and  FEBS S-T  fellowship.
L. N. is funded by  F. R. S. -FNRS  ( PDR DevoCortex -T. 0073. 15 ), the  Fonds Leon Fredericq , the  Fondation Medicale Reine Elisabeth , the  Fondation Simone et Pierre Clerdent , the  Belgian Science Policy (IAP-VII  network  P7/20 ), and by  ARC  ( ARC11/16-01 ). C. J. is supported by  CNRS ,  Institut Curie, ANR  ( ANR-10-LBX-0038 ,  ANR-10-IDEX-0001-02   PSL ,  ANR-12-BSV2-0007 ), and  INCA 2013-1-PLBIO-02-ICR-1  and  2014-PLBIO-11-ICR-1. N. Kessaris is supported by the  UK BBSRC  ( BB/N009061/1 ) and  Wellcome Trust  ( 108726/Z/15/Z ) who also provided funding for the project.
Acknowledgement The research team is grateful to all the researchers, professors, graduate students, research assistants, and librarians who accepted to participate in the studies on the MMAT. Quan Nha Hong  holds a  Doctoral Fellowship Award  from the  Canadian Institutes of Health Research (CIHR)  (# 301011 ). Pierre Pluye holds a  Senior Investigator Award  from the  Fonds de recherche du Québec -Santé (FRQS)  (# 29308 ).
Due to the configuration of Sentinel-3A repeat ground track and some CryoSat-2 passes, The present paper focuses on the calibration of both the Sentinel-3A and CryoSat-2 missions using the same facilities and similar data processing. Sentinel-3A has a 27-day repeat cycle, where CryoSat-2 has a repeat cycle of 369 days and a 30-day sub-cycle; this repetition allows them to perform the absolute calibration over the Senetosa and Ajaccio area on a monthly basis for both satellites. Due to the configuration of Sentinel-3A repeat ground track and some CryoSat-2 passes, each altimeter overflies both the Senetosa and Ajaccio sites with a time delay of about five seconds corresponding to a distance of about 37 km (Figure  1 ). As a consequence, it allows us to determine two SSH biases for each mission that should be equivalent considering that, most of the time, the sea state conditions at both sites are almost identical due to the short time delay and the short distance between sites.
From the analysis of all available calibration passes, systematic differences can be related to errors in the geodetic references or in the situ data. For T/P-Jason and ERS-Envisat-SARAL/AltiKa this was difficult to determine as each altimeter mission overflew only a single site, Senetosa or Ajaccio respectively. Since July 2016, the SARAL/AltiKa mission is in a drifting orbit phase allowing a similar study to the one presented here that is discussed in  [7]. In this paper we first present the data and the methodology (Section 0). In the results section, Section 0, we present a re-processing of the Ajaccio tide gauge data following a re-analysis of the Global Positioning System (GPS) surveys that are used to monitor the location and any vertical movement of the tide gauges (Section 0). This resulted in an updated datum and a -30 mm vertical correction to the tide-gauge-derived SSH that is used in the calibration process of all the missions calibrated using the Ajaccio tide gauge data.
Data The data sets used for Sentinel-3 and CryoSat-2 are described below. Sentinel-3A:  • Data sources: from SARvatore service at ESA G-POD (https://gpod. eo. esa. int/services/ CRYOSAT_SAR/) using baseline B products with two different retrackers. It is important to note that this product does not correspond to the current baseline C products processed by the CryoSat-2 payload data ground segment (PDGS), the main difference being the correction of a known range bias of 673 mm: SAMOSA2 retracker (default): The SAMOSA (SAR Altimetry MOde Studies and Applications) model  [11]  has been implemented in its first-order formulation (SAMOSA2) in a retracker as in  [12]. This retracker uses a look-up table to mitigate the effect of the model's approximation of the squared PTR (point target response) with a Gaussian curve, it implements a stack masking for the Doppler beams padded to zero and has in principle no enhancement in the coastal zone.
Solving the Vertical Offset in the Ajaccio Tide-Gauge-Derived SSH In  [3]  we showed that there was an anomalous SSH bias of -30. 5 ± 4. 5 mm between the Ajaccio tide gauge and our GPS-based sea level measurements. We thus retrieved the GPS data from recent surveys performed in 2013, 2014 and 2015 and computed new height differences between the antenna reference point (ARP) of the permanent receiver (AJAC) and the fundamental marker close to the tide gauge (G). This processing was done in baseline mode with the GAMIT software  [15]  and the results are shown in Table  1. The standard errors of the individual determinations range from 0. 2 to 1. 1 mm mainly due to the number of days of observation. It provides a much more coherent weighted mean value of 50. 0371 m, rather than the 2005 reference, with a standard error of the weighted mean of 0.
All the other parameters are identical to the standard processing performed for Ajaccio. Figure  4  shows that, in this case, the wet tropospheric correction is biased by -79 mm (light blue) using the Ajaccio GPS wet tropospheric correction as a reference. The 59 mm increase in the difference between using the Ajaccio and Senetosa interpolation areas illustrates the challenge to fully remove land contamination in the radiometer footprint in such complex coastal areas. In this case, the CLS correction clearly provides a great improvement even though a residual bias of -14 mm still exists (orange). In addition, the differences between the wet tropospheric corrections deduced from both the Senetosa and Ajaccio GPS data (~37 km separation distance and only five seconds time lag for a typical satellite overpass) are given.
The 59 mm increase in the difference between using the Ajaccio and Senetosa interpolation areas illustrates the challenge to fully remove land contamination in the radiometer footprint in such complex coastal areas. In this case, the CLS correction clearly provides a great improvement even though a residual bias of -14 mm still exists (orange). In addition, the differences between the wet tropospheric corrections deduced from both the Senetosa and Ajaccio GPS data (~37 km separation distance and only five seconds time lag for a typical satellite overpass) are given. It is worth noting that, if the corrections are equivalent (+1 mm) on average, the dispersion of 12 mm certainly reflects different meteorological conditions at both sites at the same time. We conclude and confirm that it is very important to use GPS measurements as close as possible to satellite radiometer data when computing wet tropospheric correction terms.
Then, the 20 most intense ions were selected for MS 2 experiments. Singly charged species were excluded for MS 2 experiments. Dynamic exclusion of already fragmented precursor ions was applied for 30 s, with a repeat count of 1, a repeat duration of 30 s and an exclusion mass width of Ϯ10 ppm. Fragmentation occurred in the linear ion trap analyzer with normalized collision energy of 35. All measurements in the Orbitrap analyzer were performed with on-the-fly internal recalibration (lock mass) at m/z 445. 12002 (polydimethylcyclosiloxane). Identification of Peptides Expressed in the Cerebellum-Peptides were identified using SpectrumMill (Agilent Technologies, version B04. 00. 127) implemented with a specific database containing 184 rat neuropeptides collected from the literature (Rat Neuropeptide Database version released on January 2017; supplemental Table  S1 ).
Data were also processed using the SwissProt Rat database. For analysis, the following parameters were used: no enzymatic digestion; precursor mass tolerance: 10 ppm; product mass tolerance: 0. 5 Da; variable modifications: cysteine sulfoxide, acetylated lysine, oxidized methionine, deaminated, pyroglutamic acid, phosphorylated S, T, and Y. Only peptides with an identification score above 10 (choose from Agilent Technologies recommendations and from the literature  (17, 18) ) and found in at least 7 samples out of 10 in one or several developmental stages were retained. Quantification of Regulated Peptides During Development-To measure peptide changes between the four groups (P8, P16, P21 and P90), Progenesis LC-MS software (Nonlinear dynamics, version 4. 0. 4441. 29989) was used. The P8 group was set as the reference to perform two by two comparisons and one P8 sample was chosen to align the LC-MS runs to account for retention time shifts.
A 2 l volume of each sample were injected onto an analytical column (PepMap RSLC C18, 75 m ϫ 25 cm, Thermo Scientific). The separation was performed in the presence of a mobile phase composed of H 2 O/0. 1% FA (buffer A) and 80% ACN/0. 1% FA (buffer B). Elution of peptides was conducted at a flow rate of 300 nL/min, using a linear gradient: from 2 to 50% buffer B over 16 min. The mass spectrometer was operated in Parallel Reaction Monitoring (PRM) mode. The resolution was set at 35,000 (at m/z 200), the Automatic Gain Controltarget at 2 ϫ 10 5 and an isolation window of 1. 6 m/z. An inclusion list was generated using the Skyline software (MacCoss Lab, version 4. 1. 0. 11714) based on analytical data (m/z and retention times) obtained with synthetic peptides in the presence of biological matrix VGF [353-372]: 821.
After 24 h in a cell culture incubator, dishes were transferred into an incubation chamber (37 °C, 5% CO 2 and 95% O 2 ) fixed to the stand of an inverted microscope (Leica DMI 6000B, Rueil Malmaison, France). Images of migrating CGN were acquired with the LAS-AF software every 2 min for 45 min in control conditions and then for another 45 min in treated conditions. Analyses were performed with the MetaMorph software (Roper Scientific). Cell Differentiation Measurement-Cell differentiation was evaluated by counting and measuring CGN neurites. CGN plated at a density of 3. 10 5 cells/ml were labeled with the tubulin tracker green kit (300 nM, Thermo Fischer Scientific) into an incubation chamber (37 °C, 5% CO 2 and 95% O 2 ) for 30 min and 5 images per well were randomly acquired with an inverted microscope (Leica DMI 6000B).
Analyses were performed by measuring for each cell, neurite lengths with the ImageJ software (version 1. 46). RNA Extraction-Cell RNA or total cerebella RNA from the six groups (P2, P4, P8, P16, P21, and P90) were extracted using Tri-Reagent (Sigma-Aldrich) and further purified with NucleoSpin RNA kit (Macherey Nagel, Duren, Germany). The concentration of RNA was measured with a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientfic) at an absorbance of 260 nm and 1 g of total RNA from each sample was reverse transcribed with the ImProm II Reverse Transcription System (Promega). Microdissection-Laser microdissection experiments were conducted as previously described  (25). Briefly, cerebella from P8 and P21 rats were cut in the frontal-plane into 14 m-thick sections. After cresyl violet coloration, sections were dehydrated in ethanol.