dataesr/scientific-paragraphs-categorization huggingface dataset:
3D modeling of GluE1 and GluE7 Models for full-length GluE1 and GluE7 were generated with RaptorX (Ka ¨llberg et al. , 2012) based on deposited three-dimensional crystal structures of the full-length AMPA-subtype ionotropic glutamate receptor from Rattus norvegicus, GluA2, bound to competitive antagonists (PDB codes 4U4G  (Yelshanskaya et al. , 2014)  and 3KG2  (Sobolevsky et al. , 2009) , respectively). Models of their respective ligand binding domains were generated with SWISS-MODEL  (Biasini et al. , 2014)  using the atomic-resolution crystal structure of the rat GluA2 LBD bound to glutamate as template (PDB code 4YU0). Model quality was assessed with MolProbity (http://molprobity. biochem. duke. edu/, RRID: SCR_014226). MolProbity scores for all models are given in Figure  1 -source data 4.
Electrophysiology Cells were visualized with an inverted epifluorescence microscope (AxioVert A. 1, Zeiss) and were constantly perfused at 22-25 ˚C with an extracellular solution containing (in mM): 145 NaCl, 2. 5 KCl, 2 CaCl 2 , 1 MgCl 2 , 10 HEPES and 10 glucose (pH = 7. 42 with NaOH; 305 mOsm/Kg). Microelectrodes were filled with an intracellular solution containing (in mM): 145 CsCl, 2. 5 NaCl, 1 Cs-EGTA, 4 MgATP, 10 HEPES (pH = 7. 2 with CsOH; 295 mOsm/Kg). Electrodes were fabricated from borosilicate glass (1. 5 mm o. d. , 1. 16 i. d. , Harvard Apparatus) pulled with a P-97 horizontal puller (Sutter Instruments) and polished with a forge (MF-830, Narishige) to a final resistance of 2-4 MW. Currents were recorded with an Axopatch 200B amplifier filtered at 1 KHz and digitized at 5 KHz using Digidata 1440A interface with pClamp 10 software (Molecular Devices Corporation).
5 s to 25 s). The paired pulses were separated 30-60 s to allow full recovery from desensitization. To estimate the percentage of recovery, the magnitude of peak current at the second pulse (P2) was compared with the first one (P1). Electrophysiological recordings were analyzed using IGOR Pro (Wavemetrics Inc. ) with NeuroMatic (Jason Rothman, UCL, RRID: SCR_004186). Figure  4  -figure supplement 3. Genes from the sponges Oscarella carmela, Sycon cilliatum and Leucosolenia complicata were obtained from Compagen (http://www. compagen. org/index. html). The following previously published datasets were used:.
Acknowledgments We thank  I Gich  ( Biomedical Research Institute Sant Pau ) for support in biostatistics and  Dr. Xavier Daura  and  Oscar Conchillo  ( Computational Biology Group  and  Data Center  from the  Institut de Biotecnologia i Biomedicina (UAB). DRV, GG, RRV, JL,  PFP , and AB work at a research institute supported by the  CERCA Programme/Generalitat de Catalunya.
After six washes with PBS with 0. 05% Tween 20, the membranes were incubated for 1 h with horseradish peroxidase-conjugated goat anti-rabbit or goat antimouse (Bio-Rad Laboratories) diluted in PBS with 5% milk. This secondary antibody was detected in a Chemidoc (Bio-Rad Laboratories) using the chemiluminescent ECL PlusTM reagent (Amersham Biosciences, GE Healthcare, Uppsala, Sweden). The signal was quantified by densitometric analysis using Image J (National Institutes of Health, Bethesda, MD, USA) software.
The cells were then washed twice in PBS, and fluorescence was measured in a quartz cuvette on a Xenius spectrofluorometer (SAFAS, Monaco, France). A second reading was performed with the addition of 100 µM H 2 O 2 in the cuvette to verify the response and absence of saturation of the CM-H 2 DCFDA probe. The signal increased immediately after the addition of H2O2 in a dose-dependent manner (data not shown).
Statistical analysis All of the data presented in this study correspond to the mean value of n experiments ± SD, with a minimum of n ≥ 3. Comparison of the data sets (control versus treated cells) was performed with Student's ttest in normally distribution data, and the Mann-Withney U-test for non-normally distributed data. SigmaStat 3. 1 (Systat Software Inc. , San Jose, CA, USA) was used for the statistical analyses. Two sets of data were considered statistically different when P < 0. 05.
Iodoacetate decreased the total ATP levels in a dose-dependent manner in DU145 cells (supplementary data Fig. S1A ) and the combination of 100 nM rotenone with 2. 5 µM iodoacetate mimicked the inhibition of 20% of the total ATP (as obtained with levobupivacaine in DU145 cells; supplementary data Fig. S1B ). This combination of inhibitors triggered a strong decrease in DU145 cell growth after 24 h of treatment, as also obtained with levobupivacaine (Fig. 4C ). To understand the basis of the cancer specific effect of levobupivacaine we considered that the repercussion of a given reduction of ATP production could have different consequences on cell proliferation, in accordance with the metabolic state of the cells and the metabolic control analysis  [26]. To evaluate the energy state of DU145 and BHP cells we measured the ATP/ADP ratio at steady-state which gives a measure of the balance between ATP synthesis and ATP consumption.
Funding The authors acknowledge financial support from  INSERM  ( U1211 ),  INCA  (Grant  2017-040 ),  CAPES-COFECUB and Fondation Arc (N. A) ,  H2020-MSCA-ITN-2016/722605 -TRANSMIT  (Translating the  Role of Mitochondria in Tumorigenesis ) and  SIRIC Brio.
Data are represented as the mean ± SD and were obtained from independent experiments with several mice per group: (B), 9; (C), 3-4. mice were irradiated using whole-genome Affymetrix MOE430 2. 0 arrays. The obtained datasets are visualized as sets of coordinates using principal component analysis (PCA) in Figure  S1A. PCA is an unsupervised pattern recognition and visualization tool used to reduce the dimensionality of datasets derived from transcriptomic arrays, making it possible to visually assess similarities and differences between cell populations  (Ringner, 2008). This PCA illustrates the transcriptomic shift of quiescent NSCs induced by irradiation concomitantly to the entry in the cell cycle of a subset of these cells we reported before  (Daynac et al. , 2013)  (Figure  S1A ). The comparative gene expression profile of LeX bright cells revealed an altered expression of 927 probes (Figure  S1B ).
Transcription Factors Enriched in Quiescent and Activated NSCs In order to identify putative transcriptional regulators of the quiescent and proliferative states of adult NSCs, we focused on transcription factors (TFs) and co-factors either enriched in LeX bright or LeX + EGFR + cells. Analysis of our dataset using public databases  (Zhang et al. , 2012)  revealed a total of 75 differentially expressed TFs, 14 of which were upregulated in LeX bright cells and the remaining 61 in LeX + EGFR + cells (Figure  3 ). Among the TFs upregulated in LeX bright cells were Sox9 and Id2, which have been previously associated with quiescent NSCs (Llorens-Bobadilla et al. , 2015) (Figure  3 ). Interestingly, Klf9, a member of the family of Kruppellike TFs found upregulated in quiescent muscle satellite stem cells  (Pallafacchina et al.
SVZ Cell Preparation Adult SVZs were dissected, dissociated, and labeled as previously described  (Daynac et al. , 2015). Briefly, dissected SVZs were digested with papain (1 mg/mL, Worthington) supplemented with Figure  6. Syndecan-1 Plays a Role in the Proliferation of Activated NSCs (A) LeX bright and LeX + EGFR + cells were purified by flow cytometry from PN10 SVZ according to SDC1 expression (SDC1 negative, light blue; SDC1 positive, dark blue) and their capacity to form primary then secondary neurospheres was assessed. The effect of Sdc1 silencing in LeX + EGFR + cells sorted from adult SVZ was evaluated on neurosphere formation (B), cell proliferation (C), and the time required for the first cell division (D). Data are represented as the mean ± SD (A and B) and scattered dot plots with median ± minimum/maximum (C and D), and were obtained from independent experiments (N = 3-10 for primary, N = 3 for secondary, and N = 4 for siRNA experiments) with three or four pooled mice.
RNA Isolation, Microarrays, and qRT-PCR NSCs were sorted into tubes containing RLT lysis buffer and total RNAs were isolated with the RNeasy Micro Kit with DNase treatment (Qiagen). For microarray experiments, RNA transcripts were converted into cDNAs and amplified using the Ovation Pico WTA System (NuGEN). cDNAs were fragmented and biotinylated; then, labeled cRNAs were hybridized to Affymetrix MOE430 2. 0 arrays according to the manufacturer's protocol at PartnerShip (Evry, France). The data were normalized with the MAS5 algorithm and quality controlled with the Expression Console software (Affymetrix). For qRT-PCR experiments, total RNAs were reverse transcribed into cDNA using the Reverse Transcription High Capacity Master Mix (Applied Biosystems) with specific primers listed in Table  S6  (Sigma-Aldrich).
Microarray Analysis Data were normalized with GC-robust multi-array analysis (GC-RMA) using log2 transformed expression levels in Genespring GX12 (Agilent Technologies). For comparative analysis of datasets, differentially expressed probes were filtered by an average expression greater than 50 in at least one population, at least 2-fold change, and a Student's t test p value <0. 05 (control LeX bright versus irradiated LeX bright ) or corrected p value <0. 05 (LeX bright versus LeX + EGFR + ). An overrepresentation analysis of GO biological processes (p < 0. 05) with Bonferroni correction and cellular component ontology analysis were carried out with PANTHER software (http://www. pantherdb. org). Statistically enriched GO terms were then hand curated into thematic categories. Heatmaps were generated with Gene-E (https://software.
ACKNOWLEDGMENTS We are indebted to  S. Vincent-Naulleau ,  V. Neuville ,  V. Barroca , and the staff of the animal facilities; to  T. Kortulewski  for videomicroscopy; to  J. Baijer  and  N. Dechamps  for cell sorting; and to  A. Gouret  and  A. Leliard  for their administrative assistance. Flow cytometry and cell sorting were performed at the iRCM Flow Cytometry Shared Resource, established by equipment grants from  DIM-Stem-Pole, INSERM, Foundation ARC , and  CEA. This work was supported by grants from  Electricitede France (EDF)  and  CEA (Segment Radiobiologie ). L. M. has a fellowship from  Region Ile-de-France  ( DIM Biotherapies ) and M. D. from  La Ligue Contre le Cancer.
Softwares and Code accessibility For the simulations we made use of the Julia language  (Bezanson et al. , 2014). The code of the simulations can be obtained from the corresponding author upon request. We made use of the XPP software  (Ermentrout and Mahajan, 2003)  for the phase-space analysis and the computation of the relaxation time constant of the dynamical system. Figures 9, 10, 11, 12 and 19 were realized using Python and the other are in the same language as the simulations. The E-statistics tests were performed using the R-Package: energy package  (Rizzo and Székely, 2014).
For ionic current measurements, the intracellular pipette solution contained (in mM) 35  NaCl, 105 CsF, 10 EGTA, and 10 HEP ES, pH 7. 4 (adjusted with CsOH). The extracellular solution contained (in mM) 140 NaCl, 2 CaCl 2 , 2 MgCl 2 , and 10 HEP ES, pH 7. 4 (adjusted with NaOH). The standard voltage-clamp protocol for measuring ionic currents consisted of steps from a holding potential of -180 mV to voltages ranging from -180 mV to +50 mV in 10-mV steps. Use-dependent inactivation was induced by depolarizing from -180 mV to 0 mV or to the indicated potentials at 0. 2 Hz or 1 Hz. Voltage-clamp pulses were generated and currents were recorded using Pulse software controlling an Instrutech ITC18 interface (HEKA). Data were analyzed using Igor Pro 6. 2 software (WaveMetrics). Na V Ab crystallization and data collection Na V Ab/WT and mutants were reconstituted into DMPC/CHA PSO bicelles (Anatrace) as previously described, mixed in a 1:2 ratio, and set up in a hanging-drop format over well solutions containing 1.
8 M ammonium sulfate and 100 mM sodium acetate, pH 4. 8. Crystals appeared in 3-5 d and grew to full size within 1 wk (∼50 µm by 100 µm). They were cryoprotected by slow addition of well solution supplemented with increasing concentrations of glucose up to 30%. Crystals were harvested in nylon loops and plunged into liquid nitrogen for data collection. Data were collected at Advanced Light Source (Beamlines BL821 and BL822). As crystals were highly radiation sensitive, exposure times were minimized to limit decay during data collection. Data reported herein were collected from single crystals. Structure determination and refinement X-ray diffraction data were integrated and scaled with DEN ZO/ SCA LEP ACK  (Otwinowski and Minor, 1997)  and processed with PHE NIX  (Adams et al. , 2010). We investigated multiple space groups and determined the reported space group (I422) by comparison of merging statistics, as well as fully refined structures determined in C2, I222, I4, and I422 with and without inclusion of weakly recorded reflections.
The Na V AbΔ28 structure was solved with molecular replacement using the Na V Ab/I217C structure (PDB accession no. 3RVY), modified by removal of the S4-S5 linker and base of S5 helix (amino acids 115-137) plus the S6 helix as the search model. Following initial phases, models of both proteins were manually rebuilt based on resulting electron density maps. Electron density was easily interpretable throughout the S6, S4-S5 linker, and C-terminal domain Na V AbΔ28. Simulated annealing omit maps were used to confirm the placement of the S6 at the activation gate, as well as the position of the S4-S5 linker. Lipids and waters were added to both models near the end of refinement. Geometry, B-factors, and electron density fits were assessed with POL YGON  (Urzhumtseva et al. , 2009). Online supplemental material Fig.
Acknowledgments This research was supported by  National Institutes of Health  research grants  R01 NS15751  ( W. A. Catterall ) and  R01 HL122808  ( W. A. Catterall  and  N. Zheng ) and the  Howard Hughes Medical Institute  ( N. Zheng ). The authors declare no competing financial interests. Author contributions: All authors designed experiments,  T. M. Gamal El-Din ,  M. J. Lenaeus , and  K. Ramanadane  carried out experiments and analyzed data. All authors contributed to writing the paper. Kenton J. Swartz  served as editor. Submitted: 21 August 2017 Revised: 22 August 2018 Accepted: 25 October 2018.
For evaluating whether the data from an eye tracker are precise enough for measuring microsaccades,  Poletti and Rucci (2016)  advocate that the measure "resolution" be used rather than the more established RMS-S2S. Resolution needs to be measured using an artificial eye that can be turned in very small steps, and visual estimation is used to assess whether the movements are visible in the recorded data from the eye tracker. As such, resolution cannot be measured with human data. Currently, resolution has an unclear and entirely uninvestigated relationship to existing RMS-S2S and STD measures of precision  (Holmqvist & Andersson, 2017, p. 190 ). Resolution measurements have only been made on the DPI and one other eye tracker. We do not know resolution values for the most used eye trackers.
ACKNOWLEDGMENTS The authors are grateful to  Graham Fleming  and  Greg Scholes  for stimulating discussions. A. B. thanks  Dieter Jaksch  and  Keble College  for their generous hospitality in spring 2016. A. B. and V. N. S. acknowledge support through the  EU Collaborative project QuProCS  (Grant Agreement 641277). M. W. would like to thank the  German National Academic Foundation  for financial support. F. S. acknowledges financial support of the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant  Agreement No. 319286  Q-MAC. The authors acknowledge support by the state of  Baden-Würtemberg  through bwHPC.
Neuroimaging Data MRI data were acquired on a 3T scanner device (Tim Trio, Siemens Medical Systems, Erlangen, Germany) with a 3D MPRAGE T1-weighted high-resolution sequence (TR: 2300 ms; TE: 2. 98 ms; FOV: 256 mm; 64 * 64 matrix; 160 sagittal slices, 1 mm3 isotropic) and analyzed with BrainVisa (v. 4. 4. 0) 2  including the Anatomist visualization module and the Morphologist segmentation pipeline  (Rivière et al. , 2009).
Probabilistic Maps of MTL Sulci Masks of the accurately labeled RS, CS proper and CS post in each hemisphere of each subject were extracted with BrainVisa's module Anatomist The masks were then normalized in the MNI stereotaxic space with SPM12 using the 'old normalize' tool 3. Each mask was smoothed with a 3 mm Gaussian kernel to increase the continuousness of the map. Mean volumes were computed over all subjects for each sulcus of interest, separately for each hemisphere. Therefore, in the obtained maps, the voxels intensities range from 0 (voxel absent in every individual mean map) to 1 (voxel present in every individual mean map). Finally, a threshold of 5% was applied to get rid of outlier voxels (i. e. , to keep voxels that were present in at least 5% of subjects corresponding to at least one subject over the whole group).
Statistical Analyses The following analyses were conducted, with a p-value of 0. 05 as the threshold for significance for each analysis: (1) We computed the proportion of Type 1 and Type 2 RS-CS proper conformations (which were observed in subject space for each sulci) and tested it against the proportions reported in adult literature with a one-proportion z-test ("proportion_z-test" function from python's StatsModels package). (2) We tested whether age, sex, brain size (volume and surface) and sulcal morphometric features, as measured in subject space, were correlated to RS-CS proper conformation, with t-tests (t-test_ind function Python's SciPy package), corrected for multiple comparisons with FDR ("fdr correction" function from python's statsmodels package). (3) We assessed the effect of the RS-CS proper conformation on the MLT sulcal and cortical spatial locations in normalized space.
To test the sensitivity of the 5% threshold applied to the statistical map defining the blobs, we repeated the analysis with a 25% threshold, i. e. , excluding voxels that are present in less than 25% of the subjects. This analysis was performed with MATLAB's permutation function "randperm. " (4) We searched for differences between sulcal variations in the location of the center of mass of each sulcus in normalized space. Namely, we computed the center of mass of each sulcus (via Python's package NumPy), separately for Type 1 and Type 2. We used permutation testing to assess the differences in the location of the center of mass for each axis (x, y, and z) between Type 1 and Type 2 sulci. Then, we analyzed the variability of these center of mass in terms of direction. For each MTL sulcus, we projected individual center of mass in 3D space separately for Type 1 and Type 2, and fitted the cloud of points with an orthogonal distance regression line using singular value decomposition (via MATLAB's function 'svd').
, 2013; Strange et al. , 2014) , with specific maturational dynamics for each HC subpart  (Ghetti and Bunge, 2012; Riggins et al. , 2015 Riggins et al. , , 2016;; Blankenship et al. , 2017). Moreover, the tripartite head/body/tail division used here allows to test more fine-grained hypotheses of HC specialization than the anterior/posterior division that is also used by some studies. Our study has several limitations. First, the wide age range (7-17 years old) of our sample and our sample size (38 subjects) limits us from performing analyses for distinct age groups because of the small sample size that such groups would have. Therefore, a more comprehensive database of structural scans in the MTL would be beneficial in the future to assess more precisely the anatomical variation of this region during development.
Background. Current classifications separate Bipolar (BD) from Major Depressive Disorder (MDD) based on polarity rather than recurrence. We aimed to determine bipolar/mixed feature frequency in a large MDD multinational sample with (High-Rec) and without (Low-Rec) >3 recurrences, comparing the two subsamples. Methods. We measured frequency of bipolarity/hypomanic features during current depressive episodes (MDEs) in 2,347 MDD patients from the BRIDGE-II-mix database, comparing High-Rec with Low-Rec. We used Bonferroni-corrected Student's t-test for continuous, and chi-squared test, for categorical variables. Logistic regression estimated the size of the association between clinical characteristics and High-Rec MDD. Results. Compared to Low-Rec (n=1084, 46. 2%), High-Rec patients (n=1263, 53.
The original study focused on mixed feature prevalence and definition in patients with MDE and analysed data of 2,811 patients with MDE, independently from whether they were diagnosed with major depressive disorder (MDD) or BD. From the 239 psychiatrists involved in the study, 237 returned their site questionnaire. The number of investigators per country ranged from 62 in Spain to 18 in Egypt. Practice settings were primary care (26%), community mental health services (23%), or hospital based (48%). The location of the practice was almost entirely urban. The mean proportion of patients who were hospitalised for the full sample was 26. 0%. Demographic features were generally similar across countries. In this study we focus only on those 2,347 patients diagnosed with MDD. This is a post hoc analysis of the original dataset.
Data Collection Participating psychiatrists completed the same case report form in all centres, consisting in meeting inclusion criteria (DSM-IV-TR MDE) and collecting sociodemographic data, like age, gender, and marital status, and clinical data, like inpatient/outpatient status, history of psychiatric symptoms (mood changes, postpartum depression, and suicide attempts), and previous psychiatric hospitalisations. Noted were all features of the current depressive episode, including bipolar symptoms listed in the DSM-IV-TR diagnostic criteria for BD  (American Psychiatric Association, 2000) , known risk factors for BD, previous response to antidepressants, including reactions, psychiatric comorbidity, and current treatment, while global functioning was assessed through the Global Assessment of Functioning (GAF) scale  (Endicott et al.
Since data collection took place before publication of the DSM-5, we applied its criteria retrospectively, based on case report forms. RBDC were met when a MDE plus three of 14 hypomanic symptoms (i. e. , irritable mood, emotional/mood lability, distractibility, psychomotor agitation, impulsiveness, verbal/physical aggression, racing thoughts, more talkative/speech pressure, hyperactivity, increased energy, risky behaviour, grandiosity, elation, and hypersexuality) co-occurred for at least one week. Revised BD-I specifier included manic episodes with the DSM-5 additional A criterion increased activity/energy without any exclusion criteria, while the revised BD-II specifier included 1-3 days hypomanic episodes  (Angst et al. , 2013). Patients meeting DSM-IV-TR criteria for BD were excluded for the purpose of the current study, leaving a sample of patients with MDD.
The cutoff for statistical significance for logistic regression was set at p<0. 05. All analyses were two-sided. When reporting logistic regression results, p<0. 05 was considered as indicative of an association with High-Rec MDD, whereas p≥0. 05 as indicative of no association with High-Rec MDD. Due consideration was given to associations at the edge of significance. All analyses were performed by using the statistical software SPSS Statistics for Macintosh, Version 20. 0 (IBM Corp. , Armonk, NY, USA).