dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION Neurons in different cortical areas are characterized by differences in the temporal stability of their firing rates  (Ogawa and Komatsu, 2010; Murray et al. , 2014)  that represents the ability of a neuron to sustain its firing rate over time, computed as the decay time constant of the autocorrelation structure during a baseline period. We refer to it as ''neural intrinsic timescale. '' For example, neurons in the frontal eye field (FEF) exhibited higher temporal stability compared with the ones in area V4. This result is consistent with the ability of neurons in FEF  (Bruce and Goldberg, 1985; Chafee and Goldman-Rakic, 2000) , but not V4  (Bisley et al. , 2004) , to maintain visual information in time without the presence of a visual stimulus. Murray et al. (2014) , analyzing multiple datasets, went further and found a matching between the hierarchy of brain areas based on their intrinsic timescales and their position in the hierarchy based on anatomical connectivity  (Felleman and Van Essen, 1991).
In this hierarchy, the prefrontal cortex (PFC) has the longest timescales; the posterior parietal cortex, the intermediate timescales; and the sensory cortex at the bottom of the hierarchy, the shortest timescales. The longest timescale, based on the examination of two datasets, was identified for the median prefrontal anterior cingulate cortex (ACC)  (Murray et al. , 2014). Other studies, using another approach, instead of focusing on the heterogeneity of timescales across areas, examined differences in intrinsic timescales across neurons within the same cortical area  (Nishida et al. , 2014; Cavanagh et al. , 2016; Fascianelli et al. , 2017) , to address whether distinct functional classes of neurons might also show timescale differences based on their specific functions. In the lateral intraparietal (LIP) area,  Nishida et al.
These studies showed comparable signal durations in PMd and PFdl, in contrast to shorter signal durations observed in ventrolateral prefrontal cortex and the globus pallidus. Using our dataset on the non-match-to-goal (NMTG) task  (Cirillo et al. , 2018) , we addressed the study of the intrinsic timescales of single neurons by computing their intrinsic features during the baseline period. Using the baseline period prevents our analysis from being influenced by relevant task parameters, and leading to define them as ''intrinsic timescales. '' In particular, we focused on the baseline activity during the holding central stimulus epoch at the beginning of each trial. This particular task epoch was suitable for the assessment of the intrinsic ability of PMd neurons to maintain a persistent activity, by preceding any stimulus-related effect on the discharge rate.
Considering comparisons between brain areas,  Murray et al. (2017)  examined datasets from the LIP area, the ACC, and the PFC and found that these brain areas showed a ranking of reward-coding timescales that was in line with the ranking of their intrinsic timescales. Therefore, neurons in different brain areas not only differ for the duration of their coding activity but also for their intrinsic timescales. In this study, we focused our analysis on the intrinsic timescales calculated during baseline but by looking at the specialization of neurons within the same area instead of focusing on differences between brain areas. We hypothesized that neurons exhibiting a long intrinsic timescale would carry more information during a delay period. Indeed, intrinsic timescales can vary between neurons even within the same brain area depending on their functional properties.
, 2014; Fascianelli et al. , 2017). Our results show that the intrinsic timescale of PMd was shorter than the timescales of prefrontal areas (>150 ms on average), with the exception of the timescale calculated for PFdl on the Pasternak's dataset  (Murray et al. , 2014) , but longer than the timescale of primary somatosensory cortex (S1; 50 ms) and medial-temporal area in visual cortex (70 ms). This result expands the hierarchy of intrinsic timescales of the cortical areas sketched by  Murray et al. (2014)  and extended by  Fascianelli et al. (2017)  to PFdl, PFp, and PFo, thus assigning to PMd a slightly lower hierarchical level than PFC areas. However, a modeling study by  Chaudhuri et al. (2015)  suggests caution to assign definitive hierarchies based on timescales. By contrasting the responses of the model to visual versus somatosensory stimulation, they found that different timescales could emerge based on input modality.
ACKNOWLEDGMENTS We thank  Mauro Ciriello  and  Rosaria Pellegrino  for their contribution in the first phase of the experiment. This work was supported by the Italian FIRB 2010 grant ( Fondo per gli Investimenti della Ricerca di Base ). We thank  Sylvia Wirth  for her helpful comments on the manuscript. Figure 5. Population auROC Values Time course of the auROC for the spatial response aligned on the peripheral targets' onset. The population of neurons with long intrinsic timescale (dark red curve, mean  G SEM , n = 26) shows higher auROC values for the spatial response than those with short intrinsic timescale (orange curve, mean  G SEM , n = 26). The black line indicates a significant difference in the 400-to 800-ms time window of the delay period (Kruskal-Wallis, p = 0. 04). iScience 10, 203-210, December 21, 2018 209.
Patient recruitment The study was approved in January 2012 by the South East Wales Research Ethics Committee and registered in February 2012 (NCT01544205). The first patient started the study in March 2012. All patients provided written informed consent. They were compensated for their time and travel costs in cash. Patients were recruited via general practitioner (GP) surgeries and Community Mental Health Teams (CMHTs) in South Wales and the National Centre for Mental Health (NCMH). To be included patients had to meet the following criteria: a diagnosis of unipolar depression, currently moderate or severe, confirmed with the Mini International Neuropsychiatric Interview (MINI), and current antidepressant treatment (with no change of dose in the preceding three months). We excluded psychotic symptoms, current substance dependence, eating disorders, claustrophobia and other MRI contraindications, and ongoing non-pharmacological treatment.
Randomization and masking Patients were randomly assigned to one of two groups using an adaptive randomization protocol developed by the South East Wales Trials Unit (SEWTU). The randomization protocol allocated patients to two groups, minimizing for differences in age, gender, duration of illness, medication type (with three categories: SSRI only; non-SSRI antidepressant; combination treatment) and baseline depression severity as measured with the Hamilton Depression Rating Scale (HDRS-17). After the patient had consented and completed all baseline measures, these were entered in a computer program (scripted in Microsoft Excel) and an allocation provided to the investigators conducting the study. Investigators running the MRI sessions needed to know group allocation in order to run the appropriate imaging protocols, but those conducting the assessments were blind to group allocation.
To control for physiological confounding factors of the BOLD signal  [18] , heart rate (HR) and respiration volume per time (RVT) were measured using pulse oximetry and a respiratory belt, respectively, and recorded with Spike2 (version 5. 21, Cambridge Electronics Design Limited, Cambridge, UK). fMRI-NF setup Acquired EPI data was submitted to real-time motion correction and spatial smoothing (FWHM 4 mm). For the localizer scan, realtime statistical analyses were carried out via an incremental general linear model (GLM) using Turbo-BrainVoyager (TBV) (Version 3. 0, Brain Innovation, Maastricht, The Netherlands). Target ROIs in the respective groups were identified during a localizer scan based on the t-statistic of the contrasts of interest, which were defined as positive vs. neutral pictures in the NFE group and scene vs.
Statistical tools and analysis The primary and secondary outcome measures for patients with complete data were analyzed using Statistical Package for Social Science (SPSS, version 23). To test for any significant differences between groups, linear regression analyses were performed on the outcome measures post-intervention (separately for the primary endpoint after session 5 and for the follow-up session) with preintervention scores as regressors of no interest. Further, the following regressors of no interest were entered to reflect the minimization procedure applied during randomization: gender, age, duration of illness, medication type, the score for the respective outcome variable at baseline, and HDRS-17 score at baseline. We deemed a Bonferroni-corrected p < 0. 002 (p = 0. 05/21 tests) to be appropriate for the secondary outcome measures.
We also analyzed pre-post differences within group means. For all pre-post comparisons, % change and 95% confidence intervals of the changes in scores were calculated separately for both groups. Remission was defined by an HDRS-17 score ≤ 7, and remission rates were calculated based on all patients for whom we had complete data up to the relevant time point. Effect sizes (ES) of clinical outcome on the HDRS-17 were quantified using Hedges g based on difference scores with respect to baseline for both session 5 (post-intervention) and FU. Confidence intervals were bootstrapped based on 10,000 iterations using the R package bootES (version 1. 3-20)  [24]. Given the absence of group effects for HDRS (Results section), we also conducted post-hoc tests for evidence for a null effect (see Suppl.
Clinical outcomes The patient flow is summarized in the CONSORT diagram (Fig. 1 ). Recruitment ended in June 2014 when the target was achieved, Fig. 1  CONSORT diagram of clinical trial and the last follow-up assessment was completed in September 2014. A total of 32 patients (16 in each group) completed the intervention. All 32 patients were included in the analysis of clinical, psychometric, and neuroimaging data at the primary endpoint. Further, 28 of 32 patients (88%) also attended follow-up and were included in the analysis of clinical and psychometric data at follow up. Randomization was successful such that there were no group differences at baseline in gender, age, handedness, HDRS-17, duration of depressive disorder or medication (Table  1 ). Second-rater scores for the HDRS-17 were available for 12 sessions, with high interrater correlation (Pearson's correlation: r = 0.
ACKNOWLEDGEMENTS We are grateful to  Professor Mary Phillips  for advice on the design of the trial and to  Professor Jeremy Hall ,  Professor Talma Hendler  and  Dr. James Kolasinski  for helpful discussions. This work was supported by  Medical Research Council (UK)  grant  G 1100629  to DEJL and a PhD studentship to IH from the  National Institute for Social Care and Health Research  ( HS/10/25 , grant holder DEJL). DMAM is supported by a PhD studentship from  Health and Care Research Wales  ( HS/14/20 , grant holder DEJL). DEJL, KH, and RG are PIs on the BRAINTRAIN consortium, supported by the  European Commission , under the  Health Cooperation Work Programme of the 7th Framework Programme , under the Grant Agreement n°602186. The  South East Wales Trials Unit (now the Centre for Trials Research)  is funded by  Health & Care Research Wales and Cancer Research UK.
Acknowledgements We apologize to colleagues whose work could not be cited due to space limitations. We thank  Nicolas Kuperwasser  for proofreading the manuscript. L. K. received a fellowship from the  Laboratory for Excellence Programme  "Revive". This work was supported by  Institut Pasteur ,  Centre National pour la Recherche Scientifique , the life insurance company " AG2R-La-Mondiale ",  Agence Nationale de la Recherche  ( ANR-15-CE37-0004-01 ),  NIH   US-French Research Proposal Grants  # 1R01DC015137-01  and  ANR-15-NEUC-0004  (Circuit-OPL), the  Laboratory for Excellence Programme "Revive" (Investissement d'Avenir ,  ANR-10-LABX-73 ) and " Biopsy" (Investissement d'Avenir ,  ANR-11-IDEX-0004-02 ).
Western Blots Membranes were probed with anti-mBDNF (1:200; Santa Cruz, N20: sc-546) and antiglyceraldehyde-3-phosphate dehydrogenase, 1:8000 (GAPDH, mouse monoclonal IgG; Cat. no. AM4300; Applied Biosystems, Streetsville, Ontario, Canada). Each sample corresponded to 6 organotypic cultures pooled together. In addition, Ctrl samples were collected for each mouse used for organotypic cultures. All samples used for western blot analysis of a specific protein were run on the same gel. Membranes were exposed to Bioflex MSI autoradiography ⁄ X-ray film for different time intervals, and only the films that showed easily identifiable, but not saturated, bands for every sample were used for quantification, using imageJ software (Wayne Rasband, National Institutes of h at 37°C. Amplification template oligonucleotides were hybridized to pairs of PLA and circularized by ligation.
Immunolabeling imaging and analysis Mice were anesthetized and perfused with saline (0. 9% NaCl) followed by 4% paraformaldehyde/phosphate buffer, pH 7. 4, then the brain was extracted and cryoprotected in 30% sucrose/PBS, and frozen in Tissue Tek. For PV, vGAT and PNN analysis on minipump-implanted brains, sections were processed in parallel and images were all acquired the same day using identical confocal parameters. Confocal images (Leica, SPE or Leica SP8) were acquired using either a 20x water immersion objective (NA 0. 7; Leica) or a 63x glycerol objective (NA 1. 3; Leica). For each animal, we acquired two confocal stacks in layer 5 in both hemispheres (infused, Ipsi vs noninfused, Contra). Data were obtained from 3 to 4 brain sections per animal. Z-stacks were acquired with a 1 µm step, exported as TIFF files, and analyzed using ImageJ software.
PV, vGAT or PNN perisomatic rings (between 7 to 10 in each stack) were outlined and the mean gray values were measured, after background subtraction. For PV/gephyrin puncta analysis, confocal images (Leica SP8) were acquired using a 63x glycerol objective (NA 1. 3; Leica). For each animal, we acquired one confocal stack with a 0. 3 µm step in cortical layer 5 from 3 to 4 brain sections per animal. Stacks were exported as TIFF files and analyzed using ImageJ software. All analysis was done by operators blind to the mouse genotype or to the specific treatment.
; https://doi. org/10. 1101/392159 doi: bioRxiv preprint 35 periodically infused to prevent dehydration. Eyes were covered with a thin layer of silicone oil to avoid corneal opacities. Recordings were made using silicon microprobes (16 channels, NeuroNexus Technologies a2x2-tet-3mm-150-121) inserted into the cortex 3. 0-3. 2 mm from the lambda point. Signals were acquired using Cheetah 5 (Neuralynx) and analyzed with custom software in Matlab (MathWorks).
Visual Stimulation Stimuli were generated in Matlab using the Psychophysics Toolbox extensions and displayed with gamma correction on a monitor (Sony Trinitron G500, 60 Hz refresh rate, 32 cd/m2 mean luminance) placed 20 cm from the mouse, subtending 60-75° of visual space. Visual evoked potentials (VEPs) VEP were recorded as described in  66. We measured contralateral to ipsilateral ratio of VEP amplitude to measure ocular dominance plasticity. Extracellular signal was filtered from 1 to 275 Hz. VEPs in response to square wave patterns with a spatial frequency of 0. 06 cpd and abrupt phase inversion (1 Hz temporal period), were evaluated in the time domain by measuring the P1 peak-to-baseline amplitude and latency. Computer controlled mechanical shutters were used to collect data from each eye.
Single-Units For single-unit recording extracellular signal was filtered from 0. 6 to 6 kHz. Sampling rate: 33 kHz. Spiking events were detected on-line by voltage threshold crossing and waveforms of 1 ms were acquired around the time of threshold crossing. To improve isolation of units, recordings from groups of four neighboring sites (tetrode) were linked, so that each spike was composed by 4 waveforms. Then waveforms were processed using the OffLine Sorter software (Plexon). Drifting sinusoidal gratings were. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted August 20, 2018. ; https://doi.
Optical images were recorded using a 12-bit CCD camera (1M60, Dalsa, Colorado Springs, USA) driven by the Imager 3001 system (Optical Imaging Inc. ©) and fitted with a macroscopic lens (Nikon, AF Micro Nikon, 60 mm, 1:2:8D). Frames of 512 × 512 pixels were acquired at a rate of 4 Hz, giving a spatial resolution of 28 µm/pixel. The acquisition was sustained for 10 min during the continuous stimulation paradigm. During episodic stimulation, frames were acquired for 20 s for every contrast and spatial frequency tested. An average of 10 repetitions was used to obtain a good signal to noise ratio. Data analysis. OIS data were analyzed with MATLAB (MathWorks, Nattick, MA). For each pixel of the cortex, a Fourier transform was applied on temporal signals collected during continuous stimulation. Fourier phase and amplitude were generated for each frequency and used to map the retinotopy and realize quantification.
Acknowledgments We thank  Drs. Graçiela Pineyro  ( CHU Ste. Justine, Montreal ),  Elsa Rossignol  ( CHU Ste. Justine, Montreal ),  Edward Ruthazer  ( Montreal Neurological Institute, Montreal ),  Dr. Keith Murai  ( CRN, McGill University, Montreal ) and  Dr. Agustin Anastasia  ( INIMEC-CONICET -Universidad Nacional de Cordoba, Argentina ) for their insightful suggestions,  Dr. Louis Reichardt  ( USF ) for providing reagents,  Dr. Vesa Kartineen  ( UMichigan ) for the p75NTR lox mouse,  Dr. Phil Barker  ( UBC, Vancouver ) for the anti-p75NTR antibody and  JF Cloutier  ( MNI, Montreal ) for the p75NTR KO mice. We would like to thank  Josianne Nuñes Carriço  and  Menna Murrah  for their technical assistance as well as the  Comité Institutionnel de Bonne Pratiques Animales en Recherche (CIBPAR)  and all the personnel of the animal facility of the  Research Center of CHU Sainte-Justine (Université de Montreal)  for their instrumental technical support.
Acknowledgements This work was supported by the  Key Scientic Research Project of Colleges and Universities of Henan Province of China  (grant no. 18A140023  and  6B140002 ), the  Aid Project for the Mainstay Young Teachers in Henan Provincial Institutions of Higher Education of China  ( 2014GGJS-283 ) and  Science and Technology Research Project of Henan Province  ( 172102210391 ).
We then used a biochemical approach to test if CRLD-1 had a PDI-like activity in C. elegans. Total worm lysate proteins from both gfp-crld-1(kr298) (wild type) and gfp-crld-1(kr302) (C30A mutant) were separated by SDS-PAGE under reducing and non-reducing conditions. GFP-CRLD-1 was revealed by western blot analysis using an anti-GFP antibody. Under non-reducing conditions we detected high molecular weight species containing the mutated GFP-CRLD-1(C30A) that were absent in the non-mutated GFP-CRLD-1 (Figure  5C ). These high-molecular weight complexes were resolved under reducing conditions. Altogether these data demonstrated that GFP-CRLD-1(C30A) behaved as a substrate trapping protein, strongly suggesting that CRLD-1 contains PDI activity required for proper synthesis of L-AChR.
Membranes were imaged with a LAS4000 (GE Healthcare Life Sciences) luminescence detector and band intensity-quantified with ImageJ software. Western blot membranes were probed with affinity-purified rabbit antibody anti-UNC-29 (1:1000 dilution; custom antibody), mouse commercial antibody anti-TUBULIN (1:1000 dilution; Sigma), mouse commercial anti-GFP (1:1000 dilution, Roche), and secondary HRP-conjugated goat anti-rabbit or goat anti-mouse antibody (1:3000 dilution; ThermoFisher Scientific) and revealed with Lumi-Light reagents (Roche).
Acknowledgements We thank  Hong Zhan  for constructs,  J Rand  for the anti-UNC-17 antibodies, the  Caenorhabditis Genetic Center  (which is funded by  NIH Office of Research Infrastructure Programs ,  P40 OD010440 ) and  Dr. Shohei Mitani  for strains. MD was supported by the  AFM , MR was supported by the  Association pour la Recherche contre le Cancer , CS was supported by a fellowship within the  Postdoctoral Program  of the  German Academic Exchange Service , a  Long-Term Postdoctoral Fellowship  of the  Human Frontier Science Program  and the  European Molecular Biology Organization , TB was supported by  INSERM. This work was supported by the  AFM-Telethon (Grant MyoNeurALP) , the  Fondation pour la Recherche sur le Cerveau  " Operation Espoir en tete 2013 ' and the  Programme Avenir Lyon Saint-Etienne.
Crystallographic data collection and structure refinement Diffraction data were collected on the EMBL beamline X12 at the DORIS synchrotron storage ring (DESY, Hamburg). 20% PEG200 was used for cryoprotection, prior to transferring the crystals to liquid nitrogen temperature. Data were collected at 100 K. The data were processed with XDS  [28]  and XDSi  [29]. The structures were solved with molecular replacement using PHASER  [30] , using the wild-type P2 structure  [14]  as template, and refinement was carried out in phenix. refine [31]. Model building was done in coot  [32]  and validation in MolProbity  [33]. The structures were deposited at the PDB with entry codes 6EW2, 6EW4, and 6EW5.
Circular dichroism spectroscopy A Chirascan Plus instrument (Applied Photophysics) was used for circular dichroism (CD) spectroscopy. CD spectra were measured at + 20 °C in a 0. 5-mm quartz cuvette. Protein concentration was 0. 25 mg/ml (14 μM) in 0. 8 mM HEPES pH 7. 5, 6 mM NaCl, 0. 4% glycerol. In mixtures with lipids, the lipids (DMPC/DMPG at 1:1 mixture) were in 100-fold molar excess (1. 4 mM). Temperature scans, at a heating rate of 1 °C/min, were run for the same samples between + 20 -+ 90 °C. Full spectra were continuously measured as a function of temperature. Temperature scan data were analyzed using Global3™ (Applied Photophysics).
Bioinformatics Using the crystal structure of wt-P2  [14]  and all the six independent F57A crystal structures described here, residue interaction networks (RINs) were generated with Chimera  [38]  through the structureViz  [39]  app for CytoScape  [40] , using default parameters without hydrogen bonds, and with own contact detection software (2. 5-5 Å). RIN analyses were performed with the RINspector  [41]  app for CytoScape. They included betweenness and residue centrality analysis. Only Z scores of ≥2 were considered relevant. The analyses were carried out with and without the bound palmitate ligand; the ligand dominated the networks when present. Sequence-based predictions of protein flexibility were done using DynaMine  [42, 43]. Structure-based prediction of point mutation effects on P2 thermal stability was done with Cupsat  [44]  and MAESTRO  [45].
Funding This work was funded by grants from the  Academy of Finland  (# 275225 , SR), the  Academy of Finland Center of Excellence program  (# 307415 , IV),  European Research Council  ( CROWDED-PRO-LIPIDS ) (# 290974 , IV),  Sigrid Jusélius Foundation (PK) , and  Emil Aaltonen Foundation (PK). TN thanks the graduate school of  Tampere University of Technology  for financial support.
6 17. 1 18. 8 R free (%) 21. 6 22. 4 23. 6 rmsd bond lengths (Å) 0. 012 0. 009 0. 005 rmsd bond angles (°) 1. 3 1. 2 0. 9 Ramachandran favoured/allowed (%) 100/100 99. 3/100 98. 1/100 # molecules in asymmetric unit 1 1 4 MolProbity score (percentile) 1. 25 (97 th ) 1. 48 (79 th ) 1. 32 (99 th ) PDB entry 6EW2 6EW4 6EW5.