dataesr/scientific-paragraphs-categorization huggingface dataset:
(2010a)  for more details). In line with  (Fornito et al. , 2011) , here we report results for a threshold that retain only edges with p b 0. 005. Results at higher (p b 0. 01) and lower (p b 0. 001) threshold values are reported in Figs. S3  and S4  respectively in Supplementary materials to show sensitivity to parameter sets. Age and duration of formal education were entered as confounding factors in the ANCOVA for both spectral and connectivity analyses. The statistical analyses were performed using the SPSS Statistics 20. 0 software package (IBM Corporation). A significance level of 0. 01 (twotailed) was applied. Corrections for multiple testing were applied using Bonferroni approach.
Acknowledgements This study was funded by the  Michael J. Fox Foundation for Parkinson's research. The sponsor was not involved in the study design or in data interpretation, writing of the report or decision to submit the article for publication. This work has also received a French government support granted to the  CominLabs excellence laboratory  and managed by the  National Research Agency  in the " Investing for the Future" program  under reference  ANR-10-LABX-07-01.
Images Acquisition and Analysis For organotypic slices, image acquisition was performed on a Zeiss LSM780 (Zeiss, Jena, Germany) confocal microscope equipped with a 63× 1. 4 N. A. oil immersion objective. Threedimensional z-stacks were collected automatically as frame by frame sequential image series (80-120 optical slices). For cultured hippocampal neurons, image acquisition was performed on a Zeiss Axio Imager Z2 equipped with a 40× 1. 4 N. A. oil immersion objective. For illustration purposes, image editing was performed using ImageJ software 1  and was limited to Sigma Plus Filter, linear contrast enhancement and gamma adjustment. Quantification was performed using ImageJ. Regions of interest corresponding to proximal axons or dendrites were selected on ankG or GFP images using NeuronJ plugin.
The AIS was automatically detected along the proximal axon tracing by a custom ImageJ script  (Leterrier et al. , 2015)   2  , which determines the point of the maximum intensity of the ankG labeling. The beginning and the end of the AIS were determined from either sides at 33% of the maximum intensity along the tracing  (Grubb and Burrone, 2010). The intensity of ankG labeling was then measured along the defined AIS segment with a 2 pixel (0. 325 µm) width for each neuron. The mean intensity along the AIS of the transfected neurons was normalized by dividing it by the mean intensity along the AIS of untransfected surrounding neurons in the same image, allowing to circumvent variations related to the immunolabeling. All intensities were corrected for background signal (taken as the mode of the whole image histogram).
To determine the intensity in channels other than ankG, the AIS segment region was detected on ankG labeling, then reported to the other channel and the intensity was measured as described above. Results are expressed as mean ± SEM. AIS/dendrite ratio is the mean labeling intensity of the proximal axon divided by the mean labeling intensity of three dendrites. The polarity index (for ankG quantification in organotypic slices) is the ratio of the mean labeling intensities: (AISsoma) divided by (AIS + soma). The width was measured perpendicularly to the axon at the point of maximum intensity of ankG along the AIS. Statistical analyses were performed using Prism 5 (Graphpad Software, La Jolla, CA, USA). Significances were tested using two-tailed unpaired t tests (two conditions) or one-way ANOVA followed by Kruskal-Wallis post-test (three or more conditions).
Electrophysiology and Data Analysis Seven or eight days post transduction, organotypic cortical slices were transferred into a recording/perfusion chamber placed on the fixed-stage of an upright microscope (BX51WI, Olympus; fitted with 4× air and 40× water-immersion objectives, a Photonics VX55 camera and a BX-FLA illumination system) and superfused at 1-3 ml/min with artificial cerebrospinal fluid (ACSF) maintained at room temperature and saturated with 95% O 2 and 5% CO 2. Voltage-clamp recordings were performed with patch pipettes pulled from borosilicate glass (World Precision Instruments) and having an electrode resistance of 1. 5-2. 5 MΩ. Selective recording of Na + currents was performed by using patch pipettes filled with an internal solution containing (in mM) 140 CsFl (to block K + and hyperpolarization-activated cation currents), 1 MgCl 2 , 10 Hepes, 2 EGTA, 2 ATP, 0, 2 GTP (pH7, 4) and a modified ACSF solution (Na + current isolation solution) containing (in mM): 120 NaCl, 3 KCl, 26 NaHCO 3 , 10 Glucose, 1 MgCl 2.
This external solution did not contain CaCl 2 (to prevent Ca 2+ currents) but was added with 10 mM tetraethyl-ammonium, 4 mM 4-Aminopyridine, 3 mM kynurenic acid (Sigma Aldrich) to attenuate K + currents and glutamate synaptic transmission. Separate recording sessions were also performed with 500 nM TTX (Tetrodotoxin, Ascent Scientific) added to the Na + current isolation solution in order to check that the recorded current was carried by Na + ions. Na + currents were evoked by voltage ramps reaching +40 mV from a holding potential of -90 mV and applied at a speed of 0. 2 mV/ms. All recordings were performed exclusively from GFP positive neurons using an Axopatch 200B (Molecular Devices). A computer interfaced to a 12-bit A/D converter (Digidata 1322A using Clampex 9. x; Molecular Devices LLC) controlled the voltage-clamp protocols and data acquisition.
The signals were filtered at 5 KHz and digitized at 20 KHz. Uncompensated series resistance was 8 ± 0. 5 MΩ. Analysis was conducted in Clampfit 10. 3 (Molecular Devices) and SigmaPlot 12 (Systat Software Inc. ). After off line linear leak subtraction, the following parameters were measured: threshold voltage for both Na + persistent current (INaP) and Na + transient currents (INaT), peak voltage for INaP and peak amplitudes for both INaP and INaT. Membrane potentials are uncorrected for the liquid junction potential (8. 9 mV). Data are shown as mean ± SEM. Mann-Whitney rank sum test was used to test for statistical differences with a significance level of p < 0. 05.
ACKNOWLEDGMENTS We thank  Dr. Sulayman Dib-Hajj  for providing the Nav1. 6-GFPplasmid;  Aziz Moqrich  for critical reading of the manuscript;  Hélène Babski  and  Constance Manso  for technical help;  Gisèle Froment ,  Didier Nègre  and  Caroline Costa  (lentivectors production facility/ SFR BioSciences Gerland-Lyon Sud  ( UMS3444/US8 )) for the production of lentiviruses.
Acknowledgements Financial support for this work was obtained from the  "Centre national de la Recherche Scientifique" (CNRS) , the  "Institut National de la Santé et de la Recherche Médicale" (INSERM) , the  Natural Sciences and Engineering Research Council of Canada  ( NSERC Discovery Grant ) and the  "Agence Nationale de la Recherche" (ANR Bee-Channels N°   ANR-13-BSV7-0010-0 ). We are indebted to  Dr. Sebastien Dutertre  ( IBMM, Montpellier, France ) for the gift of atrachotoxine, to  Eve Carreras  and  Dominique Tavan  ( Saint Mathieu de Tréviers, France ) and  Jacques Sénéchal (Avignon, France)  for their help with the honeybees, to  Philippe Richard  and  Marc Plays  ( CRBM, Montpellier, France ) for taking care of the Xenopus laevis. We thank  Michel Pélissier  ( INRA, Avignon, France ) for his technical help with the locomotion arena.
Immunoblotting Analysis. Immunoblotting was performed on total cellular extracts as previously described  52. Briefly, protein samples (40 µg) were subjected to electrophoresis in a 10% acrylamide gel and electrophoretically transferred to a nitrocellulose membrane (Bio-Rad). After blocking with Tris-buffered saline containing 4% bovine serum albumin and 0. 1% Tween 20 at room temperature, membranes were incubated with specific primary antibody overnight at 4 °C and, subsequently, with appropriate horseradish peroxidase-conjugated secondary antibody for 1 h. Immunolabeled proteins were finally visualized by chemiluminescence using the LAS-3000 analyzer (Fujifilm). Image processing was performed using Multi Gauge software (Fujifilm).
Dataset We used images of five object categories, including ducks, pigeons, cats, dogs, and cars (200 images per category). Most of the images were picked from the Imagenet dataset  (Russakovsky et al. , 2015) , and others were gathered from the web. Each image contains the side view of a different instance of one of the object categories. For each categorization level, a different combination of categories has been used. For the superordinate level (animal vs. non-animal), there was four different sets of image in two categories: one of the four animals and the car category. Also, for the basic level (bird vs. non-bird animals) there was also four sets, each of which containing one bird (duck or pigeon) and one non-bird animal (cat or dog). In the subordinate level, only duck and pigeon categories were employed.
The training phase of each session contained 20 images (10 images per category) that are randomly selected. When subjects reported their decision, a feedback was shown to them indicating whether they responded correctly or not. The recording phase contained 240 trials (120 images per category) without any feedback. Images used in the training phase are not shown in the recording phase. Subjects were seated on a comfortable chair in a dark room and were instructed to respond as fast and accurate as possible. Stimuli were presented using Matlab Psychophysics Toolbox  (Brainard, 1997)  in a 21" CRT monitor with a resolution of 800×600 pixels, frame rate 80Hz, and viewing distance of 60cm. Therefore, each stimulus covered 11 • × 11 • of visual angle. All subjects voluntarily participated to the experiments and gave their written consent prior to participation.
Discussion The aim of our study was to investigate the effect of spatial frequencies on object categorization in different levels (i. e. , superordinate, basic and subordinate levels). We constructed an object dataset containing images of cars and four animals (ducks, pigeons, cats, and dogs). Images were also filtered into LSF, ISF, and HSF bands. We performed several rapid-presentation psychophysics experiments at different categorization levels, and recorded the subjects' accuracy and RT. The same categorization experiments were also performed by two computational models. Although, the relation between the global (resp. local) visual processing and LSF (resp. HSF) information has been debated  (Morrison and Schyns, 2001; Loftus and Harley, 2004; Goffaux and Rossion, 2006; Goffaux et al.
Physical dimensions of the final structures were determined by a stylus profiler (Wyko NT1100, Veeco) and quantitative microscopy (Leica DM IL LED Inverted, Leica Microsystems CMS GmbH) through Zeiss Axiovision software (v 4. 8) measurements. PDMS pre-polymer and curing agent (Sylgard 184, Dow Corning) were mixed (10:1), degassed and poured on the original SU-8 template or an epoxy copy thereof (Epox A cast 655, Smooth-On). A laser copier transparency was placed on top of the PDMS to level the device thickness by squeezing extra PDMS out of the cavities. A thin layer (~100 µm) of PDMS was left between the transparency and the template's highest structures to provide closed somal reservoirs in place of open reservoirs. PDMS was cured at 80 °C for 20 min, peeled-off from the template, and four big seeding cavities were manually punched at the two opposite outer corners of each small somal reservoir.
Cells were distributed homogenously in all reservoir modules with a maximum number of 200 cells per reservoir module (1,600 cell/device). Extra cells were removed from the seeding cavities and 1. 5 ml pre-warmed serum-free cell culture medium was added to each MEA or coverslip in a 12-well plate. The MEA ring was then sealed with a custom-made gas-permeable PDMS cap (Fig. 1 )  19. Between experiments, the cultures were stored in a regular humidified incubator (5% CO 2 , 37 °C, 95% RH). The electrophysiology recording, microscopy, and culture medium-exchange days are given in Suppl. Table  1. To picture all network modules, 9 and 16 mosaic bright-field microscopy images were captured with 10x and 20x objectives, respectively, and then stitched together manually in ImageJ (Suppl. Fig. 2 , Video 2 and 3).
MEA electrophysiology. To record cortical network activity or electrically stimulate the cultures, we used a commercially available 60-channels MEA filter-amplifier (0. 1 Hz-25 kHz, 1200 x amplification, MEA1060-upright-standard) with an A/D conversion card (64-channels, 25 kHz sampling frequency/channel, PCIbus) and a software user interface (MC_Rack) provided by Micro Channel Systems (MCS). MEA cultures were divided into three groups. The first group included 7 MEA cultures for long-term electrophysiology and bright-field microscopy (13 DIV to 95 DIV). The data from these cultures were used for the analysis of spike and burst frequencies, spike propagation velocities and activity-velocity correlations over time. The activity of each culture was acquired on different days (Suppl. Table  1 ).
The second group of MEA cultures (n = 8) was used for electrical stimulation between 14 DIV to 75 DIV. Activity was recorded before, during and after each stimulation trial. The third group of MEA cultures (n = 4) served to study the effect of bicuculline on activity and propagation velocity. During all recordings, the temperature was kept constant (37 °C) by a built-in thermal sensor and heating element controlled by an external temperature controller (HC-1, MCS). Raw signals were recorded and analyzed offline. All signals were filtered by a second-order Bessel high-pass filter (cut-off at 200 Hz). The timestamps of the spikes in the filtered data streams were detected by a negative threshold (-4. 5 StDev of the peak-to-peak noise). After transforming the spike trains to time stamps (NeuroExplorer, Nex Technologies), the mean spike frequency on each electrode was extracted as spikes/s.
Timestamps were also used for extracting the propagation velocity between electrode pairs. Electrical and chemical stimulation. Voltage-driven biphasic stimuli (+then-,100 µs phase duration, 1 Hz) were applied to an electrode in a reservoir module for somal stimulation or to the first microchannel electrode for axonal stimulation by an 8-channel stimulus generator (MCS, STG4008) and control program (MCS, MC_Stimulus II). Three-minute baseline recordings preceded and followed each stimulation experiment not lasting more than 30 min in total. Three different stimulation amplitudes (250, 500 and 1000 mV) were tried on 12 microchannels and 12 reservoir modules of 8 different MEA cultures. Four cortical cultures were stimulated with bicuculline. After a 15 min baseline recording, half of the cell culture medium was drained, mixed with bicuculline (60 μM), returned to the MEA and incubated for another 15 min in the incubator before performing another 15 min recording.
Statistical analysis. In each MEA culture, two electrodes recorded from one of the 8 reservoir modules, and five subsequent electrodes recorded activity propagation in the same axonal bundle in 200 µm intervals inside the microchannel. The mean number of spikes per second, the number of bursts per second, various burst features and timestamps of spikes recorded from individual electrode were extracted by NeuroExplorer (Nex Technologies) and analyzed statistically (IBM SPSS Statistics 22)  21. Complex spike trains were considered as burst activity if they met the following criteria: 20 ms maximum inter-spike interval to start the burst, 10 ms maximum inter-spike interval to end the burst, 10 ms minimum inter-burst interval, 20 ms minimum burst-duration with at least 4 spikes in each burst  15.
Results were expressed as means ± StDev for reservoirs or microchannels of all MEAs. The effect of electrical stimulation through an electrode in a reservoir module was studied by recording the activity from its electrode pair and the five subsequent electrodes in its adjacent microchannel. Similarly, the effect of stimulating axons through the electrode in a microchannel most proximal to the reservoir was studied by recording the activity from the four subsequent electrodes in the same microchannel. To compare the effect of the stimulation pulse amplitude on activity, the 1 s time-period between stimulation pulses was sub-divided into shorter time-windows. Spike timestamps of each time-window were extracted by a Matlab script and aligned with the timestamp of the corresponding stimulation pulse (Suppl.
The mean activity in each time-window was averaged across 12 experiments (for 4 cultures and 12 microchannels). In each experiment, three different stimulation amplitudes (180 trials each; 1 Hz) were applied to the same electrode. Baseline activity recorded from the same set of electrodes before and after each stimulation experiment. Data are represented as mean ± StDev. *p < 0. 05 vs. baseline activity (Base) in the same microchannel. (D) Activity recorded from the corresponding reservoir module (one electrode pair in each reservoir) for the same experiments as in C. *p < 0. 05 vs. baseline activity in the reservoir module. (E) The response reliability to certain stimulation pulse amplitudes was counted in three peri-stimulus time windows over 180 trials and the percentage of successful trials (at least one spike per time-window) was calculated.
Baseline activity recorded from the same set of electrodes before and after each stimulation experiment. Data are represented as mean ± StDev. *p < 0. 05 vs. baseline activity (Base) in the same microchannel. (D) Activity recorded from the corresponding reservoir module (one electrode pair in each reservoir) for the same experiments as in C. *p < 0. 05 vs. baseline activity in the reservoir module. (E) The response reliability to certain stimulation pulse amplitudes was counted in three peri-stimulus time windows over 180 trials and the percentage of successful trials (at least one spike per time-window) was calculated. Individual points represent the average reliability percentage across 12 stimulation experiments for each stimulation pulse amplitude. *p < 0. 05 vs. the response fidelity to 1,000 mV stimuli in the same timewindow.
Results Disruption of AANAT and ASMT activities in blood platelets from individuals with ASD. We assessed ASMT enzyme activity in blood platelets from a large cohort of patients with ASD, their first-degree relatives and controls in order to achieve sample sizes of 239 patients, 303 parents, 78 unaffected siblings and 278 controls. Confirming previous results obtained on a smaller cohort  12  (Supplementary Figure  1a-c ), ASMT activity was significantly decreased in patients' platelets compared to controls, and to a lesser extent in their first-degree relatives (Fig. 1c ). A separate analysis of the two data sets is presented in Supplementary Figure  1d-e. For the first time we also investigated AANAT activity in blood platelets. AANAT activity was significantly diminished in blood platelets from patients with ASD, but not in their relatives (Fig.
Contribution of rare genetic variants in AANAT and ASMT in ASD. Considering that the biochemical impairment of melatonin synthesis observed in patients with ASD is shared by first-degree relatives (Fig. 1c , Supplementary Figure  1 ), we hypothesized that these impairments may be caused by rare mutations and hence sequenced AANAT and ASMT genes. The results were analyzed together with previously published sequencing data  12, [28] [29] [30]  : taken together, 431 individuals with ASD and 365 controls were directly sequenced for AANAT, as well as 491 individuals with ASD and 490 controls for ASMT, respectively (Table  1 ). The functional impact of each   mutation was determined in silico and in vitro after targeted mutagenesis. Overall, 4. 7% of individuals with ASD and 3. 9% of controls had a deleterious variant in one of the two genes encoding melatonin synthesis enzymes (p = 0.
This is consistent with the significant decrease of plasmatic melatonin observed in patients with ASD even when sampled in the morning  8. In gut and platelets, the reduction of ASMT activity observed in patients with ASD appeared to be correlated with an accumulation of NAS, the intermediate metabolite between serotonin and melatonin. This increase of NAS was not observed in the pineal gland, and this may also result from the different stoichiometry of the two enzymes in this tissue compared with "peripheral" sources. Indeed, in contrast with data from platelets and ileum, pineal NAS was not correlated with ASMT activity, but with AANAT activity, although with a complex pattern as observed for melatonin. In the pineal glands of ASD patients, NAS levels may be determined by its degradation (by ASMT) and its synthesis (by AANAT), both being impaired.
Acknowledgements We thank the patients and their families, and the controls who accepted to participate in this study. Some tissue used in this research was obtained from Autism BrainNet that is sponsored by the  Simons Foundation , and from the  Maryland Brain  and  Tissue Bank. The authors also acknowledge the Autism Tissue Program that was the predecessor to Autism BrainNet. The Clinical Investigation Centers of  Robert-Debré  and  Henri Mondor Hospitals  obtained and processed blood samples, the Hematology departments from both hospitals ( Dr MF Hurtaud  and  Pr M Imbert ) performed platelet counts; the  Anatomopathology department of Lariboisière Hospital (Pr F Gray ) performed immunohistochemistry analyses. This work was supported by academic institutions ( Institut Pasteur ,  Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Assistance Publique -Hôpitaux de Paris, University Paris Diderot ); academic grants:  ANR (SynDivAutism)  and  Neuron-ERANET  (EUHF-AUTISM); charity foundations:  Bettencourt-Schueller foundation ,  Orange foundation, FondaMental foundation, Conny-Maeva foundation, Cognacq-Jay foundation.
Secondary outcomes include clinical and functional measures before and after stimulation. A three-dimensional gait analysis, and evaluation of static balance on a force platform will be also conducted before, during and after stimulation. Discussion: These results should constitute a useful database to determine the aspects of complex motor function that are the most improved by transcranial direct current stimulation in patients with hemiparesis. It is the first essential step towards validating this technique as a treatment, coupled with task-oriented training.
Visits 2 and 3 locomotion/balance (V2 and V3): anodal tDCS or placebo visits. During these visits, before the cortical stimulation (anodal or placebo), all the functional and clinical data will be collected. A 3D gait analysis and instrumented balance task will be carried out before and during the 30 min of stimulation, as well as immediately after the end of the stimulation and 30 min later. Functional and clinical data will be collected again following the stimulation. The order of the placebo and anodal tDCS visits will be randomised. The visits will be 7 days apart. The study schedule is shown in Fig. 1  and the evaluations conducted during the three visits are shown in Table  1.
The 3D gait analysis will be conducted with a 3D optoelectronic system (Motion Analysis Corporation, Santa Rosa, CA, USA, sampling frequency 100 Hz) with eight optoelectronic cameras. Thirty markers will be placed on the patient's body (following the Helen Hayes model commonly used by the biomechanical community for gait analysis  [53] ). The relative displacement of each segment will be calculated from these coordinates (flexion/extension, abduction/adduction, internal/external rotation). The marker trajectories will then be filtered using a fourth-order zerolag Butterworth low-pass-filter, with a 6-Hz cut-off frequency  [54]. The data from two force platforms combined with the kinematic and anthropometric data will be used to evaluate the displacement of the COP and COM during gait and balance tasks.