dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition EEG and EMG signals were amplified (Model 3500, AM System) and digitized at 512 Hz (NIDAQ 6363, National Instruments) using a sleep recording software (MATLAB written software, DaqReverse). A 24 h baseline of spontaneous sleep-wake behaviour was recorded for all animals. Stroke and Sham animals were recorded for 24 h at post-surgery days 1, 3, 5 and 10. All optogenetic stimulations took place between 9 a. m. and 2 p. m. , with light on at 4 a. m. for ChR2 stroke , ArchT stroke , mCherry stroke and mCherry sham. Since ChR2 stroke and ArchT stroke animals showed similar functional outcomes upon neuronal manipulation during sleep, an additional ChR2-transfected set of animals received SW opto during animals' active phase (between 9 a. m. and 2 p. m. , lights on at 8 p. m, ChR2 stroke_wake ), from post-stroke day 5 until day 15.
Behavioural tests All animals were trained in four behavioural tests and engaged in daily training sessions for three consecutive days. Behavioural baselines were acquired prior to stroke/ sham surgery. Functional outcomes were verified at post-stroke days 4, 7, 10 and 15. All behavioural tests were conducted at least 3 h apart from optogenetic stimulations and during animals' active phase (between 5 p. m. and 8 p. m. ). Test sessions were recorded with a picamera (Raspberry Pi) and scored in slow motion (VideoPad software, https://www. nchsoftware. com/videopad/index. html).
Signal processing As previously described  (Jego et al. , 2013) , electrophysiological data were manually scored in 5 sec epochs and analysed using SlipAnalysis (custom written MATLAB program). Briefly, three vigilance state were identified based on EEG/EMG frequency and amplitude. Wakefulness was determined by low amplitude EEG and high activity EMG signals; NREM sleep as high amplitude and low-frequency EEG (0. 5-4 Hz) paired with reduced EMG activity; REM was characterized by theta rhythm (6-9 Hz) EEG and flat EMG. Microarousals were defined and scored as cortical fast rhythm and EMG bursts of at least 1 sec. Sleep/ wakefulness scoring was based on the visual characteristics of the contralateral EEG traces specifically. Electrophysiological analysis was completed using custom MATLAB scripts.
Automatic single SW detection Individual SW were detected during NREM sleep epochs during the first 7 h of the lights ON period in MATLAB using the SWA-MATLAB toolbox  (Mensen et al. , 2016) , with detection parameters adjusted to rodents from settings described in  Panagiotou et al. , 2017. Briefly, in a  first-pass of the data, the negative envelope across the 4 EEG channels was calculated, filtered between 0. 5 and 4 Hz (Chebyshev Type II filter design), and consecutive zerocrossings were detected. If the duration between successive downward (negative going) zerocrossing and upward zero-crossing was between 100 msec and 1 sec, then the peak negative amplitude was examined and was required to be at least 3 deviations from the median amplitude of all negative peaks in the recording. The amplitude threshold eliminates the potential individual differences of electrodes reference type, distance to those reference, and electrode depth that would affect the record amplitude.
Single unit analysis We performed spike detection and sorting as described previously  (Gent et al. , 2018). Briefly, we first extracted multiunit activity from band-pass filtered signals (600-4000 Hz, 4 th -order elliptic filter, 0. 1 dB passband ripple, -40 dB stopband attenuation), by applying a detection threshold of 7. 5 × the median of the absolute values of the filtered signal. We then extracted wavelet coefficients from the detected multiunit activity using a four-level discrete wavelet transform (Harr wavelet, 'wavedec', MATLAB), and subsequently sorted the coefficients using the super-paramagnetic clustering. We visually inspected the sorted units and excluded the clusters with a symmetric shape or an average firing rate less than 0. 2 Hz from our analyses.
Postsynaptic population To model the postsynaptic population, we inspected multiple CA3 pyramidal cell morphologies which were reconstructed from the rat hippocampus and which we downloaded from neurmorpho. org online database. This inspection was done in two ways: (i) visually, where we checked if the neurons did not look flatten and if the overall dendritic tree appeared uninjured (Fig. 1A ), and (ii) quantitatively, where we monitored the change of size in diameter of the dendrites making sure that it decreased with the distance from the soma (Fig. 1B ) as the diameter of the dendrites is of crucial importance for calculating the correct extracellular field. We decided to take all the selected reconstructions from the database of a single lab, which we chose to be the one of Amaral  (Ishizuka et al.
Excitatory unitary field potential Axonal trees of pyramidal neurons are very different from those of basket cells. They tend to be very long (200 mm for CA3b to 500 mm for CA3c pyramidal neuron  (Ropireddy et al. , 2011) , as compared to 900-1300 µm in basket cells  (Klausberger et al. , 2003) ) and longitudinal projections of single axons can extend very far (even 70% of the dorso-ventral extent of the hippocampus)  (Li et al. , 1994; Lorente de Nó, 1934; Sik et al. , 1993)  (but see  (Norenberg et al. , 2010)  in Dentate Gyrus). To model uLFPs produced by single pyramidal neurons, we used morphologically-reconstructred pyramidal neurons from rat CA3 (see Methods). We searched the NeuroMorpo. org online database for best preserved pyramidal cell axons from the rat CA3. We selected two cells: one with NeuroMorpho.
Animal preparation We collected behavioral data from 2 adult, male rhesus macaques (Macaca Mulatta). Monkeys M and N (aged 7 and 10 years, and weighing 8 and 11. 5 kg, respectively) were implanted with a scleral coil in one eye to allow measuring eye movements using the electromagnetic induction technique  (Fuchs and Robinson 1966; Judge et al. 1980). The monkeys were also implanted with a head holder to stabilize their head during the experiments, with details on all implant surgeries provided earlier  (Chen and Hafed 2013; Skinner et al. 2019). They were part of a larger neurophysiology project beyond the scope of the current manuscript.
This was also the case in more complicated variants of the task  (Willeke et al. 2019) , and it was also consistent with human results (e. g. see Fig. 10 ). We felt that this approach of large virtual target windows was better than the alternative of enforcing tiny target windows, because in the latter case, any potential increases in reaction times of saccades could have been interpreted as being the consequence of increased task difficulty or a potential speed-accuracy tradeoff. We analyzed a total of 928 trials from monkey M in this task, and 1246 trials from monkey N. We also analyzed visually-guided delayed saccades and memory-guided delayed saccades made by the same two monkeys. These data were collected during an earlier experiment, with detailed methods described elsewhere  (Willeke et al.
Acknowledgements ZMH was funded by the  Werner Reichardt Centre for Integrative Neuroscience (CIN). The CIN is an  Excellence Cluster  ( EXC307 ) funded by the  Deutsche Forschungsgemeinschaft (DFG). ZMH was also supported by the  Hertie Institute for Clinical Brain Research at Tuebingen University , and DFG-funded Research Unit (FOR1847; project:  HA6749/2-1 ). LG was supported by  CNRS  and  Fondation pour la Recherche Médicale. We thank  Aya Tarek ,  Antimo Buonocore , and  Konstantin Willeke  for help with data collection.
0-0. 3 sccm) are transparent to the eye, while the samples with oxygen deficient yttria films are grayish and opaque. Note that the yttria bandgap and optical transparency of the samples change considerably with the decrease of oxygen flow from 0. 3 to 0. 2 sccm, that is, upon the disappearance of the monoclinic reflection in Figure  1a. The reason hereof is related to an increased density of defects (oxygen vacancies) in oxygen deficient yttria, which act as scattering centers for photons and reduce the overall intensity of the transmitted optical signal. The thickness of the yttria layers is ≈15 nm and increases for oxygen deficient samples. The exact thickness values have been derived from the period of the measured X-ray reflectometry (XRR) oscillations (see Figure  S1 , Supporting Information), using an RCRefSim software  [34]  and are summarized in Table  1.
Electron energy loss spectra (EELS) of the oxygen K-edge for the yttria layers grown with an oxygen flow of 1. 0 and 0. 1 sccm are shown in Figure  2b. The ABF-STEM images for the yttria layers of the same stacks are shown in Figure  2c, d , respectively. To correlate the ABF-STEM images with the orientations of yttria grains, multislice simulation images generated using the Dr. Probe  [35]  software were overlaid on selected magnified areas of the ABF-STEM images (see Figure  2e,-g ). The stoichiometric yttria stack shows mixed cubic (332) and monoclinic (402) oriented grains and the deficient yttria stack shows a (103) oriented cubic phase. The observation of a purely cubic phase in the oxygen deficient yttria layer is consistent with the changes observed in the XRD patterns. The (103) orientation, as shown by the ABF-STEM images, resembles a forbidden reflection in XRD and is therefore not visible in the XRD pattern.
The EEL spectra were acquired with a Gatan Enfina with the entry aperture set to 1 mm. The EEL spectra were integrated over the yttria layer thickness and the background spectra were subtracted with HyperSpy software. [59] For the multislice simulations with Dr. Probe software, a voltage of 120 kV, an aperture radius α of 25 mrad, a probe size of 0. 08 nm and an ABF detector with 10-30 mrad detection range were used. The microscope aberrations were set to zero. The multislice data was generated with equidistant slicing and a thickness of ≈10 nm. TEM sample preparation was performed with a JEOL JIB-4600F focused ion beam system. Forschungsgemeinschaft under project AL 560/13-2. Also, funding from DFG grant under project number 384682067, MO 3010/3-1, and AL 560/21-1, and the European Research Council (ERC) "Horizon 2020" Program under grant no.
Acknowledgements The work leading to this publication has been undertaken in the framework of the  WAKeMeUP  project which received funding from the  Electronic Components and Systems for European Leadership Joint Undertaking  in collaboration with the European Union's  H2020 Framework Programme  ( H2020/2014-2020 ) and National Authorities, under grant agreement no  783176. Funding by the  Auvergne-Rhône Alpes Region  and the  Federal Ministry of Education and Research (BMBF)  under contract  16ESE0298  are gratefully acknowledged. E. P. acknowledges funding from the  Deutscher Akademischer Austauschdienst (DAAD). This work was supported by the Deutsche.
5a ). We next analyzed by single-gene assay ZIKV AF-and ZIKV AS-infected pericytes at 6 dpi. Both CCL5 and CXCL10 were upregulated by the two strains, as well as IFNB (Fig. 5b ). IL6 modulation was just under the 2-fold threshold compared to that for the CT but was significantly upregulated (Fig. 5b ; Fig. S5b ). Enzyme-linked immunosorbent assays (ELISAs) on mock-and ZIKV-infected supernatants collected at 4 and 6 dpi showed increases in the expression levels of CCL5 (both strains), CXCL10, and IL-6 (ZIKV AF), as well as potentially IL-8 (ZIKV AS, nonsignificantly, however) (Fig. 5c ). Altogether, this set of data showed that some of the BBB proteins are modulated by ZIKV infection in hBLECs and pericytes, even though the BBB integrity is not strongly perturbed. Proteins involved in inflammatory responses and chemoattraction as well as tight junction (TJ) proteins and adhesion molecules are affected upon infection and may trigger the recruitment of cells of the immune system and promote local inflammation.
9d  and Fig. S7d ). Moreover, staining with CD45 in consecutive slices (3-m thick) showed the recruitment of leukocytes at the BBB, some of them positive for ZIKV (Fig. 9d , arrows). To analyze the genetic modulation of neuroinflammation markers, TJ and adherens junction (AJ) proteins, as well as the levels of CAMs that we identified modulated by ZIKV in vitro, we performed RT-qPCR assays on several genes. Figure  9e  shows that ZIKV-infected brains display an upregulation in the inflammatory genes CXCL10, CCL5, TNFA, INFB, IL1B, and IL6. Moreover, we observed that ICAM1 and SELE, but not VCAM1, were also upregulated, partly consistent with our data with the human BBB in vitro model (Fig. 9f ). However, genetic analyses of several TJ and AJ genes did not show strong modulation in ZIKV-infected brain (Fig.
Human samples (CARBO cohort). CARBO (Cohort Arbovirosis) is a descriptive and prognostic study of arbovirosis in France, based on a hospital cohort of children and adults with suspected arbovirosis. The main objective of the study was to define the predictive factors of severe arbovirosis. This study is registered at clinicaltrials. gov (NCT01099852). Ethical clearance was obtained from the French National Agency for the Safety of Medicines and Health Products (ANSM) (no. IDRCB 2010-A00282-37) and by the committee for the protection of individuals. Written and signed informed consent of all subjects was obtained. For this study, patient inclusion was conducted from December 2015 to December 2016. Inclusion criteria were suspicion of ZIKV infection, ability to sign informed consent, blood or urine sample positive for ZIKV RNA by reverse transcription-PCR (RealStar Zika Virus RT-PCR kit 1.
0; Altona Diagnostics, Hamburg, Germany), and onset of symptoms of Յ21 days. Data were collected at the initial visit and, if possible, at day 3 after the onset of symptoms, between days 5 and 7, between days 8 and 10, at day 21, and at weeks 6 and 12, including sociodemographic data, comorbidities, and clinical characteristics. Neurologic manifestations were classified as either involving the peripheral nervous system (PNS) only, CNS only, or involving both PNS and CNS (mixed disorders). A diagnosis of Guillain-Barré syndrome was made using international Brighton criteria. Patients with encephalitis or acute myelitis were diagnosed according to consensus criteria.
ACKNOWLEDGMENTS We thank  Giles Uzé  for the Ifnar Ϫ/Ϫ mouse model and also  Chantal Fournier ,  Fanny Leon ,  Vanessa Daoudi ,  Jean-Marie Peloponese ,  Aurélie Covinhes , the  Montpellier RIO Imaging facility , the  University  of Montpellier A3 animal facility personnel, and CERBIM. This work/project was publicly funded by the  Montpellier University of Excellence (MUSE)  through  ANR  (the  French National Research Agency)  under the  Investissements d'avenir program   ANR-16-IDEX-0006  and the  Labex EpiGenMed Investissements d'avenir program   ANR-10-LABX-12-01 , as well by REACTing. Conceived and designed the experiments: Y. S. and S. S. ; performed the experiments: M. C. , C. D. , Y. S. , J. B. , O. C. , G. M. , K. B. , V. F. , Y. G. , F. D. B. , M. B. , L. D. , and S. S. ; analyzed the data: F.
Opsin expression analysis Image stacks were acquired from the spinal cord of 5 dpf Tg(mnx1:GAL4;UAS:opsin-FP) larvae using a 2-photon microscope and acquisition parameters described above. Maximum intensity z-projections spanning 5-10 m in depth were used to estimate opsin expression at the plasma membrane of motor neurons. First, automated cell body segmentation was performed using Cellpose to obtain 'cell body masks'  (Stringer et al. , 2020 ; https://github. com/MouseLand/cellpose). Then, 'membrane masks' corresponding to outlines of the 'cell body masks' (see Figure  1 -figure supplement 1A) were generated by running a boundary tracing routine for binary objects in MATLAB (MathWorks). For each cell, we computed the mean fluorescence intensity across all pixels in the corresponding membrane mask.
Behavioural data analysis Movie data was analysed using MATLAB (MathWorks). Region of interests (ROIs) containing individual fish were manually specified. For each ROI, the frame-by-frame change in pixel intensity -DPixel -was computed in the following way. For each trial, pixel intensity values were low-pass filtered across time frames and the absolute frame-by-frame difference in intensity (dI) was obtained for each pixel. Pixels showing the highest variance in dI (top 5 th percentile) were selected to compute their mean dI, corresponding to the ROI DPixel trace for the trial. With the exception of the larval inhibition assay (see below), onset and offset of animal movements were detected from DPixel traces in the following way. For each ROI, DPixel traces were concatenated across all trials to estimate the probability density function (pdf) of DPixel values.
For larval inhibition assays, images were background-subtracted using a background model generated over each trial (30 s duration). Images were then thresholded and the fish body centroid was found by running a particle detection routine for binary objects within suitable area limits. Tracking of body centroid position was used to compute fish speed, and periods in which speed was higher than 1 mm/s were classified as swim bouts. Bout speed was computed as the mean speed over the duration of each bout. To account for group differences in baseline coil/bout rate and bout speed in inhibition assays, data was normalised at a given irradiance level by dividing by the mean rate/speed across fish in control (no light) trials.
, 1997; Bello-Rojas et al. , 2019). We verified primary motor neuron identity in a small subset of recordings from eYFP-expressing cells in Tg(mnx1:GAL4;UAS:ChR2(H134R)-eYFP) larvae by adding 0. 025% sulforhodamine-B acid chloride dye in the intracellular solution (Sigma-Aldrich) and filling the neuron to reveal its morphology. To maximise data acquisition in our in vivo preparation, when the first attempts of primary motor neuron recordings were not successful, we recorded neighbouring, dorsally-located presumed secondary motor neurons (11 out of 90 included cells).
We generally recorded data from a single cell per larva. In a few instances, two cells from separate adjacent somites were recorded in the same fish. External bath recording solution contained the following: 134 mM NaCl, 2. 9 mM KCl, 2. 1 mM CaCl 2 -H 2 O, 1. 2 mM MgCl 2 , 10 mM glucose, and 10 mM HEPES, with pH adjusted to 7. 8 with 9 mM NaOH and an osmolarity of 295 mOsm. We blocked glutamatergic and GABAergic synaptic transmission with a cocktail of: 20 mM CNQX or DNQX, 50 mM D-AP5, 10 mM Gabazine (Tocris) added to the external recording solution. The -50 mV ECl solution contained: 115 mM K-gluconate, 15 mM KCl, 2 mM MgCl 2 , 4 mM Mg-ATP, 0. 5 mM EGTA, 10 mM HEPES, with pH adjusted to 7. 2 with 11 mM KOH solution, and a 285 mOsm. In these conditions, we calculated the liquid junction potential (LJP; Clampfit calculator) to be 12.
4 mV. The -70 mV ECl solution contained: 126 mM K-gluconate, 4 mM KCl, 2 mM MgCl 2 , 4 mM Mg-ATP, 0. 5 mM EGTA, 10 mM HEPES, pH adjusted to 7. 2 with 11 mM KOH solution, 285 mOsm and a 13. 3 mV LJP. All reagents were obtained from Sigma-Aldrich unless otherwise stated. Recordings were made with an Axopatch 700B amplifier and digitised with Digidata 1440A or 1550B (Molecular Devices). pClamp software was used to acquire electrophysiological data at a sampling rate of 20 kHz and low-pass filtered at 2 kHz (voltage clamp) or 10 kHz (current clamp). Voltage clamp recordings were acquired with full whole-cell compensation and ~60% series resistance compensation, while corrections for bridge balance and electrode capacitance were applied in current clamp mode. Cells were visualised with a 63Â/1.
Optogenetic stimulation Light stimulation was performed with either a X-Cite (Xcelitas, XT600) or a broadband white LED (Prizmatix, UHP-T-HCRI_DI) light source equipped with a combination of different bandpass and neutral density filters to modulate irradiance at specific wavelengths (see Figure  4 -figure supplement 1A and Supplementary file 4 for centre wavelengths/bandwidth and irradiance levels used to activate opsins). The onset, duration and irradiance level of light pulses were triggered and controlled via the Digidata device used for electrophysiological recordings. For all cells, data was acquired in the following order: (1) series resistance was checked at the beginning, middle and end of recording; (2) action potential rheobase was determined by injecting 5 ms pulses of current (160-340 pA) in current-clamp gap-free mode; (3) voltage clamp recording of opsin photocurrents; (4) current clamp recording of voltage responses induced by opsin activation.
Data analysis Data were analysed using the pyABF module in Spyder (3. 3. 6 MIT, running Python 3. 6, scripts available here: https://github. com/wyartlab/Antinucci_Dumitrescu_et_al_2020; Dumitrescu, 2020; copy archived at https://github. com/elifesciences-publications/Antinucci_Dumitrescu_et_al_2020), MAT-LAB (MathWorks) and Clampfit (Molecular Devices). Series resistance (Rs) was calculated as a cell response to a 5 or 10 mV hyperpolarisation step in voltage clamp from a holding potential of -60 mV, with whole-cell compensation disabled. Membrane resistance (Rm) was obtained from the steady holding current at the new step, and membrane capacitance (Cm) corresponds to the area under the exponentially decaying current from peak to holding. We used the following cell inclusion criteria: (1) cell spiking upon injection of a 5 ms pulse of current; (2) membrane resting potential < -50 mV at all times; (3) > 150 pA current injection necessary to maintain the cell at a holding potential equal to resting potential in current clamp; (4) series resistance < 6 Â pipette resistance at all times during the recording.
Acknowledgements The authors thank members of the  Bianco lab and Wyart lab  for helpful discussions. We thank staff from the  UCL  and ICM PHENO fish facilities for fish care and husbandry (UCL:  Carole Wilson  and team; ICM:  Sophie Nunes-Figueiredo ,  Bogdan Buzurin ,  Monica Dicu  and  Antoine Arneau ). Irene Arnold-Ammer  and  Enrico Ku ¨hn  ( MPI of Neurobiology ) assisted with generation of transgenic lines. We also acknowledge support from the  ICM electrophysiology core facility  (CELIS-ePhys) and  Mingyue Wu  for electrophysiology experimental assistance. PA was supported by a  Sir Henry Wellcome Postdoctoral Fellowship  ( 204708/Z/16/Z ). AD was supported by a  Marie Curie Incoming International Fellowship  ( H2020-MSCA-IF-2016  Project # 752199 ). FK was supported by a  HFSP  long-term fellowship ( LT393/2010 ).
Generation of opsins in HB's laboratory was supported by the  Max Planck Society (HB and FK)  and the  DFG  ( SPP1926   Next-Generation Optogenetics ). A  Sir Henry Dale Fellowship  from the  Royal Society & Wellcome Trust  ( 101195/Z/13/Z ) and a UCL Excellence Fellowship were awarded to IHB. CW was funded by  Human Frontier Science Program (HFSP)   Research Grant  ( RGP063-2018 ) and the  New York Stem Cell Foundation  ( NYSCF-R-NI39 ). The work on electrophysiological calibration of opsins performed in ICM has also received funding from the  ICM foundation ,  the program 'Investissements d'avenir'   ANR-10-IAIHU-06  ( Big Brain Theory ICM Program ),  ANR-11-INBS-0011  ( NeurATRIS: Translational Research Infrastructure for Biotherapies in Neurosciences ).
Available from Baier lab Software, algorithm MATLAB MathWorks RRID:SCR_001622 https://uk. mathworks. com/ products/matlab. html Software, algorithm Python Anaconda RRID:SCR_008394 https://www. anaconda. com Software, algorithm LabView National Instruments RRID:SCR_014325 http://www. ni. com/engb/shop/labview. html Software, algorithm Prism GraphPad RRID:SCR_002798 https://www. graphpad. com/ scientific-software/prism/ Data availability All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all figures.