dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  Fondation pour la Recherche Medicale DEQ20160334907-France , by the  National Agency for Research   ANR-17-CE16-0019-03  (LRR), by the  CNRS  and  Aix-Marseille Universite  ´through  UMR 7289  (PC). This work also received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR , with the references,  PER-SU (LRR)  and  ANR-10-LABX-BioPsy (LRR). The group of LRR is member of the  Labex BioPsy and ENP Foundation. Labex are supported by  French State  funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. We thank  Roxanna Ureta  for help with histology,  Lilith Sommer  for help with behavioral experiments,  Gregory Sedes  and  Nadine Francis  for help developing analysis codes, and  Richard Apps  for his insightful comments.
Peroxydase activity was detected with a Fusion TX7 camera system (Fisher Scientific). Quantification was made by densitometric analysis with the Quantity One 1D image analysis software (version 4. 4; Biorad, Hercules, CA, USA). The optical densities were normalized with respect to a "standard protein" (GAPDH/actin). For each sample, a partition ratio was calculated and normalized with respect to the sample with the highest value defined as 1.
Quantitative analysis of ataxin-3 and ubiquitin-positive aggregates and DARPP-32 depletion in immunohistochemically labeled brain slices from the LV-based model of SCA3 Brain slices were analyzed and imaged at RT with an Axiovert 200 M microscope (CCD color digital Axiocam HRc camera; Carl Zeiss), at 5× (air objective, Fluar, 0. 25 NA) and 20× magnifications (air objective, LD-PlanNeofluar, 0. 4 NA). Composite images of complete aggregate-containing or DARPP-32-depleted regions were automatically acquired using the MozaiX function of the AxioVision software. Photographs for comparison were taken under identical conditions of image acquisition, and all adjustments of brightness and contrast were applied uniformly to all images. Quantification of ataxin-3/ubiquitin-positive aggregates and determination of the area of the DARPP-32 depletion was made in 7-8 slices for each mouse, spread over the anterior-posterior extent of the striatum and separated by 200 µm.
Counting of ataxin-3/ubiquitin-positive aggregates was performed on 20×-magnified images for each hemisphere using ImageJ (National Institutes of Health). After manually thresholding images so that the recognizable ataxin-3-positive accumulations were included in the examination, particles > 3 µm 2 were automatically analyzed and counted (scale: 2 pixels/µm), and the estimative calculation of the total number of aggregates in the entire striatum was performed as previously described  [2, 3]. The extent of mutant ataxin-3-induced lesions in the striatum was analyzed by digitizing 6-8 DARPP-32-stained sections per animal (200 µm between 25 µm thickness sections), selected to obtain a complete rostro-caudal sampling of the striatum, with a slide scanner and by quantifying the area of the lesion with a semiautomated image-analysis program (ImageJ software, NIH, MD, USA), to manually define the regions with decreased antibody reactivity and quantify their area (scale: 0.
The number of Purkinje cells was manually counted for each section and for each cerebellar lobule, in a blind fashion. Data are represented as mean value of the number of Purkinje cells in the entire cerebellum or per lobule. The number of ubiquitin-positive aggregates was assessed in lobules IX and X automatically using the Cell Profiler software, from images of eight sagittal sections covering the entire cerebellum, stained with an anti-ubiquitin antibody and DAPI. Images were acquired with a Zeiss Axio Imager Z2 microscope using a 20× objective. The analysis modules applied were the same for all the animals and images. Data are represented as the average number of ubiquitin aggregates per section.
04) in SCA3 patients compared to controls (Fig. 1a, b ). Interestingly, we did not find major alterations in CYP46A1 levels in SCA3 patient 3, which also had increased levels of calbindin relatively to the remaining SCA3 patients. These data suggest that CYP46A1 levels may be correlated with the expression of neuronal markers (Student's t test; P = 0. 04). Of note, immunohistochemistry revealed that CYP46A1 levels were increased in the soma of Purkinje cells (PCs) from control individuals relatively to PCs from SCA3 patients, which presented ataxin-3-positive deposits in the nucleus, absent in control PCs (Fig. 1c ). We next evaluated the levels of CYP46A1 in brain samples from six SCA3/MJD transgenic mice expressing the N-terminal truncated human ataxin-3 with 69 CAG repeats, and six age-matched control littermates by western blot.
In particular, we analyzed CYP46A1 protein levels in the cerebellum, since in this mouse model mutant human ataxin-3 is mostly expressed in PCs  [53]  (Fig. 1d ). Results demonstrated a ~ 20% reduction in CYP46A1 protein levels (Student's t test; P = 0. 0013) in whole cerebellar extracts from SCA3 Q69 mice (n = 6) relatively to controls (n = 6) (Fig. 1d, e ), while the levels of the neuronal markers NSE and NeuN remained unchanged (data not shown). Nevertheless, a statistical significant decrease in the levels of calbindin protein, mostly present in PCs (Fig. 1d, f ) was observed. Immunofluorescence studies also revealed decreased CYP46A1 immunoreactivity in cerebellar sections from SCA3 Q69 mice compared to wild-type littermates (Suppl Fig. 1 ; Online Resource 3). To determine the impact of CYP46A1 deficiency in SCA3, we assessed cholesterol metabolism by performing quantitative measurements of cholesterol and 24S-OHC by gas chromatography-mass spectrometry (GC-MS) analysis of cerebellar extracts from SCA3 Q69 mice (n = 6) and age-matched littermates (n = 6).
| Histological studies Pancreases were either fixed and frozen in OCT, or fixed in paraformaldehyde. Sections were immunostained with antibodies against HIF1-α, and slides were then mounted with a media containing 1. 5 µg/mL of 2-(4-amidinophenyl)-1H-indole-6-carboxamidine (DAPI; FluorSave Reagent, Merck Millipore) and observed using a Nikon Eclipse 50i microscope and Nis-Elements-BR software. HIF staining was quantified on five sections of each pancreas using Nis-Elements-Br software. Results are expressed as intensity of staining relative to the total surface area. For ROS staining, pancreases were sectioned using a cryostat, distributed onto glass slides and preserved at -80°C. Total ROS level was assessed by dihydroethidium (DHE) staining for 30 minutes. Fluorescence was quantified using Nis-Elements-Br software, and results were expressed as a ratio with respect to the control.
cal help and advice. The authors are indebted to Prof. Wolf and Prof. Bachelier, the local coordination and retrieval team, and the French organ procurement agency (Agence de Biomédecine) for human pancreas procurement. The authors disclose the receipt of the following financial support for the research, authorship and/or publication of this article: this work was funded by CEED and a grant for PhD student from Société Francophone de Transplantation. F I G U R E 5 Human pancreas and human islet preservation are improved by the use of M101 for pancreas preservation. M101 was injected into the pancreas and in the preservation solution for 3 h. The impact of the molecule was observed both on the pancreatic tissue and islets after isolation. A, HIF1-α level was assessed by ELISA in pancreatic tissue after 1 h and 3 h of cold ischaemia.
B. Determination of the Mutual Information We performed the differential evolution optimisation of the models using the DEoptim R package  [31] ,  [32]. To find the binding constants, such that the mutual information between protein states and Ca 2+ signals takes a maximal value, we adopted the following procedure: 1) Define a Binding Model: Each protein is characterised by a set of binding constants (K i , with i = 1, 2, 3, 4 for proteins with four binding sites), whose values we optimise within certain bounds that depend on the binding model. In the strongly cooperative case considered in Section III, Eq. 2, we have K 1,3 = 0, K 2 = 1/K 2 d2 and K 4 = 1/K 4 d4 , where K d2,d4 are dissociation constants. The proportions of the two Ca 2+ bound and four Ca 2+ bound states are calculated from Eq.
By clamping the monolayer to 0-mV, we obtained the I sc (in μA/cm 2 ) which reflects the sum of the active ionic fluxes across the monolayer and thus the net result of its absorptive and secretory capacity. Mitochondrial metabolism. 8 × 10 4 Caco-2 cells were seeded in 25 cm 2 flasks and grown 7 days before recovery by trypsine (0. 25 g/L) in phosphate-buffered saline (PBS) containing 1 g/L EDTA. Approximatively 5 × 10 6 cells in 2 mL of an air saturated respiration medium (in mM: 20 Hepes, 200 mannitol, 5 KH 2 PO 4 , 2. 5 MgCl 2 , and 0. 5 EGTA, pH 7. 4, enriched with 0. 1% bovine serum albumin) were placed in the oxygraph chamber for oxygen consumption measurement rate. Cell respiration was recorded in real time with an "O2k"Oroboros apparatus (Innsbruck, Austria) at 37 °C. Oxygen consumption rates were obtained directly from the Datalab 4 software and were calculated as the negative time derivative of oxygen concentration in the closed respirometry chamber.
Statistical analysis. Values are expressed as means ± standard errors of the mean (s. e. m. ). The effect of treatment was analyzed with GraphPad Prism 8. 1. 1 software using a two-way analysis of variance (osmotic stress (50% or 100% hyperosmotic media) and time (2-h or 24-h treatment) or cell stage (proliferative-undifferentiated or differentiated)). Bonferroni's posthoc tests were used for pairwise comparison. Differences were considered statistically significant at P < 0. 05.
5× and 20× objectives were used to capture images and videos. Scanning electron microscope (SEM) AURIGA CrossBeam workstation (Carl Zeiss, Germany) was used for high resolution morphology characterization of FF MTs. To prevent the MTs charging under the action of electron beam the samples were coated with a 10 nm Cr layer using magnetron-sputtering (Quorum, Q150T). Single-crystal X-ray diffraction data were collected at the ID27 beamline on ESRF (Grenoble, France) using the radiation wavelength of 0. 3738Å and MAR detector. Obtained crystallographic data for D-FF microtubes reported in this paper have been deposited in the Cambridge Crystallographic Data Centre no. CCDC 1853771. Measurements were performed at 140 K. 8153 reflections were measured (3. 098° ≤ 2Θ ≤ 24. 542°), 2626 unique of them (R int = 0.
1359, R sigma = 0. 1451) were used in all calculations. ShelXT 42 software was used for structure solution by Intrinsic Phasing method. Structure was refined by full-matrix least-squares methods on F 2 using ShelXL, 43 Olex2 44 was used for solution and refinement structure as GUI. All nonhydrogen atoms were refined anisotropically. All water molecules inside the nanochannel were masking by SQUEEZE tool of Platon. The final R 1 was 0. 0881 (I > 2σ(I)), wR 2 was 0. 2354 (all data), and the goodness-of-fit was 0. 99. Mercury 3. 10  45  was used to visualize and analyze the crystal structure.
ACS Paragon Plus Environment Crystal Growth & Design 10 Computer simulation. Total energies of L-FF and D-FF rings (each ring represents one unit cell of NT and consists of 6 monomeric units) taking into account covalent and noncovalent interactions (van der Waals, electrostatic and hydrogen bonding) were calculated using HyperChem software  46. A semi-empirical approach PM3 including restricted Hartree-Fock approximation was used for analysis of individual rings (similarly to the method used in Refs. 41,47,48). Because of a large number of atoms contained in two rings, their interaction energy was calculated by means of molecular mechanics methods MM+, AMBER and BIOCHARM. 46 All these methods are suitable for the analysis of peptides, nucleic acids, proteins and macromolecules. 46.
Acoustic loudness profiles were determined for each stimulus type, i. e. , SINE, NBN, or PSHC, in random order. Analogous to the electric loudness profile, a given stimulus type was presented while monitoring subjects' loudness perception on the 10-point rating scale. Initial frequency-dependent sound pressure levels were based on pilot loudness-adjustment tests in NH subjects (n = 4, data not shown here) using the same acoustic stimuli and anchored on a 1-kHz pure tone at 65 dB SPL. Sound pressure levels were increased in steps of 1, 2, or 4 dB up to rating 7 and then decreased back in steps of 1 dB to the final rating 6. MCL sound pressure levels were determined for a given stimulus type starting at a (center) frequency of 1 kHz up to 4 kHz in half-octave steps, and then from 1 kHz down to 125 Hz in half-octave steps.
Between-subject pitch matching variability was compared with data acquired from pilot pitch matching tests in NH subjects (n = 10, data not shown here) using the same procedure and acoustic stimuli (NBN and PSHC) to test the effect of spectral slope on matching accuracy, with the standard fixed at 36 dB per octave and the comparison at 24, 36, or 48 dB per octave. Key differences in the NH experiment were that (i) standard and comparison stimuli were presented to contralateral ears, with the order randomized between subjects, (ii) the pitch matching range was up to 8 kHz compared with 4 kHz for SSD subjects, and (iii) NH subjects were also tested using the standard frequency of 4 kHz.
Materials Electric stimuli were directly transmitted to the CI using the Research Interface Box II (RIB, University of Innsbruck, Innsbruck, Austria). Acoustic stimuli were presented using a D/A converter and amplifier (24 bit, 48 kHz sampling rate, RME Fireface UC, Haimhausen, Germany), and audiometric headphones (Sennheiser HDA 200, Wedemark, Germany). All experimental procedures and graphical user interfaces were programmed in MATLAB (MathWorks, Natick, MA, United States) using the RIB library and Psychophysics Toolbox extensions  (Brainard, 1997)  to respectively drive the electric and acoustic stimulation hardware.
Analysis Data are generally presented as geometric mean, or plotted as boxplots with mean values included as circles. Within-subject comparisons were calculated using repeatedmeasures analyses of variance (ANOVA) and Tukey's LSD post-hoc test. Linear correlation between measures was tested using Pearson's r. A p-value of less than 0. 05 was considered statistically significant. Statistical analysis was performed using SPSS Statistics (IBM Corporation, Endicott, NY, United States). In addition to analyzing electrode positions using their order on the electrode carrier, angles of insertion were estimated using postoperative X-ray images acquired with the modified Stenvers' projection (cochlear view,  Xu et al. , 2000) , which has been described and illustrated in previous studies  (Verbist et al.
(2016)  at a fixed rate of 800 pps or at the place-dependent rate. While these data were not all collected on the same day and are, therefore, not directly comparable, it is worth noting that the binary search procedure could yield a much smaller variance than the adjustment procedure. This was confirmed by a two-way repeated-measures ANOVA with   et al. , 2016). Pitch matches were collected using either the binary search procedure (adaptive, from the current study) at a fixed rate of 800 pps, or the method of adjustment (adjustable, from the previous study as denoted by * ) at a fixed rate of 800 pps or at the place-dependent rate. Two-way repeated-measures ANOVA showed a significant effect of matching method, with the binary search procedure significantly lower than the method of adjustment at either rate.
system from the location of four coils placed over frontal and mastoïdian skull areas. We adapted Marti et al. 's  (22)  preprocessing pipeline. First, we identified bad MEG channels visually in the raw signal and then employed MaxFilter software (ElektaNeuromag) to compensate for head movements between experimental blocks, suppress magnetic interference from outside the sensor helmet, and interpolate bad channels  (41). We then switched to Fieldtrip for further preprocessing  (42). Continuous data were first epoched with respect to target onset (i. e. , -0. 5 to 3. 5 s). The resulting trials were downsampled to 250 Hz and any artifacted epoch was removed by means of a semiautomatic procedure: We visually inspected variance of the MEG signals to identify and reject contaminated epochs. In a last step, we performed independent component analysis separately for each channel type to remove any residual artifacts related to eye movements or cardiac activity.
Depending on the nature of the subsequent investigation, further preprocessing steps then diverged. For univariate analyses based on evoked responses (i. e. , ERFs), we only low-pass-filtered the MEG signal at 30 Hz. However, to extract the spectral component of our data, we relied on unfiltered epochs: Power estimates between 1 and 99 Hz (in 2-Hz steps) were obtained by convolving overlapping segments of the data with a frequencyindependent Hann taper (window size: 500 ms, step size: 20 ms). Multivariate analysis required additional downsampling of the signal to 125 Hz. After all necessary transformations and decompositions, we applied a baseline correction before any analysis between -200 and 0 ms. Estimating Chance-Free Brain Activity for Unseen Correct Trials. To account for chance responding on unseen correct trials, we employed a strategy developed by Lamy et al.
Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. Our group is the second in the world to have integrated EEG and fMRI for NF, therefore this dataset is unique of its kind. We believe that it will be a valuable tool to 1. Advance and test methodologies to integrate complementary neuroimaging techniques (design and validation of methods of multi-modal data integration at various scales) 2.
The degradation of EEG SNR probably represents the main issue of simultaneous EEG-fMRI recordings and several de-noising solutions have been developed  14, 15. However, so far, the corresponding datasets have rarely been released for use by other groups. Moreover, for the implementation of NF, simultaneous EEG-fMRI acquisition, artefact correction and processing need to be executed in real-time, adding complexity and challenge to hardware and software solutions. To the best of our knowledge, simultaneous EEG-fMRI in a NF loop was implemented only by another research group to train emotional self-regulation  7  : the dataset we share and describe here is therefore unique of its kind. It consist of 64-channels EEG and fMRI simultaneously acquired during a motor imagery NF task, complemented by structural MRI scans.
Recordings were performed in two studies. In the first (XP1, Figure  1 ), 10 subjects performed bimodal and unimodal NF. The second study (XP2, Figure  2 ) involved 20 subjects performing bimodal NF using a bi-dimensional or mono-dimensional visual feedback. This dataset has potential to shed light on the coupling model underlying the EEG and fMRI signals, to advance methodologies for multimodal data integration and fusion techniques and test EEG de-noising algorithms. Since data were acquired during the execution of a simple task for whom patterns of neural pathways and activations are well known (motor imagery of the right hand), this dataset constitutes a simple model to develop and validate methods of data integration at various scales (activation maps, connectome). This dataset is also of great interest for the NF research field, as it gives new perspectives to improve the estimation of NF scores, or the extraction of features of interest.
Participants All experiments were performed according to the Helsinki declaration and under approval by the Institutional Review Board. Thirty right-handed NF-naïve healthy subjects were involved, of which 12 were female (mean age 33. 7 ± 9. 9 years old). All participants signed an informed consent after having been informed about the experimental procedure. Only 26 of them signed the consent to public their anonymized data, and were therefore included in the public datasets (Data Citation 1 and Data Citation 2).
Electrodes impedances values estimated at the beginning of each run can be found in the header of the respective EEG file, as explained in the Data Records session. The subjects was then installed in the MR tube and electrodes impedances verified again after installation. Foam pads were applied to minimize head motion and for the whole duration of the experiment the subjects were asked to lie still in the MR bore. A LCD screen (NordicNeurolab Solutions, Bergen, Norway) was placed at the back of the MR scanner, a rear-facing mirror fixed on the top of the head coil allowed the subjects to view the screen. For both studies, EEG recordings were sampled at 5 kHz with the reference electrode in FCz and ground in AFz. A 1mm isotropic 3D T1 MPRAGE structural scan was acquired. fMRI acquisition was performed by means of echo-planar imaging (EPI) whose settings changed from the first to the second study, as the repetition time (TR) could be further decreased thus improving the BOLD NF updating rate.
Real-Time Data processing and NF computation During simultaneous EEG-fMRI acquisition, two main types of artefacts critically compromise the quality of EEG recordings: the rapid switching of the magnetic field gradients generate the so-called "gradient artefact", while the heartbeat-induced movements of EEG electrodes generate the ballistocardiogram (BCG) artefact. Real-time correction of EEG artefact was performed using the Brain Vision Recview software. An average artefact subtraction approach  14  was used for gradient artefact correction, with four artefact templates. EEG data were then down-sampled to 200 Hz and filtered at 50 Hz (48 dB slope) for further processing. BCG artefact correction was then performed 15 using a moving template matching method with the following parameters: pulse period 800ms, correlation threshold 0.
7 and amplitude ratio between the period examined and the pulse model ranging from 0. 6 td 1. 2. The moving pulse template was built averaging the 10 previous detected pulsed. The corrected EEG data were then sent to the NF control unit for feature extraction via a fieldtrip (https://github. com/fieldtrip/fieldtrip/) buffer solution. Real-time fMRI pre-processing (slice-time and motion correction) was performed directly by the NF control unit using a custom Matlab script based on SPM 8 (FIL, Wellcome Trust Centre for Neuroimaging, UCL, London, UK).