dataesr/scientific-paragraphs-categorization huggingface dataset:
High scores in these scales suggest a psychological profile of impulsiveness and boredom proneness. Data Collection and Processing. Materials: Motion of the head and of the dominant arm was recorded by means of a motion capture system (Optitrack device). Ten cameras were used to capture the movement of five retro reflective markers (15 mm in diameter) at a sampling frequency of 250 Hz. Markers were placed at well-defined anatomical locations on the moving arm and head, namely the acromial process, the humeral lateral condyle, the apex of the index finger, and the left and right sides of the frontal bone. Data analysis: All the analyses were performed with custom software written in Matlab (Mathworks, Natick, MA) from the recorded 3D positions of markers. Recorded signals were low-pass filtered using a digital fifth-order Butterworth filter at a cutoff frequency of 10 Hz (Matlab butter/filtfilt functions).
Cartesian velocity profiles were computed via numerical differentiation. Movement duration was determined by fitting experimental velocity profiles to minimum jerk velocity profiles  51  as they are known to capture very well the bell-shaped nature of speed profiles in such planar arm reaching movements. The jerk velocity profiles depended on 3 parameters controlling the peak velocity (a), its onset and final times (t 0 and t f ), and were expressed as follows (outside the interval [t 0 , t f ] velocity was set to zero): a t t f f f , , 0 3 0 4 0 4 0 5 0 2 0 3 f 0 =      - - - - - - - -     . (4) To do so, a global optimization using Matlab's patternsearch function was carried out and the parameters of the best-fitting minimum jerk problem were identified, i. e. movement start, end and its total duration (see  52  for a similar approach).
A mathematical analysis shows that it is possible to compute the value g(T) by solving the associated optimal control problem in fixed time T which only minimizes the effort cost L(T). Movement duration T and the initial/final states of the system can be taken from experimental data or fits of the experimental data to reduce the effect of sensorimotor noise (which makes sense since we reason at the motor planning level). Once this is done, we can compute the value g(T) as the partial time derivative of the value function of the associated fixed-time optimal control problem (i. e. without time cost) or, equivalently, as the value of the maximized Hamiltonian for that problem  53. Details for the calculation of g(T) are provided as Supplementary Information text where an example of a Matlab code is also provided.
Acknowledgements This work is supported by a public grant overseen by the  French National research Agency (ANR)  as part of the  ≪Investissement d' Avenir≫ program , through the  iCODE  project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. We thank  Jérémie Gaveau  and  Ioannis Delis  for their comments on previous versions of the article.
Statistical Analysis SPSS 13. 0 software was used for the statistical processing of our data. Quantitative data were expressed in mean ± standard deviation (SD) or median and interquartile range. Categorical variables were expressed as numbers and percentages. Data were tested for normal distribution by graphical methods. Pre and post intervention quantitative variables of normal distribution were compared using paired-t test (UPDRS III, Dyskinesia scores, Motor Fluctuations scores, PDQ39 SI, Bradykinesia subscale of UPDRS III) and pre and post intervention quantitative variables of non normal distribution (UPDRS I, UPDRS II, LEDD, PDQ 39 subscales, speech, tremor, rigidity, posture, and postural instability subscales of UPDRS III) and ordinal variables (UPDRS V and VI) were analyzed using Wilcoxon test.
All constructs were verified by sequencing (Eurofins). A cDNA encoding human actin (GenBank accession number NM_001101) was PCR-amplified and inserted into the pEGFP-C1 vector. The plasmid encoding mCherry was from Clontech (pmCherry vector) and the actin filament marker LifeAct-RFP plasmid described in ref. 21 A cDNA encoding rat MAP6-N (accession number NM_017204) was PCR-amplified and inserted into pEGFP-N1. Rat MAP6-N fragments aa 1-614 (MAP6-E) and aa 221-455 (Mc), rat MAP6-E deletions of aa 124-138 (replaced by Ala) + aa 162-171 + aa 481-495 (MAP6-E-ΔMn) and aa 225-450 (MAP6-E-ΔMc), rat MAP6-E point mutations S139A + S198A + T484 A + S537A (MAP6-E-4A) and S139E + S198E + T484E + S537E (MAP6-E-4E), according to the numbering of rat MAP6-N (NP_058900), were created by PCR and cloned into pEGFP-N1 (except into pEGFP-C1 for Mc).
Rat MAP6-N fragments aa 359-451 (R4R5) and aa 404-451 (R5) according to the numbering of rat MAP6-N (NP_058900) was created by PCR and cloned into pEGFP-C1. DsRed2 cDNA was amplified by PCR from pDsRed2-C1 plasmid (Clontech) and inserted in place of GFP in the GFP fusions of the above described rat MAP6 fragments MAP6-E, Mc and MAP6-E ΔMn. A cDNA encoding human MAP6-E (accession number AB527183) was PCR-amplified and inserted into pEGFP-C1. Human MAP6-E fragment aa 224-280 (hMc), according to the numbering of human MAP6-E (AB527183) was created by PCR and cloned into pEGFP-N1. The lentiviral plasmid encoding eGFP was from Addgene (pWPXLd vector, kind gift from D. Trono). A cDNA encoding rat MAP6-N fragment aa 221-235 (5R), according to the numbering of rat MAP6-N (NP_058900), was created by PCR and cloned into pWPXLd.
Secondary antibodies were coupled to Alexa-488, to Cy3 or to Cy5 (Jackson Immuno-Research Laboratories). TRITC-Phalloidin (Sigma Aldrich, Inc. Cat# P1951) was used to label actin filaments. Fluorescent images were acquired with a Zeiss LSM 710 confocal microscope using a ×40 and ×63 oilimmersion objective (NA 1. 25 and NA 1. 4) and ZEN 2010 software (Carl Zeiss MicroImaging). Imaging of dendritic spines. For fixed samples, images of dendritic segments of Thy1-eYFP-H mouse cortical neurons (serial sections of cortex) or 18 DIV transfected neurons visualised by soluble GFP or soluble mCherry fluorescence were obtained using a confocal laser scanning microscope (Zeiss, LSM 710). Serial optical sections (1024 × 1024 pixels) with pixel dimensions of 0. 083 × 0. 083 μm were collected at 200 nm intervals, using a ×63 oil-immersion objective (NA 1.
4) and ZEN 2010 software (Carl Zeiss MicroImaging). Images were enhanced for small structures with the LoG3D ImageJ plugin 68 using a 2 pixel radius, and thresholded to create a mask for: GFP (transfection and contour marker), Synaptophysin (presynpatic compartment) and PSD-95 (post-synpatic compartment). The mask corresponding to the overlap of the three markers was superposed to the GFP image. We manually counted the synaptic puncta present in dendritic spines or in the shaft and calculated their linear density. Mask creation and counting were done blind to the transduction condition. Electrophysiological recordings in neuronal culture. Hippocampal neurons in culture were visualised in a chamber on an upright microscope with transmitted illumination and continuously perfused at 2 ml/min with oxygenated Artificial Cerebro-Spinal Fluid (ACSF in mM: 119 NaCl, 2.
5 KCl, 1. 25 NaH 2 PO 4 , 1. 3 MgSO 4 , 2. 5 CaCl 2 , 26 NaHCO 3 , 0. 001 tetrodotoxin, 0. 05 bicuculline methiodide and 11 Glucose) at room temperature. Miniature excitatory post-synaptic currents (mEPSC) were recorded at a membrane potential of -60 mV with borosilicate glass pipettes of 4-5 MΩ resistance filled for whole-cell recordings (in mM: 117. 5 CsMeSO 4 , 15. 5 CsCl, 10 TEACl, 8 NaCl, 10 HEPES, 0. 25 EGTA, 4 MgATP, 0. 3 NaGTP, pH 7. 3). Signals were amplified with an EPC 10 Amplifier (HEKA Elektronik Dr. Schulze GmbH, Wiesenstrasse, Germany). Recordings were filtered at 1 kHz and sampled at 10 kHz using the Patchmaster Multi-channel data acquisition software (HEKA Elektronik Dr. Schulze GmbH, Wiesenstrasse, Germany). Fluorescence resonance energy transfer-acceptor bleaching. Fluorescence resonance energy transfer with acceptor bleaching (FRET-AB)  34  was performed on 2 DIV or 18 DIV neurons transfected with GFP-tagged constructs (FRET donor) and stained for actin with TRITC-phalloidin (FRET acceptor).
Fluorescent signal analysis was performed with the Nikon software Nis. All the post-bleach values were divided by the values from the non bleached area of the cells and normalised to the first 10 pre-bleach values. The first post-bleach measurement was set to zero. Raw fluorescence values were normalised by dividing by the values at each time point by the average value before bleaching. The analysis of the FRAP recovery data was performed with GraphPad Prism. For each FRAP assay, the plateau value (y 0 ), mobile fraction (A), first-order rate constant (k) and characteristic time (k -1 ) were calculated by non-linear regression, fitting the data to a mono-exponential recovery model (y = y 0 -A*exp(-k*x)). Goodness of fit was not improved when using a two-phase recovery model, therefore we retained the simpler monophasic curve.
7. 5, Molecular Devices). Samples were excited with 491 nm laser light and timelapse imaging (at 488 nm) was performed at 26 °C for actin polymerisation, during 45 min at 1 frame per 5 s with a 100-ms exposure time. The elongation rate of actin filaments polymerisation with or without Mc modules was determined on kymographs using ImageJ software and a home-made plugin (KymoTool). Elongation of single actin filaments was derived from actin length measurements on image stacks. To analyse actin nucleation, 300 nM actin (labelled as above), alone or together with 300 nM Mc modules was incubated in AP buffer containing 4 mM DTT, 1% BSA, 1 mg/ml glucose, 70 µg/ml catalase, 580 µg/ml glucose oxidase and 0. 3% methylcellulose in the perfusion chamber for 5 min at 26 °C and imaged. TIRF images were skeletonised and the number of filaments and the length of total actin network were measured.
To analyse bundling activity, 300 nM actin alone or with 150 or 300 nM Mc modules was treated as before and images were taken after 45 min. TIRF images of actin filaments were enhanced using Feature detector 69 module of Icy  70. Actin network and bundles were segmented with Fiji 71 using Otsu or Yen thresholding algorithms, respectively. The thresholded images were skeletonised and the total length of actin network or bundles was measured. The percentage of actin bundling was calculated by dividing the length of bundled actin by the total length of filamentous actin in the same field. High-speed and low-speed co-sedimentation assays. High-and low-speed cosedimentation assays were used to examine actin polymerisation and actin bundling, respectively  11. All proteins were pre-clarified at 140,000×g before each experiment.
All supernatants and pellets were resolved by SDS-PAGE and detected by Stain-Free technology in the gel (Mini-PROTEAN® TGX Stain-Free™, Biorad) (Supplementary Fig. 4 ). Protein bands were quantified from triplicate blots of independent experiments using ImageJ software (National Institutes of Health, Bethesda, MD). In post-polymerisation assays: 2 µM G-actin was incubated with 2 µM Phalloidin in AP buffer for 1 h at RT. Then, Mc modules (1-5 µM) was added and incubated for 1 h at RT. For negative staining, 1 µl of protein solution was loaded onto a carbon-mica interface. The carbon layer was floated on 2% uranyl acetate, recovered with a 400mesh copper grid (Agar Scientific), air dried, and observed with a JEOL 1200EX transmission electron microscope at 80 kV. Images were acquired with a digital camera (Veleta, Olympus) at ×100,000 magnification.
Experiments were performed at the Electron Microscopy Facility of the Grenoble Institute of Neuroscience. To detect the periodic striations of Mc-decorated single and bundled filaments, a line was traced between the edges of each filament (outside or inside the bundle), staining intensity along the line was plotted, and the distances between successive intensity minima were measured. Each value corresponds to the mean distance between successive striations of the same filament. Images of independent experiments were analysed. Actin depolymerisation by buffer dilution. To analyse actin depolymerisation, we first incubated 2 µM G-actin with 2 µM Phalloidin in AP buffer for 1 h at RT. Then, 0. Statistical analysis. All data are presented as mean ± SEM. Statistical significance of differences between conditions was calculated with Prism 5.
5 to 1 µM of Mc modules was added and incubated for 1 h at RT. G-actin was diluted from 2 to 0. 1 µM in AP buffer (alone or with 0. 5 or 1 µM of Mc modules) for 20 min, centrifuged at 100,000×g and pellets were separated on SDS-PAGE and detected by Stain-Free technology in the gel (Mini-PROTEAN® TGX Stain-Free™, Biorad). Protein bands were quantified from triplicate blots of independent experiments using ImageJ software (National Institutes of Health, Bethesda, MD). The remaining actin filaments were calculated as a percentage of the input. Kinetic depolymerisation of 2 µM of 40% pyrene-labelled actin, alone or in the presence of 0. 5 or 1 µM of Mc modules, was monitored after dilution to 0. 1 µM actin in AP buffer (alone or with 0. 5 or 1 µM of Mc modules).
Acknowledgements We are grateful to zootechnicians of the  Grenoble Institute of Neurosciences ,  S. Andrieu ,  M. Lapierre ,  F. Mehr  and  F. Rimet  for animal care. We thank  N. Collomb ,  C. Corrao ,  F. Vossier  and  C. Paoli  for technical assistance;  T. Rush  and  M. Seggio  for introducing L. P. to FRAP, TIRF microscopy;  Ju Brocard  and  G. Falivelli  for helping with virus production and  Fondation Bettencourt Schueller  for equipment. This work was supported by  INSERM ,  CEA ,  Université Grenoble Alpes  and in part by awards from the  French Agence Nationale de la Recherche  to A. A. (2010-Blanc-120201 CBioS); to A. B. , A. A. , I. A. ( 2011-MALZ-001-0217  MALAAD) and to A. A. , I. A. ( 2017-CE11-0026  MAMAs). This work was supported by the  Photonic Imaging Center of Grenoble Institute Neuroscience (Univ Grenoble Alpes -Inserm   U1216 ) which is part of the  ISdV core facility  and certified by the  IBiSA label.
Objective: To investigate the relationship between brain volume and disability worsening over ≥3 years in the natural history of primary progressive multiple sclerosis using data from the placebo group of the INFORMS trial (n = 487; clinicaltrials. gov NCT00731692). Methods: Magnetic resonance imaging scans were collected annually. Brain volume loss was determined using SIENA. Patients were stratified by baseline normalized brain volume after adjusting for demographic and disease-burden covariates. Results: Baseline normalized brain volume was predictive of disability worsening: Risk of 3-month confirmed disability progression was reduced by 36% for high versus low baseline normalized brain volume (Cox's model hazard ratio 0. 64, P = 0. 0339; log-rank test: P = 0. 0297). Moreover, on-study brain volume loss was significantly associated with disability worsening (P = 0.
Magnetic resonance imaging Magnetic resonance imaging (MRI) scans were collected at baseline and annually thereafter and were analyzed at a central facility (Queen Square MS Centre, University College London Institute of Neurology, London, UK). The analyses reported here used MRI data processed and presented in the original report of the trial. 1  Lesions were identified by visual review of scans by trained readers. T2 lesion volumes were measured on 2D proton densityweighted fast/turbo spin echo images using a semiautomated contouring tool within JIM image analysis software. Normalized brain volume and percent brain volume change were derived using SIENAX and SIENA methods, respectively, applied to 2D T1-weighted images. 15.
Prespecified statistical analyses of normalized brain volume and on-study disability worsening The following analyses were defined prior to the unblinding of the INFORMS database. Results for the placebo group are reported. parameters, similar to a model previously used in RMS. 6 he parameters tested were previously identified as significant predictors of brain volume in studies of patients with RMS. 5  The results of this statistical model were incorporated into the model that was used to predict brain volume in patients with PPMS: Model-predicted volume ¼ a þ b Â T2 lesion volume þ c Â EDSS þ d Â AGE þ e Â PPMS duration þ f Â male sex where a = model intercept; b = coefficient for baseline T2 volume; c = coefficient for EDSS; d = coefficient for age; e = coefficient for duration of PPMS; and f = coefficient for gender.
The results suggest that brain atrophy and on-study disability progression in PPMS patients in INFORMS were to some extent explained by lesion activity which had occurred prior to study entry. A key feature of this study is the insight it provides into the natural history of PPMS based on a large placebo cohort, albeit in a selected population from a randomized controlled trial. Fingolimod did not slow disability worsening in INFORMS, but it did significantly reduce inflammatory activity by 73% for number of new or newly enlarging T2 lesions and by 78% for number of Gd+ lesions, as previously reported. 1 Because the benefit/risk of fingolimod in the INFORMS study did not support the development of fingolimod in PPMS, we see no need to further explore its effects. However, a post hoc pooled analysis on the entire INFORMS dataset (fingolimod and placebo) showed essentially similar results as reported here for on-study brain volume loss and disability worsening.
Acknowledgments Novartis Pharma AG (Basel, Switzerland ) supported this study. Central MRI analysis was carried out at the  Queen Square MS Centre ,  UCL Institute of Neurology (London, UK ). Members of the central MRI analysis team were as follows:  D. G. MacManus ,  T. A. Yousry ,  C. A. M. Gandini Wheeler-Kingshott ,  € O. Yaldizli ,  J. Stutters ,  C. M. Dalton ,  V. Santana ,  A. Garcia-Gomez ,  C. Crespo ,  D. H. Miller. The  Queen Square MS Centre  is supported by the  UK MS Society  and the  UCL-UCLH joint Biomedical Research Centre. We thank the patients who participated in the study; the study-site personnel;  Ana de Vera ,  Goeril Karlsson  and the Novartis clinical team; and  Katy Demery (Novartis)  and  Matt Lewis  ( Ed4Med Ltd ) for medical writing assistance funded by  Novartis.
Recruitment of participants Participants were recruited as part of the BIOSCA study (NCT01470729)  (Garali et al. , 2017)  and only those who could perform MRI and complete the follow-up imaging protocol are reported: 15 patients with SCA1, 12 patients with SCA2, 20 patients with SCA3 and 10 patients with SCA7. Twenty-four healthy individuals of similar general characteristicsmedian age, sex and BMIwere also enrolled (Table  1 ). The general characteristics of patients and controls were matched to reduce the problems of confounding factors. Subjects were excluded from the study if they were < 18 years, were unable to perform MRI, had history of significant head injury, could not complete the study protocol or unable to comprehend an informed explanation of the study protocol. Ataxia severity was evaluated with the SARA score, which ranges from 0 (no cerebellar symptoms) to 40 (most severe cerebellar symptoms)  (Schmitz-Hübsch et al.
Imaging protocol MRI acquisitions were performed on a 3 T whole-body Siemens MAGNETOM Trio scanner (Siemens Medical Solutions, Erlangen, Germany) using a standard Siemens transmit body coil and 32-channel receive head coil array. The MRI system was upgraded during the study period and hence twelve datasets were acquired on a 3 T whole-body Siemens MAGNETOM Prisma scanner (Siemens Medical Solutions, Erlangen, Germany). Several phantom and volunteer scans were performed after the change to ensure that there was no difference in the data obtained on the new system. A 3D T1-weighted magnetizationprepared rapid gradient-echo (MPRAGE) volumetric image (T R = 2530 ms, T E = 3. 65 ms, T I = 900 ms, flip angle = 9 o , voxel size = 1 mm isotropic, field of view (FOV) = 256 × 256 mm 2 ) was acquired at baseline and follow-up for volumetric analyses.
Images were inspected immediately after acquisition and those with motion artifacts were reacquired immediately. Additionally, a T2-weighted sagittal image (T R = 4200 ms, T E = 80 ms, flip angle = 120 o , voxel size = 0. 6 × 0. 5 × 5. 0 mm, FOV = 240 × 240 mm 2 ) was acquired for each subject. The T2-weighted images were not included in volumetric analyses but were reviewed by a neurologist to rule out incidental brain abnormalities that could interfere with results. Due to method development, diffusion imaging was only performed at follow-up. Diffusion data were acquired along 60 isotropic directions with echo-planar spin-echo sequence (T R = 10,000 ms, T E = 89 ms, voxel size = 2 mm isotropic, FOV = 220 × 220 mm 2 , GRAPPA acceleration factor of 2, b value = 1500 s/mm 2 ). The directions were interleaved with five non-diffusion-weighted reference images (b0 images, b value = 0 s/mm 2 ) at every 12 directions.
Another b0 image with opposite phase-encode blip was acquired. The data were corrected for motion, eddy current geometric distortions, and susceptibility-induced off-resonance field distortions. Data are presented as mean ± standard deviation. † p ≤ 0. 001 and # p ≤ 0. 01 represent significant differences between SCA and controls obtained with the analysis of covariance, controlling for age, and with the step-down Bonferonni multiple correction. There was no difference in demographic parameters between the SCA. a Parameters at baseline. b parameters at follow-up. 0. 9 ± 0. 1 [0. 8-1. 0] 1. 0 ± 0. 1 † [0. 9-1. 3] 1. 1 ± 0. 1 † [0. 9-1. 4] 1. 0 ± 0. 1 † [0. 9-1. 2] 1. 0 ± 0. 2 † [0. 8-1. 3] CCFS b 0. 8 ± 0. 0 [0. 8-0. 9] 1. 1 ± 0. 2 † [0. 9-1. 3] 1. 1 ± 0. 2 † [0. 9-1. 5] 1. 1 ± 0. 1 † [0. 9-1.
Volumetric analyses The implementation of brain volumetry markers in large trials necessitates reproducible and reliable measurement processes. This suggests the use of automated segmentation while the gold standard in SCA evaluation remains the manual segmentation. Two strategies of segmentation were thus used and compared: manual segmentationthe gold standardperformed by one expert using ITK-SNAP  (Yushkevich et al. , 2006) , and an automated segmentation using the Freesurfer software  (Fischl et al. , 2002). Since manual segmentation is highly time-consuming, we chose to manually segment only the cerebellar vermis, the region mostly affected in this SCA and from which neurometabolic alterations were previously reported  (Adanyeguh et al. , 2015). After linearly aligning the image along the midline, the vermis lobules were delineated on the mid-sagittal slice, excluding the cerebellar tonsils (Supplementary eFig.
Diffusion tensor imaging Diffusion data with extensive artifacts were excluded after inspection. Diffusion data were not acquired for two patients with SCA1, two patients with SCA3, three patients with SCA7 and two healthy controls. Few datasets did not meet the quality control criteria and had to be rejected for one patient with SCA1, two patients with SCA2, two patients with SCA3 and one healthy control. Data were denoised  (Veraart et al. , 2016)  to improve the signal-to-noise ratio (SNR). Topup  (Andersson et al. , 2003)  and eddy  (Andersson and Sotiropoulos, 2016)  were applied to correct eddy current, motion and susceptibility-induced off-resonance field distortions. The diffusion tensor model was fitted to generate FA and radial diffusivity (RD) maps. We did not focus on axial diffusivity maps since they do not always match the underlying fiber tract direction  (O'Donnell and Westin, 2011).