dataesr/scientific-paragraphs-categorization huggingface dataset:
3-m step size) followed by deconvolution (constrained iterative method). Image analysis was performed such that genotypes were blinded to the observer during analysis. Image analysis was done using NIH software ImageJ software (Fiji, RRID:SCR_002285), according to the following procedure: for every image, the threshold for each channel was defined as two standard deviations above the mean. A region of interest (ROI) corresponding to the cell body was drawn manually and excluded from further analyses in all channels. From these images, colocalization analysis was performed with the RG2B plugin and regions of double colocalization more than two pixels in size were defined as particles. To calculate synapse density, the number of particles was divided by the length of dendrites measured using a binary mask of MAP2 labeling followed by the skeletonize function (minus the cell body).
COS7 cell spreading/contraction assay COS7 cells (RRID:CVCL_0224) were maintained in DMEM with 10% bovine calf serum, 2 mM L-glutamine, and 100 g/ml P/S. For transfection, 50,000 cells were seeded 1 d before transfection in 12-well plates. The following day, cells were transfected with a total of 2 g of DNA using lipofectamine 2000 (Invitrogen). DNA and lipofectamine were separately suspended in Opti-MEM media and then mixed. COS7 were washed in media without serum, and then the transection mix was added to the cells. Forty-eight hours after transfection, COS7 were trypsinized and reseeded onto glass coverslips (12 mm, Fisher Scientific), coated with laminin (0. 5 g/ml) for 2 h, at a density of 5000 cells. Cells were fixed in 4% PFA in PBS for 30 min at room temperature 24 h after seeding and then washed with PBS and incubated with rhodamine-tagged WGA (5 g/ml) in PBS for 10 min.
Coverslips were washed with PBS (3 ϫ 5 min) and then quickly with water before mounting with anti-fading mounting media (Dako). Imaging was performed using a 40ϫ objective on a Zeiss Axioskop fluorescent inverted microscope equipped with Xenon illumination, and images were captured using Zen software (Zeiss, RRID:SCR_014587). Cells too large to fit in one field of view and cells that appeared as bright rounded balls without any cytoplasm were excluded from analysis. The cell area of GFPexpressing cells was quantified using NIH ImageJ software (Fiji, RRID:SCR_002285) as follows. Images of rhodamine-tagged WGA membrane labeling were thresholded and made into mask and added as a ROI. ROIs were added to the GFP image and ROIs that were not GFP positive were deleted. The area of each cell (ROI) was determined using the measure tool.
This work was supported by  Canadian Institute of Health Research (CIHR)  Grants  37850  (to P. A. B. ) and  130239  (to P. S. ); the program " Investissements d'avenir"  Grant  ANR-10-IAIHU-06  (to S. B. );  Fonds de Recherche du Québec Santé  (FRQS) and McGill studentships (R. A. T. ); and  FRQS  (J. G. ). Acknowledgements: We thank  Robert Oda  for help in writing the macros used for ImageJ analysis.
Acknowledgments Research of the author is supported by the  Université Lyon 1 , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National de la Santé et de la Recherche Médicale  ( INS ERM ) and the  Association Française contre les Myopathies (AFM-Téléthon ; grant no. 18241 ). The author declares no competing interests. Eduardo Ríos served as editor.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research grants from the  Swiss Science Foundation  ( FNS 31003A_163465 ), the  Biaggi Foundation  and the  Juchum Foundation (LH) ;  eraNET neuron CNSaflame, eraNET neuron TRAINS  and,  ANR-TRAIL (JB).
However, the most complete datasets reported to date involve oculomotor control  (Yang and Lisberger, 2014; Catz et al. , 2005; Soetedjo et al. , 2008; Ke et al. , 2009) , where, as mentioned above, our algorithm may not be necessary. Beyond the predictions for the plasticity rules at parallel fibre-Purkinje cell synapses tested above, there are a number of aspects of our theory that do fit well with existing observations. The simple existence of spontaneous climbing fibre activity is one. Additional suggestive features concern the evolution of climbing fibre activity during eyeblink conditioning  (Ohmae and Medina, 2015). Once conditioning has commenced, the probability of complex spikes in response to the unconditioned stimulus decreases, which would be consistent with the build-up of the inhibition cancelling the average error signal in the olive.
Experiments were ultimately halted for external reasons, when n ¼ 58 cells were retained. The two-stage analysis is a form of multiple comparison; in consequence, the output of the statistical test on the final data has been corrected by doubling the indicated p-values in Table  1. Inspection of acquired climbing fibre responses revealed some failures of stimuli after the first in a fraction of cells, presumably because the second and subsequent stimuli at short intervals fell within the relative refractory period. As a complex spike was always produced these cells have been included in our analysis, but where individual data are displayed, we identify those cells in which failures of secondary climbing fibre stimuli were observed before the end of the induction period. Analysis made use of a modular Python framework developed in house.
Acknowledgements We are grateful to the following for discussion and/or comments on this manuscript:  David Attwell ,  Mariano Casado ,  Paul Dean ,  Anne Feltz ,  Richard Hawkes ,  Clement Lena ,  Steven Lisberger ,  Tom Ruigrok ,  John Simpson ,  Brandon Stell ,  Stephane Supplisson  and  German Szapiro. We thank  Gary Bhumbra  for sharing a native-python library for reading Clampex files. A preprint describing this work was posted on the bioRxiv repository on  2016-05-16.
The samples were labeled with anti-rat IgG-Alexa Fluor 594 diluted 1:400 in PBST + 5% NGS for 3 h at RT and washed 6 × 10 min with PBS. The brains were mounted in Vectashield mounting medium (Vector Laboratories), and confocal stacks were taken with a 1 µm step size using a Leica TCS PS2 confocal microscope. Maximum projections of image stacks were created using Leica confocal software. The mean Dilp2 immunofluorescence within each cluster of brain IPCs was calculated using the FIJI package (2. 0. 0-rc-43/1. 51p; NIH) software. The mean fluorescence within a region adjacent to the IPCs served as background and was subtracted from the mean Dilp2 fluorescence within the IPCs. Finally, a mean value representing each genotype/condition was calculated.
Acknowledgements We would like to thank  Dr. Benton ,  Dr. Hafen , the  Bloomington Stock Center , and the  Vienna Drosophila RNAi Center  for fly stocks. We thank  Pierre Leopold  and  Renald Delanoue  for preliminary discussions on this project. We would like to acknowledge the  Plateforme Dimacell at the INRA in Dijon  for the access to the confocal microscopy equipment. We also thank  Charlène Chevalier ,  Serge Loquin ,  Cindy Ménagé , and  José Solonot  for their technical assistance, and  Tomke Stürner  for comments on the manuscript. We deeply thank  Pr. David Featherstone  for discussions on preliminary results. Research in Y. G. 's laboratory is supported by the  Centre National de la Recherche Scientifique , the  Université de Bourgogne Franche-Comté , the  Conseil Régional Bourgogne Franche-Comté  ( PARI grant ), the  FEDER (European Funding for Regional Economical Development) , and the  European Council  ( ERC starting grant ,  GliSFCo-311403 ).
Data collection Electromyography (EMG) was used to measure the peak-to-peak amplitude of FDI MEPs elicited by single TMS pulses over the contralateral M1. EMG activity was recorded from surface electrodes placed over the right FDI or the left FDI (M1-Disruption or Sham sessions, respectively). EMG data were collected for 1000 ms on each trial, starting 300 ms before the TMS pulse. EMG signals were amplified, bandpass filtered on-line (10-500 Hz) and digitized at 2000 Hz for off-line analysis.
Data analysis Motor evoked potential data MEP data were collected with Signal (Signal 3. 0, Cambridge, UK) and analyzed with custom Signal scripts. MEP amplitudes were measured for each TMS pulse. Trials with background EMG activity greater than 20 μV on average (root mean square, rms), in the 200ms window preceding the TMS artifact, were excluded from the analysis. 3. 63 ± 5. 43 % of trials were discarded based on this criterion. The amplitude of MEPs elicited at each time point was averaged to obtain a measure of motor excitability at TMSBaseline and at TMS1-7 in the M1-Disruption and Sham sessions (Figure  2 ). For each session, we then expressed MEP amplitudes obtained at TMS1-7 (i. e. , after the cTBS intervention) in percentage of the amplitudes measured at TMSBaseline (i. e. , before the cTBS intervention).
The disruptive impact of cTBS on cortical activity varies between subjects  (Do et al. , 2018; Jannati et al. , 2017; Rocchi et al. , 2018). Here, we aimed at only including individuals in which cTBS effectively disrupted M1. To do so, we discarded subjects presenting percentage MEP amplitudes exceeding 2. 5 SD above the mean of the group in the M1-disruption session at one of the TMSEPOCHS or more. This led to the rejection of three subjects, who exhibited average MEP amplitudes of 148. 3 ± 12. 5 %, 139. 5 ± 10. 6 % and 188. 6 ± 20. 9% following the cTBS intervention in the M1-disruption session (all TMSEPOCHS averaged together), reflecting thus a large increase (rather than the targeted decrease) in motor excitability. The analyses of the MEP and behavioral data were performed on the remaining pool of subjects (n = 16).
Behavioral data Behavioral data were collected with LabVIEW 8. 2 (National Instruments, Austin, TX), stored in a database (Microsoft SQL Server 2005, Redmond, WA), and analyzed with custom MATLAB scripts (MathWorks, Natick, MA). Because Day 1 served as a training session (see section Blocks and sessions), the behavioral analyses focused on the data acquired on Day 2 and 3 (i. e. , in the M1-Disruption and Sham sessions).
Finally, we fitted a linear regression over the rectified positive values. We extracted the intercept and the slope of these so-called urgency functions, which we used as estimates of the initial level and the growth rate of the urgency signal, respectively. Second-order polynomial regressions were also performed on the rectified SumLogLR data but did not yield significantly better fits (i. e. , group-level Bayesian Information Criterion [BIC] values for linear and polynomial fits were 5. 75 ± 1. 61 and 5. 74 ± 1. 79 a. u. , respectively; t14 = 0. 02, p =. 984).
Statistical analysis All statistical analyses were performed with custom R Scripts (R Version 3. 4. 1, Car and BayesFactor packages). All data were examined for normality and homogeneity of variance using Skewness, Kurtosis and Brown-Forsythe tests. The significance level for all tests was set at p <. 05, except when Bonferroni corrections were applied. All results are expressed as mean ± SE.
Motor evoked potential data MEP data (expressed in percentage of MEPs at TMSBaseline) were analyzed using a twoway repeated-measure ANOVA (ANOVARM) with SESSION (M1-Disruption, Sham) and TMSEPOCH (TMS1, TMS2, TMS3, TMS4, TMS5, TMS6, TMS7) as within-subject factors. Moreover, the percentage MEP values obtained for each TMSEPOCH were compared against 100 % using Bonferroni-corrected single-sample Student's t-tests to identify any significant suppression in the M1-Disruption and in the Sham session.
Behavioral data The SRT data were analyzed using a two-way ANOVARM with SESSION (M1-Disruption, Sham) and SRTEPOCH (SRT1, SRT2) as within-subject factors. The DT, the %Correct and the %TO data were analyzed using two-way ANOVARM with SESSION (M1-Disruption, Sham) and TRIAL (obvious, ambiguous) as within-subject factors. The SumLogLR at DT was analyzed using a two-way ANOVARM with SESSION (M1-Disruption, Sham) and BIN (Bin1 to Bin7) as within-subject factors. When appropriate, Tukey's HSD post-hoc tests were used to detect paired differences in these ANOVAs. Furthermore, the intercept and the slope of the urgency functions were compared between the two sessions using Student's t-tests.
Critically, all the behavioral data collected in the tokens task were similar in the two cTBS sessions, whether M1 was disrupted or not. Based on this finding, the contribution of M1 to decision-making could be negligible. Hence, past reports of decision-related changes in M1 may reflect the influence of signals coming from upstream structures rather than an actual involvement in the deliberation process itself  (Thura and Cisek, 2017; van Maanen et al. , 2016). In a similar vein, our recent work shows that M1 disruption negatively alters value-based choices, but only when action values are freshly acquired. Such an effect of M1 disruption does not occur anymore following consolidation. This suggests that M1 contribution to value-based decision-making may vanish as subjects become more proficient at using the value information  (Derosiere et al.
MRI Data Whole Brain Analyses Data processing was performed using SPM12 release 6685 (Wellcome Department of Imaging Neuroscience, London, UK 1 ) implemented in MATLAB 7 (Mathworks Inc. , Natick, MA, USA). We processed the data via Diffeomorphic Anatomical Registration, using the Exponential Lie algebra algorithm (DARTEL,  Ashburner and Friston, 2005; Ashburner, 2007)  for the segmentation and normalization steps. The DARTEL segmentation procedure makes use of a number of tissue probability maps including GM, white matter (WM), CSF, soft tissue, skull and non-brain regions of the image. After segmentation, we performed a visual inspection and a quality check of the data, by applying the modules ''display one slice for all images'' and ''check sample homogeneity using covariance'' implemented in the VBM12 toolbox 2.
FUNDING This research was funded by  ARC2  '' Qualité de Vie & Vieillissement'' Région Rhône Alpes, France  and by  Institut Universitaire de France (IUF). The Grenoble MRI facility IRMaGe was partly funded by the  French program  '' Investissement d'Avenir' ' run by the '' Agence Nationale pour la Recherche'': Grant ''Infrastructure d'Avenir en Biologie Santé''  ( ANR-11-INBS-0006 ).
We describe progressive spastic paraparesis in two male siblings and the daughter of one of these individuals. Onset of disease occurred within the first decade, with stiffness and gait difficulties. Brisk deep tendon reflexes and extensor plantar responses were present, in the absence of intellectual disability or dermatological manifestations. Cerebral imaging identified intracranial calcification in all symptomatic family members. A marked upregulation of interferon stimulated gene (ISG) transcripts was recorded in all three affected individuals and in two clinically unaffected relatives. A heterozygous IFIH1 c. 2544T>G missense variant (p. Asp848Glu) segregated with interferon status. Although not highly conserved (Supporting figure 1; CADD score 10. 08 versus MSC-CADD score of 19. 33) and predicted as benign by in silico algorithms, this variant is not present on publically available databases of control alleles, and expression of the D848E construct in HEK293T cells indicated that it confers a gain-of-function.
Sanger sequencing of IFIH1 DNA was extracted from whole blood samples using standard methods, and sequencing performed using primers designed to amplify the coding exons of IFIH1. Purified PCR amplification products were sequenced using BigDye Terminator chemistry and an ABI 3130 DNA sequencer. Mutation description is based on the reference cDNA sequence NM_022168. 3, with nucleotide numbering beginning from the first A in the initiating ATG codon. Variants were assessed using the in silico programs SIFT (http://sift. jcvi. org) and Polyphen2 (http://genetics. bwh. harvard. edu/pph2/), and population allele frequencies obtained from the gnomAD (http://gnomad. broadinstitute. org) database.
5 μg/ml; Invivogen) using Lipofectamine 2000. Cells were lysed 16 hours after stimulation, and IFN-β promoter activity was measured using the Dual-Luciferase Reporter assay (Promega) and a Synergy2 plate reader (BioTek). Firefly luciferase activity was normalized against Renilla luciferase activity. Each experiment (n = 4) was performed in triplicate and data are presented as mean ± SEM. Statistical significance was determined by two tailed, unpaired Student's t test with * and ** indicating p values <0. 05 and <0. 01 respectively. Comparable expression of construct proteins was verified by western blotting (data not shown).
Acknowledgements Y. J. C. acknowledges funding from the  European Research Council  (GA 309449: Fellowship to Y. J. C. ) and a state subsidy managed by the  National Research Agency (ANR, France)  under the " Investments for the Future"  ( ANR-10-IAHU-01 ). L. V. E. acknowledges funding from  FWO-Vlaanderen. H. S. acknowledges the  NIH  grant  R01AI111784.
Participants were excluded if they were currently being prescribed medication for assisted detoxification. Table  1  presents data on recent and historical detoxification (for historical, specific timelines could not be recalled). Forty control participants were drawn from a larger sample of non-clinical staff and to the participants in the AUD cohort. Controls reported no history of alcohol or other drug abuse, though all but one participant consumed alcohol on a weekly or monthly basis (see Table  1 ). Two participants reported being prescribed selective serotonin reuptake inhibitors (SSRIs) for depressive illness in the past (>3 months). Smoking was more common in the AUD group (though given several temporarily sustained quit attempts, very few participants with AUD could estimate number of years using tobacco), but participants with AUD reported smoking fewer cigarettes per day than the control group.
Assessment of AUD-related behaviours AUD-related behaviours were measured by a self-report questionnaire and through clinical assessment data collected by the treatment centres. Participants were asked to indicate the average number of units of alcohol consumed per week prior to treatment (UC), years of problem drinking (YoD) (measured from time alcohol drinking behaviour had been highlighted by a medical professional), self-reported age at which alcohol use became a problem (AoPD), and the estimated age started drinking alcohol (ASD). We also sought to examine how many years of treatment participants had received, but very few participants could report a clear indication of this. The study also captured data on total/lifetime previous clinical detoxifications (a factor which may affect cognition;  Duka et al.
Discussion This study utilized a clinically validated measure, the mini-SEA, to assess social cognition in individuals with AUD compared to an age and gender matched control group. Additionally, the study aimed to deconstruct ToM subcomponents from the mini-SEA and examine the extent to which these scores were predicted by cognitive ability and AUDrelated behaviors. Firstly, the data supported the prediction that AUD is strongly associated with social processing differences compared to a control group and demonstrated that the AUD cohort showed a greater number of errors in the ToM subcomponents of the mini-SEA measure and facial emotional recognition compared to controls. As hypothesized, participants in the AUD group performed poorly on the faux pas task compared to controls, confirming earlier results by  Thoma et al.
Introduction The digital processing, analysis and archiving of databases and collections in the arts and humanities is becoming increasingly important. This is because of a myriad of possibilities that digitisation opens up that go well beyond the organisation and manipulation of the actual physical objects, allowing, for instance, the creation of digital databases that are searchable with respect to several parameters (keywords), the digital processing and analysis of objects that are non-destructive to the original object, and the application of automated algorithms for sorting newly found objects into existing digital databases by classifying them into pre-defined groups in the database. These possibilities go hand-in-hand with ever-growing advances in data science that are developing mathematical methodology for analysing and processing digital data.
Description of the dataset Our dataset is made by two manuscripts, reported in Figure  1 : William de Brailes, Last Judgement (left) and Christ in Majesty with King David playing the harp (right), Fitzwilliam Museum, MSS 330. iii and 330. v, England, Oxford, c. 1230-1250. The images to be processed are very large. tif data of 4008 × 5344 pixels and approximatively 47MB each.
Feature extraction. We now extract some intensity-type features from the image in order to build feature vectors which will be then used for classification. Namely, for every pixel x in the image we apply non-linear channel transformations to compute the HSV (Hue, Saturation, Value), the geometric mean chromaticity GMCR  [32] , the CIELAB and the CMYK (Cyan, Magenta, Yellow, Key) values (see  [33]  for more details). Once this is done, we append all these values together so that multi-dimensional feature vector ψ of the form ψ(x) = [HSV(x), GMCR, CIELAB(x), CMYK(x)] (3) is built. Note that having only the RGB intensity data at hand, unfortunately not much information can be added when building the feature vectors. In this respect, if one would have multi-spectral measurements (such as IR, UV or other types) additional features extracted from the complementary data could be added for an improved result.
A classification algorithm with training Once the feature vectors are built for every point in the image, we use the training region D 1 detected as described in Section 2. 2. 1 as a dictionary to drive the segmentation procedure extended to the whole image domain. We proceed as follows. First, we run a labelling algorithm over the whole image domain comparing appropriately the features defined in (3) in order to partition the image in a fixed number of K classes. To do that, we use the well-known k-means algorithm. After this preliminary step, we check which label has been assigned to the training [1] For our computation we used the inbuilt MATLAB Chan-Vese segmentation code. region D 1 and simply identify in the labelled image which pixels share the same label. By construction, this corresponds to find the regions in the image 'best-fitting' the training region in terms of the features defined in Section 2.
In order to solve this efficiently, a PatchMatch  [30]  strategy can be applied. Afterwards a proper image reconstruction step is performed, where for every point in D the actual corresponding patch is computed. We refer the reader to  [35]  for the numerical details. A crucial ingredient for a good performance of the non-local algorithm is its initialisation. In particular, once the inpainting domain is known, a pre-processing step where a local inpainting model, such as the TV inpainting model (  5 ) with  (6) , can be run to provide a rough, but reliable initialisation of the algorithm [3]. [2] The code is freely available at IPOL: https://doi. org/10. 5201/ipol. 2017. 189 [3] The code is freely available at IPOL: https://doi. org/10. 5201/ipol. 2012. g-tvi We report the results of the combined procedure in Figure  5  and the overall work-flow of the combined algorithm in the diagram in Figure  5.