dataesr/scientific-paragraphs-categorization huggingface dataset:
3-1. 55 s. Participants were instructed to respond to each word using their left and right index fingers, as fast as they could without sacrificing accuracy. Once the ISI had passed, the next word was presented regardless of the subject's response. Each block was followed by 5 seconds of rest. The experiment was divided in 12 runs of approximately 7 minutes. Participants were offered a break every three runs but were granted more or less pauses between runs depending on their reported state of fatigue. The stimuli totaled to 90 unique words (Lexique database;  New et al. , 2004) , 30 per semantic category (Supplementary Table  1 ). Words were also equated across three phonemic categories as per their first vocalic phoneme. Those phonemes were the French [a], [e] and  [o]. Each semantic category, then, contained ten words from each phonemic category.
Stimulation We used Psychophysics Toolbox Version 3  (Brainard, 1997; Kleiner et al. , 2007)  for MATLAB (Release 2015b; The MathWorks, Inc. , Massachusetts, United States) to implement the experimental procedure. Auditory stimulation was delivered using Nicolet TIP-300 (Madison, WI, USA) with Echodia ER3-14A foam ear tips (France). Button presses were recorded using a Cedrus Lumina LSC-400B controller.
MEG data processing To clean the MEG data from environmental artifacts temporal Signal Space Separation (tSSS;  Taulu et al. , 2005)  was performed using the MaxFilter tool (Elekta Neuromag). All remaining data processing steps were performed using the MNE software  (Gramfort et al. , 2013 (Gramfort et al. , , 2014)). The MEG data was then bandpass filtered 0. 1-15Hz with default filter parameter settings. Eye movement and cardiac artifacts were corrected using Independent Components Analysis (ICA). ICA was estimated on raw data across all runs using the FastICA algorithm  (Hyvärinen and Oja, 2000). Ocular components were detected using Pearson correlations and cardiac components using cross trial phase statistics (CTPS;  Dammers et al. , 2008) , both, with the MNE-Python default settings.
Statistical Analysis Sensor Space. We assessed category discrimination within participants and between groups using nonparametric permutation clustering tests  (Maris and Oostenveld, 2007). Within participants, we used the F-test for independent samples as contrast function in the non-parametric test. We then contrasted the ensuing sensor-wise F-statistics at the group level using the t-test for independent samples with the permutation-clustering test. Settings were consistent with the values suggested in documentation of the MNE software  (Gramfort et al. , 2014). Source space. To obtain ROI-wise inference, we then contrasted groups using t-tests for independent samples and obtained inference by comparing the observed t-value against an empirical distribution of t-values under the H0 obtained from 10000 label-wise permutations of the participants between groups  (Groppe et al.
, 2011). Multiple comparisons we adjusted using FDR-control  (Benjamini and Hochberg, 1995; Groppe et al. , 2011). Decoding. We used a logistic regression classifier with penalized likelihood (L2 norm) to learn how to discriminate single-trial semantic categories from the MEG sensors at a given time point. The regularization parameter fixed at the default value of C = 1. Out-of-sample performance was estimated using k-fold cross-validation within participants (with grouping by words) and leave-one-subject-out cross-validation across participants. All machine learning was performed using the scikit-learn software  (Pedregosa et al. , 2011). For model inspection, we extracted the learned pattern from the linear model  (Haufe et al. , 2014)  and projected it to the source model using the previously learned minimum norm inverse operator.
Neural signatures of semantic category discrimination As a first step, we assessed individual category discrimination in successful trials. Exploratory analyses suggested that average MEG signals, in both groups, varied systematically across categories. However, the direction of the effects was highly variable across participants. To summarize category discrimination, we chose an F-statistic with spatiotemporal clustering on sensor data in single participants. This allowed us to generate heat-maps of where differences occurred in time and space while avoiding cancellation when averaging over participants. We found at least one cluster with significant category discrimination in all but one blind and three sighted participants (Fig. 3A ). Those clusters overlapped in time starting at about 400 ms for the majority of the sighted and blind participants (Fig.
Acknowledgments The authors would like to thank  Nathalie George  and  Denis Schwartz  for their help in MEG acquisition,  Celine Cluzeau  for coordinating subject recruitment and data acquisition, Alexandre Gramfort,  Benjamin De Haas ,  Guillaume Dumas ,  Jacobo Sitt ,  Marijn Van Vliet  (in alphabetical order), the  Parietal Team  and the  PICNIC Lab  for stimulating discussions and feedback on our work.
Funding information This study was funded by the " Investissements d'avenir" program  ( ANR-10-IAIHU-06 ; to the  Brain and Spine Institute) , LABEX  LIFESENSES  project ( ANR-10-LABX-65 ) and  Optic2000  (to SA, through the  Vision Institute) & "Leshanot Chaim" Foundation  (to SA).
The genetics of inherited retinal degeneration is very complex as illustrated here by the case of retinitis pigmentosa (RP) (Table  1 ). We examined the expression data of the genes known to cause RP and used literature searches for refinement. Expression data include the kinetics of the transcriptome of retina of the retinal degeneration-1 (rd1) mouse  [71]  as well as the transcriptome of human retinal detachment  [72] , the mouse tissue expression profiling publicly available and sequences from normalized libraries  [73]. Among the retinitis pigmentosa genes whose profile could be examined, the rod-like expression pattern accounts for 45%. It should be noticed that because of the high proportion of rods (97%) over cones (3%) in the mouse retina, the expression profiles do not distinguish genes expressed specifically by rods (e.
Retinal Specific Expression Patterns Revealed by Transcriptomics As illustrated above by examples in the field of research on the retina, both developmental biologists and human geneticists have a strong interest in genes with retina-and photoreceptor-restricted expression profiles. The selection is generally achieved by comparing global gene expression between specimens that represent two extreme conditions. This was originally done by subtractive and differential cloning using the degenerated rd1 retina  [250]. The concept evolved thereafter to cDNA arrays and to serial analysis of gene expression (SAGE)  [251, 252]. Rapidly the need for standardization became clear, resulting in part in the increased use of commercial oligo-arrays  [72, 112, [253] [254] [255]. Each experiment generates data that are beyond what could be reported in a publication, so the research community created databases to score and exchange the increasing body of transcriptomic data.
Examples include the Gene Expression Omnibus  [256]  ArrayExpress  [257]  and the ENCyclopedia Of DNA Elements (ENCODE) databases. The major shortfall in the application of ENCODE data for ocular research is in the relative paucity of eye tissue used for analysis  [258]. The US Food and Drug Administration examined the expression array technology and concluded that the measurements were highly reproducible within and across the platforms, allowing the development, among others, of new prognostic and predictive tests for breast cancer  [259, 260]. The existence of numerous rodent models of inherited retinal degeneration led researchers to conduct comparisons between the transcriptomes of several models (Table  1 ). This includes spontaneous models, as the rd1 mouse  [261] , retinal degeneration induced by light damage  [262]  or hypoxia  [263] , models obtained by inactivation of the retinal genes by homologous recombination  [112, 264]  or by random integration of a dominant mutation in the rhodopsin gene  [111, 265] , or even using conditional inactivation in a specific retinal cell type  [266].
This novel field of research is quite dependent on the construction of repository database and on bioinformatics tools such as those provided by the PRoteomics IDEntifications (PRIDE) database  [295, 296]. The proteome of the human eye is recognized as a project on its own  [297]. Beside this repertoire of proteins expressed in the eye, differential proteomic analysis was quite successfully applied to the physiopathology of inherited retinal, degenerations by comparing disease specimens to healthy ones  [298, 299]. The approach was more rarely applied to retinal development most likely because the morphogens as the transcription factors, act at a low concentration and are difficult to detect by mass spectrometry  [300] [301] [302]. One can further argue that transcriptomics is best suited to analyze developmental processes where the organ is constructed, but proteomics is a better option to study degenerative processes, during organ deconstruction.
Acknowledgments: We thank  Naomi Berdugo ,  Raymond Ripp  and  Olivier Poch  for their contributions to the development of the functional genomics platform  Knowledge Base  for Sensory Systems (KBaSS http: //kbass. institut-vision. org/KBaSS/)that was used to analyze the expression of genes of interest. We greatly appreciated the contribution of  Donald J. Zack  to the edition of the manuscript.
Immunofluorescence After two washes with PBS, monolayers were fixed in 100% methanol at -20°C and permeabilized with TRITON x 100. Filters were then rinsed in PBS and incubated with antimouse occludin (dilution 1:100, Santa Cruz Biotechnology, CA, USA) during 1h at room temperature. After PBS washes, filters were incubated with goat anti-rabbit IgG conjugated to Alexa488 (dilution 1:1000, Life technologies SAS, Saint-Aubin, France). ProLong TM glass antifade mountant with NucBlue TM (Hoechst) stain was used as a nuclear counterstain (Thermo Fisher Scientific, Waltham, MA, USA). Images were obtained under Axiovert 200M microscope (Carl Zeiss, Göttingen, Germany) by AxioVision software.
Statistical analysis All data, presented as means ± standard error of the mean (SEM), were analysed with Graph Pad Prism Software (version 6. 01, San Diego, CA, USA). Normality of data was examined using Shapiro-Wilk test. One-way analyses of variance (ANOVA) were performed on normal data. For non-normal data or small number of observations, a Kruskal-Wallis test was performed followed by a Dunn post hoc test. Two-way ANOVA were used to assess whether there were significant differences between source of SM (pure or in total MPL) within dose (values bearing different superscript letters were significantly different). When the interaction term was not significant this analysis was followed by Bonferroni post hoc multiple comparisons test. If the interaction term was significant an unpaired test was performed to determine differences between MSM and MPL groups.
Acknowledgements Marine Milard  acknowledges a PhD grant from  French Ministry for Higher Education and Research  via EDISS and  Université Claude Bernard Lyon 1. We acknowledge  CNIEL  ( French Dairy Interbranch Organization ) for funding our study. We particularly thank  Corinne Marmonier  ( CNIEL ) for useful comments on the manuscript. We additionally thank  Constance Boyer  and  Benjamin Choque  (CNIEL) and  Sébastien Serieye  ( Sodiaal-Candia R&D ),  Nikta Vaghefi  (Savenvia),  Chalotte Baudry (Lactalis) ,  Renaud Mesdagh  ( Bongrain/Soredab ) for fruitful discussions. Authors thank  Sophie Thenet  ( INSERM UMR S1138, France ),  Agnes Girard-Egrot  ( ICBMS, Villeurbanne, France ) and  Corinne Malpuech-Brugère  ( Human Nutrition Unit, INRA-Université d'Auvergne, Clermont-Ferrand, France ) for critical discussions on the results.
Participants Fifteen adolescents with PTSD aged 13 to 18 years old, were recruited through the University Hospitals of Caen, Rennes and Rouen (France). Data of one patient were not exploitable because she slept during the resting state fMRI scan. PTSD adolescents received no psychotropic medication during the previous week and were free from other mental disorders including major depression. All presented a chronic PTSD for at least 6 months. Twenty-four typically developing adolescents with no history of trauma were chosen to match the patient group in terms of age and IQ. Healthy participants were recruited by prospecting in several junior high schools of the region (Normandy, France). Altogether, 14 PTSD patients (12 females) and 24 controls (13 females) were included in the analyses (see Table  1  for descriptive statistics).
Neuroimaging data acquisition All participants were scanned with a Philips Achieva 3. 0 T MRI scanner at the Cyceron Center (Caen, France). High-resolution T1weighted anatomical volumes were acquired using a three-dimensional fast-field echo sequence (3D-T1-FFE sagittal; repetition time = 20 ms, echo time = 4. 6 ms, flip angle = 10°, 180 slices, slice thickness = 1 mm, field of view = 256 × 256 mm 2 , in-plane resolution = 1 × 1 mm 2 ). Resting state functional volumes were obtained using an interleaved 2DT2* SENSE EPI sequence designed to reduce geometric distortions, with parallel imaging, short echo time, and small voxels (2D-T2 * -FFE-EPI axial, SENSE = 2; repetition time = 2382 ms, echo time = 30 ms, flip angle = 80°, 42 slices, slice thickness = 2. 8 mm, field of view = 224 × 224 mm 2 , in-plane resolution = 2.
Neuroimaging data preprocessing Individual datasets were first checked for artifacts through the application of the TSDiffana routines (http://imaging. mrc-cbu. cam. ac. uk/ imaging/DataDiagnostics). For each 38 individual datasets, we verified that head motion did not exceed 3 mm (translation) and 1. 5°(rotation). Data were then processed as described in  Mevel et al. (2013). Briefly, the first 6 volumes were discarded because of saturation effects. The EPI volumes were corrected for slice timing and realigned to the first volume. Data were then spatially normalized using a technique designed to reduce geometric distortion effects  (Villain et al. , 2010; Mevel et al. , 2013). This procedure includes for each individual i) a coregistration of the mean EPI volume, non-EPI T2*, T2, and T1 volumes; ii) a warping of the mean EPI volume to match the non-EPI T2* volume; iii) a segmentation of the T1 volume using voxel-based morphometry; iv) a normalization of the coregistered T1, EPI, and non-EPI T2* volumes using the parameters obtained from the T1 segmentation; v) a 4 mm full-width at half-maximum (FWHM) smooth of the EPI volumes and vii) and temporal bandpass filtering (0.
Seed-based connectivity analyses Using the GIFT toolbox (http://mialab. mrn. org/software/gift/ index. html), whole-brain resting state fMRI analyses were performed on both groups using a seed-based approach. DMN connectivity was calculated using 4-mm radius spheres located in the bilateral PCC. The coordinates were obtained from previous studies of the DMN in PTSD:  Patriat et al. (2016)  in the right PCC (MNI coordinates [2 -52 26]) and  Miller et al. (2017)  in the left PCC (MNI coordinates  [-8 -56 26] ). The PCC seeds were chosen because they represent hubs of the DMN. Seeds based on these coordinates were created using the MarsBar toolbox  (Brett et al. , 2002)  and the 38 individual mean time courses were extracted for each seed. For all participants and each seed of interest, correlations were assessed between the mean time course in the seed and the time course of each grey matter voxel.
This combination of activation thresholds was determined using the 3DClustSim program  (Ward, 2000)  to correspond to a false positive rate of p <. 05, corrected for multiple comparisons. In addition, a hypothesis-driven regions of interest (ROI) analysis of the hippocampal region (p <. 05 corrected for multiple comparisons, small volume correction or SVC) was conducted to specifically examine the correlation between PCC-hippocampal connectivity with neuropsychological and psychopathological scores, based on a priori hypotheses regarding the implication of the hippocampus in the pathophysiology of PTSD  (Etkin and Wager, 2007; Lanius et al. , 2010)  and its specific role in episodic autobiographical memory retrieval which is impaired in PTSD  (Crane et al. , 2014; Ogle et al. , 2013). Right and left hippocampal ROIs were obtained from the Automated Anatomical Labeling (AAL) toolbox  (Tzourio-Mazoyer et al.
Limitations Limitations of the current study should be mentioned. First, our sample size for the patient group was relatively small (N = 14) which has the risk that the study is under-powered. Our results should thus be confirmed in future studies with greater cohorts. Second, our sample was predominantly female and consisted of sexually-abused adolescents. This is characteristic of most resting state studies in adolescent PTSD in which more females than males are included  (Patriat et al. , 2016; Aghajani et al. , 2016; Thomason et al. , 2015; Suo et al. , 2015). It is unknown whether the findings would generalize to males. We have re-analyzed the whole dataset including females only and results remain broadly the same (data not shown), suggesting that gender does not have an effect on the present results.
Acknowledgments This study was supported by the  University Hospital of Caen , the  Caen School District  and the  Mutuelle Générale de l'Education Nationale  (insurance company). We would like to thank  C. Lebouleux ,  M. H. Noël  and  M. C. Onfroy  for their help in data acquisition,  F. Dégeilh ,  S. Egret  and  F. Mézenge  for assistance in testing participants and  P. Gagnepain ,  M. Naveau  and  B. Landeau  for programming skills. We are also thankful to the adolescents and institutions that took part in our research.
In other experiments (n = 64 mice), we stereotactically injected virus without Cre-dependence into VPM or POm, finding similar innervation patterns to those observed in the GPR26-Cre mice. In this study, we therefore combined the data from Cre-dependent and Cre-independent expression of ChR2. In order to study thalamocortical input to wS1, we obtained whole-cell membrane potential recordings from wS1 neurons in parasagittal brain slices of mice aged P42-92 containing thalamic axons expressing ChR2, and applied 1 ms blue light flashes to evoke neurotransmitter release. We pharmacologically blocked fast GABAergic synaptic transmission by applying picrotoxin (50 mM) to the extracellular solution. To prevent polysynaptic activity, we further added TTX (1 mM) and 4-AP (100 mM)  (Petreanu et al.
Borosilicate patch pipettes with resistance of 5-7 MW were used. The pipette intracellular solution contained (in mM) 135 K-gluconate, 4 KCl, 4 Mg-ATP, 10 Na 2 -phosphocreatine, 0. 3 Na-GTP, and 10 HEPES (pH 7. 3, 280 mOsmol/l). Biocytin (3 mg/ml) was added to intracellular solution. Electrophysiological data were low-pass Bessel filtered at 10 kHz and digitized at 20 kHz with an ITC-18 acquisition board (Instrutech). Data acquisition routines were custom-made procedures written in IgorPro software (Wavemetrics). Membrane potential measurements were not corrected for the liquid junction potential. For stimulation of ChR2-expressing axons, we used a 470 nm collimated blue LED system (Thorlabs) coupled to a 1 mm optic fiber (Thorlabs; 0. 48NA), which was directed at the cortical region targeted for optogenetic stimulation by bringing the end of the fiber immediately next to the brain slice.
Data analysis Electrophysiological data were analyzed using custom-made routines written in Matlab (MathWorks). The data and Matlab analysis code for generating the figures are freely available at the CERN database Zenodo https://zenodo. org/communities/petersen-lab-data with DOI: 10. 5281/zenodo. 3560697. Mean traces were calculated by averaging over 20 single trials. Membrane potential traces were aligned to the onset of the 1 ms ChR2 stimulus of the thalamocortical axons. Mean PSP amplitudes were calculated by taking the average peak and subtracting the baseline pre-stimulus membrane potential. PSP baselines were defined as the mean V m 100 ms before stimulus onset. The slope of the PSP was calculated from a linear fit to the 20-50% rise-time period. Population data are represented as mean ± SD in scatter plots.
In box plots, the median and interquartile range are shown with whiskers extending from the smallest data point comprised within 1. 5x of the interquartile range of the first quartile to the largest data point comprised within 1. 5x of the interquartile range of the third quartile. The Wilcoxon signed-rank test was used to compare two groups of paired data in Figure  1B  and Figure  1C. The following dataset was generated:.
Acknowledgements We thank  Matthieu Auffret ,  Sami El-Boustani  and  Varun Sreenivasan  for discussions. This work was supported by the  Swiss National Science Foundation  ( 310030B_166595  to CCHP), the  European Research Council  ( ERC-2011-ADG 293660  to CCHP and  ERC-2018-CoG 819496  to OY) and the  Deutsche Forschungsgemeinschaft  via the  CRC 889 'Cellular mechanisms of sensory processing'  ( TP C07  to JFS). We thank  Nicolas Zdun ,  Nils Lammers  and  Johanna Rieke  for Neurolucida reconstructions.
All experiments were performed in accordance with the Swiss Federal Veterinary Office, under authorization 1889 issued by the 'Service de la consommation et des affaires veterinaires' of the Canton de Vaud, Switzerland. Reagent type (species) or resource Designation Source or reference Identifiers Additional information Recombinant AAV-hSyn-ChR2-YFP U. Penn Addgene #26973 DNA reagent Recombinant AAV-DIO-ChR2-YFP U. Penn Addgene #20298 DNA reagent Recombinant AAV-DFO-ChR2-YFP This paper Addgene #136916 See Materials DNA reagent and methods Available from Addgene Software, Matlab analysis This paper Zenodo DOI: 10. 5281/ See Materials algorithm code and data zenodo. 3560697 and methods Available from https://zenodo. org/ Authorization for animal experiments.
Zhu JJ, Connors BW. 1999. Intrinsic firing patterns and whisker-evoked synaptic responses of neurons in the rat barrel cortex. Journal of Neurophysiology 81:1171-1183. DOI: https://doi. org/10. 1152/jn. 1999. 81. 3. 1171, PMID: 10085344 Database and Author(s) Year Dataset title Dataset URL Identifier B Semihcan Sermet, 2020 Dataset for "Pathway-, layer-and https://doi. org/10. 5281/ Zenodo, 10. 5281/ Pavel Truschow, Mi- cell-type-specific thalamic input to zenodo. 3560697 zenodo. 3560697 chael Feyerabend, mouse barrel cortex" Johannes M Mayr- hofer, Tess B Oram, Ofer Yizhar, Jochen F Staiger, Carl CH Petersen.
This paper provides resources and design recommendations for optimizing position input for pressure sensor matrices, a sensor design often used in eTextiles. Currently applications using pressure matrices for precise continuous position control are rare. One reason designers opt against using these sensors for continuous position control is that when the finger transitions from one sensing electrode to the next, jerky motion, jumps or other non-linear artifacts appear. We demonstrate that interdigitation can improve transition behavior and discuss interpolation algorithms to best leverage such designs. We provide software for reproducing our sensors and experiment, as well as a dataset consisting of 1122 swipe gestures performed on 17 sensors.