dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, TS may compromise children and adolescent patients' schooling  [15]. In this study, we aimed to give a voice to adolescents living with TS through an interview, and to report it using statistical text mining (i. e. , convert unstructured natural language to structured database able to be statistically analyzed). This approach is promising, since, contrary to qualitative approaches, it allows for the analysis of a large volume of textual data. Indeed, for the overwhelming majority of research on psychiatric and neurological conditions, natural language data are not considered, leading to a lack of consideration of the patients' lived and subjective experience. With the recent development of textual data mining, we can go beyond these limitations by examining a considerable volume of patients' speech and correlating it with clinical outcomes using a rigorous and systematic approach.
Data acquisition The 62 individuals with TS participated in a short recorded interview (less than 15 min) which was performed in a quiet room with a minimum number of individuals (i. e. , the patient, the interviewer, and eventually one additional person from the research team). Before the interview, patients were invited to develop their responses as much as possible and were encouraged to talk about different topics. The interviewer-a neuropsychologist (CAC)-remained the same for all patients. Particular attention was given to always formulate the question in the same manner and to take part as little as possible in the discussion to avoid interviewer bias. The interview was built to discuss the following points: (i) the daily life of patients in school, at home, and with strangers; (ii) the aspect of TS which had the worst consequences on their daily functioning; (iii) their thoughts regarding their future daily life as adults.
Data pre-processing The interviews were first manually transcribed by four different members of the team (i. e. , MD, EL, LP, and EW) and then divided by question, leading to the composition of five corpora. Before the corpora were statistically analyzed, a final reading was performed by CAC to homogenize several terms (to avoid the analyses considering two synonyms to be unrelated) and to add terms in the case of implicit reference. Then, the corpora were loaded in the IRAMUTEQ software (R Interface for Multidimensional Analysis of Texts and Questionnaires, version 0. 7-alpha 2), a text mining dedicated tool based on both R and Python languages  [23]. IRAMUTEQ applies text pre-processing in three steps. First, it divides each text of the corpus into smaller units on the criteria of size and punctuation, called "text segments".
, 2001;  these regions are based on post-mortem histology, and have been used in a number of previous fMRI papers). Signal extraction, ISC estimation, and inferential statistics were performed identically to the critical analyses (see Section 2. 8). ISCs from these regions were used in a reality check (see Section 3. 1), to ensure a double-dissociation obtains between visually and auditorily presented conditions in these perceptual regions. Resting state ISCs. A subset of ten participants (who completed 1-7 of the critical conditions) completed a resting state scan, which was included to ensure that data acquisition, preprocessing, and modelling procedures do not induce spurious ISCs. To this end, signal extraction, ISC estimation, and inferential statistics were performed identically to the critical analyses (see.
Critical analyses. Computing ISCs. For each participant and fROI, BOLD signal time courses recorded during each naturalistic condition were extracted from each voxel beginning 6 s following the onset of the stimulus (to exclude an initial rise in the hemodynamic response relative to fixation, which could increase ISCs) and averaged across voxels. For each fROI, participant, and condition we computed an ISC value, namely, Pearson's moment correlation coefficient between the z-scored time course and the corresponding z-scored and averaged time course across the remaining participants  (Lerner et al. , 2011). ISCs were Fisher-transformed before statistical testing to improve normality  (Silver and Dunlap, 1987). Statistical testing. In each fROI, ISCs were then tested for significance against an empirical null distribution based on 1,000 simulated signal time courses that were generated by phaserandomization of the original data  (Theiler et al.
, 1992). Namely, we generated null distributions for individual participants, fit each distribution with a Gaussian, and analytically combined the resulting parameters across participants. The true ISCs, also averaged across participants, were then z-scored relative to these empirical parameters and converted to one-tailed p-values. ISCs were compared across networks and condition types using linear, mixed-effects regressions, implemented in Matlab 2020a. ISCs were modeled with maximal random effects structure appropriate for each analysis  (Barr et al. , 2013; Baayen et al. , 2008) , including random intercepts for participants, with random slopes for the effects of interest, and crossed random intercepts for fROI and condition. Hypothesis testing was performed with two-tailed tests over the respective model coefficients, with Satterthwaite approximation for the degrees of freedom.
Further analyses performed within networks across condition types or against the theoretical null distribution (i. e. , testing the intercept term), as well as those per fROI within a network or per condition across networks, also always included maximal random effects on the remaining grouping variables. P-values in these analyses are reported following false discovery rate (FDR) correction for multiple comparisons  (Benjamini and Yekutieli, 2001). Lastly, in comparisons against baseline, per-fROI analyses against empirical null distributions are also reported, which aim to ensure that, at the finest grain (each individual fROI and condition, across participants), differences from baseline are independent of assumptions regarding data normality. These tests were also FDR corrected for multiple comparisons for all fROIs within a network, and across all seven conditions of interest.
, 2004). The ISCs during rest did not significantly differ from baseline in either the language or ToM networks, as assessed with an LME regression, or against the empirical null distribution. This analysis suggests that any above-baseline ISCs for our critical conditions are not an artifact of data acquisition, preprocessing, or analysis procedures. Replication analysis: Closer tracking of linguistic input by language regions than by domaingeneral Multiple Demand (MD) regions. We successfully replicated the key finding for the narrative condition (beta = 0. 181, SE = 0. 039, t(34. 89) = 4. 664, p = 10 -5 ; p values are FDRcorrected for the four linguistic conditions), and extended it to the audio play (beta = 0. 307, SE = 0. 052, t(34. 23) = 5. 944 , p = 10 -7 ), the dialog (beta = 0. 157, SE = 0.
A more detailed characterization of the two networks' ISC profiles. In this section, we examine more closely the detailed pattern of ISCs in the two networks of interest. The first aim of these analyses is to establish that the observed dissociation is not driven by particular conditions or regions within the networks, but rather, that different aspects of the data provide convergent support for the dissociation. The second aim is to highlight aspects of the ISC pattern that are not consistent with a complete language-ToM dissociation, and thus to evaluate the strength of counter-evidence in favor of the null hypothesis, that language and ToM are not dissociable in naturalistic cognition (see Supplemental Figure  B1  for ISCs per fROI for each network and condition). ToM network. First, the ToM network reliably tracked each of the six conditions with mental state content: story (beta = 0.
g. , in the language network, the dialog is tracked less strongly than the narrative, and in the ToM network, the Heider & Simmel-style animation is tracked less strongly than the animated film. Our dataset is not ideally suited for such investigation because it includes only a single instance of each 'condition', and no attempt was made to match the conditions on at least some dimensions that may improve homogeneity, but this is a promising direction for future work. A few other exciting future directions are worth highlighting, some of which may build directly on the approaches introduced and the findings reported in the current study. First, the language and ToM networks appear to be dissociable in the adult mind and brain. However, it is possible-perhaps even plausible-that this dissociation emerges over the course of development.
Statistical analyses Statistical analyses were conducted using Statistica 13. 3 software. To analyse differences in recall depending on age and memory task, a 2 (age-group) x 2 (retrieval task) GLM (with educational and vocabulary level as covariates) was conducted on the percentage of correctly recalled words. Next, to analyse the effect of age on updating performance and on physical activity, one-way ANCOVAs were carried out. Educational and vocabulary level were added as covariates in both of these ANCOVAs to avoid bias related to the differences between our two groups. Effect sizes for significant effects and interactions were calculated using partial eta squared (η p 2 ). Correlational analyses (controlling for age) of habitual physical activity and cognitive measures (cued recall, free recall and updating) were conducted separately in each age group to assess associations between these variables.
Acknowledgements : I dedicate this article to the memory of late French physicist  Yves Couder  , who used to say that the dynamics of morphogenesis is to be looked for in the geometry. I acknowledge the financial support of the master BioMedical Engineering of  Université de Paris Cité , of the generous donators of the Fondation CNRS and of the  SATT ERGANEO. I acknowledge the friendly support of  Amanda Brun ,  Nicolas Chevalier ,  Florence Gazeau  and  Hugo Salmon. I am grateful to anonymous reviewers for constructive remarks.
Neuroimaging Whole-brain isotropic high-resolution T1-weighted MPRAGE images (TR = 1. 9 s; Flip angle = 9 • ; TE = 2. 53 ms; TI = 900 ms; FOV = 192 × 192 × 176; voxel size = 1 mm 3 ) were acquired for 23 of the 26 DLB patients during the same day as the behavioral testing using a 3T Siemens Magnetom VERIO MRI (Siemens, Erlangten, Germany) equipped with a 32-channel coil. Data analysis was carried out using the Statistical Parametric Mapping package (SPM12; Wellcome Trust Centre for Neuroimaging). Briefly, after visual inspection and quality control, the images were segmented into 6 different maps of tissue class probabilities, including gray and white matter. These maps were subsequently spatially normalized using non-linear deformations (DARTEL algorithm) to obtain a study-specific template, which was in turn coregistered to the MNI152 template.
These classifiers were embedded in an 8-fold cross-validation framework, and performance was measured using the mean area under the curve (AUC) at each timepoint for each participant. The performances of the classifiers over time are provided in Figure  2A. At the group level, we averaged the AUC scores and tested the difference from chance level (AUC = 0. 5) at each timepoint, using the cluster-based permutation t test implemented in the permutation_cluster_1samp_test() function of the MNE Python package  101  , and controlling for the familywise error rate across multiple timepoints. 57 This procedure revealed an early significant decoding time window 250-580 ms after the onset of the central target object (cluster p value <0. 001), with maximum decoding accuracy (AUC = 0. 69, SD = 0. 07) occurring 110 ms after the appearance of the intrusive image (see Figure  2A ).
The gray dashed line indicates the time peak of maximum decoding accuracy 160 ms after the appearance of the intrusive image on the screen. The left subpanel shows the decoding AUC across frequencies at the highest decoding score timepoint. Theta frequency (3-7 Hz) encoded more information (maximum AUC reached at 5 Hz). implemented in MNE Python, resulting in 27 signals corresponding to frequencies between 3 and 30 Hz. At each timepoint, and for each frequency, we extracted the averaged AUC score using the same 8-fold crossvalidation scheme. In the resulting time-frequency heatmap, we observed that the maximum classification performance for the early presence or absence of the intrusive image (i. e. , 160 ms after the intrusive scene) depended on theta (3-7 Hz) frequencies. This early decoding also depended on alpha (8-12 Hz) and low beta (13-20 Hz) ranges (see Figure  2B ).
To further increase the reliability of our decoding procedure, we estimated the null distribution of decoding probability by repeating the classification analysis 200 times with randomly permuted labels in the training dataset (attention task), as described by Linde-Domingo and colleagues (2019). We then used a threshold corresponding to the 95 th percentile of this null distribution to accept the prediction of an intrusion. This additional step reduced the odds of the prediction of an intrusive image being induced by noise in the EEG signal. The resulting binary map (corresponding to N attention x N TNT timepoints) for a given trial was then convolved with a Gaussian kernel (with a full width at half maximum of 200 ms), producing a timecourse of mental event reactivation describing the probability of an intrusion across No-Think trials.
EEG recording EEG activity was continuously recorded by a GES 300 amplifier (Electrical Geodesics, Inc. Eugene, OR, USA) using an EGI Hydrocel Geodesic Sensor Net (HGSN-128) with a dense array of 128 Ag/AgCl sensors. Impedances were kept under 100 kU, and EEG channels were referenced to a vertex reference Cz and ground to CPPZ (fixed by the EGI system). The signal was sampled at 20 kHz frequency with a 24-bit A/D and was online (hardware) amplified and low-pass filtered at 4 kHz. The signal was filtered by a Butterworth low-pass finite impulse response (FIR) filter at 500 Hz and subsampled at 1 kHz. An electro-oculogram was performed using four electrodes placed vertically and horizontally around the eyes. EEG data were processed offline using Netstation 4. 4. 2 (Electrical Geodesics Inc. , Eugene, OR, USA).
QUANTIFICATION AND STATISTICAL ANALYSIS Preprocessing EEG analyses were performed using version 1. 0. 3 of the MNE library  101, 102  implemented in Python 3. 6, and followed recently recommended analysis steps. 103 Out of a total of 115 EEG electrodes, we removed 13 peripheral ones (F11, FT11, T9, TP11, P09, I1, Iz, I2, P010, TP12, T10, FT12, F12), owing either to poor impedance quality or lack of contact with the scalp. We low-pass filtered the raw data at a 30 Hz cutoff frequency using a FIR filter and referenced them to a common average. For the remaining 102 channels, sensors that showed no signal, constant white noise or an intermittent signal, as observed during the EEG recording, were interpolated. Cue onset was adjusted for the delay (+15 ms) between trigger generation and image appearance on the screen in the Faraday cage, measured using photodiodes during preliminary tests.
Multivariate decoding We used random forest classifiers as implemented in Scikit-learn v1. 0. 2  98  to decode the appearance of an unexpected image on the screen during the attention task and to infer the presence of memory intrusion during the No-Think trials of the TNT task. A random forest algorithm is an ensemble method that combines many decision trees in a single model  99. Each of the trees is built on bootstrap samples of the training data, and each split within a particular tree is made upon a random subset of the predictors. The advantages are that random forests can handle very high dimensional datasets, even when they contain a small number of observations or highly correlated predictor variables, and are less likely to overfit. As the number of trials could differ between conditions after preprocessing, we specified an unbalanced class weight parameter for our analysis and used 50 estimators.
Decoding intrusion versus non-intrusion in the attention task We used the preprocessed EEG recording from the 102 selected electrodes with 10-ms time bins. As some trials were excluded, based on Autoreject artefact detection  104  , and trimmed for aberrant reaction times (>3000 ms and <400 ms), the dataset contained different numbers of trials for each class. Although our design incorporated both neutral and disgusting intrusive images in the attentional and TNT procedures, we combined these stimuli here to improve the performance of the multivariate decoders. To control for overfitting and ensure that the classifiers were tested on independent data, the classification was evaluated through an eightfold cross-validation framework: each classifier was trained on 7/8 of the data and tested on the remaining 1/8.
Decoding intrusion versus non-intrusion during the TNT task, using the attention task To decode memory intrusion during the TNT task, we relied on the temporal generalization method  55  to infer the occurrence of similar intrusive patterns during this procedure, as learned from the attention task. First, we trained decoders to detect the presence of an unexpected image during the attention task. This part was performed using the SlidingEstimator() function in MNE Python. The predictors at each timepoint either side (À250 ms to 1500 ms) of the central image presentation were the raw EEG signal from the 102 selected electrodes after preprocessing, with 10-ms bins. To restrict our approach to intrusion-related activity, we only used the raw EEG signal 250-500 ms after the onset of the central object presentation (i.
For each participant and suppression trial, the classifiers provided a probability of intrusion for every timepoint of the suppression cue during the TNT task, as well as every timepoint of the attention task used for training, producing an N attention x N TNT timepoints matrix of probability. To further increase the reliability of our decoding procedure, we estimated the null distribution of intrusion probability by repeating the classification analysis 200 times with randomly permuted labels in the training dataset, as described by Linde-Domingo and colleagues (2019). We then used a threshold corresponding to the 95 th percentile of this null distribution to accept the prediction of an intrusion. This additional step reduced the odds of the prediction of an intrusive image being induced by noise in the EEG signal.
EEG and decoding statistics Statistical analyses of the behavioural data were performed using SciPy  105  and Pingouin v. 0. 5. 0 packages. 100 EEG and decoding data were analysed using the MNE library v1. 0. 3  101, 102  and MATLAB R2021b. Standard paired or one-sample t-tests were used to estimate the significance of the decoding (i. e. , AUC in time or time-frequency domain; Figure  2 ), memory strength (Figure  4 ), or intrusion slope timecourses (Figure  5 ). For the transition analysis (Figure  5 ), LME models were performed using the fitlme function of MATLAB, treating participants and item identity as random effects, and the future status of the memory (i. e. , stable or regulated) as the fixed effect of interest. Parameters were estimated using the restricted maximum likelihood fit method and degrees of freedom were corrected using the Satterthwaite method.
METHOD DETAILS Material Attention We selected 48 objects from the Bank of Standardised Stimuli (BOSS) (Brodeur et al. , 2010, 2014) and 32 scenes from the Nencki Affective Picture System (NAPS) database. 95 For the intrusion and exploration conditions, the stimuli were paired so that each object was associated with the same condition across the eight experimental blocks. The stimuli were divided into three subsets containing equal numbers of neutral and disgust stimuli (16 in each group). These three subsets were then assigned to the exploration, intrusion, and non-intrusion conditions (order counterbalanced across participants).
Considering human studies, FMT received a single approved indication so far-i. e. the treatment of recurrent Clostridioides difficile infection. More recently, the research has focused on other therapeutic avenues including in AD, although no such FMT studies are published so far in human. The unique previous study (NCT03998423; https:// www. clini caltr ials. gov/) had unfortunately to be interrupted due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) pandemia. Neverheless, FMT studies in animal models of AD-like pathology showed globally (although there are yet some discrepancies between the studies, see above) encouraging results in terms of reducing neuroinflammation, Aβ accumulation and synaptic dysfunction  [143, [161] [162] [163] [164] [165]. Overall, the available data point to manipulating microbiota and/or their metabolites as a potential innovative, attractive and non-invasive therapeutic approach for restoring optimal synaptic plasticity and associated cognitive functions in AD, at least indirectly by modulating innate and adaptive immunity both at the periphery and in the CNS (Fig.
Bioinformatic Comparison between Human and Canine Netrin-1 Human and dog Netrin-1 gene and protein sequences were compared using the NCBI database. Sequence alignment between these two species were performed with Seaview software  [25]. Canine netrin-1 protein sequences were predicted. Among all 604 amino acids composing the protein in the two species, five amino acids were different, establishing a 98. 5% homology between the two sequences. Sequence alignment showed the first two different amino acids were located before the laminin domain, the three others within the NTR domain (Figure  S2 ). Such homology justified the use of antibodies directed against human peptide for our study in dog.
Quantification of Intracellular Netrin-1 by Flow Cytometry Cells in RPMI medium (26 nodal lymphomas and five normal lymph nodes) were formalin-fixed for 15 min, and permeabilized for 10 min with a 0. 3% saponin solution (Sigma Aldrich, France). After washing, cells were incubated for 30 min with netrin-1 rat anti-mouse monoclonal antibody (clone, diluted 1:100, R&D Systems, Minneapolis, USA), and 30 min with a secondary polyclonal goat anti-rat/mouse FITC (clone F047902-2, Agilent, Carpinteria, CA). Fluorescence was measured after an ultimate washing, with a cytometer Accuri (Becton Dickinson, France). Results were analyzed with the BD Accuri C6 Software ®® (Becton Dickinson, Le Pont de Claix, France). To discriminate fluorescence intensity between culture cells, evaluation of difference in mean fluorescence intensity (∆MFI) was performed, as published in Fellman et al.
Quantification of Netrin-1 Expression by RT-qPCR Total mRNAs were extracted from frozen organ samples (8 normal lymph nodes) and fine-needle aspirates (14 nodal lymphomas) using Nucleospin RNAII kit (Macherey-Nagel, Hoerdt, France) and 1 µg was reverse transcribed using the iScript cDNA Synthesis kit (Bio-Rad, Hercules, CA, USA). A real-time quantitative PCR was performed on a LightCycler 2. 0 apparatus (Roche Life Science, city name, France) using the LightCycler FastStart DNA Master SYBERGreen I kit (Roche Life Science, Meylan, France). The hprt housekeeper gene was chosen as an internal control for all amplifications  [27]. Primers sequences were designed from the known sequences of canine orthologs of genes of interest, according to Ensembl and NCBI databases (Table  2 )  [28]. BlastN searches were performed to confirm gene specificity of the newly designed sequences and the absence of DNA polymorphisms.