dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis The data processing pipelines were implemented in Matlab 2019a (MathWorks Inc. , USA). Statistical modeling was carried out in R Studio version 4. 0. 3 (R Core Team), using the lme4 package  ( Bates et al. , 2014 )  for model fitting. Participant #6 was excluded from the analysis, due to a technical issue during data acquisition which resulted in the loss of EEG data.
Behavioral data processing Finger-taps were processed in order to return timeseries aligned to the neural ERFA, expressed in the same unit of measure (i. e. , Hz over ms). Whenever a timestamp was followed by a second timestamp with < 350 ms interval, it was considered as a false positive and therefore removed (false positives could occasionally be recorded when a participant pushed the tapping pad for too long or accidentally laid the hand on it). The intervals between the remaining timestamps were then linearly interpolated from 0 to 1 at 1 kHz sampling rate. The resulting ramp wave was scaled to 2 , providing an estimate of the fingertaps phase with a temporal resolution of 1 ms. Instantaneous frequency timeseries were computed as the first derivative of the unwrapped phase angles time series  ( Boashash, 1992 ) , and converted to Hz as indicated in  ( Cohen, 2014 ) : 𝐻𝑧 𝑡 = 𝑠 ( 𝜙 𝑡 -𝜙 𝑡 -1 ) 2  where s indicates the data sampling rate and   indicates the (unwrapped) phase angle at time t.
Neural data processing Pre-processing. The pre-processing pipeline was realized integrating functions from the Fieldtrip toolbox  ( Oostenveld et al. , 2011 )  for Matlab. Bad channels were identified by visually inspecting the raw timeseries in combination with the distribution of variance across channels. The recording was re-referenced to the average of all electrodes after rejection, to avoid noise leakage into the common average. 3. 23 bad channels per participant were removed on average (std = 1. 95). A sixth-order Butterworth high-pass filter with 1 Hz cut-off was applied to the raw recordings to remove slow drifts. We show in Supplementary material 1 that, for these parameters, the high-pass filter does not influence the oscillatory dynamics of interest in the present work. A low-pass sixth-order Butterworth filter with 40 Hz cut-off was applied to remove high-frequency muscular activity.
5. 03 artifactual components per participant were identified and removed on average (std = 4. 36). The dataset was inspected prior to ICA decomposition and following ICA back-projection to assess the quality of the artifact removal. Special attention was given to the frontal clusters of electrodes maximally contaminated by eye-related artifacts. Rejected bad channels were reconstructed after artifact removal, by computing the average activity from neighboring electrodes indicated by the template provided by ANT-Neuro for 64-channel waveguard TM original caps. No segmentation in epochs was performed up to this point, since continuous recording was needed for performing source separation.
Source separation. Generalized eigendecomposition (GED)  ( Cohen, 2022 )  was used to extract a perceptual and the sensorimotor components attuned to the stimulation frequency. The procedure described below is the same for both components, with the only difference that the respective inputs were 60 seconds of data recorded during the listening task, and 60 seconds of data recorded during finger-tapping in the absence of perturbations. This allowed to design two separate spatial filters, which were subsequently applied to the data recorded during the 405 seconds of finger-tapping in the presence of perturbations (see Fig. 1  ). As first described in the context of source separation for rhythmic entrainment  ( Cohen and Gulbinaite, 2017 ) , GED allows to avoid channel selection bias while optimizing the signal-to-noise ratio between the entrained component and the broadband neural activity.
Statistical modeling ERFAs to tempo change perturbations were modeled via polynomial fitting to the curves, downsampled by a factor of 10 for computational feasibility. For phase shifts, the discrete integral of the ERFA from 0 to 1500 ms interval was computed via trapezoidal method with unit spacing, using the trapz() Matlab function. This returned signed areas under the curves, which we interpreted as a measure of entity of the error correction sensitive to the direction of the corrective response. Baseline normalization is crucial in order to obtain signed areas, as data need to be zero-centered. ). The spatial pattern of the perceptual component did not exhibit the centro-frontal activation expected from EEG auditory evoked responses  ( Nozaradan et al. , 2011 ; Nozaradan et al. , 2012 ; Nozaradan et al.
The fitting was performed with the sigm_fit() Matlab function, defining no fixed parameters. For the neural ERFAs, the effects on the response variables of interest were tested in 3 × 2 factorial designs with Direction (Negative, Null, Positive) and Component (Perceptual, Sensorimotor) as factors. For the behavioral ERFAs, the effects on the response variables of interest were tested in 3 × 1 factorial design with Direction (Negative, Null, Positive) as the only factor. When pairwise comparisons between Negative and Positive directions were performed after omnibus tests, Bonferroni correction was applied for a significance level of  = 0. 05.
Acknowledgments The present study was funded by  Bijzonder Onderzoeksfonds (BOF)  from  Ghent University (Belgium) , in the context of a joint-PhD project with the  University of Lille (France)  ( I-SITE ULNE program ; grant number:  01D21819 ). The authors are grateful to  Ivan Schepers  for building the hardware of the finger-tapping device, to  Canan Nuran Gener  for her precious help in collecting the data, and to the three anonymous reviewers whose thorough comments considerably improved the quality of the manuscript.
Apparatus Stimuli were presented on an LCD monitor (Samsung 2232RZ, 47 cm wide) with a refresh rate of 100 Hz and a resolution of 1680×1050 pixels. Stimulus presentation and data collection were performed using MATLAB with the Psychophysics Toolbox  (Brainard, 1997)  and the Eyelink Toolbox  (Cornelissen et al. , 2002). Viewing was binocular and movements of the right eye were monitored with an EyeLink 1000 Plus (SR Research, Mississauga, ON, Canada) at a sampling rate of 1000 Hz. Head movements were restrained with a chinrest located 70 cm from the screen.
Behavioral analyses Linear mixed models were fitted using the 'lme4' package  (Bates et al. , 2015)  and the lmerTest package  (Kuznetsova et al. , 2017)  in R (R Core  Team, 2018). Factors with two levels were coded using sum contrasts  (Schad et al. , 2020). The models were adjusted to accommodate convergence issues. Specifically, the parsimony principle guided the selection random effects  (Bates et al. , 2018). Indeed, for some analyses, the inclusion of the maximal random structure led to convergence failures. In these cases, we performed a Principal Component Analysis (PCA) to isolate the random effects that contributed the least to model fitting and we removed them from the final model. Significance of the fixed effects was determined using type-II Wald tests with the 'car' package in R  (Fox & Weisberg, 2011).
Pupil analyses Raw pupil data were firstly filtered with a high-pass non causal Finite Impulse Response (FIR) filter of 1/12 Hz 1  in order to remove slow oscillations in the pupil response not related to the task. We then extracted two types of segments: 1) from -100 to +1200 ms time-locked to stimulus onset, and 2) from -1400 to +400 ms time-locked to perceptual response onset. Individual segments were corrected with a 100 ms baseline. Specifically, we subtracted the average pupil size observed from -100 ms to 0 ms prior to stimulus onset for each trial from each time point of the corresponding trials. Blinks were linearly interpolated. The correct interpolation of blinks was verified through visual inspection of each segment. Visually inspecting the segment allowed us also to remove trials containing artifacts (e.
Given that the stimulus-locked segment was not contaminated by blinks, we used this time window for the classification. The classification procedure implemented a Monte Carlo cross-validation method  (Dubitzky et al. , 2007). Notably, each classifier was trained on 90% of the available dataset and tested on each of the remaining trials. This procedure was repeated 1000 times. Each time a random 90% of trials was used as training set and the rest as test set. To avoid classification biases at each repetition of the procedure we matched the number of high and low confidence trials to classify. Classification accuracy was estimated by calculating for each time-point the proportion of trials that the classifier correctly identified as high or low confidence trials. The mean classification performance of the 1000 shuffling within each participant and time-point was taken as the classifier accuracy for that specific participant and time-point.
Subject demographics Postmortem human grey matter from the ACC (Brodmann's area 24) was obtained from the NIH NeuroBioBank. Specimens were obtained across six biorepositories: University of Miami Brain Endowment Bank, University of Maryland Brain and Tissue Bank, Harvard Brain Tissue Resource Centre, The Human Brain and Spinal Fluid Resource Centre, Mt. Sinai Brain Bank and the Brain Tissue Donation Programme at the University of Pittsburgh. The cohort consisted of 44 individuals with MDD and 36 unaffected comparison subjects (herein referred to as controls). All demographic information and medical data were provided by the NIH NeuroBioBank. This study was approved by the University of Wollongong Human Research Ethics Committee (HE13/069). All groups were matched for the demographic variables, age, postmortem interval (PMI), RNA integrity number (RIN), hemisphere, and sex (all p > 0.
Statistical analysis SPSS was used for all statistical analysis (Version 28, IBM, Armonk, NY, USA). Extreme outliers were identified in diagnostic and sub-diagnostic groups via boxplot (defined as three times the interquartile range) and excluded. Data for each gene or metabolite was tested for normality using Shapiro-Wilk test and Q-Q plot assessment. When data was not normally distributed, data was natural log transformed to achieve relatively normal distribution. If normal distribution was not achieved, non-parametric analyses were used. To examine sex-diagnosis interactions, in addition to any main diagnosis or sex effects, two-way ANOVAs or two-way ANCOVAs were used. Covariates were identified via Spearman's correlations between relevant genes/metabolites and PMI, RIN or age in MDD and controls separately.
Covariates were subsequently included if they were found to significantly contribute to the two-way ANOVA model. Where a significant interaction was identified, Bonferroni simple main effects were used to determine specific changes. One-way ANOVAs or ANCOVAs with a Bonferroni post-hoc were used to examine suicide subgroup differences (controls, MDD-suicide, and MDD-non-suicide) for gene expression and metabolite levels. Metabolites and genes of interest were investigated for significant correlations using Pearson or Spearman's correlations where appropriate. Significance was set at p < 0. 05. All data are presented as means ± SEM.
FUNDING This study has been conducted with the support of the  Australian Government Research   Training Programme Scholarship  awarded to SJB. XFH is a recipient of a  National Health and Medical Research Council (Australia)   Investigator Grant  (# 1176503 ). CSW is funded by the  NSW Ministry of Health, Office of Health and Medical Research. CSW is a recipient of a  National Health and Medical Research Council (Australia) Principal Research Fellowship (PRF)  (# 1117079 ). Open Access funding enabled and organized by  CAUL  and its  Member Institutions.
Statistical analysis Data are expressed in percentage for categorial variables and in median for continuous variables (interquartile range  [IQR] ). Bivariable association was evaluated by using Mann-Whitney U test for unpaired data or Wilcoxon signed rank test for paired data. All analyses were performed at a two-sided alpha level of 5 %. P-value adjustment for multiple analysis was performed with Bonferroni correction. A p-value of <0. 05 was considered to indicate statistical significance. All analyses were performed with Prism 9 v9. 4. 0.
Tumor volumes, radiographic features, clinical presentations, and clinical outcomes were evaluated. Results: The 107 patients included 22 patients with grade 3 tumors, 39 with grade 4, and 46 with grade 5. No statistical differences were noted among groups for demographic data or complication rates. Unlike grades 3 and 4 patients, grade 5 patients presented with worse hearing (p < 0. 001), larger tumors (p <0. 001), lower rates of gross total resection (GTR), longer hospital stays, and higher rates of vestibular dysfunction. Conclusions: With edema detected in 43% of this cohort, we propose this radiographic feature of grade 5 holds relevance to further guide the surgeonpatient discussion for treatment. Special considerations are warranted for grade 5 vestibular schwannomas given the preoperative findings of worse hearing, lower rates of GTR, longer hospital stays, and 96% who pursued postoperative vestibular therapy.
, 2015; Moreno-Bote, 2010; Pleskac & Busemeyer, 2010; Yeung & Summerfield, 2012). However, most such attempts have required additional assumptions and components on top of the DDM itself, which detracts from the elegance of the original model and its ability to so reliably account for key decision variables. Given the success of the DDM in accounting for other key aspects of choice (i. e. , choice probability and RT), and assuming that confidence about a choice is inherently linked to those other aspects  (Kiani & Shadlen, 2009; Van Den Berg et al. , 2016) , one might conclude that the DDM should be able to simultaneously account for all three variables without the need for any ad hoc features. Calder-Travis et al. (2016)  conducted a thorough analysis of various sorts of DDM found in the literature, specifically focused on their ability to predict choice confidence in a variety of datasets.
Results CLDN1 is overexpressed in an HCC subtype characterized by a progenitor phenotype and an "immune-low" type of TME To investigate the role of CLDN1 in HCC, we first analyzed CLDN1 mRNA and protein levels in individuals with HCC. Computational analysis of data retrieved from Genomic Data Commons Data Portal and the human protein atlas  6  revealed that CLDN1 is the most highly expressed Claudin family member in HCC at both the mRNA (p <0. 0001, Fig. 1A ) and protein levels (Fig. S1A ). Indeed, 75% of liver tumors show medium or high CLDN1 expression (Fig. S1A ). Moreover, CLDN1 is significantly upregulated in pre-malignant dysplastic nodules of cirrhotic liver (GSE102383, p = 0. 03, Fig. S1B ), as well as HCC tissue compared to matched non-tumorous adjacent liver (GSE113996, p = 0. 02, Fig.
S1C ). Immunostaining of CLDN1 in healthy and HCC tissue confirmed CLDN1 overexpression, particularly in high grade tumors (p <0. 0001, Fig. 1B-C ; p = 0. 06, Fig. S1D , Table  S3 ). Interestingly, CLDN1 showed aberrant non-junctional localization in HCC in contrast to primarily junctional localization in healthy liver tissue (Fig. 1D ). The predominantly non-junctional localization of CLDN1 in HCC tissues was further confirmed by double immunohistochemistry showing absent co-localization of CLDN1 with TJ protein ZO-1 (Fig. 1E ). HCC is characterized by a strong intra-and inter-tumoral heterogeneity and various molecular phenotypes. 1  Characterization of CLDN1 expression in a large HCC single-cell RNA sequencing (scRNAseq) dataset revealed that tumor cells are the key cell population with robust CLDN1 expression (Fig.
1F , Fig. S2A-B ). Gene set enrichment analysis (GSEA) revealed that highly CLDN1-expressing tumor cells (Fig. 1G , Fig. S2C-D ) exhibited a progenitor or stem cell-like phenotype (false discovery rate [FDR] <0. 05, Fig. 1H ). Similar results were obtained in various independent large HCC patient cohorts with available bulk transcriptomic data (GSE5975, p <0. 0001, Fig. S2E , GSE112791, FDR <0. 001, Fig. S2F ). ScRNAseq data from HCC tumors revealed a paucity of immune cells and suppression of immune signatures in tumors with low CLDN1 expression in tumor cells (FDR <0. 05, Fig. 1I , Fig. S3A-B ), suggesting a functional impact of CLDN1 expression on the tumor immune microenvironment (TIME). Signatures of angiogenesis and desmoplastic reaction on the other hand were significantly induced (FDR <0.
01, Fig. 1I ). Similar results were obtained in an independent large patient cohort on the bulk transcriptomic level (GSE112791, FDR = = Enrichment of gene sets related to stemness and progenitor cells in CLDN1 high tumor cells (GSE151530, FDR <0. 05, Kolmogorov-Smirnov test). (I) Enrichment of gene sets related to an immune-enriched or immune-active tumor immune microenvironment in tumors with low CLDN1 expression. Bars indicate NES of significant enrichments (GSE151530, FDR <0. 05, Kolmogorov-Smirnov test). (J) Enrichment of genes specific for Boyault Liver Cancer Subclass G1 in tumors with high CLDN1 expression (GSE20140, n = 164 FDR <0. 001, Kolmogorov-Smirnov-test). (K) CLDN1 expression in HCC tissue predicted to confer response (n = 42) or resistance (n = 98) to sorafenib treatment (GSE109211, p <0.
0001, Student's t test). (L) CLDN1 mRNA expression in the non-cancerous tumor-microenvironment of individuals with HCC with (VM1, n = 9) or without (VM0, n = 11) venous metastasis (GSE5093, p <0. 0001, Mann-Whitney U test). (M) Recurrence-free survival in individuals with high (50% above median, n = 26) vs. low (50% below median, n = 26) CLDN1 expression in tumor adjacent liver tissue (GSE76427, p = 0. 008, log rank test). Boxplots represent median (-), 1 st and 3 rd quartile (bottom and top of the box) and single data points (). Vertical bars show NES of significantly (FDR <0. 05, Kolmogorov-Smirnov test) altered gene sets. *p <0. 05, **p <0. 01, ***p <0. 001, ****p <0. 0001. FDR, false discovery rate; FPKM, fragments per kilobase million; HCC, hepatocellular carcinoma; HpSC-HCC, progenitor-like stem cell signature; MH-HCC, hepatocyte mature HCC; NES, normalized enrichment score; NJ, non-junctional; VM0, HCC adjacent tissue without venous metastasis; VM1, HCC adjacent tissue with venous metastasis; t-SNE, t-distributed stochastic neighbor embedding.
0. 04, Fig. S3C ). As expected, given the correlation with a progenitor phenotype and an immune-low or -excluded TME, HCCs with high CLDN1 expression showed enrichment for genes specific for the Boyault G1 subclass of HCC 7 (FDR <0. 001, Fig. 1J ). While we did not detect an association of CLDN1 expression in HCC tumors with survival (Fig. S3D ), the association with transcriptomic signatures of sorafenib resistance  8  supports a prognostic function of CLDN1 (GSE109211, p <0. 0001, Fig. 1K ). Furthermore, high CLDN1 expression in HCC adjacent liver tissue was associated with metastatic behavior of the corresponding tumor and with worse postresection recurrence-free survival (GSE5093, p <0. 0001, Fig. 1L , GSE76427, p = 0. 008, Fig. 1M ). Collectively, these data suggest that CLDN1 is a potential hallmark for tumor cell differentiation and TIME.
Inc. The protocols were reviewed and approved by the Institutional Animal Care and Use Committee of CrownBio prior to execution. Mice were randomly assigned to the study groups. For details on methodology, please see the supplementary information. Human samples and patient cohorts Human liver tissue samples for ex vivo perturbation studies were derived from patients with liver resections at the Pôle Hépato-digestif, Strasbourg University Hospitals (2014-2021). All patients provided written informed consent within the ethical principles of the declaration of Helsinki, approved by the local and national ethics committee (comités de protection des personnes, protocol RIPH2, LivMod-IDRCB 2019-A00738-49, ClinicalTrial NCT04690972). Sampling of human liver tissue for immunostaining was conducted by Charles River Laboratories and US Biomax Inc.
.  Electrical measurement Before electrical tests of memcapacitors, the silicon terminal is grounded. C-f, C-V, C-t, and pattern recognition data were measured and collected using a semiconductor parameter analyzer (Keithly S4200A). LTP and LTD tests were carried out with an Agilent E4980A LCR meter. All capacitance value is measured under the ac voltage signal with an amplitude of 0. 2 V and a frequency of 1 kHz after removing the biased voltage pulses.
We searched the database using a predefined search algorithm to identify potentially eligible studies. Articles in which patients had isolated anterograde amnesia with no retrograde amnesia were withdrawn because of the probable different brain mechanisms implicated in their pathophysiology  (12). We discarded studies involving EEG investigation because of the poor spatial resolution of this technique. Studies only involving structural techniques were also put aside as they did not add any functional information. All duplicate studies were removed. We independently selected studies based on their titles. Then, all online abstracts were reviewed, and full-text papers were retrieved when relevant. The procedure followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis criteria (PRISMA) (13).
Although this may be linked to a probable sampling bias due to the limited number of patients included in the review, it is possible that sex plays a role in the results. Secondly, patients included in the review were very heterogeneous in term of clinical features (ranging from GARA to FRA and sometimes LOI) and comorbidities. It is obvious that the actual presence of an active comorbidity such as depression or functional neurological disorder may have influenced the results of the studies. We tried to compare GARA and FRA patients but failed to draw any conclusion because of the disparate imaging techniques employed to assess the brain modifications, and we were not able to analyze imaging data based on a LOI grouping because of the heterogeneity of such a thing. This heterogeneity adds another constraint to the generalization of these data.
In silico docking The model of the iPRC1 VHH was generated by homology modelling using SWISS-MODEL  [28] [29] [30] [31]. Docking of the iPRC1 CDR3 on the ICL3 of either inactive FSHR (PDB: 812H) or activated FSH-FSHR-Gs complex (PDB: 812G) was performed using the standard 'EASY' access settings on HADDOCK 2. 4  [32, 33]. The generated HADDOCK models were visualized, superimposed onto each other and analysed using the PYMOL Molecular Graphics System, Version 2. 0 (Schr€ odinger, LLC, New York, NY, USA).
Here, the interaction between iPRC1 and the human FSHR was assayed by a BRET method using the VHH C-terminally fused to the Venus BRET acceptor, and the FSHR or the arginine-vasopressin 2 receptor (AVPR2) fused to the RLuc8 BRET donor. With increasing amounts of transfected VHH-Venus plasmids (Fig. 3A ), a dosedependent and much higher binding of iPRC1 to FSHR than to AVPR2 was observed, even though a weak binding signal was detected with AVPR2. There was no impact of the presence of the ligand on iPRC1 binding, which indicates that iPRC1 has no preference for inactive or ligand-induced active conformations of the FSHR. The maximum binding estimated from fitting the data to specific binding curves with Hill slope showed a statistically significant difference for FSHR, when compared to AVPR2 (Fig.