dataesr/scientific-paragraphs-categorization huggingface dataset:
In this regime, the model captures the multistability and noise-driven exploration of the dynamic repertoire explored previously in computational studies  [2, 31, 32, 51, 52]. The data features extracted from the time series provides a link between the empirical data and the model. Here, the functional structure in the brain is carried by the rare high amplitude cofluctuation events as it was previously demonstrated in empirical fMRI data  [19, 21, 53] , and in simultaneous EEG and fMRI measurements  [44]. Similarly, recent modeling study has shown the role of structural modules of the network in shaping the co-fluctuation events  [54] , which is aligned with the brain network as the symmetry breaking gives rise to the low-dimensional dynamics. The slow time scale fluctuations of the dynamical functional connectivity reflect the movement of the brain activity between the low-and high-activity subspaces of the manifold.
Empirical data and spatial analysis The functional gradient on empirical data was computed from the group connectivity matrix of the Human Connectome Project dataset using the brainspace toolbox  [93]. For a simulated resting state session with G w , the time in avalanche was computed for each node as total time for which the r i (t) was above the threshold of 3 standard deviations, and the event z-score as a sum of z-scored BOLD signal in time-points marked as events. The nodes were then groupped according to the cortical hieararchy  [76]  projected to the Desikan-Killiany parcellation. A parcellation-based BOLD signals of a resting-state session from a subject from the Human Connectome Project  [94]  were used to validate the separation of the events in the low-dimensional embedding. The data consisted of 1200 time points sampled at 720 ms in the Desikan-Killiany parcellation  [89]  with 70 cortical regions.
Finally, the plates were washed by the same method again and 150µl of reading buffer was added to each plate and read using a Mesoscale Discovery instrument (MSD SECTOR Imager 2400). The unknown levels of cytokines and chemokines released in the cell culture media were determined by the Meso Scale software. Both raw data statistical analysis (using one way Anova test followed by Dunnett's multiple comparison test), and graphs representation were performed using GraphPad Prism 5 software.
1). BioMAP pro les can be compared against a proprietary reference database of > 4,000 BioMAP pro les of bioactive agents (biologics, approved drugs, chemicals and experimental agents) to classify and identify the most similar pro les. Common biomarker readouts are annotated when the readout for both pro les is outside of the signi cance envelope with an effect size > 20% in the same direction. Concentrations of test agents that have 3 or more detectable systems with cytotoxicity are excluded from similarity analysis. The similarity between agents is determined using a combinatorial approach that accounts for the characteristics of BioMAP pro les by ltering (Tanimoto metric) and ranking (BioMAP Z-Standard) the Pearson's correlation coe cient between two pro les. Pro les are identi ed as having mechanistically relevant similarity if the Pearson's correlation coe cient is ≥ 0.
BioMAP® Diversity PLUS® To further investigate the anti-in ammatory effects of the sage across a broader set of cellular contexts, as well as seek new insights into potential mechanisms of action, the extract was assessed using the BioMAP® Diversity PLUS® Panel. This systems biology approach provides 148 biomarker readouts across 12 different primary cell-based disease models, advanced analytics, and a comprehensive reference database for insights on mechanism of action, e cacy and safety. The panel is typically applied in pharmaceutical development, but has been used by the U. S. Environmental Protection Agency, and has previously been shown to successfully work for plant extracts  (43). Figure  5  summarises the results of pro ling sage extract in BioMAP® Diversity PLUS®. The extract showed dose-dependent effects on multiple in ammation and immunomodulatory markers across multiple cell systems; biomarker annotations included reductions in ICAM-1, VCAM-1, IL-17 and IL-10 among other markers (Figure  5 ).
In addition to the reduction of in ammation markers in this panel, there was also a reduction of Plasminogen activator inhibitor-I (PAI-I), EGF receptor (EGFR), Collagen-I (Col-I) and Col-III; suggesting that the sage extract may also modulate tissue remodelling in response to perturbation (Figure  5 ). This cell panel, as well as the 3C (Venular endothelial cells stimulated with TNFα, IL-1β, and IFN ), and SAg (Venular endothelial cells and peripheral blood mononuclear cells stimulated with TCR ligands) also showed antiproliferative effects (Figure  5 ), which may relate to the tissue remodelling effects, but also to potential anti-cancer activities proposed for sage previously  (23). In addition to offering independent support for anti-in ammatory effects, and suggesting potential tissue remodelling and antiproliferative effects for the sage extract, the BioMAP® Diversity PLUS® Panel also provides the possibility to map a test agent's response pro le against a database of known agents to suggest potential mechanistic similarity with any of these agents.
Consistent with this, a previous analysis of the sage extract on the BioMAP® Diversity PLUS® Panel, run across a slightly different concentration range, showed a below threshold connection to two COMT inhibitor agents among the top three hits: Entacapone and Phenazopyridine (Additional le 3). The other agent in the top three was SR1001, a retinoic acid-related orphan receptor-α (RORα)/RORγ ligand, which could offer a potential mechanism for the IL-17-related effects of sage  (53). However, it must be reiterated that these database hits were below the level of signi cance (r < 0. 7), so they cannot be considered as sharing mechanistically relevant similarity based on this analysis alone. Given the signi cant connection to caffeic acid, and previously demonstrated activities of this compound (36), the ability of the sage extract to inhibit COMT and MOA activity was assessed in vitro.
Acknowledgements The authors would like to thank  Kate Feeney  of  Euro ns Discovery  for assistance with the enzymatic assays performed by Euro ns Discovery. The authors would also like to thank  Alison O'Mahony  formerly of  Euro ns Discovery (DiscoverX)  and  Sheryl Denker  of  Euro ns Discovery (DiscoverX)  for assistance with the BioMAP® Diversity PLUS® Panel analysis, as well as with discussion and interpretation of the results.
, 2011). To achieve this goal, we analyzed our data in two different ways. First, we examined qualitative differences between patterns of thought captured by self-and probe-caught experience sampling. Here, we derived patterns of experience from both self-and probe-caught methods separately and compared these to those seen across the entire data set. This allows us to establish the degree to which patterns formed by each method yield descriptions of experience which differ from those seen when analyzed collectively. Second, we examined whether the different methods of experience sampling capture 'quantitative' differences in a common set of experiences. In this analysis, we derived common patterns of experience using data from both methods and examined whether moments characterized by different methods differentially contribute to this common 'experiential' space  (Ho et al.
, 2019; and Maillet & Schacter, 2016). See Table  S3  in the supplementary materials for a comparison of the models in a Bayesian ANOVA. 12. 33 [9. 86; 14. 81]  5. 66 [3. 25; 8. 07]  Principal Component Analysis identifies three types of thoughts Several participants did not report any mind-wandering experiences with the self-caught method. Thus, data from one young and 10 older adults have been excluded. The following analyses, therefore, will be conducted on data from 35 young adults and 28 older adults. To examine the MDES data, the set of questions was decomposed using principal component analysis (PCA), applying varimax rotation (e. g. Ho et al. , 2020; Karapanagiotidis et al. , 2017; Konu, Mckeown, et al. , 2020; Konu, Turnbull, et al. , 2020; Martinon et al. , 2019; Smallwood et al.
26% of the overall variance. In the left upper panel is an illustration of the 0-Back task used. Note that the correct response of the example target trial is "circle". Decomposition of the experience sampling data collected during this task revealed three components across all conditions. Based on their loadings the three components were summarised as "Detailed task-relevant", "Off-Task social episodic", and "Off-task verbal self-relevant". The word clouds summarise these loadings in which the colour of the word describes the direction of the relationship (red = positive, blue = negative) and the size of the item reflects the magnitude of the loading. The scree plot for this decomposition is presented on the right upper panel. Bar-plots in the left lower panel show the mean ratings for each item that these components are derived from, for both young and older adults.
Detailed task-relevant Off-Task social episodic Verbal self-relevant Qualitative differences in patterns of ongoing thoughts between groups and sampling methods. Having described the patterns that account for the most variance within the MDES data, we examined the qualitative similarity in thought patterns captured by different experience sampling methods and in different age groups. To achieve this goal, we generated new factor solutions while separating young and older adults' data as well as the probe-caught and self-caught data (4 cells of our study). In these new PCAs we specifically asked for 3 components to maintain similarity with the original solution (see  Ho et al. , 2020  for a previous example of a similar analysis). Next, we compared these three new factors to the original omnibus solution described above, which used the combination of all the data regardless of age or method sampling (see Figure  1 ).
This analysis allows us to evaluate the possibility of qualitative differences in the patterns of experience across methods or age groups by testing the null hypothesis that the PCA patterns captured by the omnibus analysis are only present in only some subsets of the data. To evaluate the correspondence between the new factors and the omnibus factors, while accounting for the hierarchical structure of the data, linear mixed models were used. The MIXED procedure in SPSS 26. 0  (Peugh & Enders, 2005)  with Maximum Likelihood Estimation was used. For these analyses, all dependent and independent variables were grand mean centred to ensure clarity in interpreting any effects. All linear mixed models included one random effect (the intercept for each participant) to control for the dependency arising due to repeated sampling of data within participants.
175 and p=. 271, BF 10 =0. 408, respectively). See Table  S8  in the supplementary materials for a comparison of the models in a Bayesian repeated-measure ANOVA. To understand how changes in thought patterns assessed by different measures and in different age groups are associated with variation in self-reported motivation levels, we ran mediation analyses following the procedure advocated by  Preacher and Hayes (2004). We used the most recent version of PROCESS (version 3. 5) for SPSS 26. 0  (Field, 2018). We selected the following specifications: model number 4, 5000 bootstrap samples, and 95% confidence intervals. Based on our results, age-related differences were found in probe-caught detailed task-relevant thoughts, probe-caught off-task social episodic thoughts, and self-caught off-task social episodic thoughts.
It is worthwhile noting, however, that ten older adults (compared to 1 younger) did not self-catch thoughts, highlighting the age variability in the dataset. The extra demands during the self-catch condition mirror similar tasks employed in the prospective memory literature, where age effects are evident  (Gonneaud et al. 2011 ). Regardless, it remains an open question the extent to which features of age-related changes in experience are primarily driven by organic changes in cognition, lifestyle differences, or familiarity with university settings (e. g. McVay et al. , 2013). Increased verbal features in off-task experiences for older individuals may be related to the increased reliance on semantic knowledge and crystallized intelligence as we grow older (e. g. Riby et al. 2004 ), which may have links to organic changes in neural function  (Spreng et al.
9  Indeed, when we quantified AS areas 1 day later in development, at E14. 5, we observed an increase in median AS area (Figure  1D ). To identify the origin of this time-dependent variation in median AS area, we cultured embryonic cortical explants in vitro and performed live imaging with an en-face view of the ventricular wall (Figure  1E ). We validated our culture conditions using an H2B reporter mouse line to visualize progenitor nuclei. We confirmed that apical progenitors divide properly, with intact interkinetic nuclear migration and division events (Movie S1 and data not shown). Development of apical domains were followed for 14 hours (Movie S2), and each image was segmented (Movie S3) to quantify epithelial growth dynamics. These movies revealed an increase in median AS area during the 14 hours time lapse (Figure  1D, E, F ), indicating that the increase in AS area is intrinsic to the tissue.
| Image analysis Huygens, IMARIS, and ImageJ software were used for image processing and data analysis. For AS size measurement, we extracted from the 15 μm z-stack 2-3 actin stained images at the most apical z-levels, with minimal pollution from more basal actin network. Averaged actin images were then segmented using Tissue Analyzer plugin in ImageJ. 35 Global apical areas and AS of tagged cells were calculated automatically from segmented images using the EpiTools plugin in Icy. 36 Dividing apical surfaces were selected based on the following criteria: constriction of round apical surfaces followed by appearance of two smaller apical surfaces and correlating with progression through mitosis at nuclei level. AS size measurement of dividing cell was done by manually tracing cell limits in ImageJ.
ACKNOWLEDGMENTS The authors would like to thank members of the Davy team, especially  Thomas Jungas  for informal discussions and for insightful comments on the manuscript and  Fraha Haidar  for technical help. Thomas Mangeat and the  Microscopy Core Facility LITC (Light Imaging Toulouse CBI) of CBI (Centre de Biologie Intégrative de Toulouse)  provided invaluable help with laser ablation experiments. We thank the  CBI-bigA  facility engineers for their help and feedback with statistical analyses and data visualization. The authors would also like to thank the Anexplo-CBI animal facility platform for housing the mice. This work was funded by the  Fondation pour la Recherche Médicale  ( DEQ20180339174 ) and by core funding from  CNRS  and  Université Paul Sabatier.
Acknowledgements We thank members of the  Deczkowska lab  for fruitful discussions and  Kyle Everton Lamber  for editing the manuscript. The figures were prepared with  BioRender. com. Research in Deczkowska lab  is supported by core  G5  funding from  Institut Pasteur ,  Ville de Paris EMERGENCE(S)  grant and  Alzheimer's Association Research Grant  ( AARG-22-917964 ). RS is a student from the  AIRE master program  funded by the  Bettencourt Schueller foundation  and the  EURIP graduate program  ( ANR-17-EURE-0012 ). DSF is supported by the  cooperative Erasmus þ Program  developed by  Universitat de Barcelona and Paris-Sorbonne Universite.
Participants The study design was a retrospective case collection. We initially screened intraoperative stimulation data of 571 consecutive awake surgeries performed at our institution from 2015 to 2021. Of these, we selected patients meeting the following criteria: (i) at least one site inducing intraoperative jargonaphasia during a naming task in three non-consecutive trials, (ii) perioperative language assessment, (iii) a histologically-proven diffuse low-grade glioma to maintain homogeneity across the sample. Intraoperatively elicited semantic jargonaphasia consisted in reproducible (three nonconsecutive stimulation trials) sequences of utterances (real words) that were semantically and contextually unrelated to the target word. Conversely, neologistic jargonaphasia was classified as reproducible (three non-consecutive stimulation trials) but unintelligible sequences of utterances (pseudoword or non-word) that replaced the target word.
Intraoperative tasks were performed as a double-task (a behavioural task (Fig. 1 ) combined with rhythmic contralateral arm movement)  [20]. An experienced speech therapist (SMG) performed the cognitive monitoring, which was blinded to the time of stimulation. Once resection proceeded subcortically, this was interrupted whenever behavioural impairments were induced by subcortical stimulation. After tumour removal, cortical and subcortical epicenters were identified with sterile number tags and a picture was acquired to allow offline data processing  [21].
Extraoperative language and neuropsychological assessment Naming abilities (DO 80), non verbal semantic associations (Pyramid and Palm tree test (PPTT))  [24] , verbal fluency (number of non-repeated animals/non-repeated words starting with the letter "P" that could be pronounced within 2 min) and reading ability (ECLA 16þ composed of text reading reading of regular, irregular and pseudo-words)  [25]  were assessed preoperatively, postoperatively (2e4 days after surgery) and at a 3-months follow-up. This was accompanied by a qualitative evaluation of spontaneous speech. Additional neuropsychological tests were performed according to tumour location and clinical need. All language assessments were performed by the same experienced speech therapist (SMG). Performance raw scores were aligned to published French normative data (adjusted according to educational level, age, and sex) and converted into z-scores, considered impaired for values below À1.
Imaging acquisition and processing Structural MRI datasets were collected preoperatively, postoperatively and at a follow-up of 3 months on a 3T MR imaging scanner (Skyra, Siemens Medical Systems, Erlangen, Germany), as part of the routine management protocol. For the purpose of the current work, standard gadolinium-enhanced isovolumetric T1weighted images (repetition time 1. 880/1. 700 ms; echo time 3. 4/ 2. 5 ms; inversion time 1100/922 ms; field of view 256 Â 256/ 250 Â 250 mm; flip angle 15/9 ; voxel size 1 Â 1 Â 1 mm 3 and 176 axial slices) were used. Fluid-attenuated inversion recovery (FLAIR) sequences were also acquired for each patient at each time point. The individual brain anatomy with the related resection cavity was normalized to a template of 152 patients (MNI; Montreal Neurological Institute) using an enantiomorphic normalization from SPM12.
3-month postoperative T1 MRI were used to exclude immediate postoperative brain-shift. As previously mentioned, MNI coordinates of each stimulation point were recorded using operative reports and intraoperative photographs. To increase the accuracy of this re-positioning work, a 3D pial-mesh reconstruction of each normalized 3-month postoperative MRI was generated using Brain VISA Software (Version 5. 0, CEA I2BM, CATI Neuroimaging, France). This mesh allows a 3D navigation through anatomical structures and an automatic MNI coordinates-surface-matching of the 3D model with a high level of inter-rater reliability  [26].
Disconnectome analysis To investigate white matter pathways corresponding to different intraoperative impairments, a disconnectome analysis was performed using each stimulation site as ROI. Following normalization to MNI, a spherical volume of interest (VOI) with a 2. 5 mm radius was centred on the coordinates of each effective site in each patient using MarsBaR. The sphere diameter was 5 mm to match the tip distance of the bipolar probe. The probability of disconnection induced by each stimulation was computed as a disconnectome map in BCBtoolkit software  [12]. The maps were generated from the tractograms of 20 unrelated right-handed adults processed using High Angular Resolution Diffusion Imaging tractography (spherical deconvolution) from the 7T dataset of the Human Connectome Project.
For each lesion, the tractograms were averaged together so that each voxel represented a probability of disconnection from 0 to 1  [12]. Each patient's disconnection profile was then used to investigate if disruption of specific white matter pathways were associated with language interference. To do so, non-parametric statistical comparisons were performed on disconnection maps using FSL's randomise with 5000 permutations and threshold-free cluster enhancement  [27]. Family-wise error rate (FWE)-corrected one and two sample t-tests were used to assess which disconnection profiles were associated with each language impairment. To recognize the involved white matter tracts, we considered voxels significant at p < 0. 05corrected based on the FSL-randomise contrast maps and white matter tracts from atlas computed on 1065 healthy subjects from the same 7T HCP dataset  [28]  were superimposed.
Statistical analysis Statistical analysis was performed using SPSS Statistics (16, IBM, Armonk, NY, USA). Normality of variable distribution was evaluated using a Kolmogorov-Smirnov test. Non-parametric voxel-based one and two sample t-tests were performed with FSL using thresholdfree cluster enhancement and FWE-corrected  [27]. A Wilcoxon signed-rank test was used to evaluate variation in language and neuropsychological performance at the different time-points.
Data and software availability The clinical data is available on reasonable request to the first or last author. Software used for this study included: SPM12 (http:// www. fil. ion. ucl. ac. uk/spm); MarsBaR (http://marsbar. sourceforge. net); ITK-SNAP (http://www. itksnap. org); BrainVISA (https:// brainvisa. info/web/); DSI Studio (http://dsi-studio. labsolver. org/); MRIcron and MRIcroGL (https://www. nitrc. org); Surf Ice (https:// www. nitrc. org/projects/surfice/); Cogent 2000 (http://www. vislab. ucl. ac. uk); FSL (https://fsl. fmrib. ox. ac. uk/fsl).
Discussion Direct electrical stimulation in awake neurosurgical patients represents the most reliable method to causally assess function of language pathways  [29]. In this study, we reviewed intraoperative stimulation data of 571 awake surgeries to show that direct stimulation caused jargonaphasia in only 17 patients. This is, to our knowledge, the largest intraoperative dataset for this disorder to date. Furthermore, we showed for the first time that jargonaphasia occurred following subcortical stimulation, indicating this rare language dysfunction may reflect a transient disconnection of subcortical white matter anatomy. Therefore, we used disconnectome lesion-symptom methods  [12]  to identify networks involved in neologistic and semantic jargonaphasia. On one hand, our results advocate for a continuum among variants of jargonaphasia determined by common subcortical damage.
Tool use and language are two highly refined human abilities which may have coevolved, thus resulting in neural commonalities. We recently provided evidence for shared neural resources between tool use and syntax. However, the debate is still open on whether semantics and phonology also recruit neurofunctional supplies common to tool use. Here we tested the hypothesis that activity within the tool-use network contributes to semantic and phonological neural representations. To this aim, we identified the tool-use network with fMRI while participants used pliers. The same participants underwent, a semantic and a phonological task to evidence the underlying networks. Through a series of representational similarity analyses we tested whether activity in tool-use clusters contributed to neural representations of semantic and phonological stimuli.