dataesr/scientific-paragraphs-categorization huggingface dataset:
Centered log-ratio transformation was performed to normalize reads across cells before scaling, and then principal component analysis was performed on the normalized and scaled data. Dimensional reduction of protein expression data was performed using UMAP embedding and K-nearest neighbors and Louvain clustering algorithms to identify cell populations based on ADT sequencing before undergoing manual cell population labeling based on cell-surface marker expression. Genotype data were then merged, and relative mutated proportions were calculated for each cluster having at least 10 cells for interpretation.
Acknowledgments R. Kim  was a recipient of a PhD grant with financial support from  ITMO Cancer of Aviesan  on funds administered by  INSERM. The study was supported by a grant from  Force Hémato  ( 2020 ). The authors thank the patients and all the GRAALL investigators, physicians, and biologists who contributed samples and data for this study. The authors thank the  Saint-Louis Molecular Hematology lab  for technical support, especially  Hélène Boyer ,  Emilie Gaudas ,  Léna Yousfi , and  Océanne Richard. The authors also thank  Stéphanie Mathis ,  Pierre Lemaire , and  Clémentine Chauvel  for the flow cytometry evaluation of ALL diagnostic samples. This work benefited from the genomic platform facility of  Institut de Recherche Saint-Louis (IRSL) , supported by  Association Saint-Louis.
The EEG recording system comprised 127 active wet electrodes mounted on an elastic cap with an equidistant arrangement (waveguard original -ANT Neuro, Hengelo, The Netherlands). An electrode located closest to the standard Cz position (10-20 international system) provided the reference for all other electrodes and an additional one on the left earlobe served as electrical ground reference. After placing the cap on the participant's head, we performed a head scan to collect the position of individual electrodes using a depth perception camera (RealSense D435 -Intel, Santa Clara, CA, USA) in combination with the RecFusion software (v2. 3. 0, ImFusion GmbH, München, Germany). We placed visual markers on the location of 3 fiducial landmarks of the skull (nasion and helix/tragus intersection of left and right ears) for later coregistration of the scan with MRI data.
Prior to recording for the experimental session, we lowered the impedance of the electrodes with the scalp below 20kΩ. We acquired raw EEG data with eego mylab software (v1. 9. 1, ANT Neuro) at a 1kHz sampling rate, continuously within each block. To simultaneously record all streams of data (eye tracking, EEG and protocol events), we used the Lab Streaming Layer protocol  (Kothe, 2014)  and the LabRecorder software (v1. 13. 0) to collect them into a single XDF file per block.
Data analysis Behavior Data were extracted from the XDF files of each participant using the pyxdf library in Python 3. 8. Analyses were conducted using R version 4. 0. 3 in RStudio version 2022. 12. 0+353 (R Core Team, 2020; RStudio Team, 2022). To assess participants' navigational performance, we computed the number of errors per intersection during the navigational task. This number could vary from 0 to 2, 2 being the total number of wrong directions at a single intersection. We also reported the number of errors made during the recognition task. Using the answers from the post-experiment questionnaire, we grouped participants into two categories: those who used a mental map to orient (from the beginning or at some point during the experiment) and those who never did. As an exploratory analysis, we also classified participants according to the reported number of objects they relied upon for orientation at each intersection: individuals who selected a subset of objects vs.
Source reconstruction modeling We relied on a fMRI informed source reconstruction model tailored to each participant  (Cottereau, Ales and Norcia, 2015). Participants completed fMRI experiments as part of the SilverSight cohort study, we therefore had MRI anatomical scans and relevant fMRI localizer data at our disposal to build such a model. MRI data was collected using a 3 Tesla Siemens MAGNETOM Skyra whole-body system (Siemens Medical Solutions, Erlangen, Germany) equipped with a 64-channel head coil. Acquisitions took place at the Quinze-Vingts National Ophthalmology Hospital in Paris. For anatomical scans, we used 2 types of sequences: a T1*-weighted MPRAGE 3D sequence (voxel size = 1 x 1 x 1. 1 mm, TR/TE/Angle flip = 2300 ms/2. 9 ms/900 ms/9°, matrix size = 256 x 240 x 176) and a T2-weighted 3D sequence SPACE CAIPI (voxel size: 1 x 1 x 1.
We selected this tool to improve the automatic estimation with the T2weighted image. We further checked the automatically generated surfaces and we applied manual corrections when required. In parallel, we analyzed the localizer task using the SPM12 toolbox implemented in Matlab R2019a (release 7771, Wellcome Department of Imaging Neuroscience, London, UK). We realigned the functional images and co-registered them to the T1-weighted image. We then computed forward and inverse deformation fields from the individual T1 image to the MNI-152 space. Finally, we normalized all images using the forward deformation field, we resampled the functional images to 3 x 3 x 3 mm voxel size (4th degree B-spline interpolation), and we smoothed them using an 8 mm FWHM Gaussian kernel. We fit the localizer data to a single participant general linear model for block design.
The model included five categories of interest (scenes, faces, objects, scrambled objects, rest) as regressors, convolved with the SPM canonical HRF. We also added the 6 movement parameters as regressors of no interest in the model and we high-pass filtered each voxel's time-series (1/128 Hz cut-off). For each participant, we delineated the PPA, OPA, and MPA using the contrast [Scenes > (Faces + Objects)]. We identified significant supra-threshold voxels on individual t-maps that were closest to the reference coordinates for the scene-selective regions  (Ramanoël et al. , 2020)  using false discovery rate correction for multiple comparisons (p < 0. 05) at peak-level. We regrouped peak coordinates for each young and older participant into Supplementary Table  1. We selected the best 2D images of the head scan in the RecFusion software to improve the visibility of the electrodes on the 3D model.
We subsequently imported the model in the get_chanlocs plugin (v3. 00) of EEGLAB (v2021. 0,  Delorme and Makeig, 2004)  to manually point the positions of the 3 fiducials and of each electrode. We then retrieved the 3D position of the electrodes in a coordinate system defined by the fiducials. We built the forward model matrix using  MNE-Python tools (v0. 23, Gramfort, 2013). We first defined the midgray surface halfway between white matter and gray matter surfaces. We used a tessellation of this midgray surface with 10242 regularly spaced vertices (hemisphere-wise) as the basis for our source space. Each of these vertices held a current dipole oriented orthogonally to the cortical surface in order to diminish the number of parameters to be estimated in the inverse procedure  (Hämäläinen et al.
, 1993; Cottereau, Ales and Norcia, 2015). We individually tessellated inner skull, outer skull and outer skin surfaces into 5120 faces meshes defining the boundaries between the cerebrospinal fluid and the skull, the skull and the scalp and the scalp and the air, respectively. We assigned a standard 0. 33 S/m conductivity to brain/cerebrospinal fluid and scalp compartments. The skull thickens and densifies across the lifespan, which modifies its electrical properties. Taking the latter into consideration we chose to assign age-dependent conductivity measures for the skull. We estimated skull conductivity at 0. 02145 S/m for young participants (1:15 ratio around 30 years old) and at 0. 0099 S/m for older participants (1:33 ratio around 75 years old)  (Michel and Brunet, 2019). We coregistered the electrode positions to the MRI head surface using MNE-python coregister function which finds the rigid body transformation that minimizes the distance between fiducials coordinates in both reference frames.
We then reconstructed the removed channels by spherical interpolation of neighboring channels and we applied re-referencing to the common average. In a subsequent timedomain cleaning, we detected and removed artifactual periods with the APP pipeline  (da Cruz et al. , 2018). On the cleaned dataset, we performed an independent component analysis (ICA) using an adaptive mixture independent component analysis algorithm  (AMICA plugin, v1. 5. 1, Palmer et al. , 2008). The ICA was preceded by a principal component analysis reduction to the remaining rank of the dataset taking into account the number of channels interpolated. For each independent component (IC), we computed an equivalent current dipole model with the DIPFIT plugin for  EEGLAB (v3. 7, Oostenveld and Oostendorp, 2002). We used the ICLabel algorithm (version 1.
3, Pion-Tonachini, Kreutz-Delgado and Makeig, 2019) with the "default" option to give an automatic class prediction for each IC. Guided by this automatic prediction, we proceeded to a visual check of candidate IC properties (outputted by pop_prop_extended function of EEGLAB) and kept those that contained sufficient evidence for brainrelated features. Finally, we back projected the selected ICs in the channel (i. e. , electrode) space. On the cleaned-with-ICA full dataset (i. e. , data from all trials, regardless of whether they were labeled as artifactual periods earlier), we ran a final artifact check with the APP pipeline. We used this analysis to automatically flag any epoch containing at least one data point classified as an artifact by both runs of the APP pipeline. These data points contain transient artifacts that were sufficiently strong to be detected prior to ICA but that were not satisfyingly dealt with after removal of non-brain ICs.
Before epoching the dataset we applied a bandpass filter between 1. 25Hz and 42Hz (zero-phase Hamming windowed finite impulse response filters: high-pass filter with 1 Hz cut-off frequency and 0. 5 Hz transition bandwidth and low-pass filter with 47. 25 Hz cut-off frequency and 10. 5 Hz transition bandwidth). We then epoched the dataset into [-2,+4] second fixed time windows centered around the event of arriving at an intersection. We considered all intersections excluding start positions. We discarded epochs that were previously flagged as well as those that did not contain any fixations. We present the detailed count of epochs included in the EEG analysis in Supplementary Tables  2  and 3. For the following analyses, we used functions from the FieldTrip-lite plugin  (v20210601, Oostenveld et al.
, 2011)  for EEGLAB. At the epoch level, we first computed event-related spectral perturbations (ERSPs) for each channel (with a 25 Hz sampling rate in the temporal domain and with a linear scale between 2 and 40 Hz for the spectral domain) using the ft_freqanalysis function. The latter was based on a frequency-wise combination of Morlet wavelets of varying cycle widths  ('superlet' method: Moca et al. , 2021). We discarded data from electrodes for which the position was not determined with sufficient confidence on the 3D model. Then we proceeded to solving the inverse problem with the complex Fourier coefficients as inputs using the ft_sourceanalysis function and the MNE method. To summarize activity from the OPA, PPA and MPA, we averaged power estimations over all dipoles included in the ROI and combined its contribution from both hemispheres.
Acknowledgements We would first like to express our most sincere gratitude to all the participants involved in this experiment. We thank  Fabienne Tzvetkov-Ricard  and  Sonia Combariza  from the  Aging in Vision and Action laboratory at the Vision Institute  for their help in enrolling participants. We also wish to thank  Bilel Benziane  for his insightful suggestions on task design in Unity and  Arnaud Kasteler  for his help in collecting the data. This research was supported by the  French National Research Agency  ( ANR-18-CHIN-0002 ), the  LabEx LIFESENSE  ( ANR-10-LABX-65 ), the  IHU FOReSIGHT  ( ANR-18-IAHU-01 ), the  Fondation pour la Recherche sur Alzheimer (FRA)  and the  Fondation pour la Recherche Médicale  ( FRM -FDT202204014752 ).
Code availability Custom codes used to run the behavioral and eye tracking analyses and to plot the results are available at: https://osf. io/fr5tv/?view_only=413c295de14c477e83cdf1f30e177110. We wrote the codes using R version 4. 0. 3 in RStudio version 1. 4. 1103 (R Core Team, 2020; RStudio Team, 2021). All custom software for the EEG analysis can be found at https://osf. io/fr5tv/?view_only=413c295de14c477e83cdf1f30e177110. We wrote the EEG analysis pipeline in Matlab R2019a (Mathworks Inc. , Natick, MA, USA).
Acknowledgements The authors thank the electron microscopy core facility of the  Institut Charles Sadron  and the ellipsometry core facility of the  LCP-A2MC (Université de Lorraine , http://lcp-a2mc. univ-lorraine. fr). W. W. acknowledges financial support from the  China Scholarship Council. This work was part of the  Interdisciplinary Thematic Institutes QMat and HiFunMat of the University of Strasbourg ,  CNRS , and  Inserm. It was supported by the following programs:  IdEx Unistra  ( ANR-10-IDEX-0002 ), SFRI  STRATUS  project ( ANR-20-SFRI-0012 ), the  Labex CSC  project  ANR-10-LABX-0026-CSC , the  Labex NIE  project  ANR-11-LABX-0058-NIE ,  USIAS  ( ANR-10-IDEX-0002-02 ), under the framework of the  French Investments for the Future Program , and the  ERC  (project  788482   MOLUSC ).
Introduction TSC is a multisystem genetic disorder characterized by age-related development of benign tumors across several organs including the brain, skin, kidneys, heart, and eyes. TSC is caused by inactivating mutations in either TSC-1 or TSC-2 genes  [1] [2] [3]. The gene mutation results in an overactivation of the PI3K-Akt-mechanistic (formerly mammalian) target of the rapamycin (mTOR) signaling pathway, which plays a crucial role from brain development processes to aging  [4]. Overall, more than 2000 pathogenic TSC1/2 variants have been described in the Leiden Open Variation Database (www. lovd. nl/TSC1 and www. lovd. nl/TSC2), making the genotypephenotype correlation challenging. Identification of a pathogenic variant or deletion in TSC1/2 is sufficient for the diagnosis of TSC, regardless of clinical findings  (Northrup 2021)   [5].
Moreover, three international randomized controlled trials (RCTs) have changed clinical practices in the treatment of epilepsy associated with TSC. The EPISTOP (NCT02098759) trial showed that pre-symptomatic treatment with vigabatrin may modify the natural history of seizures with a delay in seizure onset and a reduction in seizure severity and the risk of epileptic encephalopathy (EE)  [18]. The EXIST3 trial demonstrated the efficacy of mTOR inhibition with adjunctive Everolimus in reducing seizure frequency in patients with TSC-associated refractory seizures  [19]. Finally, the GWPCARE6 trial documented efficacy and safety of Cannabidiol (CBD) in reducing TSC-associated seizures compared with placebo  [20]. CBD is now approved by Food and Drug Administration (FDA) and European Medicine Agency (EMA) for the treatment of seizures in patients with TSC.
Predictors of drug resistance TSC-associated epilepsy management is still a challenge because of high percentage of patients developing drug resistant epilepsy (DRE). Data from the TOSCA registry revealed that patients presenting with focal seizures (whether or not combined with IESS) were more refractory than patients presenting with epileptic spasms only, but the prediction of DRE is still challenging  [17]. In a prospective study, infants with an early seizure onset continued to perform worse at 24 months of age, in comparison with children with a later seizure onset  [56]. Data from the TSC Natural History Database, including 1965 subjects with TSC, showed that early focal-onset seizures and treatment-resistant infantile spasms were clinical predictors for DRE  [57]. Infantile spasms were also among strong indicators of DRE as reported in a systematic TSC review  [58].
Acknowledgments The contribution of  Sergiusz Jozwiak  and  Katarzyna Kotulska  has been partly supported by grant  EPIMARKER  of the  Polish National Center for Research and Development  no. STRATEGMED3/306306/4/ 2016 , grant  VIRAP  (Project number  2019/ABM/01/00034 ) and grant  RaRE- TS  (Project number  2020/ABM/01/00054 ) of the  Medical Research Agency, Poland , and a statutory grant of the  Children's Memorial Health Institute  financed by the  Polish Ministry of Science and Higher Education. Eleonora Aronica  acknowledges support by The  Netherlands Organisation for Health Research and Development (ZonMw). Rima Nabbout ,  Eleonora Aronica ,  Arianna Benvenuto ,  Martha Feucht ,  Floor Jansen ,  Katarzyna Kotulska ,  Lieven Lagae ,  Sergiusz Jozwiak , and  Paolo Curatolo  were supported by the  7th Framework Program  of the  European Commission  within the  Large-Scale Integrating Project EPISTOP  (Proposal No.
602391-2 ; www. epistop. eu). Nicola Specchio  is supported by # NEXTGENERATIONEU (NGEU)  and funded by the  Ministry of University and Research (MUR) ,  National Recovery and Resilience Plan (NRRP) , project  MNESYS  ( PE0000006 ) - A Multiscale integrated approach to the study of the nervous system in health and disease  (DN. 1553 October 11 , 2022).
The second way was to analyze the sample in UHPLC/HRMS 2 and to plot mass spectral similarity networks with the GNPS platform and Cytoscape software to refine identification. By combining the information obtained, we were able to propose an identification for 104 detected molecules, including 7 nitrogenous, 28 lipidic and 67 phenolic compounds, leading to the first detailed phytochemical analysis of corn silk extract.
To obtain an overall characterization of an extract, coupling between liquid chromatography and mass spectrometry is increasingly used. High-resolution mass spectrometry provides precise mass information, thus assisting in the identification of unknown compounds. In recent years, support for chemical analyses and mass spectrometry data processing has been developed through bioinformatics. Tools and web-platforms have multiplied to help with the study of metabolites and their identification. In 2016, Rathahao-Paris et al. reviewed the interest of working in high resolution mass spectrometry to identify metabolites  [26]. They also showed the interest of mathematical tools, such as the mass defect and the van Krevelen diagram, in helping to visualize and characterize metabolites. Given that in LC-MS 2 it is not possible to build a global database for all types of technologies, a group of researchers has created a platform to display fragmentation similarities between metabolites.
The diagram can then be used to quickly determine whether the compounds of an unknown complex mixture belong to the different molecular families according to their crude formula (Table  1 ). ≥0. 3, ≤0. 9 ≥0. 6, ≤1. 5 Figure  2  shows the Kendrick (Figure  2A ), van Krevelen (Figure  2B ) and double bond equivalent (DBE) (Figure  2C ) diagrams. They were constructed from the exact mass and molecular formula calculated from the pseudo-molecular ions ([M-H] -) detected in the corn silk extract. In order to define zones corresponding to the main molecular families of the extract on the diagrams, a database of natural compounds was extracted from the catalog of Extrasynthese (https://www. extrasynthese. com/ accessed on 1 December 2019)  [36] , and the areas corresponding to lipids, mono-and diglycosylated flavonoids have been circled on the diagrams.
To refine the identification proposed from the Kendrick, van Krevelen and DBE diagrams, the exact mass and molecular formulae obtained were compared with the molecules described in the literature of Zea mays and listed in the lotus database (https: //lotus. naturalproducts. net/ accessed on 7 August 2022)  [37]. This comparison confirms the propositions of molecular formulas of the concordant molecules and enables dereplication. Thus, this corn silk extract contains 14 known phenolic compounds which have already been described in the literature (indicated by an asterisk * in Table  3 ). This method also highlighted numerous undescribed molecules. Thus, in order to characterize them, a data dependent acquisition was carried out to obtain fragmentation spectra.
Discussion Nurraihana et al. (  2018 ) carried out a first identification of 21 flavonoids of corn silk extract by LC/MS  [39]. Some molecules characterized have common molecular formulas with those of this analysis; however, MS data led to the interpretation of different structures. For example, the molecular formula C 26 H 28 O 13 was characterized in their extract as being the molecule mirificin, which corresponds to an isoflavone, daidzein 8-C-glucoside 2 -O-apioside. In comparison, in this extract it was characterized as a flavone, apigenin 6-C-deoxyhexose 8-C-pentose. The compound characterization remains the main challenge, because of the lack of a universal and complete database, due to the fact that the fragmentation spectra depend on the conditions of analysis and equipment. This is why Desmet et al.
5 min. The injection volume was 0. 6 µL. The equilibration time between two injections was 5 min. UHPLC was coupled with mass spectrometry detection performed on a maXis UHR-Q-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany). The instrument was used in negative electrospray ionization (ESI-) mode. The capillary voltage was maintained at -4 kV, the gas flow to the nebulizer was set at 2 bar, the drying temperature was heated at 200 • C and the drying gas flow was 10. 5 L/min. Mass spectra were recorded in the data dependent acquisition (DDA) mode with an m/z range of 50-1650 for MS spectra and an m/z range of 230-660 for MS 2 spectra. The collision-induced dissociation (CID) energy was applied at 30 eV. Two precursor ions with intensities higher than 1000 au were selected per fragmentation cycle among the most intense ions to be fragmented.
The van Krevelen diagram gives another mapping of the corn silk extract. This representation associated with other molecular formulas coming from databases makes it possible to position molecular families and to highlight the most dominant families. [29]. In parallel, the Bruker LC-HRMS/MS data were converted into mzXML format by using MS convert (from the Proteo wizard package), a text-based format used to represent mass spectrometry data describing the scan number, precursor and MS 2 ion m/z and intensity required for the generation of a molecular network (MN). This file was submitted to the GNPS (Global Natural Product Social Molecular Networking) web-based platform to generate an MS-based molecular network  [45]. The following parameters were applied to create the molecular network. The mass tolerance for precursor ions was 0.
MS 2 peaks were grouped at 0. 01 Da. To converge the LDA model, 1000 iterations were used. The minimum intensity of MS 2 peaks to include in the MS2LDA analysis was set at 100 au. The number of unsupervised Mass2Motifs MS2LDA was set at 300. MolNetEnhancer is a tool to annotate the network with the chemical classes  [47]. This workflow combines the outputs from molecular networking, and here, MS2LDA and chemical classification. A library search was carried out with a minimum of 6 matched peaks, a score threshold at 0. 6 and a maximum analog search mass difference of 100. The molecular network was visualized using the Cytoscape software (version 3. 7. 2). The MN is accessible at the GNPS web site with the following links (MN; MS2LDA; MolNetEnhancer): https://gnps. ucsd. edu/ProteoSAFe/status.