dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006; Yu et al. , 2011) , topological  (Gardner et al. , 2021; Giusti et al. , 2015) , and theoretical justification for quantifying how the synergy-redundancy balance evolves across the lifespan to mitigate cognitive decline  (Gatica et al. , 2021; Luppi et al. , 2022 Luppi et al. , , 2023;; Stanford et al. , 2022). Considering the breadth of current neuroimaging datasets, the use of sophisticated techniques designed to yield high inter-and low intra-individual variability is timely. The proportional thresholding for the topological structure  (Jiang et al. , 2023)  may lead to the exclusion of weaker edges that are neurobiologically meaningful  (van den Heuvel et al. , 2017). Therefore, assessing how a data-driven topological filtering scheme such as OMST (Orthogonal Minimum Spanning Tree;  Dimitriadis et al.
Acknowledgments This work has been supported by the  ANR  project  ANR-15-IDEX-02. This project has received financial support from the  CNRS  through the  MITI interdisciplinary programs. The  Cambridge Centre for Ageing and Neuroscience  (Cam-CAN) research was supported by the  Biotechnology and Biological Sciences Research Council  (grant number  BB/H008217/1 ).
Acknowledgements This work was supported by the  ANR  Grant  STORM  No. 16-CE35-0007  and the  IRD JEAI  Grant No. AAP2018_JEAI_PALUNEC. We are grateful to  NIGNAN Saibou  from the  Institut de Recherche pour le Développement  and  Dr. OUOBA Y. Hermann  from the  University Joseph KI-ZERBO/University Center of Ziniaré, Burkina Faso  for plant species identification. We would like to thank the biochemistry and  microbiology laboratory  of the  Département Technologie Alimentaire (DTA)  of the  Institut de Recherche en Sciences Appliquées et Technologies (IRSAT) of Bobo-Dioulasso  for technical support, particularly  Dr. Abel Tankoano ,  Hamadou Rouamba  and  Inessa Ouattara  for their assistance in determining the degree Brix and total sugar content of the flowers. Thanks to all of the IRSS/DRO technicians and students, notably to  Yasmire R.
This research was supported by the  Research and Technology Innovation Fund ,  Hungarian Brain Research Program  ( National Brain Research Program , project  2017-1. 2. 1-NKP-2017-00002 , to DN); the  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 , to DN); the  Hungarian Scientific Research Fund  ( NKFIH-OTKA PD 124148 , to KJ;  NKFIH-OTKA K 128016 , to DN); the  National Research, Development and Innovation Office  ( NKFIH FK 128100 ,  NKFIH-1157-8/2019-DT , to PS) and  Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences  (to KJ). We thank  Rafael Pedrosa  for his invaluable help with the graphical illustrations. CC-BY-NC-ND.
Genetic analyses Excluding the AMP-PD cohorts, with whole genome sequencing (WGS) data, the other centers (Table  1 ) were genotyped using the OmniExpress, NeuroX,  46  NeuroChip 47 or MegaChip GWAS array according to the manufacturer's instructions (Illumina Inc. ). Quality control was performed following standard pipelines (detailed in https://github. com/neurogenetics/GWAS-pipeline) using Plink 1. 9. 48  In brief, we filtered out heterozygosity outliers using an F-statistic cut-off of <-0. 15 or >0. 15. Individuals with a variant call rate <95% and sex mismatch were excluded. Variants missing in >5% of the participants, with disparate missingness between cases and controls (p<1E-04), or significantly deviating from Hardy-Weinberg equilibrium in controls (p<1E-04) were also removed. We used GCTA to check for relatedness closer than first cousins between participants (PIHAT>0.
PD risk variant-based polygenic risk score To assess the impact on LID of the cumulative genetic risk for PD we calculated polygenic risk score (PRS) for each PD patient including the 90 variants associated with PD in the most recent GWAS meta-analysis in Europeans. 54  PRS calculation was performed based on the weighted allele dose as implemented in PRSice2 using default clumping (https://choishingwan. github. io/PRSice/). 55  To investigate the association between the PRS and LID risk we performed logistic regression, while to evaluate the association between PRS and progression to LID we performed Cox regression. The analyses were adjusted for PCs, PD AAO, sex, HY and levodopa dosage, cumulative in logistic regression and at the last time point in Cox regression. These analyses were repeated using PRS as a continuous variable and then as a discrete variable by dividing the PRS into quartiles.
Cephalon Inc ,  Lundbeck Inc ,  John Blume Foundation ,  Smart Family Foundation ,  RJG Founda-tion, Kinetics Foundation ,  National Parkinson Foundation ,  Amarin Neuroscience LTD ,  CHDI Foundation Inc ,  NIH (NHGRI  and  NINDS) , and  Columbia Parkinson'sDisease Research Center. ZGO is supported by the  Fonds de recherche du Québec -Santé (FRQS)   Chercheurs-boursiers award , and is a  William Dawson Scholar. YLS is supported by the  HBHL Graduate student fellowship. ProPARK is funded by the  Alkemade-Keuls Foundation ,  Stichting Parkinson Fonds ,  Parkinson Vereniging , and The  Netherlands Organization for Health Research and Development ; Udall is supported by the  NINDS. This work at the  Mayo Clinic Florida  was supported by: the  Haworth Family Professorship in Neurodegenerative Diseases fund , and The  Albertson Parkinson's Research Foundation.
TRACS analysis: The library reference file containing a list of all sgRNAs and their sequences (CSV file), raw reads for the pooled sgRNA library (FASTQ files (L0) and raw reads (FASTQ files) for all T0 and Tf timepoints and replicates for Cas9-positive and Cas9-negative cells were loaded into TRACS (https://github. com/developerpiru/TRACS). TRACS then automatically trimmed the reads using Cutadapt (v1. 15), built a Bowtie2 (v2. 3. 4. 1) index and aligned the reads using Samtools (v1. 7). TRACS used the MAGeCK read count function (v0. 5. 6) to generate read counts and incremented all reads by 1 to prevent zero counts and division-by-zero errors. The TRACS algorithm was then run using this read count file to determine the Library Enrichment Score (ES), Initial ES, Final ES and the Enrichment Ratio (ER) for each gene.
VisualizeTRACS was used to generate graphs of Initial and Final ratios. Pathway enrichment analyses: For GO-CRISPR screens and multi-omics comparisons, we used the filtered list of genes obtained using TRACS and performed gene ontology and pathway enrichment analysis using the ConsensusPathDB enrichment analysis test (Release 34 (15. 01. 2019), http://cpdb. molgen. mpg. de/) for top-ranked genes of interest. padj values and ER values for each gene were used as inputs. The minimum required genes for enrichment was set to 45 and the FDR-corrected padj value cutoff was set to < 0. 01. The Reactome pathway dataset was used as the reference. For each identified pathway, ConsensusPathDB provides the number of enriched genes and a q value (padj) for the enrichment. Bar plots were generated in R 3.
The supernatant liquid for each sample was collected and the pellets reextracted with RIPA containing the above inhibitor cocktails. Following centrifugation, the supernates for each treatment were collected and pooled. Protein was determined by Bradford TCGA patient data analyses: We used the cBioPortal for Cancer Genomics (https://www. cbioportal. org)  (49)  to analyze Netrin pathway alterations in Ovarian Serous Cystadenocarcinoma patients (TCGA, PanCancer Atlas) for genomic alterations and mRNA expression. OncoPrints were generated for mRNA expression (mean z-score threshold ± 1. 5), and mutations and putative copy number alterations for the following genes: NTN1, NTN3, NTN4, NTN5, UNC5A, UNC5B, UNC5C, UNC5D, DCC, DSCAM. Survival analysis of NTN1, and -3 utilized patients from TCGA, PanCancer Atlas dataset and RNA-seq expression (high expression z-score threshold > 1.
Dataset description The dataset  (19, 56)  includes 15 PD patients (7 males and 8 females, median age = 63± 8 years) and 16 healthy participants (7 males and 9 females, median age = 60. 5±8 years). The median disease duration is 3±2 years (range 1 to 12 years). PD patients were diagnosed by a movement disorder specialist at Scripps clinic in La Jolla, California. Dopaminergic medication significantly improved motor symptoms, as measured by the motor section of the Unified Parkinson's Disease Rating Scale-UPDRS III  (25) , as performed in a paired Wilcoxon test (Z = 2. 9388, p = 0. 0033). Participants were right-handed and provided written consent in accordance with the Institutional Review Board of the University of California, San Diego, and the Declaration of Helsinki. Details on the demographic information of each participant are available in the original studies from this cohort  (19, 56).
The Equations  (10)  and  (11)  show the MIC computation between two time series X and Y. The mutual information 𝐼 0 is computed to different grid combinations 𝑔 ∈ 𝐺 12. The mutual information values are normalized by the minimum joint entropy log # min{𝑛 1 , 𝑛 2 }, resulting in an index in the range 0-1. Finally, the quantified coupling between X and Y corresponds to the normalized mutual information resulting from the grid that maximizes the MIC value. m(X , Y) = max 0 ∈ 4 $% 𝐼 0 log # min{𝑛 1 , 𝑛 2 } (10) MIC(X , Y) = max 5 $ × 5 % 7 8 𝑚(𝑋, 𝑌)  (11)  where B = 𝑁 !. : , and N is the dimension of the signals  (23). The source code implementing MIC is available online at https://github. com/minepy. The visualization of the brain networks coupled with heartbeat dynamics was performed using Vizaj  (62).
are available at https://github. com/diegocandiar/robust_hrv. The functions for the computation of time-/github. com/diegocandiar/heart_brain_conn. The functions for the computation of MIC values (23)  are available at https://github. com/minepy. The functions to perform cluster /github. com/diegocandiar/eeg_cluster_wilcoxon. The data analysis was performed using Fieldtrip toolbox (57) , available at https://github. com/fieldtrip/fieldtrip varying connectivity and brain-heart coupling are available at https:/permutation analyses (24) are available at https:/.
Participants Eighteen healthy young males (age: 22. 6 ± 5. 7 years old; height: 180 ± 5. 7 cm; weight: 74. 5 ± 7. 8 kg) gave their written informed consent to participate in the present study. Participants reported a regular practice of Parkour, at a wide range of expertise. Their total training volume (in hours), estimated with their experience (in years) and training frequency (in hours/week) has been evaluated and ranged from 156 to 10,608 hours (mean: 4343 ± 3508). Research protocol was approved by the regional ethic committee (CPP-Est-IV) under the number 18/47, registered on ClinicalTrials. org (NCT03937115), and conducted in accordance with the last version of the Declaration of Helsinki.
The subjective probability of success (𝑝 # ) was inferred from the target size. The model assumes that subjects have a representation of their precision following a Gaussian assumption, meaning that the subjective distribution of their performance could be defined by its mean (the required 1 second to reach target center) and its width (i. e. , standard deviation) captured by a free parameter 𝜎. Thus, the probability of success was the integral of this Gaussian bounded by the target window: 𝑝 -= 1 𝜎√2𝜋 ; 𝑒 !(/!0) " 12 " 𝑑𝑥 0( 3%45 1 0! 3%45 1 This model was inverted for subjects separately using the Matlab VBA toolbox (available at https://mbb-team. github. io/VBA-toolbox/), which implements Variational Bayesian analysis under the Laplace approximation  (Daunizeau et al. , 2014). Computational analysis of stimulation effect on choice.
Finally, it is unknown whether lysosomal activity relates to quiescence heterogeneities between NSCs. To address these issues, we focused on NSCs of the zebrafish adult pallium, which are easily accessible for transient functional manipulations in vivo, and for which we generated a comprehensive 3 scRNAseq database and in situ readouts identifying qNSC heterogeneities correlating with differences in quiescence depth or length  [11, 13]. We report here that lysosomal activity is necessary for NSC quiescence in this system, and we identify the lysosomal protein Prosaposin (Psap) as a critical player in this activity. First, psap expression is highest in deltaA neg qNSCs, which display deepest/longest quiescence. Second, psap knockdown promotes qNSCs transition through the deltaA pos state of shallower/shorter quiescence, and NSC activation.
Pathway enrichment analysis: To identify the pathways enriched among the genes significantly upregulated or downregulated (FDR<0. 05) between two given cell types, we used the functional annotation chart module of the Database for Annotation, Visualization and Integrated Discovery (DAVID). Pathway annotations were taken from the KEGG database. A threshold of 5 was applied on gene counts in such a way that only terms harboring at least 5 enriched genes were considered in the analysis. The cutoff for EASE score was set at 0. 05. Data are displayed as the enrichment score [-log10(p-value)] of the KEGG pathways displaying an EASE<0. 05. The number of genes (of the differentially expressed gene list assessed) associated with an enriched term is indicated above the bar corresponding to the enrichment score.
Geneset enrichment analysis (GSEA): The datasets (i) "deeply" qNSCs (her4 high ) (dRFP high /GFP neg ) vs. aNSC (dRFP pos /GFP pos ) and (ii) adult qNSCs 3. 5 mpf (dRFP pos /GFP neg ) vs. juvenile qNSCs 1. 5 mpf (dRFP pos /GFP neg ) were analyzed. Geneset enrichment analysis was performed using GSEA software 4. 1. 0. Zebrafish ensemble gene IDs were converted to human gene symbols, by collapsing the dataset to gene symbols with a dedicated "chip" file. We used the gene sets associated with the KEGG canonical pathways available from the c2. cp. kegg. v7. 5. 1 database of the Broadinstitute. The background was obtained by permutating 1000 times the gene sets. Gene sets larger than 500 and smaller than 15 were excluded. The metric used for ranking gene was Signal2Noise and the enrichment statistic was normally weighted.
Focusing on proteostasis, we observed enrichment of the proteasomal pathway in aNSCs (Figure  S1e ) and enrichment of the lysosomal pathway (Figure  S1f ) and the lysosome-related pathways autophagy (fig. S1Af ) and endocytosis (Figure  S1g ) in RFP high qNSCs. When operating gene ontology (GO) analysis, we identified the KEGG lysosomal pathway as highly enriched in RFP high qNSCs (Figure  1b ). Second, we also generated a dataset in which we compared the bulk transcriptome of qNSCs between 1. 5 and 3. 5 mpf fish, stages between which deeper quiescence is progressively instated (Tables  S3  and S4 ). GO analysis identified the KEGG lysosomal pathway as significantly enriched at 3. 5 mpf (Figure  1c ), pointing to a pathway that becomes more prominent with time, as previously suggested between adult and aged mice  [20].
Sortilin1 is ubiquitously expressed in many tissues but most abundant in the central nervous system  [41]. The zebrafish duplicates sort1a and sort1b were expressed together at a high level in qNSCs in our bulk dataset, and also detectable in our single cell NSC dataset of the adult zebrafish pallium  [38]  (Figure  S3 ). To knockdown Sort1 function, we used previously validated splice-morpholino oligonucleotides (MOs) directed against sort1a and sort1b [42], which we electroporated in vivo into adult NSCs in Tg(gfap:dTomato) adult fish following intracerebral injection. We found that abrogating Sort1 function led to a significant increase in the proportion of aNSCs among cells electroporated with sort1a/sort1b MOs compared to control MOs (median and IQR: control 5. 45 %, 4. 55-11. 11 % vs.
psap is heterogeneously expressed in NSCs during their quiescence phase To further challenge whether NSC quiescence involves proteolysis control by lysosomes, as proposed  [19, 20] , and to unravel through which molecular players, we analyzed the expression of the genes that contributed to the enrichment of the lysosomal pathway in qNSCs in our RNAseq datasets. This led us to select psap, a gene encoding a glycoprotein involved in the activation of hydrolases in the lysosomes  [43] , and to date solely implicated in lysosomal proteostasis. In addition, cells deficient for Sort1 exhibit impaired trafficking of Psap to the lysosomes and enhanced release of Psap into the extracellular medium  [44] [45] [46] [47]. We found that psap was expressed at levels 3. 5 times higher in qNSCs vs. aNSCs, in a manner very reproducible among replicates (Figure  2a ).
It is also enriched 2. 3 times in 3mpf compared to 1. 5mpf qNSCs (Table  S3 ). RNAScope ISH experiments for psap further revealed heterogeneous expression levels among NSCs (Figure  2b ). Finally, and importantly, psap transcript levels in individual qNSCs in our scRNAseq dataset were stronger in cells transcriptionally distant from pcna-expressing aNSCs, ascl1a-expressing pa-qNSCs and deltaA pos NSCs (Figures  2c  and S4a-c ). To assess the relationship between psap, the number of lysosomes and qNSC states associated with different quiescence depth/length, we first tested whether deltaA expression already correlates with distinct metabolic states signed by NSCs harboring more or fewer lysosomes. We used double transgenic Tg(gfap:dtomato);Tg(deltaA:egfp) fish, allowing us to dissociate dTomato pos ,GFP pos deltaA pos NSCs and dTomato pos GFP neg deltaA neg NSCs for flow cytometry analysis after lysotracker staining (Figure  S4d ).
Acknowledgments: We are greatly indebted to  Jean Giacomotto  and  Alisha Tromp  for their help with the RNAi protocol and for providing the 3' entry backbone for these constructs. We thank  Sébastien Bedu  and  Nicolas Dray  for help in generating the Tg(mcm5:nls-rfp) line. We thank the ZEN team and  Thomas Wollert  for input, and the  Institut Pasteur CB UTechS  service platform for expert assistance with FACS analysis.
Funding: This research was funded by the  ANR (Labex Revive) ,  La Ligue Nationale Contre le Cancer  ( LNCC EL2019 BALLY-CUIF ), the  Fondation pour la Recherche Médicale  ( EQU202203014636 ),  CNRS , and  Institut Pasteur. Institutional Review Board Statement: The animal study protocol was approved by the  Ethics Committee n°39 of Institut Pasteur  (authorization # 36936 , April 26 th , 2022) and  DDPP-2021 -021 of the  Direction Départementale de la Protection des Populations de Paris. Informed Consent Statement: Not applicable.
The distribution was obtained with a bootstrap procedure. We generated 1,000 surrogate datasets by randomly selecting trials with an equal probability sampling from the initial datasets, and computing the optimal phase from each surrogate dataset, for each participant, frequency, and target position. 2) We converted the obtained optimal phases into complex vectors. The phase-locking values were fitted to a linear mixed effects model with frequencies and target positions as fixed effects, and participants as random effect. Post-hoc tests allowed to assess which specific frequencies displayed a shift in the optimal phase. Here, the optimal phases were converted in radians. We created a one-cycle sine wave with the phase offset of the oscillating disk and extracted the phase values in radians along the entire cycle.
The optimal phase, i. e. , phase of highest performance, was computed for each target position on data averaged across participants. To study whether the optimal phase shifted as a function of target positions, we computed the pairwise phase difference and assessed the significance using a Monte Carlo procedure. 50 000 datasets were created for each target position by randomly assigning detection performance according to the average values for each pair of positions (positions 1 and 2, 1 and 3, 2 and 3), and by considering the original distribution of the number of targets across participants and luminance values. The data were fitted to the sine function (EqS1) and the pairwise phase difference was obtained for each pair of positions. We obtained a surrogate of 50 000 phase differences for each pair of positions.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  852139 -Laura Dugué ) and the  Agence Nationale de la Recherche (ANR) -Deutsche Forschungsgemeinschaft ( DFG) program  (grant agreement No  J18P08ANR00 -Laura   Dugué ). We also thank  Kirsten Petras ,  Andrea Alamia ,  Rufin VanRullen  and  Frédéric Chavane  for their useful comments on the manuscript.
applied participant-by-participant to obtain rose plots of the individual optimal phase with the function circ_plot from the Circular Statistics Toolbox ([128], P. Berens, CircStat: A Matlab Toolbox for Circular Statistics, Journal of Statistical Software, Volume 31, Issue 10, 2009, http://www. jstatsoft. org/v31/i10), for each frequency and target position. The optimal phase in radians was compared between target positions by computing the pairwise phase difference with the function circ_dist from the Circular Statistics Toolbox. Statistics were performed with a Monte Carlo procedure. 50 000 datasets were created for each target position and frequency, by randomly assigning detection performance according to the average values for each pair of positions (positions 1 and 2, 1 and 3, 2 and 3), and by considering the original distribution of the number of targets across participants and phase bins.
Acknowledgements This work was supported by grants from the  French Agence Nationale de la Recherche  ( ANR-21-CE16-0025-01  and  ANR-22-CE16-0014-02 ), the  Fondation pour la Recherche Medicale  (Equipe FRM  EQU202203014688 ), the  French government  in the framework of the  University of Bordeaux 's  IdEx (Investments for the Future e France 2030 program / GPR BRAIN_2030 ). A CC-BY public copyright license has been applied by the authors to the present manuscript, https://creative commons. org/licenses/by/4. 0/. This work was also conducted in the scope of the  Transborder Joint Laboratory (LTC CoMorPD). We are grateful to  J. Baufreton ,  M. Deffains ,  A. Dubreuil ,  F. Georges ,  A. Leblois  for insightful scientific discussions and comments on the manuscript.
DOPPLER SIGNAL ANALYSIS Imaging in awake mice required careful removal of motion artifacts due to head movements or behavioral movements such as grooming. We followed the analysis previously described in  (Rabut et al. , 2020)  by first using a SVD clutter filter to separate blood motion from tissue motion, and then by thresholding tissue motion and Doppler signal to identify the frames with motion artifacts. Several thresholds were investigated by carefully examining the tissue motion signal and the threshold that removed most of the motion artifacts was chosen. We kept and concatenated epochs of at least 50 consecutive time points. The concatenated cleaned frames were filtered using a low-pass filter with a cut-off frequency of 0. 1 Hz to extract the steady-state. A polynomial fit of order 3 was applied to detrend the signal and, finally, global variations in the brain were suppressed by removing the first eigenvector of the dataset before connectivity analysis (Figure  1F ).
Acknowledgments The authors wish to thank  Nathalie Ialy-Radio  for animal husbandry and the  CNRS, INSERM  and  ESPCI  for their financial support. This work was supported by a funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  754387  ( PhD fellowship Miss Koorliyil ) and from the  Agence Nationale de la recherche  (Project 'PINCH',  18-CE37-0005-01 ). In addition, this work was supported by the  Chair in Biomedical Imaging of the AXA Research Fund  and the  European Research Council (ERC)  Advanced Grant FUSIMAGINE.