dataesr/scientific-paragraphs-categorization huggingface dataset:
e. m). On substituting these parameters in the free energy equation, we were able to extract the nucleation barrier ΔG(n c ) and the critical cluster size n c ∝ (R c )  3. ΔG(n c ) and critical radius of cluster (R c ) were normalized with respect to untreated control dataset. Analysis of SAP97/hDLG cluster size using super-resolution microscopy. A typical single-cell SMLM (single-molecule localization microscopy) experiment acquired with the microscope setup as described previously produced a set of 20,000 images that were analyzed to extract single-molecule position and dynamics. Single-molecule fluorescence events were localized in each image frame and tracked over time by a wavelet segmentation algorithm and simulated annealing, respectively. The algorithm used to derive quantitative data on protein localization and dynamics was run using the PALM-TRACER  40  plugin within the MetaMorph software environment.
The numbers thus obtained were correlated with numbers of standard scaffolding molecules such as PSD95, Bassoon, AMPA Receptors, Amyloid Precursor Protein and beta secretase molecule BACE1 in primary hippocampal neuronal cultures and APP and beta secretase molecule BACE1 in Neuro-2a cells  40, 41, 81. The derivation of the polynomial curve fitting of the normalized probability distribution function of SAP97/hDLG cluster size was followed from a previous study  34, 48. The bin size of the probability distribution for different pharmacological conditions was chosen between Δn = 40, without the function hitting a noise floor. After a trial-and-error approach with various bin sizes, the range of bins was decided for the different datasets. The cluster numbers in all conditions ranged from 1200 to 1500.
Values are expressed as mean ± s. e. m. The critical cluster size (Rc) and nucleation barrier (ΔGc) data from multiple conditions were compared and tested for significance using Brown-Forsythe and Welch's ANOVA with Dunn's correction, with p < 0. 001 as indicated by ****. Linear regression of bn was performed for all pharmacological conditions to explore the value and slope of the bulk energy component. The normality of distributions of dSTORM data was checked by D'Agostino & Pearson omnibus normality test and was found to be non-normal. The frequency distributions for constructing the probability density function histograms and the cumulative probability distribution function curves were normalized. K-S test was used to compare the distributions of control, Tg, w7 and BAPTA datasets. For K-S test, the confidence level was set to 0.
001 due to the high sample size and p < 0. 001 was indicated by ****. A previously published method was followed to generate the rank-ordered plots of treated vs. control datasets of average fluorescence intensities and puncta area  48. The data from all cells were pooled for the control and treated conditions. The exact number of puncta were chosen randomly from both control and treated datasets. These data were rank-ordered from lowest to highest, and the treated data was plotted against the control data, and a linear fit was calculated. The slope of the linear regression gave an arbitrary scaling factor. The accurate scaling factor was acquired using a previously established method  48, 85. Multiple scaling factors were applied to scale down the treated distribution within a range around the arbitrary scaling factor.
Acknowledgements The authors thank the  Bioimaging facility at IISc, India. We also thank the  Central Animal Facility at IISc, Bangalore, India. We thank  Prof. A Chishti  for the SAP97/hDLG plasmids used in this work. We thank  Prof. Aravind Penmatsa  and  Mr. Smriti Nayak ,  Molecular Biophysics Unit, IISc  for their generous help with the protein purification. We extend our gratitude to  Prof. Ibrahim Cisse ,  MIT, USA  and  Dr. Arjun Narayanan ,  Max Planck institute for physics of complex systems, Germany  for their comments and suggestions to improve the phase separation analysis of SAP97/hDLG. We thank  Prof. Samir K Maji ,  Dept. of Biosciences and Bioengineering, IIT Bombay  for his comments to optimise the in-vitro imaging of SAP97/hDLG condensates. We also extend our gratitude to  Ms.
Phenotyping Here, we will use the term "trait" for any plant feature for which raw data were collected, whatever the year and condition. However, in our analyzes we use the term "response variable" because: (1) for some traits, data were acquired in different years and conditions, and hence analyzed separately;  (2)  we also combined several traits to define new variables. In the end, we thus analyzed 152 response variables from 127 traits. In 2011 and 2012, the trial was not irrigated, and all the plants of the panel cultivars and controls were concurrently phenotyped. For each plant, we recorded the number of clusters (NCBLU) and harvested 3 clusters at 20 Brix, which provided the sampling date (SAMPLDAY, in days since January 1). We recorded mean cluster weight (MCW, in g), mean cluster length (in cm), mean cluster width (in cm), and cluster compactness (from 1 to 9 on the OIV 204 scale; OIV, 2009).
2016). Six variables were computed from these traits: the sum of glucose and fructose (GLUFRU), glucose divided by fructose (GLUONFRU), malate divided by either tartrate (MALTAR), shikimate (SHIKTAR), or citrate (CITAR) and the sum of glucose and fructose (GLUFRUTAR). In 2014 and 2015, irrigation was applied to blocks C, D, and E only  (Pinasseau, Vallverdu-Queralt, et al. 2017) , and only panel cultivars were phenotyped. As above, 3 clusters per plant were harvested at 20 Brix, providing the MCW (in g). Berries sampled from different blocks with the same water treatment were pooled per cultivar. More details on berry sampling and processing, as well as polyphenols and d 13 C measurements and analysis are described elsewhere  (Pinasseau, Vallverdu-Queralt, et al. 2017). From the data available on the 105 different polyphenols in mg per berry  (Pinasseau, Verbaere, et al.
Statistical modeling of phenotypic data We performed a 2-stage analysis of each response variable using univariate regression models. In the first stage, estimates of total genotypic values were obtained (detailed in this section). In the second stage (see next section), these were regressed on SNP genotypes to identify QTLs, estimate their allelic effects and assess prediction accuracy. To decrease the influence of potential outliers, all polyphenols (the compounds as well as the calculated variables) had their raw data automatically transformed with the natural log. For the other traits, when their raw phenotypic data were too skewed as visually assessed, they were also log-transformed (see Supplementary Fig. 5  and Supplementary Table  4 ).
Estimation of genotypic values For each response variable, a global linear mixed model was defined with multiple fixed effects [for the 2011-2012 data set: block, year, block-year interaction, PRUW, NBWS, vigor, and all 5 viruses, PRUW and vigor being discarded when vigor itself was the response; for the 2014-2015 data set: irrigation, year, irrigation-year interaction, Brix (as there can be small deviations from 20 Brix)], and all 5 viruses, as well as the volume and weight of juice samples for BDAM and PDMS) together with 2 random effects (genotype and genotype-year interaction). The global model was fitted by ML with R/lme4 version 1. 1. 19 (Bates et al. 2015). The output was given to R/lmerTest version 3. 1-2  (Kuznetsova et al. 2017)  to use its function "step. " Backward elimination of random-effect terms was performed using likelihood ratio test, followed by backward elimination of fixed-effect terms using F-test for all marginal terms, i.
was assessed by ELISA from plants at INRAE Vassal (Marseillan, France). Flower sex (OIV 151) and berry skin color (OIV 225) of each panel cultivar were retrieved from (Laucou et al. 2018) and completed with the database of INRAE Vassal germplasm repository (https:// bioweb. supagro. inra. fr/collections_vigne/Home. php?l=EN).
Acknowledgments We thank all the agents from the grapevine germplasm collection maintained at  INRAE Vassal, Val erie Miralles  and  Jean-Franc¸ois Ballester  from  AGAP-PPB  for phenotyping of organic acids,  Pierre Mournet  from  AGAP-GPTR  and the GenoToul platform for genotyping by sequencing,  Bertrand Pitollat  and the  South Green  platform for computing, and  Philippe Chatelet  for useful suggestions on the text.
Funding This work was funded by several projects:  GrapeReSeq  ( ANR ,  2009-2011 ),  DLVitis  ( ANR ,  2010-2012 ), Innovine (KBBE, 2014(KBBE, -2015)), " Cr eer les c epages de demain avec les outils d'aujourd'hui " (CASDAR, 2011-2013), and  FruitSelGen  ( INRA m eta-programme Selgen ,  2015-2016 ).
Data availability The data that support the findings of this study are openly available, for sequences at the NCBI as BioProject PRJNA489354 and, for all the rest, at the Data INRAE repository at https://doi. org/10. 15454/8DHKGL. The code that supports the findings of this study is openly available at the Data INRAE repository at https://doi. org/10. 15454/8DHKGL. Many of the custom functions we used are available in a R package for reproducibility purposes, rutilstimflutre  (Flutre 2019). Supplemental material is available at https://doi. org/10. 15454/8DHKGL.
Gene expression data derived from 572 unique donors, aged 0 to 102 years, were pooled across ve databases: the Allen Human Brain Atlas (AHBA)  12  , the BrainCloud 13 , the Brain eQTL Almanac project  14  , the BrainSpan (http://brainspan. org), and the Genotype-Tissue Expression Project  15. The virtual histology analyses were anchored on the AHBA database where gene expression levels across all donors (n = 6) have been mapped to the 34 left-hemisphere Desikan-Killiany cortical regions. Therefore all subsequent analyses were restricted to these regions. The expression values of the mapped samples in the AHBA were mean averaged across microarray probes to provide a single value for each gene for a given sample. Median averages summarized expression values within each of the 34 left-hemisphere cortical regions of a speci c donor, and the median average across donors provided a single value for each region.
The latter represents the median expression pro le for a given gene across the 34 cortical regions. Similarly mapped gene expression levels are also available in the BrainSpan database. Comparison between the mapped samples of the AHBA and the BrainSpan datasets were used to identify genes with consistent expression across both datasets. This analysis yielded 2,511 genes consistently expressed in the cortex of which 2,321 were also available in all ve databases. Of the consistently expressed genes, 604 were cell-speci c 16 and were clustered into 9 panels: S1 pyramidal neurons (n = 73), CA1 pyramidal neurons (n = 103), interneurons (n = 100), astrocytes (n = 54), microglia (n = 48), oligodendrocytes (n = 60), ependymal (n = 84), endothelial (n = 57), and mural (pericytes and vascular smooth muscle cells; n = 25).
e. , knots). GAMM derivatives (implemented with the gratia package) were used as estimates of cortical thinning in each region for every 1-year shift in chronological age (Supplementary Fig. 2 ). As the age range of the ENIGMA Lifespan dataset is 4-89 years, these analyses generated 86 age-speci c inter-regional cortical thinning pro les across the 34 cortical regions, each representing the spatial variation in cortical thinning at that speci c age. Virtual histology involved computing the Pearson's correlation coe cient between the cortical thinning pro les (N = 86) and the expression pro les of each of the cell-speci c gene panels (n = 9). Positive and negative coe cients indicate that higher gene expression levels were respectively associated with less or more pronounced cortical thinning.
In late-life, higher expression levels for marker genes for microglia and CA1 pyramidal cells were associated with more pronounced cortical thinning. Cortical thinning pro les were then averaged to generate a single life-stage inter-regional cortical thinning pro le for each life-stage. We then used gene co-expression and over-representation analyses to test for differing biological processes and cellular components underlying cortical thinning across early-, mid-and late-life stages  17. Gene expression values across the ve databases were available for 15,380 genes including 2,321 genes that were consistently expressed in cortical regions. A co-expression matrix (2321 × 15380) was determined using linear mixed models (implemented with lme4 package) adjusted for age and sex with a individual participant identi ers as random intercept.
Cell-speci c Gene Expression Pro ling The Allen Human Brain Atlas (AHBA) provides comprehensive information about gene expression based on postmortem brain time from 6 adult donors (http://www. brain-map. org). Gene expression data were obtained from isolated RNA hybridized to custom 64K Agilent microarrays (58,692 probes). Geneexpression data were mapped to the Desikan-Killiany atlas in FreeSurfer space yielding up to 1,269 labeled samples per brain inside or close to a FreeSurfer cortical region  9. The expression values of these samples were mean averaged across microarray probes to provide a single expression value for each gene for a given sample. Using established methods  10  , we extracted gene expression values from the 34 cortical regions on the left hemisphere for which information was available for all donors.
Median averages were used to summarize expression values within each cortical region and donor, which was followed by the median average across the 6 donors. The same approach was employed for data from the BrainSpan Atlas (http://brainspan. org), another post-mortem dataset with gene expression data mapped to FreeSurfer space. We then identi ed 2,511 genes whose regional expression pro les were consistent across donors and between the AHBA and BrainSpan datasets. All further analyses focused on these consistent gene sets. We used previously published lists of genes expressed in speci c cell types based on single-cell transcriptomes from 3005 cells from the somatosensory cortex (S1) and the CA1 hippocampus of mice  31. Mice genes were converted to human gene symbols  16  with the "homologene" R package.
Images were inspected slice by slice using orthogonal and external surface displays. Following qualitative assessment, the regional parcellations were marked on a binary "pass" or "fail" scale. Participants with regions marked as "fail" were removed. After these two steps, each contributing cohort forwarded data from individuals with successful parcellations to the Icahn School of Medicine at Mount Sinai, where regional cortical thickness estimates identi ed as outliers using ve median absolute deviations (MAD) were removed.
Estimates of Cortical Thinning The association of age with cortical thickness in each region were quanti ed using cubical splines speci ed in generalized additive mixed models (GAMM; implemented with the mgcv package in R) adjusted for sex as xed effect and scanner identi ers as random intercepts and optimized for the number of convergence points (i. e. , knots). GAMM derivatives (implemented with the gratia package) were used as estimates of cortical thinning in each region for every 1-year shift in chronological age (Supplementary Fig. 2 ). Correlations between cortical thinning measures extracted with different number of knots ranged 0. 75-0. 99 (mean r=0. 96) (Supplementary Table  3 ) demonstrating the robustness of the results to the GAMM parameters.
Gene Co-expression Networks We pooled data on gene-expression levels in post-mortem cortical brain tissue from 572 unique donors, aged 0 and 102 years at the time of death, from ve databases: the AHBA  12  , the BrainCloud 13 , the Brain eQTL Almanac project (BrainEAC)  14  , the BrainSpan (http://brainspan. org) and the Genotype-Tissue Expression Project (GTEx)  15. Gene expression was quanti ed using microarrays in AHBA, BrainCloud, and BrainEAC and RNA sequencing in BrainSpan and GTEx. Gene symbols in each database were updated, the gene expression values were log10 transformed, scaled within each cortical region. Gene expression values for 15,380 genes were available in all ve databases which included 2,321 genes consistently expressed in cortical tissue genes panel. Data were pooled to generate a co-expression matrix for each 2,321 consistent gene using linear mixed models as implemented with lme4 in R.
Behavioral analysis of mussel larvae by Image-J The first step consisted in converting the video into AVI format compatible with the ImageJ software. This was made by the freeware VirtualDub (www. virtualdub. org). Image-J software (http://imagej. nih. gov/ij/index. html) and the wrMTrck plugin  (Nussbaum-Krammer et al. , 2015)  were used for the analysis of mussel larvae's swimming behaviour. As this is the first study of the swimming behavior of mussel larvae, specific parameters for mussel larvae were tested (Table  2 ). The films (. avi) were opened as a stack of images. Each image is made binary by applying automatic "thresholding" based on the entropy of their histogram and the detection of larvae is launched. Every object larger than 400 pixels and smaller than 1500 pixels is selected and considered as larvae.
Acknowledgments Khouloud Boukadida  was recipient of a Ph. D. scholarship from the  European Union  in the framework of the  Erasmus Mundus EU Metallic II Program. This work formed part of the  LABEX COTE cluster of excellence "Continental  to coastal Ecosystems: evolution, adaptability, and governance". This project was supported by the  University of Bordeaux, Ministry of Higher Education  of the tunisian government. , and the  Laboratory of Agrobiodiversity and Ecotoxicology  ( LR21AGR02 ).
| Analysis of the data As previously published,  7, 8, 13  we studied the full dataset of users meeting the eligibility criteria. We computed the frequency of three medication schemes: observations under no medication, monotherapy (including frequency of use of each medication group) and co-medication. For each medication group or combination, we computed the median VAS Global allergy symptoms (VAS assessing on a 0-100 scale how much allergic symptoms are bothering the patient on that day), as well as the median VAS Work (VAS assessing on a 0-100 scale the impact of allergic symptoms on work that day) and the respective confidence intervals. To assess whether medication use followed any seasonal pattern, we analysed the monthly use of different medication groups and co-medication schemes. To address across-Europe diversity, we performed separate analyses with data from Southern European countries (Greece, Italy, Portugal and Spain), Central-Western European countries (Belgium, France and Switzerland) and Northern-Eastern European countries (Austria, Czech Republic, Denmark, Finland, Germany, Lithuania, Netherlands, Poland, Slovenia, Sweden and the United Kingdom).
Data analysis 2. 5. 1. Sleep Onset Analysis Wakefulness and sleep stages were scored off-line (Noxturnal software, v. 4. 4. 2) on EEG, EOG and EMG signals by a sleep expert according to both the reference 30s-epoch sleep classification (American Academy of Sleep Medicine and  Iber, 2007 )  and the Hori scale of drowsiness  ( Hori et al. , 1994 ) , based on epochs of 5 sec. ( fig. 1. A ). We also defined different sub-stages within N2 sleep: in addition to the Hori stage 9 (H9), that we scored as epochs containing the first sleep spindles in the first 15 s. of N2 sleep, we separated the 'early-N2' from the consolidated or 'late-N2', with epochs respectively in the first 5 minutes of N2 or later. We also differentiated epochs with sleep spindles, K-complexes or slow waves in addition to the theta background, which were manually detected.
PE was computed in theta, alpha, beta and gamma. SMI and wSMI were computed in theta and alpha bands. All markers were computed for each epoch and channel, using the interval between the baseline and 600 ms (the onset of the last stimulus). PSD was computed using the welch method  ( Welch, 1967 ) , dividing the signal into segments of 512 ms, with 400ms overlap between segments. Segments were windowed using a Hanning window and zero padded to 4096 samples. The power in each band was computed by integrating the power within the band. Kolmogorov-Chaitin complexity was computed by compressing a discretization of the signal using a histogram approach with 32 bins. Permutation Entropy was obtained by computing the entropy of a symbolic transformation of the signals  ( King et al. , 2013 ). SMI and wSMI is then computed from the same symbolic transformation, but data was first filtered using Current Source Density estimates  ( Kayser and Tenke, 2006 )  to diminish the volume conduction.
Software and Data MEG data was preprocessed with Fieldtrip  ( Oostenveld et al. , 2011 ). Markers were computed using NICE ( Denis A  Engemann et al. , 2018 )  and MNE-Python  ( Gramfort et al. , 2013 ). Decoding was computed using scikit-learn  ( Abraham et al. , 2014 ; Pedregosa et al. , 2012 ; ). All scripts used for the analysis after pre-processing the data are available at https://github. com/fraimondo/lg _ sleep _ paper. Raw and epoched MEG data are subject to the data privacy, ethics requirements and informed consents signed by the participants at the moment of acquisition. It can be shared upon reasonable request to authors and with a formal data sharing agreement. The MEG-extracted markers for the analysis are available from figshare with the DOI: 10. 6084/m9. figshare. 14039315.
Responsiveness is driven by the ongoing brain state We then tested if responsiveness during these vigilance stages could be related to the overall ongoing brain state. We evaluated the capacity to distinguish the responsive (MR + ) from non-responsive (MR-) trials on a trial-by-trial basis, based on the MEG markers computed just before the global deviant stimuli (between the onsets of the 1 st and the 5 th sounds). To this aim, we trained Extremely Randomized Trees classifiers (ET) to distinguish between markers computed during trials where the subject did respond (MR + ) and trials where the subjects' response was absent (MR-). We first measured the ROC-AUC using cross-validation on separate classifiers trained with data from single drowsiness stages. We found above chance classification in every stage, namely AUC = 0.
Acknowledgment We are grateful to  Stanislas Dehaene  for his essential support in data acquisition and for helpful comments, to  Bernadette Martins  and the  Neurospin  staff for administrative support, to nurses  Laurence Laurier ,  Véronique Joly-Testault , and  Gaëlle Mediouni  for their help in recruitment and data acquisition and to  Kaustubh Patil  for his feedback on predictive modelling. This work was supported by grants from  Institut National de la Santé et de la Recherche Médicale (INSERM)  and  Journées de Neurologie de Langue Française  (to M. S. ),  Stic-Amsud  " RT-BRAIN  " (to J. D. S. ) and  Wallonia-Bruxelles International IN Excellence Grant  (to F. R. ). The Neurospin MEG facility is sponsored by grants from  INSERM ,  CEA ,  Fondation pour la Recherche Médicale , the  Bettencourt-Schueller Foundation , and the  Région i le-de-France.
Acknowledgements. This work is part of the  ANR  project  EQUIPEX CRITEX  (grant  ANR-11-EQPX-0011 ) and relies on the  Ploemeur Critical Zone Observatory H + (SNO H + , http://hplus. ore. fr/en/ploemeur, last access: 4 May 2022). We are grateful to the  OZCAR research infrastructure  (http://www. ozcar-ri. org/,last access: 4 May 2022). We thank  Mikhail Smilovic  for revising an earlier version of the manuscript for grammar and syntax. Financial support. This research has been supported by  Université de Rennes 1. Review statement. This paper was edited by  Gerrit H. de Rooij  and reviewed by  Ty P. A. Ferre  and one anonymous referee.