dataesr/scientific-paragraphs-categorization huggingface dataset:
986 and 0. 987, respectively, close to the theoretical maximum of 1. 0 that can only be achieved with perfect type classification for all neurons in the dataset. For subsets of 47 genes, the latent representations were obtained with the second linked autoencoder, and the corresponding RI distributions are colored red. A set of 47 DE genes achieves average RI = 0. 964. These results confirm the idea that autoencoder-based low dimensional representations of gene expression can be used for accurate type classification. The 47 NP genes can be used to classify neuron types well, with average RI = 0. 925 and a majority of the neurons (62%) classified correctly at the type level (with nearly uniform performance across all neuron types, see Figure  4 -figure supplement 1). This RI performance is significantly higher (p<0.
Prediction of peptidergic networks from neurotaxonomic NP gene expression profiles Figure  6  displays weighted adjacency matrix plots representing predictions of neuron-type-specific and neuron-subclass-specific peptidergic coupling from selections drawn from VISp and ALM of the 37 cognate NP gene pairs. The prediction matrices A-E are outer products (CPM*CPM units) of vectors representing expression (CPM units) of an NPP gene (columns) and a cognate NP-GPCR gene (rows) across all VISp or ALM neuron types. The predicted coupling matrices in F matrices are similar except that factor vectors are down-sampled by averaging neuron-type-specific CPM values within each of the subclasses (see Materials and methods for more details). The online version of this article includes the following source data for figure  5 : Source data 1.
Cell types, cluster ids and color codes for VISp region. Source data 2. Cell types, cluster ids and color codes for ALM region. Table  3. The 18 NPP and 29 NP-GPCR genes of Tables 1 and 2 constitute 37 cognate NPP/NP-GPCR pairs and predict at least 37 potentially distinct peptidergic modulatory networks. The 37 pairs are enumerated here along with indications of the expected primary GPCR signal transduction class for each NP-GPCR  (Alexander et al. , 2017 ) and a fraction denoting frequency with which the given cognate pair occurs as a fraction of all neuron pairs surveyed. Pastel table fill colors denote G-protein transduction class as in Tables  1  and 2   6A  and B, these exemplify the wide variety of neuron-type-specific coupling motifs resulting from transcriptomic prediction. Most coupling matrices (i.
Conclusions Because single-cell RNA-seq data enable prediction of complete protein parts lists of individual neurons, they open powerful new perspectives on neuronal differentiation, function and network architectures. The power of these new perspectives has been further enhanced by parallel development of transcriptomic neurotaxonomies. Here we have exploited both a pioneering large-scale RNA-seq dataset and its data-driven neurotaxonomy to pursue a new perspective on local neuropeptidergic modulatory signaling in mouse cortex. This work has revealed a surprisingly highly structured and abundant expression of cortical NPP and NP-GPCR genes: dozens of neuropeptide signaling genes are expressed at very high levels in very distinctive and highly conserved patterns. While entirely consistent with previous bulk transcriptomic and proteomic observations, it is only with the advent of the RNA-seq combination of single-cell resolution with genomic depth that this extreme structure and abundance has come into focus.
Data and software resources The present study is based on analysis of a resource single-cell mRNA-seq dataset acquired at the Allen Institute  (Tasic et al. , 2018)  and available for download at http://celltypes. brain-map. org/rnaseq/ These RNA-seq data were acquired from a total of 22,439 isolated neurons, with detection of transcripts from a median of 9462 genes per cell (min = 1,445; max = 15,338) and an overall total of 21,931 protein-coding genes detected. Neurons were sampled from two distant and very different neocortical areas: 13,491 neurons from primary visual cortex (VISp), and 8948 neurons from anterior lateral motor cortex (ALM). Tasic et al. , harvested tissue specimens from a variety of transgenic mice expressing fluorescent proteins to enable enrichment of samples for neurons and for relatively rare neuron types by FACS sorting after dissociation.
This enrichment procedure resulted, by design, in a disproportionate representation of GABAergic neurons, canonically ~20% of neurons  (Sahara et al. , 2012) , such that the sampled neuron population is roughly half GABAergic (47%) and half glutamatergic (53%). The resource publication  (Tasic et al. , 2018)  should be consulted for full details of neuronal sample and library preparation, sequencing and data processing. Source data, spreadsheets, R scripts and other code used to generate all tables and figures presented here are available at https://github. com/AllenInstitute/PeptidergicNetworks (Gala, 2019 ; copy archived at https:// github. com/elifesciences-publications/PeptidergicNetworks). A derived data set np_gpcr_cpm. csv was used for analyses summarizing CPM, region and metadata for the NPP and NP-GPCR genes.
Data metrics The Tasic 2018 single-cell RNA-seq data tables report the abundance of transcripts from individual neurons in both 'counts per million reads' (CPM) and 'fragments per kilobase of exon per million reads mapped' (FPKM) units. Our analysis of this data compares gene expression levels quantitatively, with two distinct use cases: (1) comparisons across large sets of different genes, and (2) comparisons of the same gene across different individual cells, cell types and brain areas. We have relied upon FPKM data  (Mortazavi et al. , 2008; Pimentel, 2014) , for use case 1 (i. e. , the Tables  1  and 2  comparisons across genes). For use case 2 (as in all figures below), we have preferred the CPM units, because these units were used to generate the Tasic 2018 neurotaxonomy. While choices between CPM and FPKM units here should have little impact upon outcomes, it would seem inconsistent to use FPKM units to compare across cell types discerned on the basis of CPM units.
Selection of the 18 NPP gene set As noted in Introduction, usage and definitions of the term 'neuropeptide' vary widely across the current literature. It therefore seems unwise at present to claim that any attempted consensus list would accurately circumscribe all neuropeptides. For the purposes of the present work, we have relied therefore on the reasonably exhaustive list of 96 classical and candidate human and mouse NPP genes put forth in a widely cited publication  (Burbach, 2010)  and related website (http://neuropeptides. nl/, last accessed 10 October 2019). To reconcile this list to current mouse gene nomenclature, we used both the HGNC nomenclature ((https://www. genenames. org/, last accessed 10 October 2019) and the Mouse Genome Database (MGD) (http://www. informatics. jax. org, last accessed 10 October 2019).
The result is the list of 94 putative mouse NPP genes presented in Supplementary file 3, which also tabulates the pFPKM values and percentile scores compiled for each NPP genes from the Tasic 2018 dataset. These 94 NPP genes were further segregated using a preliminary (early 2018) version of the Tasic 2018 neurotaxonomy to select NPP genes exhibiting median CPM expression levels > 10 in one or more neuron type in VISp and ALM cortex. This screening resulted in the list of 39 such NPP genes represented in Supplementary file 4, with most exceeding the 10 CPM threshold by a large margin (observed range was 24-4100 CPM). Supplementary file 4 also tabulates criteria that drove inclusion of only the 18 NPP genes represented in Table  1  while 21 other cortically expressed NPP genes were excluded.
Acknowledgements We wish to thank the  Allen Institute for Brain Science  founder,  Paul G Allen , for his vision, encouragement and support. This work was supported in part by award number  R01NS092474  from the  Office of the Director of National Institutes of Health  and award number  R01MH104227  from the  National Institute of Mental Health. The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health.
Data availability The present study is an analysis of a large transcriptomic dataset that is now freely available for download in its entirety at http://celltypes. brain-map. org/rnaseq/ and is described fully in a rigorously peer-reviewed publication (Tasic, et al. , Nature 563:72-78, 2018). All code and intermediate data products involved in preparing this manuscript are freely available from a well-documented GitHub repository: https://github. com/AllenInstitute/PeptidergicNetworks (copy archived at https:// github. com/elifesciences-publications/PeptidergicNetworks). The following previously published dataset was used:.
Acknowledgements A. G. is a Ramón y Cajal fellow ( RYC-2016-19466 ). This work was supported by the  NARSAD foundation  (young investigator grant  2016 (Ref. 24803 ) to A. G. ) and  Ministerio de Ciencia e Innovacioń  ( SAF2015-67474-R ;  MINECO/FEDER  to S. G. ). J. -A. G. lab was supported in part by  Inserm , the  Université Pierre et Marie Curie  ( UPMC, Sorbonne Université ), the  Fondation pour la Recherche Médicale , and the  Bio-Psy (Biology for Psychiatry) laboratory of excellence. O. A. -M. was funded by the  ISCIII/SERGAS  thought a research contract " Sara Borrell " ( CD14/ 00091 ). JA lab was supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  ( SAF-2014-57160R ,  SAF-2017-88076-R ),  Fundació Marató   TV3  to J. A. and  Instituto Carlos III: Centro de Investigación Biomédica en Red sobre enfermedades neurodegenerativas (CIBERNED) , and  RETICS RD12/ 0019/0002  to J.
g. , language). Hence, examining the variability of brain lateralisation phenotypes across species may provide insights into the evolution of hemispheric asymmetries. To conclude, we strongly agree with Guy Vingerhoets' perspective and challenges  [15]. We believe that actions are needed to take the next critical steps towards multidimensional participantcentric analyses. Taking other successful initiatives  [35]  as an example, the initial step would be for researchers who investigate functional brain lateralisation to unite and pool efforts to create an extensive high-dimensional comprehensive database to harvest together as a consortium. This effort ensures a large database and productive collaborations within an open science framework to take the next steps together.
Datasets Lexical frequency effects were investigated for consistent word misperceptions in noise in two recent, extensive, open-source datasets of Spanish (SP)  11  and English (EN) words  12. Consistent misperceptions are defined in these datasets as tokens for which no fewer than six listeners reported the same misperception in response to a given target word presented in noise. Both datasets were elicited in a similar manner, but while the EN dataset used three maskers (speech-shaped noise, SSN; 3-talker babble-modulated noise, BMN3; 4-talker babble, BAB4), the SP dataset additionally employed 1-talker babble-modulated noise (BMN1) and 8-talker babble (BAB8). Babble maskers were generated by random concatenation of target words to reach the required babble density  11, 12. Misperceptions were elicited at a range of SNRs (Table  1 ), values chosen in pilot tests to maximise the chance of consistent confusions, motivated by the finding that too-high SNRs lead to few errors, while too-low SNRs tend to produce inconsistent errors.
Estimates for the SP dataset are derived from the CREA Spanish word frequency list  13  , expressed in occurrences per million words, while equivalent data for the EN dataset come from the SUBTLEX-UK corpus  14  , expressed in Zipfs. The Zipf scale is defined as log10 (frequency per billion words) and ranges from around 1 (very low frequency words) to 7 (extremely common words, mainly function words and pronouns). For example, in the current datasets, common words "por" and "we" have a Zipf value around 7 and the far less common words "bromeas" and "fifteenth" have values near to 2. The Zipf scale is argued to avoid the problem of interpreting negative values that arise from log-transformed counts per million words that occur when counts are derived from very large corpora 14. To ease comparability in the current study, lexical frequencies in the SP dataset were derived by converting values to Zipfs.
Words from the masker Misperceptions in the EN and SP datasets occasionally correspond to complete words contained in the BAB4 masker. Since maskers were constructed from the same set of speech materials as the target words, a mean lexical frequency difference of zero is to be expected for these cases, leading to a potential source of bias. Such cases amount to some 316 tokens (around 5% of the combined datasets), of which 269 occur in the EN dataset. After excluding these cases, the lexical frequency difference for the BAB4 masker in EN increases from 0. 18 to 0. 31 Zipfs, while for SP the increase is more modest, from 0. 09 to 0. 12 Zipfs. However, a significant masker effect remains [F (2, 4806) = 47. 1, p <. 001, η 2 = 0. 019], indicating that the occasional reporting of complete words from the babble might account for part of the limited lexical frequency effect in the EN dataset, but has almost no impact on the SP corpus.
Word length differences Shorter words tend to be more common, and different maskers may result in different patterns of phoneme deletion. For example, the quasi-stationary SSN masker might be expected to leave more energetic target components near to syllable nuclei intact, while maskers with significant temporal modulation might produce a more uniform pattern of deletions across phonemes. For the misperceptions of the current datasets, length in phonemes is indeed inverselyrelated to lexical frequency [EN: r = -0. 38, SP: r = -0. 30, both p <. 001]. However, a significant masker effect remains, albeit with a reduced effect size, after excluding target/misperception pairs of differing phoneme length [F (2, 1999) = 21. 2, p <. 001, η 2 = 0. 021]. Combining the equal-length criterion with exclusion of words from the masker (Section 3.
Duplicate target-misperception pairs Due to the procedure used to generate new speech-in-noise tokens on demand during elicitation of the EN and SP datasets, which involved random selection of a target word from a base corpus, both datasets contain a number of duplicate target-misperception pairs (note that even though targets were presented multiple times, they may have come from different talkers and were mixed at varying SNRs with potentially different maskers). For instance, the target word 'perverse' (3. 13 Zipfs) was misperceived as the more common word 'reverse' (4. 15 Zipfs) on two occasions. A re-analysis limited to unique pairs only (counts of which are indicated in Table  1 ) produces a highly-similar pattern to that seen in Fig. 1  and a clear effect of masker type [F (2, 3945) = 70.
As an alternative proxy for pure energetic masking, glimpse percentages were computed for the two datasets (Fig. 2 ). Glimpse percentages have been shown to provide reasonable first-order predictions of intelligibility for a range of different speech and masker types  17. for the ranges of SNRs used here. Moreover, glimpse percentage is lower for the three pure energetic maskers than for the two babble maskers, suggesting that part of the lexical frequency effect seen in Fig. 1  could be due to scarcity of information about the target that is predicted to survive masking. Nevertheless, energetic masking cannot entirely explain the across-masker disparity between lexical frequency differences. For example, BMN1 and BAB8 are both predicted to leave the target word occupying around 20-22% of the spectrotemporal plane, yet the BMN1 masker results in a far larger lexical frequency effect.
Discussion In two extensive corpora, misperceptions reported by listeners were words of a higher lexical frequency than their corresponding intended target words. Lexical frequency difference shows an apparent dependence on the type of masker, being substantially larger for three pure energetic maskers than two babble maskers (Fig. 1 ), but some of the effect may be due to differences in the amount of acoustic information which survives masking according to a glimpsing model (Fig. 2 ). This outcome supports the finding of an increased lexical frequency effect at lower SNRs 7. It is conceivable that increased acoustic uncertainty favours the use of word frequency priors. An example from the EN dataset illustrates this possibility: "clinic" with a lexical frequency of 3. 9 Zipfs was misperceived as "finish" (5.
One possibility is that the additional acoustic-phonetic information contributed by an informational masker acts as a source of constraint on possible word hypotheses, which in turn limits or eliminates a role for lexical frequency. This notion can be illustrated with an example from the EN dataset: target word "wife" (5. 2 Zipfs) was misperceived in BAB4 for the less frequent word "twice" (4. 8 Zipfs); an inspection of the words making up the babble provides clear evidence for a word-initial /t/ and a word-final /s/ with a temporal alignment appropriate for their incorporation in the reported word. Here, the ability to fit alternative word candidates is constrained by elements in the babble, attenuating the influence of lexical frequency. The limited room for manouevre in the presence of additional phonetic cues contrasts with the uncertainty created in the face of missing information due to pure energetic masking.
2 Zipfs for the datasets of the current study. Since low frequency targets are a priori more likely to result in higher frequency responses than targets of a higher mean lexical frequency, it is understandable that Felty et al. 9  observed a lexical frequency effect at higher SNRs than those used in the current study. A further difference between the two studies is the nature of the speech material making up the babble. In Felty et al. 9  the masking material came from a different talker than that of the target words, while in the current study the target talker could also appear in the babble. Informational masking effects are thus expected to be higher for our stimuli, and as a consequence it seems likely that speech fragments from the masker were more easily misallocated into the final word misperception.
Medical records were reviewed retrospectively and data were collected for demographic details (sex, age of onset), biological results including CSF, main clinical features, final diagnosis, and treatment. Brain and spinal cord MRI were reviewed. Final diagnosis was made using the most recent criteria for MS, NMO, CIS and lupus erythematosus (LE)  [3] [4] [5]  Medical records were reviewed retrospectively and data were collected for demographic details. Expanded Disability Status Scale (EDSS) was obtained during relapse.
Acknowledgment. We are indebted to  Ray Cooke for copyediting. Disclosures. Commission Nationale Informatique et Libertés (CNIL) approved the Observatory of Multiple Sclerosis (OFSEP)  project. Contributors. NL MB and JCO contributed to the writing of the manuscript. NL, JCO, CCD, MC, PL, JPC, GE, AN, DB, JC, PC, MB, CD, AM and BB contributed to reviewing the manuscript, the recruitment of patients and data collection.
5-1 kHz and analyzed using IGOR Pro and Neuromatic v2. 6 software. All drugs for electrophysiological experiments were obtained from Tocris Biosciences or Sigma-Aldrich, unless otherwise stated. Experimental design and statistical analysis. Statistical analyses were performed with Prism6 (GraphPad Software). First the normality of dataset was tested using the D'Agostino-Pearson omnibus normality test. If data were normally distributed, a Student's t test, one-way ANOVA or two-way ANOVA was performed; otherwise, nonparametric tests such as Mann-Whitney test (for unpaired data) were used. Data distributions were analyzed using the Kolmogorov-Smirnov (KS) test; for miniature events the distribution curve was calculated for each cell and all cells averaged per condition to create a single curve.
IMPLEMENTATION We use a Microsoft Hololens with a Pupil Labs Eye tracker. The device is equipped with one eye camera 1. The computer vision algorithms used to detect and track the pupil center positions reduced the frame rate by 5%. A C# desktop software was built using the EmguCv 3. 1 2 on an XPS 15 9530 Dell Laptop 1 Sampling rate: 120 Hz, resolution: 640 × 480 pixels 2 An OpenCV 3. 1 wrapper for C#   64 bits with an Intel(R) Core(TM) I7-4712HQ CPU 2. 30GHz, 4 core(s), 8 processes, 16GB of Random Access Memory, 2GB swapping Memory.
Here we use a large, multi-cohort, lifespan sample (n = 21 251; age 4-97 years) of individuals to investigate the relationship between the inter-regional profile of cortical thickness, estimated from MRI, and the inter-regional profile of NR3C1 and NR3C2 expression, obtained from the Allen Human Brain Atlas. With cumulative exposure to glucocorticoids with aging, we hypothesize that cortical thickness may become more strongly associated with the distribution of NR3C1 and NR3C2 cortical expression across the lifespan. Furthermore, we investigate neural cell types that may be involved in mediating the relationship between glucocorticoid exposure and cortical thickness. Finally, we examine whether expression of NR3C1 and NR3C2 in the human cerebral cortex varies across the lifespan, using data from 502 post-mortem donor brains (4-102 years of age).
Participants A group of 18 international cohorts (14 cross-sectional), with a combined total of 21 251 participants (54. 9% female), contributed data for this study. All cohorts contributed demographic (age and sex) and neuroimaging data from cognitively healthy participants across the lifespan (age range, 4-97 years). All included participants had at least 1 T1-weighted brain scan. Basic demographic and cohort details can be found in Table  1.
Neuroimaging For each participant, cortical thickness was estimated for 34 cortical regions (left hemisphere) using the FreeSurfer pipeline with the Desikan-Killiany Atlas  (Desikan et al. 2006 ). We used left hemisphere values of cortical thickness to ensure compatibility with gene expression data available in the Allen Human Brain Atlas (left hemisphere only, see below). Note that interregional profiles of cortical thickness in the right hemisphere are virtually identical to those in the left hemisphere; in a sample of 2091 participants (3514 scans), we observed a strong correlation between the left and right inter-regional profiles (r = 0. 99). Specifics on MRI protocol and FreeSurfer version can be found in Supplementary Table  S1.
The final result is a single gene expression (median) value for each of the 34 regions. Given the lifespan nature of our study, we examined whether NR3C1 and NR3C2 expression varies with age. To do so, we obtained mRNA expression values from the following (additional) sources of gene expression in the human brain: BrainCloud (Supplementary,  Colantuoni et al. 2012; Jaffe et al. 2015) , Brain eQTL Almanac (BrainEAC;  Trabzuni et al. 2011) , BrainSpan  (Li et al. 2018) , and Genotype Tissue Expression (GTEx;  Ardlie et al. 2015). Each source acquired tissue samples from human post-mortem donor brains. The extent of tissue sampling ranges from a single cortical region (BrainCloud) to 34 cortical regions (Allen Human Brain Atlas). When applicable, we excluded donors younger than 4 years of age (the youngest age in the in vivo MRI dataset).