dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We gratefully thank  Prof. Ryan S. Gray  for providing us with Tg(scospondin-GFP) embryos,  Prof. Sylvie Schneider-Maunoury  and  Dr. Christine Vesque  for providing us the Ras-eGFP plasmid,  Dr. Stephane Gobron  for the antibody directed against the Reissner fiber. We thank  Monica Dicu  and  Antoine Arneau  for fish care, the  ICM. Quant imaging facility for instrument use, scientific and technical assistance and  François-Xavier Lejeune  for advice on statistics. This work benefited from equipment and services from the iGenSeq (RNA sequencing) and iCONICS (RNAseq analysis) core facilities at the  ICM (Institut du Cerveau, Hôpital Pitié-Salpétrière, PARIS, France). We thank for critical feedback all members of the  Wyart lab  (www. wyartlab. com). This work was supported by, the  HFSP  Program Grants # RGP0063/2018 , the  Fondation Schlumberger pour l'Education et la Recherche  ( FSER/2017 ) for C.
Both capsaicin responsive (TRPV1+) and non-responsive (TRPV1-) neurons fluxed Ca 2+ in the presence of HKCA effluence (Fig  1A ). This is consistent with our prior observations and suggests that multiple populations of sensory afferent neurons respond to C. albicans. β(1-3)-glucan is a well-defined component of the C. albicans yeast cell wall that drives recognition of C. albicans by cells of the innate immune system through interaction with the c-type lectin receptor Dectin-1  [21]. Although mRNA encoding for Dectin-1 (i. e. Clec7a mRNA) is not abundant in DRG single cell RNAseq datasets  [9, 22] , incubation of cultured DRGs with C. albicans-derived soluble β-glucan is sufficient to trigger release of the neuropeptide CGRPα in a Dectin-1 dependent manner  [18]. Unexpectedly, we observed that equal numbers of sensory neurons isolated from wild-type (WT) or Clec7a -/-mice fluxed Ca 2+ in response to HKCA effluence (Fig  1C ).
albicans infectivity relative to control mice. Thus, there is no clear role for P2RX7 and P2RX4 in the recognition of eATP or for recognition of ATP from other potential sources in the host during C. albicans infection. In contrast, WT mice treated with two P2RX2/3-specific antagonists, PPADS and A-317491, had increased CFU in a skin infection model. In the skin, P2RX2/3 expression is largely restricted to neurons. P2RX3 is expressed in many non-peptidergic fibers  [12]  and very highly expressed in the MRGPRD subset  [9]. Approximately 92% of MRGPRD-expressing neurons flux Ca 2+ in response to 50µM ATP  [41]. P2RX2/3 is not highly expressed in immune cells based on publicly available datasets (Immgen) or in other cells in the skin  [51] , nor is it highly expressed in glia  [10]. We thus focused on the MRGPRD-expressing subset of nonpeptidergic sensory afferents.
Calcium imaging Coverslips coated with neurons were transferred to a dish containing 2 mL 0. 5% BSA in normal recording buffer (NRB: 1x HBSS with1. 4mM NaCl, 0. 9mM MgCl2, pH 7. 4, osmolarity to 320 mOsm with sucrose), with 0. 025% Pluronic F-127 (Promocell, Heidelberg, DE) and 1µM Fura-2AM (Molecular Probes, Eugene, OR), then incubated 30 minutes in the dark at 37°C, 5% CO 2. Labelled neurons were placed in a 32°C heated recording chamber and superfused with NRB. Data was acquired on a PC with Metafluor software (Molecular Devices) via a CCD camera (RTE/CCD 1300; Roper Scientific). The ratio of fluorescence emission (510 nm) in response to 340/380 nm excitation (controlled by a λ 10-2 filter changer; Sutter Instrument) was acquired at 1 Hz during drug application. High K + (50mM KCl), 1µM Capsaicin and 10µM ATP were applied using a computer-controlled perfusion fast-step system (switching time <20 ms; SF-77B perfusion system; Warner Instruments).
Determination of ATP level of C. albicans Aliquots of supernatant were collected from C. albicans cultures during growth and centrifuged at 3000 rpm for 5 mins at 4°C to remove yeast. ATP level in supernatant was determined using ATPlite luminescence assay system (PerkinElmer, Waltham, MA), as previously described  [48]. To determine intracellular ATP level, the C. albicans pellet at each indicated time was washed in PBS and incubated for 30 min with ATPlite lysis buffer at room temperature. The extract was then spun down at 3000 rpm for 5 mins and the supernatant was transferred to a fresh tube for the ATP assay. Measurements were made with SOFTmax Pro for Lmax 1. 1 software and a Lmax Microplate Luminometer (Molecular Devices, San Jose, CA).
Cell/ Neuron Quantification Quantification of cre-mediated eYFP+ co-expression with antibodies against TRPV1, IB4, and CGRP in TRPV1-TdT DRG neurons were each determined for 9 DRGs pooled from 2 mice. Data is represented as the percentage of cells expressing the indicated marker that co-express TdT. Quantification of neuron loss after DT treatment was characterized as a percentage of IB4 or GFRα2 neurons in β-tubulinIII labeled cell bodies. At least 5 sections from lumbar neurons of 3 DT treated and 3 PBS treated mice were analysed. Langerhans and DETC cells were counted from at least two separate images for each of 3 PBS and 3 DT treated mice. In cross sections of the ear, the number of GFRα2 labeled neuron terminals crossing the dermal epidermal border was pooled for 4-5 images from 4 mice for each treatment condition.
Acknowledgements We thank the  Division of Laboratory Animal Resources of the University of Pittsburgh  for excellent animal care. This work benefitted from SPECIAL BD LSRFORTESSATM funded by  NIH   1S10OD011925-01. This work was supported by the  National Institute for Health (NIH)  grants  T32AI060525  (JAC),  R01AR071720  ( DHK, KMA, BMD ),  R01AR067187  (DHK) and  European Research Council   Advanced Award  ( 340087 RAPLODAPT  to J. B. ).
However, its genome is not available for a genomic comparison. Genome sequencing was performed by paired-end strategy with Illumina Mi-seq technology. Genome assembly was carried out with the hybrid spades program  [1]  using only the pairedend strategy and standard parameters. A draft genome of 4 071 551 bp was obtained with 105 scaffolds and with a GC content close to 75%. A total of 3740 genes were predicted, plus 50 tRNA, and we detected one gene of 16S rRNA, one for 23S rRNA and one for 5SrRNA. Annotation was performed using PROKKA software  [2]  and brief analyses of BLAST protein confirmed that the isolated strain was a Cellulomonas; we named the isolated strain IHU. This represents the first draft genome available for C. hominis in a public database (embl website under number CABEGA010000001-CABEGA010000105).
The closed reaction vessel was positioned on the vibratory ball mill, and in situ Raman monitoring was initiated together with the start of milling, which was performed at 30 Hz for 30-90 min. Product Identification by Nuclear Magnetic Resonance (NMR). The identity of the final product dantrolene was confirmed with NMR and by comparing spectra with the NMR spectral data previously described in the literature. 51 Chemical shifts (δ) of 1 H NMR spectra are reported in ppm relative to residual solvent signals (DMSO in DMSO-d 6 : δ = 2. 50 ppm). 1  H NMR spectra were recorded at 400 MHz (Figure  6 , black spectrum), 600 MHz (Figure  7 , red spectrum), and 300 MHz (Figure  7 ). In Situ Raman Spectroscopy for Silver Sulfadiazine Synthesis. Raman spectroscopy measurements for the silver sulfadiazine synthesis employed a portable Raman system with a PD-LD (now Necsel) BlueBox laser source having an excitation wavelength of 785 nm and an OceanOptics Maya2000Pro spectrometer coupled with a B&W-Tek fiber optic BAC102 probe.
8) and focused to a cooled charge-coupled device (CCD) detector. In situ Raman spectra were collected across the range 1875-150 cm -1. A laser exposure time of 1 s with 10 accumulation was selected to collect one spectrum every 30 s during milling. The P h AT approach allowed a greater volume to be analyzed in a single measurement than measurements that use a backscattered probe geometry. This larger sampling volume allowed a more representative, repeatable, and robust measure- ment of the process because the measurement was not as sensitive to sample placement with regard to the laser focus. Raman Data Analysis. Utilizing both the Kaiser Hololab calibration accessory and a Raman calibration standard, calibrations of the spectrograph, laser excitation wavelength, and instrument spectral response were performed to ensure high spectral quality.
All Raman spectral data were processed by GramsAI (Thermo, Inc. , Waltham, MA) for visual inspection. Chemometric calculations were performed by using the multivariate curve resolution-alternating least squares (MCR-ALS) in Matlab. Multivariate Analysis of Raman Spectra. Chemometric analysis was performed by using the multivariate curve resolution-alternating least squares (MCR-ALS) method directly on the baseline-subtracted Raman spectra. MCR-ALS is based on a linear model assuming the generalized law of Lambert-Beer where the individual response of each component is addable. The aim of this method is the decomposition of the original data matrix, which contains all the spectra recorded during the dantrolene synthesis, into the product of two matrices, one that contains the concentration profiles and the other corresponding to the so-called reference Raman spectra.
Data in Figure  5c  are an excellent starting point to carry out a quantitative kinetic analysis. To this aim, the data have been normalized taking into account the larger experimental uncertainties affecting the estimation of the intermediate fraction. The obtained datasets are plotted in Figure  8  as a function of time t. We find that the variation of reactants α r , intermediate α i , and product α p fractions is best described with the equations α = + - kt kt (1 ) exp( ) r α α α α = - + + + + - Ä Ç Å Å Å Å Å Å Å Å Å Å É Ö Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ kt kt kt kt kt (1 ) ( ) 2 ( ) 6 ( ) 24 ( ) 120 exp( ) i max r max   Quite interestingly, the 0. 14 min -1 value is not far from those of 0. 19 and 0. 17 min -1 obtained for the silver sulfadiazine formation. We ascribe the result to the similar mechanical processing conditions used in the silver sulfadiazine and dantrolene syntheses.
Collection and integration of multiple brain samples in a large compendium A large exploration of multiple databases and resources was done to obtain a collection of datasets of samples of post-mortem human brains from neurologically healthy individuals with different ages. In this way, we managed to prepare a compendium of more than two thousand samples that had in all cases an adequate phenotypic characterization, plus genome-wide expression data. The sample compendium is described in Table  1 , which includes datasets of cortex, hippocampus and cerebellum (in a total collection of 2202 samples). At the same time, information about the age of each individual, donor of samples, was collected. All these data were used for the integrative transcriptomic profiling of the human brain throughout different age stages, from children to the elderly, organizing the samples in similar time intervals, as indicated in Table  1.
Gene expression normalization, signal calculation and age correlation Each of the 4 datasets collected was treated and analysed individually, as a strategy to provide independent support and confirmation of the results from each data series. All the data management, analysis and integration were done using R (programming language and environment for statistical computing, https://www. r-project. org/). We also used several software packages and algorithms from Bioconductor (https://www. bioconductor. org/). Normalization and signal expression calculation were performed using affy  [16]  and RMA  [17]  algorithms, in the case of the expression datasets obtained with Affymetrix platforms (that were high-density oligonucleotide microarrays Human Exon 1. 0 and Human Genome U133 Plus 2. 0). In the case of the expression datasets obtained with Illumina platform, the normalization and signal expression calculation were performed using the R package beadarray  [18].
The Goodman-Kruskal gamma correlation is a measure of ordinal (rank) association between two variables with ranked quantities  [23]. Before these analyses, the age of the individuals was divided in ageperiods or age-stages by approximate decades (Table  1 ), so that the different consecutive stages included a similar and balanced number of samples. In this way, each of the analysed datasets included 6 or more stages, from very young ages (corresponding to children: D0 = 1-9 years old, D1 = 10-19 years old) to old ages (i. e. elderly people: D6 = 60-69 years old, D7 = 70-79 years old, D8 = 80-89 years old, etc) (Table  1 ). Due to the fact that dataset 1  [10]  included many samples of young people (children), we divided these samples into two (following the strategy used by Kang et al. in their work): stage D0i = corresponding to ages between 1 and 6 years; and stage D0ii = between 6 and 12 years.
Datasets It is important to underline that the categorization of the individual signals in age-stages, carried out in this study, was essential to achieve an accurate capture of gene expression signatures, and their evolution and sustained changes during the life of neurologically healthy individuals. Therefore, the approach provided a robust and consistent gene signature of the brain, which was validated in several independent cohorts and that evolved constantly through the ages, being a signature specifically linked to aging. In order to delve into the results obtained by the gamma statistic, we applied another algorithm to verify whether the main trajectories or trends found with gamma could be found with a different method based on coexpression and clustering of genes. In this way, we applied to the genes, included in the signature derived from the gamma, a fuzzy cmeans algorithm  [24] , using functions from mFuzz  [25]  and e1070 packages.
This method discovers groups of genes (i. e. , clusters) that follow similar coexpression patterns across the ordinal covariate (the age-stages). Therefore, we used this analysis to corroborate, within each of the 4 datasets, the finding of groups of genes that show a significant trend of upward or downward expression with increasing age. The fuzzy analysis also showed that these two main trends of increase and decrease in expression over time were reproducible and consistent.
Concurrent functional enrichment analysis of the aging gene signature After the identification of the aging gene signatures in cortex, hippocampus and cerebellum, we performed a functional enrichment analysis of the largest signature (which was the one derived from cortex) to discover the main biological processes and pathways overrepresented or re-pressed (i. e. significantly enriched in this set of genes). This analysis was done using several enrichment tools: DAVID (https://david. ncifcrf. gov/) [26]  and GeneTerm Linker  [27, 28]. In particular, the second software tool, GeneTerm Linker (http://gtlinker. cnb. csic. es/), performs concurrent functional enrichment analysis in several annotation databases at the same time: the 3 sections of Gene Ontology (GO-BP, GO-MF, GO-CC), KEGG pathways and InterPro motifs and domains.
iRegulon relies on the analysis of the regulatory sequences around each gene in the gene set to detect enriched TF motifs or ChIP-seq peaks, using databases of nearly 10,000 TF motifs and 1000 ChIP-seq tracks. It associates enriched motifs and tracks with candidate TFs using a Normalized Enrichment Score (NES) and determines the optimal subset of direct target genes  [29]. The NES score is a z-score indicative of the significance. The default NES cutoff in iRegulon is 3. 0, corresponding to a FDR between 3% and 9%.
All the method was optimized for running on a Graphics Processing Unit (GPU). The input data used was the gene expression matrix corresponding to 1077 protein coding genes (i. e. the genes included in the aging gene signature derived from cortex) plus the corresponding chronological age of the person for each sample; using 652 samples (70% of total) for the training of the neural network predictor, and 280 samples (30% of total) for the validation. The total set of samples (652 + 280 = 932), used for the construction of the DLNN, corresponded to the combination all the cortex samples from datasets 1, 3 and 4 (set 1 with 429 samples, set 3 with 374 samples, and set 4 with 129 samples) (Table  1 ). We selected these three datasets because they were produced with similar microarray expression platforms (from Affymetrix), and, more important, because they were the ones with the highest coverage over the human transcriptome, mapping to a common set of about twenty thousand human genes (including the 1077 protein coding genes of the signature).
Comparatuve overview of transcriptomic samples from three regions of the brain using t-SNE Before entering the study of changes in gene expression with age, we performed an analysis of the whole transcriptomic signal in the samples collected in different datasets. Two datasets of the collected compendium (Table  1 ) included samples from the three regions studied in the brain: dataset 1, included 429 samples of cerebral cortex, 35 of hippocampus and 34 of cerebellum; and dataset 3, included 374 samples of cortex, 121 of hippocampus and 130 of cerebellum. Using the complete transcriptome expression signal of these samples, we applied the machine learning algorithm t-SNE (t-distributed Stochastic Neighbour Embedding) for the visualization of each dataset (Fig. 1A, B ). The t-SNE is a non-linear dimensionality reduction technique for embedding high-dimensional data in a low-dimensional space  [33].
The visualization of the two main dimensions obtained for dataset 1 (Fig. 1A ) and dataset 3 (Fig. 1B ) show a clear grouping of the samples according to the 3 regions of the brain, indicating a major separation of the cerebellum versus the cortex and hippocampus. Moreover, the analysis also suggests that the transcriptome of these two regions, cortex and hippocampus, is considerably similar; as shown by the fact that the values of distance between the data points of t-SNE are closer for these regions (in Fig. 1B ) or even mixed (in Fig. 1A ). A similar gene expression profiling and cellular composition of cerebral cortex and hippocampus was already found and reported in the first anatomically comprehensive atlas of the human brain transcriptome  [7]. We also performed with each of the 4 datasets an analysis applying the t-SNE dimensionality reduction method to explore the overall structure and relationships within each set of the main variable factors: (i) the brain regions and (ii) the age stages.
The results of these analyses (performed for each of the 4 datasets independently) are included in the Supplementary Fig. 1S. As we indicated above, we present in Fig. 1  two plots that visualize the t-SNE output obtained for dataset 1  [10] , and dataset 3  [11]. These two sets include samples from 3 brain regions: cortex, hippocampus and cerebellum. The segregation of the age groups in these analyses is not so clear (Supplementary Fig. 1S ). They use the whole gene expression data of the samples revealing the variance from many possible factors. Therefore, to find the specific gene factors linked to the age stages, the data should be investigated using more discriminating analytical methods, since the t-SNE only reflects the overall main variance of the transcriptomic data.
Integrative analysis measuring expression changes and trend of brain genes over age stages To build a transcriptomic landscape and gene regulatory profile of human brain related to aging, we managed to integrate a large collection of transcriptomic datasets from human post-mortem samples from brains of healthy individuals with different ages, normalized individually per dataset. The compendium includes more than two thousand samples with phenotypic characterization and genome-wide expression data (Table  1 ). The largest number of samples correspond to brain cortex, with 429 samples in dataset 1  [10] , 453 samples in dataset 2  [12] , 374 samples in dataset 3  [11] , and 129 samples in dataset 4  [14]. Several algorithms (described in Materials and methods) were used to identify the expression profile of the genes that follow a trajectory correlated with the increasing age, from young individuals to old people (i.
e. , with aging). These analyses found genes that present a significant and consistent up-regulation or down-regulation with age stages in the 4 independent datasets. The analyses were performed individually, selecting the samples from each dataset and each region of the brain, and then combining the results of all the datasets for each one of the regions: cortex, hippocampus or cerebellum. In this way, the analyses provided a signature of 1148 genes for cortex, 874 genes for hippocampus and 657 genes for cerebellum. All these genes showed significant gamma rank correlation with age. The 3 signatures, including all the corresponding statistical parameters, are provided in Supplementary Tables 2S, 3S and 4S. Regarding the methodology, it is important to underline that the 4 datasets were treated and analysed as independent.
This provided robust analysis, since we reported the agreement of the results in different brain transcriptomic studies. Working in this way, a cross-dataset normalization of the transcriptomic data was not needed. We did not put together or mix the RNA signal from different platforms, labs or studies, and so there was no batch effect that could dramatically affect the outcome of the meta-analysis. Of course, each independent dataset was internally normalized, so that all samples in each study were comparable. Another critical point in our methodology is the fact that in the entire study we established age stages by transforming the continuous variable age in a discrete variable. Datasets usually have scarcity of samples corresponding to many age time-points (i. e. , in some series we did not have people younger than 20 years old, or older than 70 years old).
Transcriptomic profiles of brain cortex and hippocampus with age Following the methodologies described above, we performed a complete analysis of the expression trajectories throughout the age stages of each gene, in each of the 4 datasets in the 3 brain regions studied. With this approach, we obtained profiles (as the ones presented in Fig. 2 ), that are independent for each dataset and each brain region. The most significant genes followed two major profiles: one that steadily increases expression over the progressive stages of life, and another in which the expression decreases across age stages. One of the upregulated genes with most significant correlation in the cortex transcriptome data is HLA-DPA1 (Fig. 2A, B, C, D ), which corresponds to a protein member of the major histocompatibility complex (MHC class II, DP alpha 1) and plays a central role in the immune system by presenting antigen peptides derived from extracellular proteins.
The observation that HLA proteins increase in brain with age had been reported by Darmanis et al. [30] , who observed the expression of major histocompatibility complex genes in a subset of adult human neurons, but not in fetal neurons. It is expected that molecules expressed in antigen presenting cells increase levels in elderly people, since their brain immunological system has been exposed to many more antigenic elements. Indeed, our transcriptomic profiling also revealed that several other HLA genes were up-regulated with age: HLA-DMB, HLA-DPB1, HLA-DPB2 and HLA-DRA. With respect to genes down-regulated with age, we present in Fig. 2(E, F, G, H ) the profile in the 4 datasets of the gene that showed the most significant down-regulation: the heat shock protein DNAJB5 (member of the HSP40 family).