dataesr/scientific-paragraphs-categorization huggingface dataset:
Standard image transformations are proposed to the user and their execution has been optimized to deal with large datasets. Nutil can be used independently or conjointly with the Quint workflow. Quint is a suite of tools that allows for the quantification and the spatial analysis of selected features in series of histological section images of rodent brain within a known atlas space. It combines several pre-existing tools for pre-processing, registration to 3D reference atlas (mouse and rat) and object segmentation, for the quantification of specific parameters in regions defined by the atlas. The Quint suite allows the user to perform in a convenient way, a quantitative analysis at different levels of granularity on large imaging datasets.
APPLICATIONS OF EXISTING DATA SHARING AND DATA ANALYSIS SOLUTIONS MR imaging is a non-invasive versatile technique that allows to assess to various anatomical, functional or physiological parameters. Then, the T1 and T2 relaxation times are tissue and region-dependent parameters that may reflect structural alterations and may be used as biomarkers for various pathologies. The goal of Deruelle et al. was to define maps of T1 and T2 values for specific rat brain regions. To serve as reference, such maps should be defined based on a sufficient number of healthy animals reflecting inter-individual variability. They designed a multi-center study which included two data provider centers and three image processing pipeline provider centers. They used the SAS architecture  (Kain et al. )  and showed first, that MR data from 40 rats acquired in two centers could be successfully combined; second, that a good reproducibility could be obtained when using different processing solutions.
Their findings contribute to a better understanding of the physio-pathological mechanisms triggering the onset of the Alzheimer's disease. All generated datasets generated are available to the scientific community and may be pooled with new data to reinforce the robustness of the findings. Interestingly, all the papers of the RT Appning concern the investigation of the rodent brain with both in vivo, MR imaging, and in vitro, histological imaging. Indeed, neuroimaging is the domain for which scientists have developed to date several solutions for sharing data and process large data repositories. This is certainly due to the impressive quantity of neuroimaging studies performed in the recent years, generating large-scale databases. This is also stimulated by publications showing the poor replicability and reproducibility of the results obtained  (Carp, 2012; Button et al.
Due to this low number of parameters that are not linearly dependent on each other, convolutional networks are not necessary. By investigating simulations, we demonstrate that our strategy called Phasor-Net is efficient for retrieving all biexponential decays components (lifetimes and proportions). We compare this strategy with standard fitting method based on the minimization of the error estimated from maximum likelihood (MLfit). We demonstrate that Phasor-Net is more precise and less biased than standard fitting approaches for realistic SNR simulated data that could be encountered in FRET experiments. We finally apply successfully our method to determine biexponential decays parameters for FLIM experiments in living cells. Theoretical framework of Phasor-Net. In order to simplify the artificial neural network, the temporal decays are first converted into four physical parameters that can be calculated easily: the phasor coordinates (g,s), the mean lifetime τ m and the amplitude-weighted lifetime < τ >.
Material and methods "Phasor-based" neural network (Phasor-Net). The 4 parameters (g, s, τ m and < τ >) are first calculated for each temporal decay. These parameters are then used as inputs of a neural network made only with dense layers. In this work, we have used six hidden neurons which fulfill the fact that the number of hidden neurons should be less than twice the inputs size  43. For determining the optimal number of hidden layers, we have tested several networks constituted with different numbers of hidden layers ranging between 5 and 17. For each architecture, we have calculated the relative squared error between the targeted and the estimated values. For using Phasor-net with a specific FLIM system, it is first necessary to generate realistic simulated samples for training (files used for simulations are available here: https:// github.
com/ ayler ay/ Phaso rNet). To mimic experimental conditions, several values are required such as: the number of channels, the temporal resolution of one channel, the laser period, or the FWHM of the IRF. These values should correspond to the experimental parameters of the specific FLIM system (except for IRF as detailed in the discussion section). The training of our neural network was performed with the MATLAB neural network toolbox. Files are available here: https:// github. com/ ayler ay/ Phaso rNet. We used the Bayesian regularization training algorithm that requires more time but results in good generalization for difficult, or noisy datasets. All hidden layers were activated with a sigmoid function, except the output layer that was activated with a linear function (fitnet)  44. Phasor-Net was trained with 50,000 simulated biexponential decays composed with random lifetimes (τ 1 and τ 2 ) ranging between 0.
19  whose codes are available here: https:// github. com/ jason tsmit h2718/ DL4FLI. It is developed with Tensorflow  46  and the machinelearning library Keras  47. The training was performed on 300,000 simulated biexponential decays with random lifetimes (τ 1 and τ 2 ) and random proportions a 1. We used the same ranges as those applied previously for Phasor-Net: [0. 2-3] ns for lifetimes and [0. 1-0. 9] for a 1. The maximal photon count of temporal decays was comprised between 250 and 1000 photons, as described in  19. Each temporal decay is finally normalized by dividing by its maximal photon count. 15% of these simulations (corresponding to 45,000 samples) were used for validating the training process with a batch size of 20. The mean square error (MSE) was applied as loss function and the RMSprop algorithm with an initial learning rate of 10 -5 was used as optimizer of this convolutional neural network.
For exhaustive comparison, the biexponential decays components are also resolved with standard fitting method. In this work, we used the Matlab package called "Tcspcfit" that has been developed by Enderlein and Erdmann which is based on an efficient Nelder-Meade simplex algorithm. Codes are available here: https:// www. uni-goett ingen. de/ de/ 513325. html. We used a convolved autoregressive model for fitting multiexponential decay curves by minimizing the quadratic error estimated from the maximum likelihood (MLfit) because it is more robust than algorithm minimizing the least square deviation (more details can be found in  48  ). The initial guess for lifetimes were estimated automatically and they were constrained in the same ranges as those of the training datasets. We have limited the number of iterations to 6000 in order to guarantee that the quadratic error has been minimized correctly and the tolerance was fixed to 10 -12 for ensuring accurate results.
Acknowledgements This work has been supported by the  Agence Nationale de la Recherche  (contract " Dynamic-12-BSV5-0018-02 "), the  EIPHI Graduate School  (contract " ANR-17-EURE-0002 "), the  Région Bourgogne Franche Comté  (contract CIME), the  Région Hauts de France  (contract  Photonics4Society ), the  GDR IMABIO , the  LabEx CEMPI  (contract " ANR-11-LABX-0007 "), the  EquipEx ImaginEx BioMed , the  European Regional Development Fund (ERDF)  and the  European Union  ( FEDER-FSE Bourgogne 2014-2020 ).
2009) , and miRNA production  (Kim et al. 2009). Mutations of HAG1 were found to be associated with the alternation of generations, namely abnormal flower development and reduced fertility  (Bertrand et al. 2003; Vlachonasios et al. 2003). Observed loss of fecundity was attributed to a reduced stamen height in early-arising flowers and an increase in stamen number in early-arising flowers in contrast to late flowers that are able to produce small siliques with a few seeds  (Vlachonasios et al. 2003). HAG1/GCN5 belongs to the GNAT TR family defined by the presence of the GNAT acetyltransferase domain (PFAM PF00583)  (Wilhelmsson et al. 2017). Multiple sequence alignment shows the conservation of the GNAT domain with its Coenzyme A binding pocket (Fig. S2 ). Our phylogenetic analysis of the HAG1 clade (Fig.
SWI3A/B Chromatin-remodeling complexes (CRC) constitute a switch point between signaling pathways and chromatin-based 1 3 control of transcription. They are involved in transcriptional activation as well as repression  (Sarnowski et al. 2005; Roberts and Orkin 2004). SWI3 is part of the SWITCH/ SUCROSE NONFERMENTING (SWI/SNF) CRC, which is involved in ATP-dependent alteration of DNA-histone contacts  (Sarnowski et al. 2005). According to the TAPscan classification  (Wilhelmsson et al. 2017 ) SWI3 is part of the MYB-related TF family characterized by the Myb_DNA-binding domain (PF00249). However, according to the PFAM domain description the domain family contains not only DNA binding domains but also the SANT domain  (Aasland et al. 1996). The two domains show high structural similarity  (Grune et al.
Selfing and crossing analysis The number of sporophytes developed 30 days after watering by Reute wildtype as well as loss of function mutants were counted in three independent mutant lines according to  Hiss et al. (2017)  using a Leica S8Apo binocular (Leica, Wetzlar, Germany). The F1 offspring (sporelings) were counted as described in  Hiss et al. (2017)  using a fluorescence stereomicroscope SteREO LumarV12 (Carl Zeiss, Oberkochen, Germany). Microsoft Excel 2016 (Microsoft) was used to perform statistical analyses and data visualization. Fisher's exact test was conducted using https:// www. socsc istat istics. com/ tests/ fisher/ defau lt2. aspx.
The obtained male and female lines were analyzed using primers surrounding the targeted locus (Fig. S6 ). First, absence of the wild-type locus was tested using primers Mapoly0187s0003#1 and Mapoly0187s0003#3 and then the mutant locus was analyzed using primers Mapoly0187s0003#1 and Mapoly0187s0003#2 (Fig. S16 ). (http:// madla nd. scien ce, DFG priority program 2237), and SAR is grateful for funding by the DFG  (RE 1697/15-1, 19-1, 20-1). The IJPB benefits from the support of the LabEx Saclay Plant Sciences-SPS (ANR-10-LABX-0040-SPS). KSR acknowledges financial support from the National Science Foundation (NSF 1758497) and National Institutes of Health (NIH 5R25GM107760-07).
Acknowledgements We thank  Tom Dierschke ,  Nora Gutsche  and  Sabine Zachgo  for their help with establishing M. polymorpha transformation,  Florence Charlot  for her help with establishing the CRISPR-Cas strategy in P. patens, and  Marco Göttig  and  Jason S. Henry  for excellent technical assistance. The following plasmids were kindly provided by  Takayuki Kohchi & Sumihare Noji  via Addgene: pMpGE_ EN03 (#  71535 ), pMpGE010 (#  71536 ), pMpGWB401 (#  68666 ).
Results Foxm1 is specifically expressed in the regenerating spinal cord We compared the transcriptome of isolated spinal cords at 1 day post-amputation (1 dpa) and 3 dpa to spinal cords from intact tails (0 dpa, Fig  1A ). Principal component plot, dendrogram of sampleto-sample distances and MA plot of the log2 fold change (FC) of expression in relation to the average count confirmed the quality of the data (Fig EV1A  -D ). Between 0 dpa and 1 dpa, 2447 differentially expressed (DE) transcripts (|log2(FC)|> 1 and FDR < 0. 01) were identified (1,125 down-and 1,322 upregulated). Between 0 dpa and 3 dpa, 5,383 genes are differentially expressed  (2, 637 upregulated, Fig EV1E, Dataset EV1). To identify the most enriched biological processes by gene ontology (GO), a non-biased hierarchical clustering for all DE genes was performed  (Fig 1B).
C The dataset was uploaded on the Ingenuity Pathway Analysis software (Qiagen). Genes with a |log2(FC)|> 1 and P_adj < 0. 01 were considered. The software identified upstream regulators based on the changes in expression levels of known downstream targets. Each upstream regulator is attributed a z-score, corresponding to the negative log of the P-value derived from the Fisher's exact test. D Changes in the expression of foxm1 and known downstream targets in the whole tail comparing day 0 and day 3 (WT_d0d3) and in the spinal cord comparing day 0 and day 1 (SC_d0d1) and day 0 and day 3 (SC_d0d3). The whole tail dataset was obtained from  (Chang et al, 2017). E Tadpoles at NF50 were amputated, fixed at the indicated time and then processed for whole-mount in situ hybridisation using a probe specific for foxm1.
Characterisation of foxm1 + cells during regeneration We then analysed the changes in the transcriptome during regeneration by combining both 0 dpa and 3 dpa scRNA-seq datasets  (Figs 3 and EV4A and B). We noticed that whilst the different cell types were clustering together, there was a clear shift between cells from 0 and 3 dpa possibly due to a batch effect (Fig We next assigned a cell type identity for each cluster based on the most significant differentially expressed genes (Dataset EV2, Fig  3F  and G ). The same clusters as 0 dpa were identified with the addition of the two 3 dpa-specific clusters (lep + and foxm1 + clusters). We then performed pseudo-time analysis at both timepoints to explore the origin of foxm1 + cells (Fig  EV4C-E, Trapnell et al, 2014). Unsupervised pseudo-time using the Monocle algorithm places the root on progenitor clusters (Fig EV4C ) with one branch terminating with differentiated neurons.
Cell cycle dynamics during regeneration The result of our GO analysis led us to analyse changes in cell cycle dynamics between 0 and 3 dpa (Fig  4 ). We first used computational inference of cell cycle state on our scRNA-seq dataset  (Fig 4A and B, Aztekin et al, 2019). Whilst clusters representing neurons are mainly in G1/G0 both at 0 and at 3 dpa, this proportion decreases in differentiating neurons and progenitor clusters have the highest proportion of cells in G2/M and S phases. The small number of cells in G2, M and S phases in neuronal clusters is probably due to some sox2 + cells present in these clusters  (Fig 3D). Interestingly, more than 50% of the cells in the foxm1 + cluster appeared to be in S phase. To confirm these changes in cell cycle dynamics, we performed PCNA staining on sections at 0, 3 and 5 dpa (Fig 4C  -E ).
, expressing sox2 and sox3) and differentiating progenitors (diff. prog. , expressing neurod1, neurog1) and neurons (neur. , expressing snap25). The total does not always amount to 100% as some clusters have cells from both 0 and 3 dpa. The bar boxes in red represent the foxm1 positive cluster, and the cluster numbering refers to the cluster identity defined in Fig 3G. C Representative sections labelled with an anti-PCNA antibody (red) and DAPI (blue) at the indicated day after amputation (dpa). The white arrowheads point to cells in S phase in the spinal cord and the yellow arrowhead at cells in G1, G2 and M phases. The right panels correspond to the inset indicated as a white box in the middle panels. D The ratio of PCNA + per total number of cells (DAPI) in the spinal cord was determined at the indicated times after amputation (n = 3, with a mean of 14 sections per data point).
A Representative sections of spinal cords from tadpoles injected with EdU at 3 dpa and fixed at 5 dpa. After sectioning, the samples were labelled with antibodies against Sox3 (green), EdU (magenta) and DAPI (blue). White arrowheads show Sox3 positive extensions. B Sections of spinal cords at 5 dpa in the stump (non-regen. ) or in the regenerate (regen) labelled with anti-Sox3 (green), anti-Acetylated Tubulin (AcTub, magenta) and DAPI (blue). White arrowheads show Sox3 positive extensions. C Quantification of the images shown in (A). The ratio of Sox3 + per total number of cells (DAPI) in the spinal cord was determined in the stump (non-regen. ) and the regenerating spinal cord (regen. ) in wt and foxm1 -/-tadpoles (n = 3-8, with an average of 15 sections per data point). D Quantification of the proportion of cycling progenitors (Sox3 + EdU + /DAPI, S3 + E + /DAPI), the proportion of progenitors having divided (Sox3 + EdU + /Sox3 + , S3 + E + /S3 + ) and the proportion of progenitors self-renewal (Sox3 + EdU + /EdU + , S3 + E + /E + ) in the regenerate.
The number of reads per transcript was determined using HTseq, and the idxstats files were used to identify differentially expressed (DE) genes using the general linear model glmQLFit in DESeq2 with R. DE genes with a |log2(FC)| > 1 and FDR < 0. 01 were considered significant. For the clustering, the k-mean was determined at 5 using the Elbow method and enriched gene ontology (GO) terms were identified using Fidea (D'Andrea et al, 2013). The full RNA-seq dataset was then uploaded onto the Ingenuity Pathway Analysis (IPA, Qiagen) software. Upstream regulatory analysis was performed for DE genes with a |Log2FC|> 1 and FDR < 0. 001 (992 genes for 0 dpa/1 dpa and 2720 genes for 0 dpa/3 dpa). This analysis predicts upstream molecules such as transcription factors that may be causing the observed change in gene expression (Kr€ amer et al, 2014).
• Data pre-processing: The sequence files from the sequencer were processed using 10x Genomics custom pipeline Cell Ranger v2. 2. 0. The fastq files were aligned to the custom genome using the default parameters of Cell Ranger. The pipeline identified the barcodes associated with cells and counted UMIs mapped to each cell. Cell Ranger uses STAR aligner to align reads to the genome discarding all the counts mapping to multiple loci. The uniquely mapped UMI counts are reported in a gene by cell count matrix represented as a sparse matrix format. The Cell Ranger's aggr command was used to aggregate the samples from 0 and 3 dpa whilst keeping the default down-sampling parameter enabled. • Filtering: Low-quality cells were removed from the dataset to ensure that the technical noise did not affect the downstream analysis.
This pool-based size factors are then deconvoluted to find the size factor of each cell. These normalised data are then logtransformed with a pseudo-count of 1. • Visualisation and Clustering: The first step for visualisation and clustering is to identify the highly variable genes (HVGs). To do this, we first decomposed the variance of each gene expression values into technical and biological components and identified the genes for which biological components were significantly greater than zero. These genes are called HVG. HVGs were then used to reduce the dimensions of the dataset using PCA. The dimensions of dataset were further reduced to 2D using t -SNE, where 1-14 components of the PCA were given as input. The cells were grouped into their putative clusters using the dynamic tree cut method.
Dynamic tree cut method identified the branch cutting point of a dendrogram dynamically and combined the advantage of both hierarchical and K-medoid clustering approach. Pseudo-time trajectory was performed using the Monocle 2 algorithm  (Qiu et al, 2017). • Batch correction. Two approaches were followed to batch correct the data: (i) using the integration strategy in-built to Seurat  (Butler et al, 2018)  and (ii) using the Harmony method for single-cell data integration  (Korsunsky et al, 2019). Batch-effect quantification and batch correction method selection were conducted in an unbiased way using the k nearest-neighbour batch-effect test (kBET, B€ uttner et al, 2019). For Seurat integration, 500 highly variable genes (HVGs) were first detected using the FindVariableFeatures() function set to the vst selection method.
The integrated dataset was obtained by subsequently calling the FindintegrationAnchors() and IntegrateData() functions, both set to a total of 30 dimensions to generate a series of anchors across the data provided in separate Seurat objects. For Harmony, a single Seurat object with the combined data for both timepoints was forwarded to the Harmony::RunHarmony() function in R whilst indicating a sample grouping variable. To evaluate the effectiveness of each method, an evaluation approach was performed using the kBET::kBET() function in R as described in  (Tran et al, 2020). • Identification of marker genes: To identify the marker gene for a cluster, we compared a given cluster with all other clusters and changes in expression were tested using the sSeq package. We selected the marker genes for the cluster using manual curation of DE genes with an FDR < 0.
Isolation of RNA and qPCR RNA was isolated from total embryos, tail regenerates and isolated spinal cord using TRIzol (Life Technologies) according to manufacturer's instructions. cDNA was generated using the Reverse Transcriptase AMV kit (Roche) for whole embryos and tails and Sensiscript Reverse Transcription Kit (Qiagen) for isolated spinal cords. qPCR analysis was performed on the StepOnePlus Real-Time PCR System using SYBR-Green reagents (Applied Biosystems). Expression was normalised to the expression levels of ef1a or odc, and expression values were calculated using the ΔΔCt method. The following primers were used (5'-3' sequences): foxm1 fwd 5'-AAAGAGGAAGAGAGTGCGCC, rev 5'-TGGCATTTAGCTGCTCC TCC; cyclinb3 fwd 5'-CTGCACTTCCACCATCCAATCCA, rev 5'-CAA CTATATGCGGGACAGAGAG; cdc25b fwd 5'-GCCCAAACCCCTCGA GAAGA, rev 5'-GCCATCGAAGGTGCGTAGCCT; ntubulin fwd 5'-GG CAGTTACCATGGAGACAGT, rev 5'-GCCTGTGCCACCACCCAGAGA; sox2 fwd 5'-CATGATGGAGACCGATCTCA, rev 5'-CTTACTCTGGTT GGAGCC; and ef1a fwd 5'-GGATGGAACGGTGACAACATGCT, rev 5'-GCAGGGTAGTTCCGCTGCCAGA.
Acknowledgements This work was supported by a  Wellcome Trust   Seed Award  (ref  205894/Z/ 17/Z ), the  Manchester Regenerative Medicine Network (MaRMN)  and  The University of Manchester Strategic Fund (ISSF)  to K. D and MRC single-cell centre award to S. M. B. (ref  MR/M008908/1 ). D. P. and L. S. P. are supported by  BBSRC-DTP  PhD studentships. L. S. P. is supported by a  Biotechnology and Biological Sciences Research Council   Research Training Support Grant  ( ref BB/M011208/1 ). We thank the  EMBL core facility  for performing the bulk RNA-seq experiments, the  Genomics Core Facility  ( University of Manchester ) for the scRNA-seq, the  Genome Editing Unit and Histology Core Facilities  for use of genotyping and cryotomy equipment ( University of Manchester ) and the  National Xenopus Resource  ( NXR, Marine Biology Laboratories, Woods Hole, USA ) for their bioinformatics workshop (National Xenopus Resource RRID: SCR_013731 ).
Data and software availability • Bulk RNA-seq data: ArrayExpress at EMBL-EBI E-MTAB-8785 (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-8785/). • Single-cell RNA-seq: ArrayExpress at EMBL-EBI E-MTAB-8839 (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-8839/). Expanded View for this article is available online.
1 ). We tested for a relationship of shape changes with relative neck length and also conducted "virtual experiments" using digital 3D surface models of the vertebrae to assess the range of motion (ROM) between C7 and T1 to characterize specifics of neck elongation in giraffes in contrast to other relatively long-necked species as well as to all other species analyzed. A phylomorphospace and a phenogram were created to visualize how shape diversity of C7 and T1 as well as morphological disparity between C7 and T1 (quantified as Procrustes difference) relate to phylogeny, respectively. We then directly studied the association between the morphological data (shape, relative neck length) and the functional data (ROM) using phylogenetic generalized least squares regression (PGLS). We expected to find similarity of C7 shape shared by relatively long-necked species (see below) and quantified this potential convergence.