dataesr/scientific-paragraphs-categorization huggingface dataset:
Applications Application of CD spectroscopy in combination with BeSt-Sel analysis covers all areas of protein science. BeStSel has been used in over 1,000 scientific studies since its first publication in 2015 (4). We made a searchable database of these works at the webserver, providing valuable examples for users. The broad applicability of BeStSel is represented by the vast variation of studies conducted using the algorithm. Some notable examples are presented below. An increasing number of users are applying the method to investigate the effects of nanoparticle-protein interactions on protein structure. Barbalinardo et al. (23)  report that lysozyme amyloid fibrils are less cytotoxic in the presence of gold nanoparticles than fibrils alone. They investigated the conformational changes of fibrils caused by gold nanoparticles.
NEW FEATURES After the first release in 2015  (Micsonai et al. , PNAS) , the next larger update of BeStSel was introduced in 2018 when fold prediction was improved on the basis of the CATH 4. 2 and the WKNN search engine was built-in  (Micsonai et al. , NAR 2018). The current version of the webserver uses updated background databases. The DSSP algorithm was replaced by the 3. 0 version, which solved issues with assigning residues in -helices as ␣-helix. The BeStSel basis spectra were re-optimized on the new assignations resulting improved accuracy (Supplementary Tables  S1-S2 ). Moreover, wavelength ranges for CD spectrum analysis are now available at 5 nm increments. Fold prediction was further enhanced by processing the CATH 4. 3 data. The background databases are up-to-date, 184 307 PDB structures are now recognized for secondary structure analysis and fold classification.
The updated single domain dataset used as a basis for fold prediction contains 61932 single domains based on CATH 4. 3 covering 43 architectures, 1467 topologies and 6540 homologies. A recent addition is the binary classification of ordered-disordered structures based on the CD spectra, which helps the experimental identification of intrinsically disordered proteins (IDPs) and the verification of the results of bioinformatics tools. One of the new features is that users can upload any 3D structures in PDB format and have the eight secondary structure components of BeStSel calculated along with DSSP and SELCON components for comparison. This is crucial for the experimental verification of MD simulation results or in silico models, such as AlphaFold2 (46) structures, making the structural comparison with the results of CD spectrum analysis possible.
CONCLUSIONS The BeStSel web server provides the BeStSel method for the community to analyze protein CD spectra for secondary structure composition and protein fold prediction. The eight secondary structure components give detailed structural information from the CD spectra including the ␤structure composition (orientation and twist). The method has an accuracy superior to any previously available methods on any type of secondary structures. It is especially usable for ␤-sheet-rich proteins, protein aggregates and membrane proteins. Single and multiple CD spectra can be analyzed and the protein fold can be predicted with a few clicks. Adjustable wavelength ranges, scaling of spectra, links to corresponding PDB structures make the site a swiss-knife for CD users. A new module of the webserver helps to distinguish intrinsically disordered proteins by their CD spectrum.
Background concentrations above the roughness sublayer can be provided by 3D CTMs, such as Polair3D from the Polyphemus air-quality modeling platform  [25, 26, 31]. The objective of this study is to evaluate the parametrizations that are currently used in MUNICH for the horizontal and vertical transfers as well as to develop a new parametrization as accurate as the existing ones but with a simpler formulation. It is an adaptation of a parametrization originally designed to model flows in sparse and dense vegetated canopies, depending on the leaf area or spacing between trees. The parametrizations are evaluated by comparisons to Computational Fluid Dynamics (CFD) simulations in street canyons. They are performed with Code_Saturne  [32] , an open-source code that can represent air and pollutant flows in a street canyon  [33].
"It solves the Navier-Stokes equations for 2D, 2D-axisymmetric and 3D flows, steady or unsteady, laminar or turbulent, incompressible or weakly compressible, isothermal or not, with scalars transport" if required (https: //www. code-saturne. org/, accessed on 17 December 2021). This code has been adapted to atmospheric flows and can simulate the humid atmosphere with radiative heat transfer (not used here). Code_Saturne has been evaluated with the results of the Mock Urban Setting Test (MUST). This is a near full-scale experiment conducted in Utah's West Desert area based on the release of a neutral gas in a field of regularly spaced shipping containers  [33, 34]. In addition, Code_Saturne was used to simulate thermal effect and pollutant dispersion in a real urban neighborhood of Toulouse, France  [35]  and micro-scale heterogeneities of turbulent variables for different wind directions over a complex semi-urban area located near Paris  [36].
Street Canyon Geometry In the CFD code Code_Saturne, the computational domain is represented by a geometry and a mesh. In the present study, three street canyons are considered: a wide street canyon (WC) with the same characteristics as the one used in the study of  Kim et al. [24] , an intermediate canyon (IC) and a narrow canyon (NC). Their main characteristics are presented in Table  1  and Figure  1 : Code_Saturne simulations are performed in a periodic 2D-canyon of length L = 1 m. This configuration is equivalent to an infinite street. The periodicity in the Y direction enables simulations with a wind that is not perpendicular with respect to the canyon axis. The flow and the wind speed vector are 3D. Note that the building height H varies, but the street and building widths (W and B) are constant for the three canyons and equal to 27.
5 m. The mesh used is homogeneous (hexahedral elements) with only one cell for the discretization in the Y direction. The size of each cell in the X and Z directions is 0. 5 × 0. 5 m. To compare to MUNICH, the values of the fields averaged over a given cell are located at the center of the cell. The present configuration does not reproduce the complex 3D effects (e. g. , variations of width and height of buildings or obstacles in the canyon) encountered in real applications. However, it is suited for comparison with the homogeneous street hypothesis used in MUNICH. To simplify the reading of the figures, a color code is introduced: red symbols for the wide canyon (WC), purple ones for the intermediate canyon (IC) and blue ones for the narrow canyon (NC). A list of the abbreviations, parameters and variables used can be found in Appendix A.
Data Availability Statement: The last version of MUNICH source code is available online at https: //doi. org/10. 5281/zenodo. 4168984 (accessed on 20 December 2021) and https://github. com/cerealab/munich (accessed on 20 December 2021). For the three 2D canyons considered in the present study, the mesh, the source code and the XML setup file allowing reproduction of the CFD results using Code_Saturne version 6. 0 are available online at https://gitlab. enpc. fr/alice. maison/tree_ parametrization (accessed on 3 December 2021) and at http://dx. doi. org/10. 17632/fzfrjsz3mv. 2 (accessed on 3 December 2021) under the GNU GPL2. 0 licence.
Therefore, the power of current scRT studies is shadowed by the previously mentioned limitations and the rare stochastic RT events may not be reflected in currently available scRT with limited sample sizes. Here, to overcome current restrictions in scRT studies, we put forth a uniform computational framework named Kronos scRT to investigate scRT based on single-cell copy number variation (scCNV) detection from single-cell Whole Genome Sequencing (scWGS) data. Our pipeline can be used to analyze datasets from various experiments including classical single-cell Whole Genome Amplification (scWGA), droplet-based 10x Genomics Chromium scCNV solution, single-cell Highthroughput Chromosome conformation capture (scHi-C) and other related data, obtained either from FACS sorted cells or directly from asynchronous cycling populations.
The framework described here allows us to increase the number of cells used to analyze scRT by at least 10-fold (>1,000 cells in one experiment) compared to previous studies. By analyzing published data and the new dropletbased scWGS data generated in the current study, we obtained large amounts (up to 1,353 S-phase cells for a given cell type from a single experiment, 4,724 cells analyzed in total) of scRT data of different mammalian cell lines. The obtained scRT data allow us to construct distinguished S-phase progression trajectories of different cell types as well as to identify coexisting sub-populations. In addition, our results show that incorporating significantly more cells enables us to stretch the study of DNA replication heterogeneity with unprecedent detail. Our analysis demonstrates that, for given genomic regions, although most cells replicate within a close timing range around the population average RT, replication can also happen stochastically in any given window during S phase.
Results Kronos scRT: a computational tool allowing extraction and analysis of scRT data from scWGS data of asynchronous cells. We have developed Kronos scRT, a tool that computes scRT under a unified framework and in a comprehensive manner (Fig. 1a  and Supplementary Fig. 1a ). At first, single-cell DNA sequencing reads are aligned to the provided reference genome and counted over regular bins (by default, and in our analysis 20 kb), of which the size can be adjusted depending on the average coverage of the experiment. Read counts are then corrected for GC content and mappability bias. An option to blacklist genomic regions is available as well (Methods). The data are then segmented and copy number variation (CNV) is called independently within each individual cell. At this stage, two additional parameters are calculated: the cell ploidy, as the cell weighted mean copy number (CN), and the intracellular bin-to-bin variability, calculated as Depth Independent Median Absolute deviation of Pairwise Differences (DIMAPD) (Methods).
1b ). S-phase cell ploidy must therefore be adjusted before proceeding with the downstream analysis. Adjustment can be automatically calculated (Fig. 1c ) or manually imposed by the user (Methods). The cells shown in Figure  1b -d come from a MCF7 breast cancer cell line dataset created using 10x Genomics microfluidic system. According to ATCC (American Type Culture Collection), this cell line has 80 autosomes in G1 (ploidy: 3. 64), which agrees with the estimation of Kronos scRT (Fig. 1b-c  and Supplementary Fig. 3b ). In addition to the ploidy estimation step, cells with a low coverage are filtered out. To evaluate the minimum number of reads required to obtain a stable CNV calling with each experimental setting, we selected G1/G2-and S-phase cells with relative high coverages and down-sampled them.
Each point is a single cell and the color is assigned based on a cut-off automatically calculated, or manually imposed, regarding the population variability (Methods). High variability is associated with S-phase cells (green), while low variability is associated with G1/G2-phase cells (blue). An unknown region can be manually set (yellow). The vertical black line represents the median ploidy of the population. Reported data are from asynchronized MCF7 cells enriched for the S phase. The green and purple arrows show the S-phase progression of the first and second part of the S phase, respectively (Methods). c Data presented in (b) after S-phase progression correction. The color of the second portion of the S phase have been changed to purple. As in (b), the green and the purple arrows indicate the correct S-phase progression.
The black curve reports the S-phase density distribution that is used to calculate the parameters to adjust the S-phase progression (Methods). d Read down-sampling for the dataset of MCF7 cells obtained by the 10x Genomics system. G1/G2-and S-phase cells are plotted in orange and green, respectively. Boxplots report the percentage change of the mean ploidy after down-sampling compared to the original value. The red dashed line indicates a change of 5%. Cells with more than 450 reads per Mb were used for the downsampling, and the number of cells used for each down-sampling is reported below the boxplot. e A normalized copynumber (nCN) distribution of a representative single cell in black. In blue, the Euclidian distance between the real data and a binarized system computed with a certain nCN cut-off.
Determination and analysis of scRT from scWGS data of sorted S-phase cells. scRT studies usually require cell phase sorting via FACS because they lack of an in silico phase separation as presented in the current study. Nevertheless, cell phase information can be integrated to the analysis to label cells using the WhoIsWho module (Fig. 1a ). To test the applicability of Kronos scRT's framework, we used previously published scWGS data derived from sorted mid-S-phase mouse embryonic stem cells (mESC, n=67 cells) and mESC differentiated for 2 days to EpiLCs followed by five days of Embryo Bodies culture, which result into neuroectoderm cells (hereafter called NE-7d, n=45 cells)  22. The scRT profiles of these two cell types were determined using Kronos scRT, and the phase was assigned using the FACS metadata (Supplementary Table  1  and 2 ).
To demonstrate that Kronos scRT is efficient in detecting scRT profiles, even with the utilization of a small number of sorted mid-S-phase cells, we calculated the correlation between pseudo-bulk RT issued from scRT of each sample in the current study and BrdU-IP-issued bulk RT data generated by Takahashi and colleagues  22  (Fig. 2a ). We obtained a Spearman correlation of 0. 890 for mESC and 0. 902 for NE-7d cells (Fig. 2b  and Supplementary Fig. 2a ), demonstrating the robustness of our method and computational pipeline. In addition, in agreement with previous studies  [21] [22] [23]  , the obtained mid-S binary replication signals show that cell-to-cell variability exists but is limited and that RT organization is largely conserved in single-cells. We were then interested in quantifying the replication variability within the cell populations by calculating, in each dataset, the Twidth values, defined as the time needed for a given genomic region to be replicated from 25% to 75% of cells in a S-phase lasting 10 h 21.
Kronos scRT can extract scRT from various single-cell DNA sequencing data. Although, we performed experiments on scWGS, created ad hoc for the scRT analysis, this is not a requirement when using Kronos scRT. As long as the single-cell sequencing data maintain the copy number information and they are derived from cycling cells, Kronos scRT can process them and extract scRT profiles. Amongst the published datasets, scHi-C data generated in Nagano et al. 2017 31 are a perfect example to demonstrate this. The dataset includes cycling mESC grown either on a feeder layer in ES-DMEM with fetal bovine serum (mESC Serum) or without feeders and adding PD and CHIR inhibitors, two inhibitors that favor the maintenance of a naive ground state for mESC (mESC 2i)  31. Despite being paired-end sequencing data, due to their nature, they were loaded into Kronos scRT as single-end data.
The Metafer imaging platform (MetaSystems) was used for automated acquisition of the chromosome spread. Picture triplets were merged with Fiji (v2. 1. 0) and the resulting images were manually scrutinized for chromosome 3 enumeration. Representative pictures were acquired using a Deltavision Core system (Applied Precision). 10x Genomics data processing Data produced with 10x Genomics system (various human cell lines) were processed using Cell Ranger DNA (10x Genomics software, version 1. 0. 0). The resultant bam file was subset using 10x Genomics subset-bam version 1. 0 to obtain single cell bam files. Duplicated reads were removed using Picard MarkDuplicates (version 2. 6. 0, http://broadinstitute. github. io/picard) and the resulting files were used as input for the CNV module of Kronos scRT (Supplementary Fig.
Trimming and aligning reads The fastqtoBAM module of Kronos scRT uses demultiplexed fastq files as input and removes standard adaptors from reads. Adaptor trimming is performed using Trim Galore (version 0. 4. 4, https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/), a modified version of cutadapt and FastQC. After trimming, reads are aligned to the provided reference genome (in our study, human genome version hg38 and mouse genome version mm10 were used) using RBowtie2 package (version 1. 4. 0) reporting only the best mapping for each read. SAM files are then sorted, converted into BAM files using Rsamtools (version 1. 34. 1) and deduplicated using Picard MarkDuplicates (version 2. 6. 0). The fastqtoBam module was used to process all the mouse data analyzed in the current study.
Calculate bin mappability and GC content for copy-number estimation The binning module of Kronos scRT is used to calculate mappability and GC content in each genomic bin. This information is later used by the CNV module to normalize read counts and select which bins will be considered for further analysis. By default, bin size is 20 kb, but, depending on the sample average sequencing dept, it can be adjusted by the user. Moreover, only autosomal chromosomes are used by default, but the user can decide to keep either or both sex chromosomes as well. GC content is simply calculated as the frequency of C and G in the reference sequence belonging to a bin. To calculate mappability, this module simulates 1X coverage reads from a reference genome, adds mutations with an error rate of 0. 1% (that can be adjusted by the user to fit the error rate of their own datasets  45  ) and re-maps the reads on the reference genome using Rbowtie2 with the same settings used in the fastqtoBAM module.
For the sorted G1 and Mid-S cells of the mouse datasets the closest ploidy to 2 was selected. ix Where is the value of for which is minimized (formula viii), Rn is the read count in the segment n, and CNn is an integer that represent the copy number of the segment n. The mean ploidy of a cell can then be calculated as follow: x Where P is the mean ploidy, Sn is the size of the segment n, and CNn is the copy number of the segment n. The difference between the absolute minima and its closest relative minimum is used to evaluate how good is the CN calling, for values below 2 the CN is not considered reliable (ploidy confidence). Negative values of ploidy confidence are imposed as suggested by 10X Genomics. Bins included in the provided blacklist are removed, as done here with the binning for the mESC and mNE-7d cells.
Single-cell replication profiling and scRT calculation As already mentioned, the DIMAPD parameter can be used to distinguish the replicating cells (i. e. S-phase cells) from those which are not (i. e. G1/G2-phase cells). The automatic threshold is a reasonable choice if the cell population has not been sorted to enrich for the S phase. For the Sphase enriched cells, the diagnostic module can be used to select more adequate thresholds manually. The thresholds used in this study are reported in Supplementary Table  2. If available, FACS metadata can be integrated through the WhoIsWho module of Kronos scRT. As shown in Fig. 1b , the function that we use to identify CN (formula viii) introduces some constraints in the calculation of mean ploidy. Firstly, it is not possible to distinguish between G1 and G2 cells that co-occupy the same area (Fig.
Coverage thresholds for each dataset are reported in Supplementary Table  2. The genome is then binned again, in this case, in bins of 200 kb to calculate scRT, but the size should be adjusted based on sequencing depth. A weighted median CN is then calculated, where the weights are the sizes of overlap between each 200 kb bin and the previously calculated segments. The G1/G2-phase population is used to calculate a median pseudo-bulk CN profile and it is used to normalize each cell in S phase as follow (formula xi): xi Where nCNn,x is the normalized copy number of the bin n in the S-phase cell x. CN200n,x is the copy number of the bin n in the S-phase cell x before normalization, and CNGn is the CN of the bin n in all the G1/G2-phase cells. Each S-phase cell profile is then binarized. To do so, Kronos scRT identifies a nCN value for which the following target function is minimized (xii) (Fig.
e. very early and/or very late) are filtered out. Replication tracks per percentage interval are then averaged together to create pseudo-bulk RT and compared with bulk RT. In this study, bulk RT of mESCs and NE-7d issued from BrdU-IP samples had coordinates converted to mm10 with R package liftOver (v1. 10). Human bulk RT were converted from hg19 to hg38 using ucsc-liftOver tool (v366).
e. 10x Genomics system, scWGA and scHiC. Downsampling was performed using Picard DownsampleSam (version 2. 6. 0, http://broadinstitute. github. io/picard). For each down-sampling coverage, cells with higher RPMb were used. RPMb thresholds were set in our study as the value at which at least 75% of cells have a ploidy estimation that does not differs more than 5% from the original value. If the users do not define a specific threshold, Kronos scRT applies a threshold of 160 RPMb per haploid genome by default, which approaches the highest threshold we found from different datasets analyzed in the current study (Fig. 1d  and Supplementary Fig. 1c ).
Dimension Reduction Kronos DRed is the dimension reduction module, which uses genome-wide scCNV or scRT data to transform the data and provide a low-dimensional representation reflecting the important features (e. g. cell type, cell population, etc. ). For CNV data, original values are used, while for RT data, simple matching coefficient distances calculated from R package ade4 (v1. 7) are used. t-SNE  46  and UMAP  26, 27  are performed with R packages Rtsne (v0. 15) and umap (v0. 2. 7), respectively. For t-SNE, perplexity corresponds to a fiftieth of the number of cells or a minimum value of 10 accordingly, theta to 0. 25, and partial PCA is performed allowing t-SNE coordinates to be calculated under 5,000 iterations.
Acknowledgements Work of C. L. C lab is supported by the  YPI program  of  I. Curie , the  ATIP-Avenir program  from  Centre national de la recherche scientifique (CNRS)  and  Plan Cancer  from  INSERM , the  CNRS 80|Prime interdisciplinary program , the  Agence Nationale pour la Recherche (ANR)  and  Institut National du Cancer (INCa). J. M. J. is supported by a  PSL-Qlife fellowship  ( ANR-17-CONV-0005 ). High-throughput sequencing was performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOS-Inserm_12554 ).
Data availability The scWGS data of mouse were obtained from GSE108556. The scHi-C data of mouse were obtained from GSE94489. The bulk RT data were obtained from GSM923442 for MCF7, GSM923449 for HeLa, GSM923451 for GM12878 (B-lymphoblastoid cell line) and GSE108556 for mESC and NE-7d cells. The mm10 blacklist can be located at https://github. com/Boyle-Lab/Blacklist. The raw and processing generated in the current study will be submitted to SRA and GEO, respectively.