dataesr/scientific-paragraphs-categorization huggingface dataset:
0 au (i. e. 54 fs). The field has been chosen along the molecular symmetry axis (z) and the 6-311++G** basis set and B3LYP functional were used. The propagation was carried out for a total time of 3500 a. u without any numerical instabilities. As shown in Fig. 5 , the induced dipole does not follow the applied field adiabatically when a In Fig. 6  we report the base-10 logarithm of the spectral intensity for the embedded water molecule and we compare it to the HHG of the isolated water calculated that has the same geometry it has in the cluster model. In the case of the isolated water molecule we are able to observe relatively well defined peaks up to the 21th harmonics. We mention that this finding qualitatively agrees with data obtained by Sun et al. 20  (see Figure  3  of Ref. 20  ). An important parameter in the analysis of the HHG spectrum is the value of the energy cutoff (E cutof f ), which is related with the maximum number of high harmonics (N max ≈ E cutof f /ω 0 ).
Conclusions and perspectives In this work we have focused on the implementation of the Frozen Density Embedding scheme in the real-time TDDFT. We have integrated the Psi4Numpy real-time module we recently developed within the PyADF framework. We have devised a real-time FDE scheme in which the active density is evolved under the presence of the embedding potential. This implementation relies on a multiscale approach, since the embedding potential is calculated by means of PyADF, while the propagation is carried out by Psi4Numpy. We tested the implementation on a simple water cluster showing that the time needed for the propagation scales linearly with the cluster size. We studied many low-lying transitions in the case of a water molecule embedded in ammonia, and we showed that the shift of excitation energies with respect to the isolated water molecule is in good agreement with the results obtained using linear response FDE TDDFT implemented in ADF.
CRJ and ASPG acknowledge funding from the Franco-German project CompRIXS (Agence nationale de la recherche ANR-19-CE29-0019, Deutsche Forschungsgemeinschaft JA 2329/6-1). ASPG further acknowledges support from the CNRS Institute of Physics (INP), PIA ANR project CaPPA (ANR-11-LABX-0005-01), I-SITE ULNE project OVERSEE (ANR-16-IDEX-0004), the French Ministry of Higher Education and Research, region Hauts de France council and European Regional Development Fund (ERDF) project CPER CLIMIBIO. CRJ acknowledges funding from the Deutsche Forschungsgemeinschaft for the development of PyADF (Project Suresoft, JA 2329/7-1).
Cohesin AID depletion was triggered using 500 mM of Indole-3-acetic acid sodium salt (auxin analog) (Sigma-Aldrich Cat #I5148) final, diluted in culture medium. TetO promoters were induced using 1 μg/ml doxycycline final, diluted in culture medium. Single-molecule imaging in CTCF-AID cells was performed after 1 day of auxin treatment, to minimize secondary effects. ChIP-seq and Hi-C was performed after 2 days of auxin (+dox) treatment to enable comparison with previous ChIPseq and Hi-C data  3. 5C was performed after 4 days of auxin (+dox) treatment, where the effect of CTCF depletion and the difference with the CTCF full-length transgene rescue were maximal 3. Plasmid construction. Plasmids were assembled using Gibson assembly (SBI MC010B-1) or restriction ligation. Mouse cDNAs were used for CTCF and cohesin transgenes, and cloned after by reverse transcription of mESC (E14Tg2a) mRNAs (SuperscriptIII, ThermoFisher).
We cotransfected this plasmid together with the sgRNA vector pX459-EN2328 (derived from pX459, Cas9-2A-puro, Addgene #62988-see Supplementary information). We used the Neon system (Thermofisher) using a 100-µL tip with 1 million cells at 1400 V, 10 ms, and 3 pulses with 15 µg of pEN715 and 5 µg of pX459-EN2328. One day later, puromycin was added at 1 µg/mL. One day later, cells were split for limiting dilution in 10-cm plates with puromycin. Starting 1 day later, the medium was changed daily without puromycin. Single colonies were picked manually and genotyped by PCR. Deletions were also confirmed from cDNA generated from the selected clones, and that no WT CTCF cDNA was produced by these cells. These mutant cells did not exhibit noticeable growth defects. The list of cell lines generated in this study and the corresponding CRISPR sgRNAs can found in Supplementary Data 1.
The next day, DNA was cleaned either using Ampure Beads (FLAG ChIPs) or Qiagen PCR cleanup minelute kit, eluting in 32 mL. DNA was then used for library preparation exactly as described  3  , using the entire eluate for ChIP-seq and 40 ng for inputs. ChIP-seq analysis. Mapping and peak calling were performed as exactly as described previously 3 using mm9 assembly: Fastq files were trimmed using the fastq-mcf program, aligned to the mm9 reference genome with the bowtie2 software  57. Reads with a mapq score of 30 or greater were retained, using Samtools. Heatmap visualization and integration with RNA-seq was performed using the Easeq version 1. 03 software  58. Published 3 CTCF ChIP-seq peaks in untreated and auxin-treated CTCF-AID mESCs were used to identify total and auxin-sensitive CTCF peaks.
The fraction of reads in peak scores were calculated by the proportion of uniquely mapping reads within auxin-sensitive CTCF peaks compared to the total number of uniquely mapping reads, and excluding genomic regions known to display artificial ChIP-seq signal  59  retrieved from https://sites. google. com/site/anshulkundaje/projects/blacklists. The RAD21 ChIP-seq presented in Fig. 1 , and identified as Rad21_ (2017_protocol) in Supplementary Data 2, was generated in parallel of the CTCF ChIP-seq data published 3 in GEO series GSE98671. We used matching inputs for the analysis as those were generated in parallel (see Supplementary Data 2). These samples were generated using the commercial Active motif spike-in reagents (spike-in chromatin cat#52083 and spike-in antibody cat#61686), where spike-in calibration yielded consistent results.
The list of FLAG (CTCF) peaks from cells expressing the full-length FLAG-CTCF, used in Fig. 3 , is provided as Supplementary information in the GEO series of this paper. It corresponds to the overlapping peaks from libraries ENC178 and ENC205 (Supplementary Data 2), excluding blacklisted genomic intervals. For mapping Drosophila RAD21 enrichment at CTCF sites in Kc167 cells, published  44  datasets from accession GSE63518 were mapped to dm3, and peak calling was performed as exactly as described previously  3. Chromosome-conformation capture carbon copy (5C). 5C was performed exactly as described  3  with the same 5C oligonucleotide pool, which corresponds to a single alternating design of 486 Forward and 504 Reverse oligos, spanning 4. 5 Mb across mm9 chrX:98837477-103425147. Note that all cells used here are XY with a single active X chromosome.
5C analysis. Sequencing and mapping were performed as described 3 using mm9 reference coordinates. Matrices were then iteratively corrected at the fragment level and normalized to sum to 1e6. Iterative correction was performed on raw unbinned matrices (fragment level from the alternating 5C primer design) using iter-ative_correction_asymmteric with default values (cooltools, https://github. com/ open2c/cooltools). 5C heatmap data depicted in the figures were obtained after binning the corrected matrices at 15 kb by taking the median over all primer pairs that fall within each pair of bins. To minimize possible artifacts when calculating insulation scores, we binned the matrices at 20 kb by taking the mean over all primer pairs that fall within each pair of bins. The first two diagonals of the binned matrix were then filled with the mean of the second diagonal.
Combined insulation scores for each sample were calculated for the binned corrected matrices by aggregating over the same set of boundary positions across samples. Boundaries were identified in untreated CTCF-AID mESCs without any CTCF transgene (GEO accession GSE98671 samples GSM2609248, GSM2609253, and GSM2609256)  3  by taking the minima of the insulation profile, as described previously  3. Insulation scores were calculated with a 100-kb window, as described previously  3. These minima were then filtered to exclude those that are shared with those upon auxin-mediated degradation of CTCF-AID for 4 days in mESCs (GSM2609254 and GSM2609259) (to eliminate CTCF-independent boundaries-e. g. , compartment transitions). Combined insulation scores averaged across all replicates (Fig. 2 ) were calculated as the mean across boundary positions and averaged across replicas, for each cell line separately.
Hi-C analysis. We processed each Hi-C dataset using distiller (https://github. com/ open2c/distiller)  60  , mapping reads to mm10 and saving processed data in the cooler format (https://github. com/open2c/cooler 61 ) at 10-kb resolution. For each library, around 70% of initial reads were valid Hi-C pairs with >90% in cis. We used iterative correction  62  to remove biases using cooler balance (filters: mad_max=8, min_count=20). Aggregate boundary analyses used 4753 boundaries called on Bonev et al. 40  10kb binned ESC data. Data by Bonev et al. were also mapped using distiller to mm10 and iteratively corrected. Boundaries were called using calculate_insulation_score with a 200-kb window, and with additional stringent thresholds boundary_strength >0. 25, log2_insulation_score <0. Average boundary maps for individual datasets were constructed using CoolerSnipper from cooltools (https://github.
com/open2c/ cooltools)  63  to collect and aggregate ±400-kb regions around each boundary, and then normalized to the average value of the second diagonal. To quantify boundary strength for a dataset, we took the ratio between map values for positions spanning the boundary to those shifted to be completely upstream or downstream of the boundary, as performed in earlier studies  64. Here, we used positions separated by up to 80 kb and shifted the areas by 80 kb upstream and downstream. Aggregate peak analyses used 4450 dots (Hi-C loops) called on Bonev et al. 40  10-kb binned ESC data, processed as above. Dots were called using cooltools dotcaller with default parameters. Average peak maps were constructed using CoolerSnipper from cooltools to aggregate ±200 kb paired regions around each set of peak anchors, and then normalized to the average value of 200 kb inward from both anchors.
Analysis of single-particle-tracking data. To localize the single emitters and build the trajectories, we used SLIMfast 68 , implemented in Matlab and based on the MTT algorithm  69. The point spread function of a single emitter is fitted with a 2D gaussian, whose center corresponds to the position of the fluorophore with a subpixel resolution. Analysis of bound fractions-To quantify the fraction of bound molecules, we used data acquired at 5-ms exposure in a continuous imaging regime. The actual frame-rate acquisition is 197 Hz (5. 08 ms), due to the frame-transfer lag to the camera. We chose to use the data from the fastest acquisition rate to include the fastest-diffusing population, which blurred when imaging with 50 ms of exposure time. Particles were tracked as described above, and we computed the distribution of the step sizes of the protein of interest.
PðtÞdt. Given the intrinsic limitations of single-molecule imaging when probing very stable binding events (as for cohesin), we use the Survival Probability curves to qualitatively sample the discrepancies between the different biological conditions. Analysis of diffusion dynamics-The trajectories obtained from experiments at 50 ms were analyzed with custom codes implemented in Matlab. First, we computed the time-averaged mean-squared displacement (MSD) as MSD = 〈xt + nΔt -xt〉, where x(t) is the position at time point t, n = 1, 2 …, N, with N = maximum number of time points in a trajectory, and 〈〉 indicating the ensemble average over all the possible time lags of one individual trajectory. We selected the trajectories with at least ten localizations. In spite of the low JF549 ligand concentration, the beginning of the videos is very dense in point emitters.
Using different thresholds did not affect the conclusions. For the boxplots presented in Fig. 4  and extended Fig. 4 , we used a threshold of 5000 for the red channel (and no threshold for the green channel), in reference to the data in the Source Data file. Boxplots show the results measured over at least 30 LacO arrays across at least two independent transfections carried on different days, typically. Flow cytometry. mESCs were dissociated with TryplE, resuspended in culture medium, spun, and resuspended in 4% FBS-PBS before live flow cytometry on a MACSQuant instrument (Miltenyibiotec). Dissociation, wash, and flow buffers were supplemented with auxin, when appropriate, to avoid re-expression of the CTCF-AID-eGFP fusion. Analysis was performed using the Flowjo sowftware. Western blots. mESCs were dissociated, resuspended in culture medium, pelleted, washed in PBS, pelleted again, and kept at -80 °C.
Acknowledgements This paper is dedicated to the memory of  Maxime Dahan , who supervised L. C. We thank  Julia Ronsch ,  Christel Picard , and  Edith Heard  for support with tissue culture. We are grateful to  Mathieu Coppey  for mentoring L. C. ;  Luke Lavis ,  Pierre-Antoine Defossez ,  Heinrich Leonhardt , and  David Spector  for reagents;  Anders Hansen  and  Maxime Woringer  for insight into single-molecule imaging;  Mads Lerdrup  for support with the Easeq software;  Joke van Bemmel ,  Anton Goloborodko  and  Erika Anderson  for critical reading of the paper. We thank the  Gladstone Flow Cytometry, Genomics, Histology and Microscopy , and  Stem Cell Cores  for their excellent services. Research in the groups of BGB and KSP was supported by  NHLBI  ( Bench to Bassinet program   UM1HL098179 ),  Gladstone institutes (including BioFulcrum program ),  Younger Family Fund , and  UCSF Cardiovascular Research Institute.
Research in the group of MD was supported by the  FRM  grants  FDT201805005225  and  DEI20151234398 , the  France-BioImaging infrastructure Grant  ( ANR-10-INBS-04  Investments for the future), and the  Institut Curie  ( PIC3i  project). L. A. M. and M. D. were supported by the  MIT-France Seed Fund  and  NIH  grant  GM114190. E. N. was supported by an  HFSP postdoctoral fellowship and UCSF. L. C. was supported by the  international Curie PhD program.
To fully utilize the MS  1  and MS 2 data collected during a non-targeted metabolomics experiment in liquid chromatography coupled to tandem MS (LC-MS 2 ), we have created an online and streamlined workflow (Fig. 1a ) infrastructure that supports the outputs of feature detection and alignment tools for FBMN analysis (https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking/), including the standard output format for analysis of small molecules (mzTab-M)  6. The diversity of supported software, each offering different functionalities and modules, serves experimentalists, bioinformaticians, and software developers. FBMN is the second most commonly used analysis tool within the GNPS environment (Fig. 1b ), with more than 6,767 jobs performed in 2019, and has already been used in more than 80 publications since its introduction in November 2017.
The molecular networks generated with FBMN enable the efficient visualization and annotation of isomers in LC-MS  2  datasets, as demonstrated below with LC-MS 2 data from a drug discovery project from Euphorbia plant extract  7  (Fig. 2a, b ) and the detection of human microbiome-derived lipids belonging to the commendamide family  8  , detected in fecal samples from the American Gut Project (AGP  9  ; a crowd-sourced citizen-science microbiome project; Fig. 2c, d ). In both cases, FBMN resolved positional isomers/stereoisomers in the molecular networks that have similar MS 2 spectra but distinct retention times, that would not have been resolved with classical MN. The uses of FBMN facilitated the discovery of antiviral compounds  7  (Fig. 2c ), and the annotation of commendamide isomers  9  and of a putative new derivative, the N-(dehydrohexadecanoyl)glycine (Fig.
FBMN enables robust statistical analysis by providing accurate relative ion intensities across a dataset. This capacity is demonstrated with a serial dilution series dataset of the NIST 1950 serum reference standard, containing 150 spiked standards. Here the LC-MS 2 data were processed with MZmine 11 or OpenMS  10  for FBMN (Fig. 2g, h ). A linear regression analysis was used to evaluate the relative quantification between classical MN and FBMN. Figure  2h  shows that for FBMN, relative quantification had a coefficient of determination (R 2 ) value distribution mostly above 0. 7, whereas this was not found when the precursor ion abundance was obtained from classical MN via spectral counts (Fig. 2g ). The improved distribution of correlation coefficients toward 1 indicates a more linear response between molecular concentration and ion abundance, which improves the accuracy and precision of the quantification of results.
In addition, FBMN facilitates For both methods, the mS data files are converted to the mzmL format using tools such as proteowizard mSConvert  21. the classical mN method runs entirely on the GNpS platform, in which mS 2 spectra are clustered with mS-Cluster and the consensus mS 2 spectra obtained are used for molecular network generation. For FbmN, the user first applies a feature detection and alignment tool to process the LC-mS 2 data (such as mZmine, mS-DIAL, XCmS, OpenmS, progenesis QI or metaboScape) instead of using mS-Cluster (classical mN) on GNpS. results are then exported as a feature quantification table (. tXt format) and mS 2 spectral summary (. mGF format) or an mztab-m file and uploaded to the GNpS web platform for molecular networking analysis with the FbmN workflow. b, Graphs show the number of molecular networking jobs performed on GNpS.
An example of such isomer separation using trapped IMS (TIMS) coupled to LC-MS 2 is shown in Supplementary Fig. 1. Available on the GNPS web platform at https://gnps. ucsd. edu/, FBMN is ideally suited for advanced molecular networking analysis, enabling the characterization of isomers, incorporation of relative quantification and integration of ion mobility data. FBMN analysis is recommended for a single LC-MS 2 metabolomics study, but its applicability is limited when applied across multiple studies due to different experimental conditions and possible batch effects. Moreover, the use of FBMN for the analysis of very large datasets (containing several thousand samples) is limited by the scalability of most feature detection and alignment software tools. Thus, while FBMN offers an improvement upon many aspects of molecular networking analysis, classical MN remains essential for meta-analysis of large-scale datasets and is convenient for rapid analysis of LC-MS 2 data with less user-defined parameters; one important aspect of molecular networks obtained with FBMN is the use of adequate processing steps and parameters, which otherwise could negatively affect the resulting molecular networks.
To facilitate dissemination and education of the FBMN method and the supported processing software, we have created detailed tutorials and step-by-step instructions, available at https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking/. The FBMN workflow not only offers automated spectral library search and spectral library entry curation, but is also integrated with other annotation tools available on the GNPS environment, such as MASST  20  , while promoting data analysis reproducibility by saving the FBMN jobs on the user's private online workspace. The GNPS environment conveniently enables the user to evaluate different parameters and share the results via a URL for publication. experiment per sample). by merging mS 2 spectra of eDtA eluting over 2. 5 min into one representative mS 2 spectrum, FbmN recovered the molecular similarity of in-source fragments observed for eDtA.
This simple mapping enabled us to relate LC-MS feature information or statistically derived results to the molecular network nodes. This approach was also used for the integration of other tools with FBMN and does not require third-party software, as proposed previously  22, 23. Finally, the FBMN workflow also supports the mzTab-M format  6  , a standardized output format designed for the report of metabolomics MS data-processing results. In this case, the mzTab-M file is used instead of the feature quantification table and requires the input of the mzML files instead of the MS 2 spectral summary file. Support for the mzTab-M format enables the possibility to perform FBMN with any existing and future processing tools that support this standardized format. The FBMN workflow has been integrated into the GNPS ecosystem and thus benefits from the connection with other GNPS features, for example, the possibility to perform automatic MS 2 spectral library searching, the direct addition and curation of library entries, the search of a spectrum against public datasets with MASST  20  and the visualization of molecular networks directly in the web browser  24  or with Cytoscape  25.
The FBMN workflow is available on the GNPS platform (https://gnps. ucsd. edu/) via a web interface (Supplementary Fig. 2 ). Jobs are computed and stored on the computational infrastructure of the University of California San Diego Center for Computational Mass Spectrometry. Each finished job is saved in the private user space for future examination and has a permanent static link that enables data sharing and collaborative analyses. We strongly recommend the sharing of this static link along with data publications using GNPS workflows to facilitate accessibility and reproducibility of results. Instructions to perform FBMN with the supported tools and input file format requirements are provided in the GNPS documentation (https://ccms-ucsd. github. io/ GNPSDocumentation/featurebasedmolecularnetworking; Supplementary Fig.
3 ). Processing mass spectrometry data for FBMN. FBMN supports the output from several feature detection and alignment processing software programs. Depending on the type and size of MS data and the intended user (for example, bioinformatician, mass spectrometrists and biologists), different software might be more appropriate. In general, tools with a graphical user interface (GUI), for example, MZmine  11  , MS-DIAL  26  , MetaboScape and Progenesis QI, are convenient for data visualization and empirical parameters optimization, but often have limited scalability, which might prevent their usage for large datasets (>500 files). For these large datasets, tools that were designed to operate on a cluster/cloud computer are preferred (XCMS  27  , OpenMS 10 and, to some extent, MZmine). Regardless of the software or application, the processing steps and parameters used should be determined according to the recommendations from tool developers and experienced users through community feedback.
Finally, automated optimization modules can be used to finely tune parameters, which is particularly valuable when using command-line interface tools  28, 29. While we acknowledge that many tools and configurations are available to analyze MS data, we provide a summary of processing steps on the supported tools in the FBMN documentation (https:// ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking). These steps constitute an aggregation of institutional knowledge from tool developers and experienced tool users that do not encompass all possible applications, but rather provide a starting point for new users. Generation of a representative MS 2 spectrum from a LC-MS 2 file. The selection of the representative MS 2 spectrum for detected features in the MS 2 spectral summary file is performed using several methods.