dataesr/scientific-paragraphs-categorization huggingface dataset:
MERRA-2 and ERA 5 Reanalyses MERRA-2 (Modern Era Retrospective analysis for Research and Analysis -2) is a reanalysis data developed at the Global Modelling and Assimilation office (GMAO). These data have 72 sigma pres sure vertical layers with a spatial resolution of 0. 625° × 0. 5°  (Bosilovich et al. , 2015)  and are available from 1980 onwards. The reanalysis also assimilates data from Infrared Atmospheric Sounding Interferometer (IASI) and Cross Infrared Sunder and Advanced Technology Microwave Sounder on board Suomi NPP. An adaptive bias correction scheme is also applied to the radiance data while assimilating the datasets in MERRA-2. A comprehensive discussion on this assimilation system is given in  Gelaro et al. (2016). We have also used the ECMWF Reanalysis 5 th Generation (ERA5) data, which has 137 vertical hybrid sigma pressure levels, which extends from the surface to 0.
Ozone variability and trends across the latitudes Figure  1  shows the latitudinal distribution of (zonal mean) stratospheric ozone from a merged satellite dataset (GOZCARDS). The data show high ozone mixing ratios (10-11 ppm at 25-35 km) in the tropics (30° N-30° S), which decrease toward the high latitudes (2-5 ppm at 25-35 km). Since the production of ozone is effective in the tropics, ozone mixing ratios are highest in the tropical middle stratosphere (25-35 km). As the intensity of atmospheric transport is different with seasons, there are also analogous changes in ozone distribution across the latitudes and altitudes. The seasonal variability of ozone is minimum in the tropics and very high in the polar regions with respect to the latitudinal distribution of sunlight and variability of the dynamical processes.
, 2010). Here, we have used the data from OMPS for the year 2015 to show the changes in TCO, since there was a pronounced Antarctic ozone hole in that year. Figure  3  shows the TCO averaged over the tropics for the period 1978-2022, which is within 250-280 DU over this time period from all available measurements and reanalysis datasets. We also observe a decrease in peak TCO in the tropics during the period 1995-1999 (around 255 DU) when compared to the previous and following years (> 255 DU). However, there is an increase in TCO post-1997 and there is no significant difference (10-15 DU) in TCO among different data sets during the entire period. Furthermore, the bias in measurements from different instruments is within 5-10 DU, which shows that the data are robust and there is no substantial loss of ozone in the tropics.
(2020) , as they also find statistically significant ne gative trends in the tropical lower stratosphere (up to -3%/yr), but positive trends in the middle and upper stratosphere in spring and summer. We examined another dataset, the MERRA-2 reanalysis (Fig. S3 ), to find the trends derived from SWOOSH and GOZCARDS, and they replicate the information deducted from the previously discussed datasets. The trends are statistically nonsignificant in both periods, except in the topmost altitudes in the upper stratosphere. The trends computed with MERRA -2 data show slightly higher values (0. 03-0. 05 ppm/yr in the middle stratosphere and 0. 02-0. 03 ppm/yr in the lower stratosphere) at all altitudes in both periods. The negative trends in polar regions and positive trends in the tropical and southern middle latitudes are stronger than those found with SWOOSH, and are within ±0.
However, note that the ozone peak is around 30-35 km in these latitudes when we consider volume mixing ratios (molar mixing ratios), and thus, analyses of Lu (2022) miss the major part of tropical ozone. When we examine the column values, they are never below 220 DU and there is no big threat from UV radiation. Lu (2022)'s claim is solely with respect to a decadal dataset, which has only four profiles in each decade (see Fig. S10 ) and the data set is available only for the lower stratosphere. On the o ther hand, here we have analysed a set of satellite, balloon-borne ground-based and reanalysed data to examine tropical ozone, and find that the claims are not properly based on measurements or model simulations, and the data  Lu (2022)  used are inadequate to analyse tropical stratospheric ozone.
Data availability. TOST data is available via https://woudc. org/archive/products/ozone/vertical-ozoneprofile/ozonesonde/1. 0/tost/, GOZCARDS and MERRA-2 data are available on https://disc. gsfc. nasa. gov/, ERA 5 data are available on https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview, SHADOZ is https://doi. org/10. 5194/egusphere-2023-2574 Preprint. Discussion started: 23 November 2023 c Author(s) 2023. CC BY 4. 0 License.
Acknowledgments The authors thank  Lilia Kuzmenko  and  Mykhailo Shekera  for their help on the project during their Master 2 internship,  Eléonore Réal  for help with SDS-PAGE electrophoresis,  Thiebault Lequeu  for help with antibody puri cation,  Halina Anton  and  Maxime Lehman  for their help with cell culture. This work was supported by the  Labex NIE ,  ITI QMAT ,  GDR Imabio , and  COST CM 1403 « The European upconversion network». Y. M. is grateful to the ' Institut Universitaire de France (IUF) ' for support and providing additional time for research. The imaging was supported by the  Imaging Center PIQ-QuESt  (https://piq. unistra. fr/).
4. 0) and Cutadapt (v1. 8. 2). 33  Trimmed reads were then mapped to the mouse reference genome mm10 using the STAR (v2. 7. 0a) aligner  34  and the corresponding gene annotation from GENCODE: release 23/GRCm38. p6. The following parameters were used for mapping of RNA-seq reads against the reference genome: --seedSearchStartLmax 20 --outSAMattributes NH HI NM MD --alignEndsType EndToEnd. Gene counts were generated using FeatureCounts (Subread v1. 5. 1). 35  Since iMEF RNA-seq data include SE50 and PE100 datasets, we removed any potential batch effects with the removeBatchEffect function of the BioconductoR limma package  36  prior to conducting the differential expression analysis with DESeq2. 37.
Clinical data All tissue samples were fully anonymized before processing and sequencing. Study approval was first provided by the institutional review board and local ethical committee (Groupe Thématique de Travail -Hematology section, Institut Curie), under project ID BS#2014-311, when written informed patient consent was available, or else by the Comité de Protection des Personnes Sud Méditerrannée I, under project ID#RCB2019-A000620-57, when written consent was not available due to death or lost to follow-up. RNA-seq raw data from patients included in the PRIMA trial (NCT00140582) were courtesy of Drs. Huet, Tesson and Salles (Hospices Civils de Lyon, Pierre-Bénite; INSERM U1052, Université de Lyon; Carnot Calym, Pierre-Bénite; France).
Somatic mutation calling and annotation All of the variants were filtered through the COSMIC Cancer Gene Census (CGC) v86 (cancer. sanger. uk). Mutations affecting these putative driver genes were annotated as driver mutations if they passed the following filters: • The mutation must be detected with an allelic frequency >5% in the sample, with a coverage > 100x, GnomeAD all must be <0. 5% • We selected frameshift indels, as well as stop-gain mutation types. Intronic mutations were removed. Genes identified as recurrently mutated in two large studies of FL were systematically screened. 59, 60  Data availability RNA-seq, CUT&RUN-seq and ChIP-seq data for this study have been deposited in the Gene Expression Omnibus under accession no. GSE218717.
Acknowledgments The authors thank  Drs. Huet ,  Tesson  and  Salles  ( Hospices Civils de Lyon, Pierre-Bénite ;  INSERM U1052, Université de Lyon ;  Carnot Calym ,  Pierre-Bénite ; France) for kindly providing RNA-seq raw data from patients included in the PRIMA trial (NCT00140582). Work in the lab of R. M. was supported by funding from the  Foundations ARC  and FRM. P. R. was supported by the  MD-PhD program  at the  Institut Curie. L. R. was a recipient of a postdoc fellowship from the ARC. D. H. was supported by a postdoctoral fellowship from the FRM (no. SPF20150934266 ). Highthroughput sequencing was performed by the NGS platform of the  Institut Curie , supported by grants  ANR-10-EQPX-03  and  ANR-10-INBS-09-08  from the  Agence Nationale de le Recherche (investissements d 'avenir) and by the  Canceropôle Ile-de-France.
SMYD2 regulates breast cancer metastasis Breast cancers are complex cellular ecosystems and bulk sampling methods may mask pertinent information because of the high heterogeneity of cancer tissue. To identify key lysine methyltransferases regulating breast cancer metastasis, we utilized a single-cell RNA-seq dataset of a large cohort of breast cancer patient samples representing all major clinical subtypes  12. Restricting our analysis to neoplastic cells-only, classified in low or high metastasis potential populations based on metastasis genes signature  23  , we identified SMYD2 and EZH2 as the top two lysine methyltransferases significantly enriched in pro-metastatic cells (Fig. 1a ). While EZH2 has previously been linked to breast cancer metastasis  [24] [25] [26]  , the participation of SMYD2 has never been demonstrated.
Bioinformatic analyses We analyzed publicly available single-cell RNA sequencing datasets of breast cancer patients (GEO; accession number: GSE176078)  12  consisting of 130,246 cells derived from 26 primary tumors including 11 ER+, 5 HER2+ and 10 TNBCs, from which 24,489 malignant epithelial cells were selected to assess their metastasis potential. Metastasis signatures were extracted from a previous study  23  and assigned to each cancer cell using UCell  51. The cells were ranked according to the metastasis signature scores and were further categorized into a "Metastasispotential-high" group (top 25%) and a "Metastasis-potential-low" group (bottom 25%). Differentially expressed genes between the two groups were identified by FindMarkers function in Seurat  52. The Log 2 fold change of the average expression of active lysine methyltransferases was then scaled to calculate the Z-score.
The threshold was set up to the median expression value. We then compared overall survival probabilities between the two groups of patients with "low" or "high" expressions using the log-rank statistical test. We considered the association with survival significant if the obtained log-rank p-value was less than 0. 05. The survival analysis was performed using the "survminer" package of R. Other gene expression studies were performed using TNMplot  53  and bc-GenExMiner v4. 8, compiling TCGA and SCAN-B RNA Seq datasets  54.
MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Mascot Distiller (Matrix Science) was used to produce mgf files before identification of peptides and proteins using Mascot (version 2. 7) through concomitant searches against a database containing the sequences of proteins of interest (homemade), classical contaminants database (homemade) and the corresponding reversed databases. The Proline software  57  was used to filter the results (conservation of rank 1 peptides, peptide length ≥ 7 amino acids, identity threshold of peptide-spectrum-match < 0. 01, minimum peptidespectrum-match score of 25, and minimum of 1 specific peptide per identified protein group). Peptides of interest were subsequently targeted by LC-Parallel Reaction Monitoring using the same nanoLC-MS system.
For this purpose, the peptides were sampled on a pre-column (300 μm x 5 mm PepMap C18, Thermo Scientific) and separated in a 75 μm x 250 mm C18 column (Reprosil-Pur 120 C18-AQ, 1. 9 μm, Dr. Maisch). The MS and MS/MS data were acquired by Xcalibur (Thermo Fisher Scientific). Peptides and proteins were identified and quantified using MaxQuant (version 1. 6. 2. 10, Tyanova, Temu, and Cox 2016) using the Uniprot database (Homo sapiens reference proteome, 20180526 version) and the frequently observed contaminant database embedded in MaxQuant. Trypsin was chosen as the enzyme and 2 missed cleavages were allowed. Peptide modifications allowed during the search were: carbamidomethylation (C, fixed), acetyl (Protein N-ter, variable) and oxidation (M, variable). The minimum peptide length and the minimum number of unique peptides were respectively set to seven amino acids and one peptide.
Maximum false discovery rates -calculated by employing a reverse database strategy -were set to 0. 01 at peptide and protein levels. Quantification of SILAC ratios was performed using default settings. Proteins identified as outliers in both experiments are assigned as significant interactors. Amino acid complements (L-lysine-2HC, L-arginine-HCl, 2H4-L-lysine-2HCl, 13C6-L-arginine-HCl, Lproline), media, and serum used for SILAC were purchased from Silantes.
Homology modeling of FMNL3 GBD-FH3 domain The structural model of the FMNL3 GBD-FH3 domain was built by using the SWISS-MODEL server  60  by adding as templates the X-ray structures of human FMNL2 GBD-FH3 / Cdc42 GppNHp (PDBe code: 4YC7); and the structure of human FMNL1 (PDB code: 4YDH). The sequence identities between FMNL3 GBD-FH3 (residues 33 to 419) and FMNL1 and FMNL2 were 80. 0 and 88. 6% respectively. The calculated homology model of the FMNL3 GBD-FH3 domain presented Qmean Z scores of -2. 86 and 3. 14, indicating a very good global-quality model  61. The root-mean square deviations calculated in Coot 62 over carbon-alpha atoms of FMNL3 GBD-FH3 with respect to FMNL1 and FMNL2 were 0. 18 and 0. 74 Å respectively. The PyMOL Molecular Graphics System (v. 2. 5. 1, Schrodinger, LLC) was used to prepare structural models.
In silico docking of methylated K334-BCAR3 into FMNL3 GBD-FH3 domain The secondary structure propensity of BCAR3 residues 319 to 397 was calculated using the prediction sever PSIPred  63  , indicating that the region with residues 321 to 342, bearing Lysine 334, is predicted to fold into a helix. The three-dimensional structure of this BCAR3 helix and its docking into FMNL3 were calculated in Coot 62 via mutagenesis and local energy refinements by using as a template model the structure of Cdc42 in complex with FMNL2 (PDB code : 4YC7). Specifically, the position of methylated K334 BCAR3 was modeled by using the guanidinium group of Arginine 65 within helix 3 of Cdc42. No steric clashes or violations of the interatomic Van der Waals radii were found after BCAR3 K334me1 was placed at the hydrophobic pocket of FMNL3 GBD-FH3.
ACKNOWLEDGMENTS Authors would like to thank  Alexei Grichine ,  Mylène Pezet  and  Jacques Mazzega  from the  Optical Microscopy -Cell Imaging facility at the Institute for Advanced Biosciences , for their help in imagery analyses. N. R. was supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ), the  French National Cancer Institute  ( INCA_ 16066 ),  la Ligue contre le cancer (Comité Saone et Loire , 2021) and  Gefluc Isère  2020. P. K. M. was supported by  NIH  ( R01CA236118 ,  R01CA236949 ,  CA266280 ),  DoD   PRCRP Career Development Award  ( CA181486 ),  CPRIT IIRA  ( RP220391 ) and  CPRIT Scholar in Cancer Research  ( RR160078 ). The proteomic experiments were partially supported by  French National Research Agency  under projects  ProFI (Proteomics French Infrastructure ,  ANR-10-INBS-08 ) and  GRAL , a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ).
Size-exclusion chromatography (SEC) SpCAF-1 subunits interaction was performed by mixing 2. references were set indirectly relative to DSS using frequency ratios  38. Chemical shift index were calculated according to the sequence-specific random coil chemical shifts  39, 40. Structural models of the SpCAF-1 WHD domain were computed from NMR data with CS-ROSETTA  41  version 1. 01. First, the MFR program from NMRpipe 42 was used to search a structural database for best matched fragments based on the protein backbone 15 N,  13  C,  13  CA,  13  CB and 1 HN chemical shifts. Then the ROSETTA 3. 8 software was used to generate 27753 models by fragment assembly and full-atom relaxation. These models were rescored by comparing the experimental chemical shifts with the chemical shifts predicted by SPARTA  43  for each model.
Structural Modeling Sequences of S. pombe Pcf1 (Q1MTN9), Pcf2 (O13985), Pcf3 (Q9Y825), H3 (P09988), H4 (P09322) and PCNA (Q03392) were retrieved from UniProt database  49. These sequences were used as input of mmseqs2 homology search program  50  used with three iterations to generate a multiple sequence alignment (MSA) against the uniref30_2103 database 51. The resulting alignments were filtered using hhfilter  52  using parameters ('id'=100, 'qid'=25, 'cov'=50) and the taxonomy assigned to every sequence keeping only one sequence per species. To increase the number of sequences in the alignment of S. pombe Pcf1 we independently generated MSA using mmseqs2 starting from the S. cerevisiae or the human homolog of Pcf1 (Q12495 and Q13111, respectively) and the resulting alignments were combined with the one of SpPcf1.
Acknowledgements: We thank the  Alain LECOQ  and  Denis SERVENT  from giving access to the CD spectro-polarimeter. This work was supported by grants from the  INCA  ( 2016-1-PL BIO-03-CEA-1 ,  2016-1-PLBIO-03-ICR-1 ),  ANR  ( ANR-16-CE11-0028 ;  ANR-20-CE18-0038 ;  ANR-21-CE11-0027 ;  ANR-21-CE44-0009-01 ) the program labeled by the  ARC  foundation  2016  ( PGA1*20160203953 ), , the  Fondation LIGUE  " Equipe Labellisée 2020 " ( EL2020LNCC/ Sal ), and by french infrastructures, the  Synchrotron Soleil  ( 20191119 ;  20210745 ), the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-0005  and the  IR INFRANALYTICS   FR2054. It benefited from the  ERC-2015-ADG-694694  " ChromADICT ", the  Ligue Nationale contre le Cancer (Equipe labellisée Ligue)  and  ANR-11-LABX-0044 5.
Histones were added alone or previously complexed with histones chaperones. e Cartoon representation of the complex between human histones H3H4 (light blue and cyan), Asf1 (light grey) and Mcm2 (dark grey) (PDB: 5BNX). f AlphaFold2 model of Pcf1 (353-385) (as red cartoon), corresponding to the segment of the ED domain indicated in red, in complex with histones H3H4 (light blue and cyan surface) superimposed with Mcm2 and Asf1 as in panel e. The two insets represent zoomed views of the sidechains of the conserved Pcf1 L359 and F380 residues (red sticks) binding into H4 and H3 pockets, respectively. The same four and five branch stars are used to label these positions in the logo panel c.
A first global model with full-length Pcf1, Pcf2 and Pcf3 was generated to map the regions of Pcf1 binding to Pcf2 and Pcf3 and to obtain the pLDDT scores shown in Figure 1b for Pcf1, Figure S1f for Pcf2 and Pcf3. Next, three models of the complex corresponding to independent modules of the complex were generated using different delimitations: model_1 (presented in Figure S1g-i) with Pcf1(403-450)-Pcf2(1-453) (MSA with 2180 species, 501 positions), model_2 (presented in Figure S1j-l) with Pcf1(200-335)-Pcf3(1-408) (MSA with 2148 species, 544 positions), model_3 Pcf1(352-383)-H3(60-136)-H4(25-103) (presented in Figure 2f and S2d) (MSA with 3530 species, 188 positions). Concatenated mixed MSAs were generated using the delimitations defined above and used as input to run 5 independent runs of the Alphafold2 algorithm with 6 iterations each 54 generating 5 structural models using a local version of the ColabFold interface 51 trained on the multimer dataset 55 on a local HPC equipped with NVIDIA Ampere A100 80Go GPU cards.
Its user-friendly graphical interface and command-line interface allow for easy installation and execution on various hardware platforms. The software employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM supports preprocessing, spot detection, mask detection, and trace generation, generating human-readable reports and intermediate results for data validation and further analysis. Moreover, it offers additional features for data formatting, result display, and post-processing. pyHiM's scalability and parallelization capabilities enable the analysis of large, complex datasets in a reasonable time frame. Overall, pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis, foster collaborative developments, and promote the growth of a user community to drive discoveries in the field of chromatin organization.
To address these needs, we introduce pyHiM, an open-source, modular and scalable software toolbox specifically designed for sequential spatial genomics data analysis (Fig. 1a ). pyHiM comes with extensive user and developer documentation, as well as tutorials that illustrate typical analysis pipelines (Fig. 1b ). It can be easily installed using standard package management tools (conda and PyPi, Fig. S1 ), and conveniently runs in both Windows and Linux (Fig. 1c ). A single human-readable configuration file is used to centralize all analysis parameters and can be edited thanks to a user-friendly graphical user interface (GUI) (Fig. S1 ). In addition, a command-line interface enables execution on multiple hardware platforms, from laptop computers to high-performance computing (HPC) clusters.
Contact maps computed using the 2D pipeline show all the relevant features of 3D maps (long-range contacts, TADs, etc), but require ~5x less computation time (Figs. 2e-f ) and can therefore be used to quickly assess the quality of the acquired dataset before full 3D analysis. Finally, a critical aspect of multiplexed DNA-FISH imaging is the amount of data generated, typically ~1-3 Tb per experiment depending on the number of cycles and the number of FOVs. To handle and analyze such large volumes of data in a reasonable time, we have implemented a parallelization mode based on the Dask Python package. For this, pyHiM analyzes data associated with different hybridization cycles in parallel, while keeping the technical aspects transparent to the user, leading to a drastic shortening in computation time (Figs.
Biological samples can display local deformations (typically in the hundreds of nm range) during the long-term acquisition times of a HiM dataset. These distortions cannot be properly corrected by global 2D realignment routines. To tackle this issue, we developed a new registration method that performs local 3D registration. In this method, images are first globally realigned in 2D. Next, fiducial images are decomposed in 3D blocks and each block is realigned by 3D cross-correlation and re-interpolation. The resulting local block corrections are stored as an ASTROPY table  21  that is used by the register_localizations module (see section below). More details on the methods and the execution of this module can be found in our online description of the alignImages3D module.
Acknowledgments This project was funded by the  European Union 's  Horizon 2020 Research and Innovation Program (EpiScope , Grant  ID 724429 , M. N. ). We acknowledge the  Bettencourt-Schueller Foundation  for their prize 'Coup d' élan pour la recherche Française ', and the  France-BioImaging  infrastructure supported by the  French National Research Agency  (grant  ID ANR-10-INBS-04 , '' Investments for the Future' '). Part of this work was funded by the cloudFISH project from  France-BioImaging.