dataesr/scientific-paragraphs-categorization huggingface dataset:
FluoSim was thoroughly validated against experimental data obtained on the canonical neurexin-neuroligin adhesion complex at cell-cell contacts. This unified software allows one to model and predict membrane protein dynamics and localization at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments. Critical cellular functions such as membrane adhesion, receptor-mediated signaling, or synaptic transmission, involve the diffusional trapping of specific molecules in sub-cellular compartments 1,2. To quantitatively describe such molecular dynamics in living cells, several fluorescence imaging techniques are currently available 3 4 : (1) single particle tracking (SPT) which resolves the motion of individual proteins at camera frame rate; (2) photoactivation and photo-bleaching methods, namely PhotoActivation of Fluorescence (PAF) and Fluorescence Recovery After Photobleaching (FRAP) which infer protein turnover at the population level; and (3) Fluorescence Correlation Spectroscopy (FCS), which analyzes molecular dynamics by correlating intensity fluctuations.
Thus, there is a pressing need for computer simulators that could unify those different imaging modes in a unique framework, estimate their respective biases, and serve as a predictive tool for experimenters, with the aim to quantitatively decipher protein organization and dynamics in living cells. Several particle-based packages relying on Monte Carlo simulations already exist to predict random motion and multi-state reactions of biological molecules, but either they do not integrate fluorescence properties or are limited to a specific type of imaging mode, and are usually not performing real-time visualization  [11] [12] [13] [14] [15] [16] [17] [18]. In this context, we provide here fast, robust, and user-friendly software (FluoSim) that allows real time simulation of membrane protein dynamics in live-cell imaging (SPT, FRAP, PAF, and FCS) and super-resolution (PALM, dSTORM, uPAINT) modalities.
Results General principle of FluoSim. The FluoSim interface looks like performing a real experiment: the user imports a 2D cellular geometry from a microscopy image, and populates it with a realistic number of molecules (a parameter which depends on protein expression level, cell surface area, and labeling density) (Fig. 1a , Supplemental software and accompanying user manual). Kinetic parameters characterizing the diffusion and trapping of molecules in cellular regions of interest (ROI), are entered as inputs (Table  1 ). At each time increment (typically ~ 1-100 ms, adjusted to sensor acquisition rates), the algorithm updates the instantaneous positions of all molecules based on random number generation. Photo-switching rates of organic fluorophores or fluorescent proteins attached to the proteins of interest, further determine the fluorescence intensity associated to each molecule over time.
The algorithm is optimized to visualize in real time the cellular system and provide postprocessing information including molecule trajectories, image stacks, and output graphs (i. e. histograms of diffusion coefficients, FRAP, PAF, and FCS curves). Examples of simulated data sets for a realistic range of parameter values are given in Fig. S1. Moreover, since the positions of molecules are known with near-infinite accuracy, the program can generate super-resolved images comparable to those obtained with PALM or dSTORM, after introducing additional parameters describing protein labeling density, fluorophore duty cycle, and localization precision of the system. Finally, various levels of noise were introduced in the simulator, including a Poisson shot noise characterizing intensity fluctuations of individual fluorescent molecules, and a Gaussian readout noise applied around the camera offset  20, 21.
This approach led to the simulation of very realistic images that closely matched those obtained by Total Internal Reflection Fluorescence (TIRF) imaging of single GFP-tagged molecules immobilized on a glass substrate (Fig. S2 ). A complete view of the parameters used in each experimental paradigm is available from the individual examples provided in the software menu. Experimental system to validate FluoSim. To thoroughly validate FluoSim, we performed SPT, dSTORM, FRAP, and FCS experiments essentially on the canonical neurexin-neuroligin complex that mediates trans-synaptic adhesion in neurons  22. We used COS-7 cells as a model expression system because they form large and flat lamellipodia that can be approximated as 2D environments for membrane diffusion. Cells were separately electroporated with recombinant GFP-tagged neurexin-1β (GFP-Nrx1β) and mCherry-tagged neuroligin-1 (Nlg1-mCherry), then cultured together for 24 h (Fig.
Discussion In summary, FluoSim allows a prediction and comparison of membrane protein dynamics in a wide range of fluorescence cell imaging modes, with a precise control of the relevant kinetic, photo-physical, and acquisition parameters determining measurement outputs. The program is intended to help biologists adjust and interpret their experiments on a variety of cellular systems, and serve as a teaching resource in bio-imaging programs. FluoSim reproduced a wide range of experimental results (SPT, FRAP, PAF, FCS, SRI) on the Nrx1β-Nlg1 membrane complex using a unique set of parameters extracted from published in-vitro studies and/or taken from our own measurements  26, 27  , thereby giving strong credit to the correlative approach. The program is very fast and robust, and should be applicable to model a wide range of 2D-like dynamic molecular systems experiencing membrane diffusion and transient confinement, for example integrins at focal contacts in fibroblasts  34, 35  , cadherins at cell-cell contacts  [36] [37] [38]  , neuronal adhesion proteins and neurotransmitter receptors at synapses  24, 39, 40  , and trapping of membrane molecules by lipid rafts or cytoskeletal interactions  [41] [42] [43].
Compared to existing software such as PyFRAP, SuReSim, FERNET, or MCell  [13] [14] [15]  , FluoSim integrates many fluorescence modalities into a single program and achieves real-time display (Supplementary Table  1 ). In its present version, FluoSim is limited to Brownian motion and first order molecular reactions, but sub-or super-diffusive behaviors as well as more complex multi-state molecular reactions might be implemented on a case-by-case basis, depending on user needs (Table  1 ). Finally, with the advent of single-molecule based super-resolution microscopy such as PALM and STORM, many new questions arise regarding the degree of labeling needed, and the number of single molecule localizations to accumulate in order to reconstruct a realistic image of biological structures, with the risk of finding artificial properties in under-sampling conditions  10.
The 642 nm laser beam was parked on a region of interest, either on the free cell membrane, or in the cell-cell contact area. The laser was kept at low power (3% of 100 mW, i. e. ~ 0. 2 mW at the objective front lens) to avoid photo-bleaching the organic dye. Using glass coverslips coated with polylysine and higher concentrations of Atto647N-conjugated nanobody (100 nM) to provide a uniform density, we independently measured the Gaussian intensity profile of the laser beam, which gave a standard deviation σ FCS = 0. 26 µm (FWHM = 0. 6 µm). Intensity fluctuations due to Nrx1β molecules entering and leaving the laser spot by membrane diffusion, were detected using a second camera (Hamamatsu Orca Flash 4. 0) on the opposite port of the microscope. A square area of 16 × 16 pixels (1 µm × 1 µm) centered on the laser spot was chosen, and streams of 60,000 images at binning 4 were acquired through the Hokawo software (Hamamatsu) at 200 Hz or 500 Hz, for the study of cell-cell contacts or free regions, respectively.
Acknowledgements We thank  M. Missler ,  P. Scheiffele , and  J. Lippincott-Schwartz  for the generous gift of plasmids;  B. Tessier ,  Z. Karatas , and  S. Benquet  for molecular biology;  R. Sterling  and the cell culture facility of the Institute;  J. B. Sibarita ,  C. Butler , and  A. Kechkar  for the kind gift of the  PALM-Tracer program ,  M. Sainlos  for the gift of dye-conjugated GFP nanobody,  R. Vafabakhsh  ( Northwestern University, Evanston, USA ) for help with single molecule pulldown,  E. Isacoff  ( University of California, Berkeley, USA ) for providing generous access to laboratory resources,  L. Barusseau  ( Explora Nova ) and  F. Levet  for programming tips,  A. Disher  ( Hamamatsu Photonics ) for help on camera acquisition parameters for FCS,  E. Nehme  ( Technion, Haifa, ISR ) for helpful advice on Deep-STORM,  O.
Rossier  and  G. Giannone  for scientific insight,  A. Mehidi  and  T. Orré  for interesting feedback, and  A. Florea  for help with the FluoSim movie. This work received funding from the  Fondation pour la Recherche Médicale ("Equipe FRM "  DEQ20160334916  to O. T. ), the  French Ministry of Research  (fellowship to M. L. ), the  Agence Nationale de la Recherche  ( Grants «SynAdh »  ANR-13-BSV4-0005-01  and  «Synthesyn »  ANR-17-CE16-0028-01  to O. T. ), the national infrastructure  France BioImaging  (Grant  ANR-10INBS-04-01 ), the  French-American Commission  ( Fulbright grant  to O. T. ), and the  Conseil Régional Aquitaine  ( «SiMoDyn » to O. T. ).
In Silico Identification and Phylogenetic Reconstruction of ST8Sia Sequences To investigate st8sia genes' expansion and distribution in vertebrates, we performed public database screenings in the National Center for Biotechnology Information (NCBI), ENSEMBL, and Phylofish databases  [37]  using a BLAST strategy  [38]. The obtained results led to the identification of more than 700 ST8Sia-related sequences (Supplemental Data 1) in chordate genomes, including 71 ray-finned fish genomes (68 Teleosts genomes). Putative ST8Sia sequences with significant similarity to the known human ST8Sia based on the presence of the sialylmotifs L, S, III and VS found in all GT29 sialyltransferases, and of family motifs characteristic for the ST8Sia family were selected, and multiple sequence alignments were performed to select the complete open reading frame.
A simplified dataset was constructed including sequences of Agnathans (Lethenteron camtschaticum, Petromyzon marinus, Eptatretus burgii), Chondrichthyans (Callorhinchus milii, Squalus acanthias, and Heterodontus zebra), basal Actinopterygians (Acipenser sinensis, Amia calva, and Lepisosteus oculatus) and basal Teleosteans such as the Elopomorphs Anguilla anguilla and Mastacembelus armatus, in addition to two Teleosts, the Beloniforme O. latipes (medaka) and the Characiforme Astyanax mexicanus (cave fish). The potential orthology of the selected sequences was assessed through the construction of phylogenetic trees (Figure  2 ). The topology of these trees indicated two major phylogenetic groups of mono-α2,8-sialyltransferases on one hand, and oligo-and poly-α2,8-sialyltransferases on the other, as previously described  [7, 14].
Missing Sialyltransferase Fish Order st8sia2 Siluriformes, Gymnotiformes, Esociformes st8sia4 To determine whether the expansion of st8sia genes observed in Actinopterygii could be associated to WGD or smaller scale duplication events, we took advantage of the improved genome sequencing of several critical species for basal Vertebrates as Agnathans (Lampreys and Hagfish) and for Actinopterygii as Chondrostei (Sturgeons) and Holostei (Gars and Bowfin) (Figure  1 ). A simplified dataset was constructed including sequences of Agnathans (Lethenteron camtschaticum, Petromyzon marinus, Eptatretus burgii), Chondrichthyans (Callorhinchus milii, Squalus acanthias, and Heterodontus zebra), basal Actinopterygians (Acipenser sinensis, Amia calva, and Lepisosteus oculatus) and basal Teleosteans such as the Elopomorphs Anguilla anguilla and Mastacembelus armatus, in addition to two Teleosts, the Beloniforme O.
Despite the fact that the ST8Sia III-r sequences appear to be restricted to Teleosteans, including Elopomorphes, and are lost in Minimum evolution phylogenetic tree of 89 chordates ST8Sia. The evolutionary history of 89 ST8Sia (see names and sequences in Supplemental Data 1) was inferred using the minimum evolution (ME) method. The optimal tree drawn to scale with the sum of branch length = 16. 02931149 is shown. The evolutionary distances were computed using the JTT (Jones-Taylor-Thornton) matrix-based method and the rate variation among sites was modeled with a gamma distribution (shape parameter = 5). The ME tree was searched using the close-neighbor-interchange (CNI) algorithm at a search level of 1. The neighbor-joining algorithm  [39]  was used to generate the initial tree. The analysis involved 89 amino acid sequences and all positions with less than 95% site coverage were eliminated.
A total of 226 positions were in the final dataset (see multiple sequence alignments in Supplemental Data 2). Evolutionary analyses were conducted in MEGA7. 0  [40]. The nine Vertebrate subfamilies of ST8Sia (ST8Sia I to ST8Sia IX) are indicated by various colors. In the mono-α2,8-sialyltransferases group, a series of Agnathan sequences are found at the base of each of ST8Sia I and ST8Sia V. The results corroborate previous findings suggesting the emergence of these two subfamilies around 596 and 563 million years ago (MYA), well before vertebrates emergence and prior WGD R1 and R2  [14]. Consistent with our previous data  [17] , we identified st8sia7 genes in the jawless vertebrates Lethenteron camtschaticum, Petromyzon marinum, and Eptatretus burgeri genomes. Thus, these genes might have arisen from the ancestral st8sia6/7/8 gene after the first WGD R1 event (~552 MYA), although timing of these events with respect to the divergence of agnathans is still a matter of debate  [41, 42].
In Silico Identification and Phylogenetic Analysis of ST8Sia Sequences A local alignment BLAST approach was used to retrieve the vertebrate st8sia nucleotide sequences with significant homology to the mammalian sequences from the genomic and Transcriptome Shotgun Assembly (TSA) divisions of the GenBank/EBI databases at the National Center for Biotechnology Information (NCBI) (last accessed on 27 September 2019), ENSEMBL (release 97) and from the PhyloFish database  [7, 14, 37]. The protein sequence analysis was performed using the Expert Protein Analysis System (ExPASy; Swiss Institute of Bioinformatics, Switzerland; website (https://www. expasy. org/)). Sequence alignments were performed using the clustalW (PRABI; https://npsa-prabi. ibcp. fr/cgi-bin/ npsa_automat. pl?page=/NPSA/npsa_clustalw.
html). Phylogeny was determined aligning the known vertebrate ST8Sia sequences with MUSCLE in MEGA7. 0  [40]. The multiple sequence alignments of the selected vertebrate ST8Sia sequences were conducted using MUSCLE and Clustal Omega algorithms in MEGA7. 0 and manually refined (see Supplementary Data 1 and 2). Phylogenetic trees were produced by the neighbor-joining (NJ), maximum likelihood, and minimum evolution method in MEGA 7. 0  [40, 69]. To determine the consequences of duplication or loss of genes of a given order of Actinopterygian, we considered what happened on its closest paralogue. The amino acid substitutions that occurred at its base were deduced using the parsimony method implemented in Protpars program (PHYLIP package vers. 3. 69) [70].
Sequence Alignments, Motifs Analysis, and 3D Simulation of PSTD Multiple sequence alignments were performed with MUSCLE of EMBL-EBI (version 3. 8; https: //www. ebi. ac. uk/Tools/msa/muscle/) from selected species using published sequences (accession numbers in Supplemental Data 1). The sequences were conducted, edited, and annotated in Jawa Alignment Viewer Jalview 2. 11. 0, and manually refined  [67]. The 3D structure of the human PSTD of ST8Sia II as well as ST8Sia IV was generated in YASARA (Version 19. 9. 17) using the following Protein Data Bank (PDB) entries: ST8Sia IV (code: 6AHZ) (PDB, https://www. rcsb. org/pdb/home/sitemap. do https://www. wwpdb. org/pdb?id=pdb_00006ahz). The ST8Sia II was generated in YASARA changing the amino acid in the positions N3, K4, L5, and K6, corresponding to H3, H4, V5, and N6, respectively.
To this end, we identified discriminating sequence motifs to derive the oligonucleotides for st8sia2-r1 (sense, 5 -AGCCTCATCAGGAAGAACATCC-3 ; antisense, 5 -TTCCCTACGATGGCACAGCGT-3 ) and st8sia2-r2 (sense, 5 -CGTTCAACAGGAGCCTCTCTAA-3 ; antisense, 5 -TTCCCTACGATGGCACAGCGC-3 ). Moreover, we designed a st8sia4-specific primer pair (sense, 5 -ATGATAAGGAAGGACGTGCTGC-3 ; antisense, 5 -TGTTGAGCGTTCGGCGTCTGT-3 ). These RT-qPCR primers were designed (Pyrosequencing Assay Design software v. 1. 0. 6; Biotage, Uppsala, Sweden) to synthesize amplicons between 121 bp and 226 bp. eef1a1a2 (encoding eukaryotic translation elongation factor, variant a2), rpl9, and rpl32 (ribosomal proteins L9 and L32) were selected as reference genes  [71]. The RT-qPCR analyses were conducted with the LightCycler 96 System (Roche, Mannheim, Germany) using the SensiFAST SYBR No-ROX Kit (Bioline, Luckenwalde, Germany).
Data Availability To identify the maraena whitefish ST8Sia II sequences, the orthologous sequences from rainbow trout and Atlantic salmon were aligned with the software Bowtie2 (v 2. 2. 4) to our RNA-seq read collection from maraena whitefish  [72]. The alignments were then indexed and sorted with the software package Samtools (v. 16) and the final consensus sequences were obtained with the Ugene software (v 1. 29).
Figure2. Minimum evolution phylogenetic tree of 89 chordates ST8Sia. The evolutionary history of 89 ST8Sia (see names and sequences in Supplemental Data 1) was inferred using the minimum evolution (ME) method. The optimal tree drawn to scale with the sum of branch length = 16. 02931149 is shown. The evolutionary distances were computed using the JTT (Jones-Taylor-Thornton) matrix-based method and the rate variation among sites was modeled with a gamma distribution (shape parameter = 5). The ME tree was searched using the close-neighbor-interchange (CNI) algorithm at a search level of 1. The neighbor-joining algorithm [39]  was used to generate the initial tree. The analysis involved 89 amino acid sequences and all positions with less than 95% site coverage were eliminated. A total of 226 positions were in the final dataset (see multiple sequence alignments in Supplemental Data 2).
The authors acknowledge the financial support of the  CNRS , the  University of Lille (FST) , the program  PHC Procope 2019  (project  42533RC ), and the  German Academic Exchange Service (DAAD)  for financial support (PN: 57446225 ). The authors are very grateful to  Brigitte Schöpel ,  Christian Plinski ,  Torsten Viergutz  for the Laboratory support, and to  Olga Plechakova  for her assistance with the GT-database. We thank  Ralf Bochert  ( Landesforschungsanstalt Mecklenburg-Vorpommern, Germany ) for providing maraena whitefish. The authors acknowledge the  Research Federation FRABio (Univ. Lille, CNRS ,  FR3688 ,  Biochimie Structurale et Fonctionnelle des assemblages Biomoléculaires ) for providing the scientific and technical environment conducive to achieving this work, the  Ministère de l'enseignement supérieur, de  for providing research fellowships to M.
We assume that the antennas are deployed in an urban microcell (UMI) scenario. The channel model is presented in Figure  2. The parameters used for the channel simulation are presented in Table  2. Running a large number of simulations, we empirically fitted the distributions of the gain components into statistical models. We chose the distributions for the channel and antenna gain by determining the best fit out of the common distributions using MATLAB. The distributions' parameters summarized in Table  3  were determined accordingly.
The method is exact and proven to be O(nq log(n)), n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. Conclusions: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.
The overall complexity of our tree aggregation method is O(nq log n), with q being the number of sources and n the number of individuals. The rest of the paper is organized as follows: first, we give an overview of the methods that address a similar problem in the literature, in different yet related communities (machine learning, phylogenetics, bioinformatics). This leads us to introduce the rationale for developing our own method for recovering a single hierarchy from multiple data sets, that we describe in the next section. In particular, we detail the algorithm that aggregates multiple tree structures with a low computational burden. Numerical and statistical performances of the aggregation methods are then studied on simulations. Finally, we illustrate our method on two multi-omics data sets, in breast cancer and cell differentiation.
Implementation We implemented the mergeTrees algorithm in an R/C++ package called mergeTrees available on CRAN  [26]. In our analyses, we always rely on Ward's hierarchical clustering and Euclidean distances. With multivariate data, we use the implementation available in the R-base function hclust  [27]. With vector data, we use the O(n log(n)) implementation available in the ward_1d function of the package univarclust  [28].
Cell-type differentiation The first data set concerns the inflammatory bowel disease and is presented by Ventham et al. [30]. Methylation (485577 features) and gene expression (46835 features) data were available for 199 samples. Different cell-types were considered: CD14 (57 samples), CD4 (51 samples), CD8 (47 samples) and whole blood (44 samples) were sequenced, originating from 61 individuals. All methylation and gene expression data are freely available at NCBI GEO database  [31]  (accession GSE87650). Individual clusterings based on the methylation and gene expression data show that the observations tend to cluster based on the cell-type of the sample. We therefore compared the results of the three clustering methods to the cell-type repartition of the samples. Results are presented in Table  2  and in Fig.
Clustering was first performed for each dataset separately. These individual clusterings were not found to be related to age or stage of the cancer. The protein and RNA-seq analyses reflected the ER/PR status the best. We therefore compared the results of the consensus methods to these clinical variables in order to quantify their medical relevance. The subtype was also included, as it is related to the ER/PR status and is often of interest in such studies. Results are shown in Table  3. Regarding the NID values based on the individual clusterings at the top of Table  3 , the protein expression dataset is the most informative in the task of retrieving the ER/PR status, as well as the cancer subtype. The RNA-seq data perform nearly as well, whereas the methylation and miRNA data provide very little information with regard to these clinical variables.