dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluating the coverage of a reaction database is interesting in order to assert the coverage of the known reactome by a given set of rules, but it cannot be used to assert the efficiency of a retrosynthesis tool. Indeed, the coverage of a reaction database depends mainly on the database from which the rules were inferred and how exhaustive the cross-links are between those two. Ideally, we would desire a set of rules being able to recover all known biochemical reactions. It means that anything less than 100% coverage evidences that the set of rules is incomplete and that more data could have been aggregated. Note that in this work we focused our efforts on MetaNetX for the sake of simplicity but it is clear that more data can be imported from other databases such as BRENDA  (Chang et al. , 2015).
To the author's opinion, a better indicator of retrosynthesis tools efficiency should be found in the coverage of known pathways realized in a metabolic engineering context. This is precisely what we did using the LASER database as a reference for examples of successfully engineered metabolic pathways. In that way we provided a comprehensive overview of the capabilities of our tool in order to identify metabolic engineering solutions to bioproduction for well-studied cases. The main source of misprediction that we observed in our analysis came from cases in which additional compounds absent from E. coli metabolism were needed to perform the synthesis. Indeed, we performed all computations within five iterations from E. coli, with target compounds that were not necessarily produced in this chassis nor at five enzymatic steps; moreover, some substrates could be supplemented in the media of the chassis organism.
, level followed by manual 2011) curation BNICE (Hadadi et al. , 2016a) Automated from KEGG followed by manual curation PathPred (Moriya et al. , 2010) Automated from the KEGG RPAIR database GEM-Path (Campodonico Computed from 3rd EC et al. , 2014) level METEOR (Marchant et al. , Knowledge-based expert 2008) system (Lhasa Ldt. ) Reverse Pathway Engineering Automated from the (THERESA) (Liu et al. , BioPath database 2014) (Molecular Networks GmbH) RetroPath (Carbonell et al. , Automated computation on 2014b) MetaCyc RetroPath2. 0 (this study) Automated computation on MetaNetX.
For data collection, crystals were equilibrated in reservoir buffer with 30% glycerol and flash frozen in liquid nitrogen. Diffraction data were collected at 100K on the 19-ID beamline at Argonne National Laboratory. Data sets were processed using HKL2000 and the CCP4 suite. The structures of were determined by molecular replacement. For substrate-bound Fab1bound IDE structure, the unbound IDE-Fab 1 complex (4IOF) was used as a search model and no NCS average for C2 symmetry of IDE dimer was applied during the refinement to avoid the bias. For the crystal structure of Fab H11-E -bound IDE, the closed-state IDE (2WBY) and Fab in unbound IDE-Fab 1 complex (4IOF) were used as the search model. Model building and refinement were performed by using REFMAC, PHENIX, and COOT  (Adams et al. , 2011; Emsley et al.
, 2017). Images were processed using software integrated into the Appion  (Lander et al. , 2009)  pipeline except where stated. Frames were aligned using MotionCor2 software with dose weighting  (Zheng et al. , 2017) , particles were picked and extracted automatically using DoGpicker  (Voss et al. , 2009). Particle stacks were then passed to RELION2. 1  (Scheres, 2012)   zero and outside Fab regions set to value one. Subsequently the masked refinement was carried out with C1 symmetry using RELION 2. 1  (Scheres, 2012). The final resolution was estimated using Fourier Shell Correlation (FSC = 0. 143) in RELION 2. 1 (Figure  2 -figure supplement 9C and D). The anisotropy was analyzed using 3DFSC  (Tan et al. , 2017). The cry-oEM data collection and processing statistics are listed in Supplementary file 2.
Kinetic reaction was triggered by diffusion of twofold molar excess of insulin into the jet of IDE from adjacent flow layers of buffer above and below. The thickness of the sheet was controlled by the flow rate ratios of the inlet channels. The observation channel is 1 mm deep (i. e. X-ray pathlength), 0. 2 mm wide and 25 mm long. During the flow experiment, the observation channel was sampled by a grid scan of 10 Â 30 positions and SAXS data was collected at each grid point. The total flow rate determined the maximum accessible time corresponding to the exit end of the observation channel. The time window of 2 s was performed by using the flow rates of 68, 2, 9 mL/min for side, diagonal and center syringe pumps, respectively. Increasing the flow rates to 682, 22 and 90 mL/min allows the kinetic study in the time window shorter than 0.
Instruments settings was optimized to minimize the back-exchange  (Walters et al. , 2012). The data was acquired in both data-dependent MS/ MS mode and MS1 profile mode and Proteome Discoverer software (ThermoFisher) was used to identify proteolytic peptides. The deuterium content of the peptides for each time point was calculated by DXMS Explorer (Sierra Analytics Inc, Modesto, CA), with corrections for back-exchange  (Zhang and Smith, 1993). H/D exchange experiments performed using our automated system typically produce deuteron incorporation measurements with a standard deviation of less than 2% of the mean of triplicate determinations  (Chang et al. , 2015; Hsu et al. , 2009a; Hsu et al. , 2009b). In the present work, just as in our previous studies, only changes in deuteration level greater than 10% were considered significant  (Burke et al.
Data and software availability The cryoEM maps have been deposited in the Electron Microscopy Data Bank with accession codes EMD-7041, EMD-7092, EMD-7065, EMD-7090, EMD-7062, EMD-7093, EMD-7066, and EMD-7091. The atomic models have been deposited in the Protein Data Bank under accession code EMDB 6B3Q, 6BFC, 6BF8, 6B7Y, 6BF6, 6B70, 6BF9, 6B7Z and 6BF7. The crystal structures of Fab1-bound IDE in complex with insulin or Ab have been deposited in the Protein Data Bank under access code 5WOB and 4M1C, respectively. The crystal structure of Fab 1 -bound IDE in complex with insulin and Fab 1E has been deposited in the Protein Data Bank under access code 5CJO. The crystal structures of IDE in complex with Fab H11-E has been deposited in the Protein Data Bank under access code 5UOE. Shohei Koide, Anthony A Kossiakoff, Resources, Provided critical reagents; David R Liu, Resources, Performed HDX-MS and analysis, Provided critical reagents; Sheng Li, Resources, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing-original draft, Writing-review and editing, Designed the project, Performed HDX-MS and analysis; Bridget Carragher, Clinton S Potter, Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing-original draft, Writing-review and editing, Designed the project, Oversaw EM grid preparation, data acquisition and Wei-Jen Tang, Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing-original draft, Project administration, Writing-review and editing, Designed the project, Built and refined cryoEM structural models, Built and refined structural models.
Insulin A and B chains in ribbon representation are colored in magenta and cyan, respectively. Top: Insulin alone structure where the intra-and inter-molecular disulfide bonds are marked by arrows. Middle: IDE-bound insulin structure that contains partially unfolded insulin (PDB code: 2WBY). The N-terminus binding exosite (Exo) and catalytic zinc ion are marked by arrows. Until now, the structure of IDE in complex with the fully unfolded or cleaved insulin is not determined. DOI: https://doi. org/10. 7554/eLife. 33572. 002.
Acknowledgements We are grateful to  Ryan M Hoffman in Andrew Ward 's lab at the  Scripps Research Institute  for initial negative stained EM data acquisition and analysis, to  Srinivas Chakravarthy at BioCAT, APS  for assisting with SAXS data collection and analysis, to  Juan P Maianti  for synthesizing 6bK, and to  Steve Meredith  for synthetic Ab. This work was supported by the  NIH  grants  GM81539  and  GM121964  to  Wei-Jen Tang ,  GM103310  to  Bridget Carragher  and  Clinton S Potter ,  Agency for Science, Technology and Research Singapore  to  Yong Zi Tan ,  GM103622  to  Tom Irving at BioCAT, APS , and  DARPA   Fold Fx program  ( N66001-14-2-4053 ),  NIH   R35 GM118062 , and the  Howard Hughes Medical Institute  to  David Liu. The  Simons Electron Microscopy Center  is supported by a grant from the  Simons Foundation  ( 349247 ).
Funding Funder Grant reference number The Journal of Biological Chemistry 284: 23652-23661. DOI: https://doi. org/10. 1074/jbc. M109. 021857,PMID: 1 9556238 Jucker M, Walker LC. 2013. Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature 501: 45-51. DOI: https://doi. org/10. 1038/nature12481,PMID: 24005412  Kim J, Wu S, Tomasiak TM, Mergel C, Winter MB, Stiller SB, Robles-Colmanares Y, Stroud RM, TampeR, Craik CS , Cheng Y. 2015. Subnanometre-resolution electron cryomicroscopy structure of a heterodimeric ABC exporter. Nature 517: 396-400. DOI: https://doi. org/10. 1038/nature13872,PMID: 25363761  King JV ,  Liang WG ,  Scherpelz KP , Schilling AB, Meredith SC, Tang WJ. 2014. Molecular basis of substrate recognition and degradation by human presequence protease.
05. 022 ,PMID: 17681537  Kurochkin IV ,  Guarnera E, Berezovsky  IN. 2018. Insulin-degrading enzyme in the fight against alzheimer's disease. Trends in Pharmacological Sciences 39: 49-58. DOI: https://doi. org/10. 1016/j. tips. 2017. 10. 008,PMID: 29132916 Kurochkin IV. 2001. Insulin-degrading enzyme: embarking on amyloid destruction. Trends in Biochemical Sciences 26: 421-425. DOI: https://doi. org/10. 1016/S0968-0004(01 ) 01876 -X,PMID: 11440853  Lander GC ,  Stagg SM ,  Voss NR, Cheng A, Fellmann D, Pulokas J, Yoshioka C, Irving C, Mulder A, Lau PW ,  Lyumkis D, Potter CS , Carragher B. 2009. Appion: an integrated, database-driven pipeline to facilitate EM image processing. Journal of Structural Biology 166: 95-102. DOI: https://doi. org/10. 1016/j. jsb. 2009. 01. 002 ,PMID: 19263523 Leissring MA, Farris W,  Chang AY ,  Walsh DM ,  Wu X ,  Sun X, Frosch MP , Selkoe DJ.
Optical clearing with organic solvents efficiently improves in-depth 3D imaging of thick samples by LSFM  15. Here, we adapted clearing with benzyl alcohol-benzyl benzoate (BABB) to spheroids stained with propidium iodide after fixation to improve in depth resolution for the analysis of the 3D shape of nuclei (Fig. 1b ). The automatic segmentation of nuclei in 3D LSFM images was not possible using open-source or commercial software tools that were not efficient enough to segment large collections of nuclei from a huge dataset, such as in LSFM z-stacks of spheroids. Therefore, we designed a specific semi-supervised algorithm to efficiently fit ellipsoids in 3D to nuclei (Fig. 1b ) and to extract the c, e1, e2 and e3 coordinates as well as the L1, L2 and L3 lengths. The algorithm is available and distributed as a plugin for Icy, a widely used open community platform for bioimage informatics (https://www.
Nucleus geometry analysis. The FitEllipsoid plugin for Icy was used  16, 40. Briefly, from the propidium iodide fluorescence signal, a few points on the nucleus boundary were manually delimited on the xy, yz and xz views on a central z-plane of each nucleus analysed. Then, an ellipsoid was fitted automatically to the set of selected points. The ellipsoid geometrical parameters were then saved by the plugin. A MATLAB algorithm was used to determine the geometry of nuclei within the spheroid from this data. The user provides the image stack of the spheroid and a file with the parameters of all the identified ellipsoids (nuclei). The algorithm first identifies the spheroid boundary from the stack of images and extracts the convex hull. Then, for each ellipsoid it finds the closest point on the envelope, and finally calculates the angle between the ellipsoid longest axis and the normal to the surface.
Briefly, sample chambers in hydrogel (Phytagel, 10 g/L in PBS) were fabricated and filled with culture medium or melted agarose. Spheroids were transferred into the chambers 24 hours prior acquisition. LSFM setup, 3D and time-lapse image acquisition were as previously specified  12. Chambers were hanged with a rod and immersed in medium in the physiologic chamber. Image stacks of 170 to 300 µm from the spheroid periphery to the centre, with 1 µm step, were acquired every 4 minutes for a maximum of 4 hours. Analysis of the cell division orientation axis. Mitotic cells were visualized in 3D fluorescence images acquired by LSFM using the Imaris software. The metaphase plate was segmented and represented as an ellipsoid flattened along one axis that indicates the division axis orientation. The ellipsoid descriptive parameters (the x, y and z coordinates of the centre of the mass and the x, y and z coordinates of the three orthogonal axes) corresponding to the segmented object were then extracted.
The last metaphase plate detected before anaphase onset was used to determine the shortest axis of the ellipsoid that corresponds to the division axis orientation. This orientation refers to a coordinate reference that is the normal to the spheroid surface at the closest point. To obtain this reference, a MATLAB program was used to determine the spheroid convex hull and calculate the distance of mitotic cells from the surface. The angle of the ellipsoid shortest axis with the normal to the surface was then calculated to obtain the orientation of the division axis. Analysis of the duration of mitotic steps. Stack images were downloaded with Imaris to allow their 3D visualization. For each detected dividing cell, DNA condensation was determined as the first frame where the nucleus appears non-homogeneous.
Acknowledgements We gratefully thank the members of our groups for discussions and interest in this project. The authors wish to acknowledge the  TRI-Genotoul  and ITAV imaging facilities. Annaïck Desmaison  was a recipient of a doctoral fellowship from  ARC (Association de la Recherche contre le Cancer) ,  Ecole de l'Inserm-Liliane Bettencourt and Fondation Bettencourt-Schueller. This work was supported by the  CNRS ,  Université Paul Sabatier ,  Région Midi-Pyrénées ,  La Ligue  contre le  Cancer comité de la Haute-Garonne  and  Agence Nationale de la Recherche ANR  ( 12-BSV5-0008-01 ).
Data analysis. Graphs and data were analysed with GraphPad Prism version 6. 00, GraphPad software, La Jolla California USA, www. graphpad. com and in R (R Core Team 2017, https://www. R-project. org/). The non-parametric Mann-Whitney test was used to compare data from different conditions. Stacks were analysed using the Amira, Imaris and Fiji 41 software programs.
For each phenotype comparison of two cell clusters, CytoCompare provides a distance measure as well as a p-value asserting the statistical significance of the difference. CytoCompare can import clustering results from various algorithms including SPADE, viSNE/ACCENSE, and Citrus, the most current widely used algorithms. Additionally, CytoCompare can generate parallel coordinates, parallel heatmaps, multidimensional scaling or circular graph representations to visualize easily cell cluster phenotypes and the comparison results. Conclusions: CytoCompare is a flexible analysis pipeline for comparing the phenotypes of cell clusters identified by automatic gating algorithms in high-dimensional cytometry data. This R package is ideal for benchmarking different clustering algorithms and associated parameters.
Additionally, no visualization methods have been proposed to represent such phenotypic comparisons in easily interpretable ways. To answer that need, we developed CytoCompare, an R package to compare the phenotypes of cell clusters identified by automatic gating algorithms. The aim of CytoCompare is to facilitate the easy identification of similar and different cell clusters identified by various bioinformatics approaches. For each comparison of two cell clusters, a distance measure is computed, based on the expression distribution densities of selected markers. Our approach is based on the Kolmogorov-Smirnov distance to quantify marker differences. Additionally, CytoCompare provides a p-value for determining whether two cell clusters are statistically different or not. CytoCompare proposes several visualizations methods, such as parallel coordinates, parallel heatmaps, dendrograms, multidimensional scaling (MDS)  [19]  or circular graphs representations  [20, 21] , which make cell cluster phenotypes and phenotypic comparisons easily understandable and explicit.
Availability of cytometry profiles All the cytometry profiles used to illustrate CytoCompare are available on the FlowRepository database  [22]  under accession number ''FR-FCM-ZZ99". This public dataset consists of 15 mass cytometry profiles of 25 cell markers obtained in the context of a macaque vaccine study, as described in  [10]. Five cynomolgus macaques were vaccinated with a recombinant Modified Vaccinia virus Ankara (MVA) expressing HIV clade B antigens, which is a candidate HIV vaccine, with the aim to compare B cell responses at baseline, and 8 and 28 days after a second immunization.
Implementation in R CytoCompare has been implemented in R. The ggplot2, ggdendro, ggrepel, grid, igraph, MASS, RJSONIO, and XML R packages are currently required for running CytoCompare. The flowCore and flowUtils Bioconductor packages are also required for running CytoCompare. The exact left-tailed binomial test, used in the statistical approach, is based on the 'binom. test()' function available in R. Comparison and intermediary results generated by CytoCompare are handled as S4 objects to allow users to access and export them easily. CytoCompare is freely distributed under the GPL-3 license and is available on https://github. com/tchitchek-lab/CytoCompare.
SPADE and viSNE/ACCENSE analyses The SPADE analyses used to illustrate CytoCompare were performed using the publicly available SPADE R package  [5]. The viSNE/ACCENSE analyses were generated using the ACCENSE software  [8]. SPADE and viSNE/ACCENSE analyses were generated using different settings as detailed in the results section. Cell clusters correlating with the anti-MVA titer were identified using the SPADEVizR  [4]  R package, based on data published in  [10].
Thus, the cell clustering results generated using different parameters need to be compared to unravel the effects of these parameters. The third situation takes place when ascertaining the reproducibility of the clustering results by repeating the analysis using the same settings. Expression-based algorithms are relevant approaches to identify rare cell populations but require extensive computation capabilities. To overcome these computing difficulties, SPADE, viSNE/ACCENSE, and Citrus algorithms perform the identification of cell populations on a restricted cell subset of the input dataset. This process, named down-sampling, is usually stochastic and can lead to the identification of different cell populations through multiple runs of the algorithm. Cell clustering results need then to be compared to verify whether they are reproducible or not.
A schematic circular graph representation generated by CytoCompare is provided in Fig. 2D. In such representation, each node corresponds to a cell cluster. Two cell clusters are linked if their p-value is higher than a specific threshold (set by default to 0. 05). Nodes can be colored to clarify from which approach the cell clusters have been obtained. To offer a greater readability, the starts and the ends of links can be colored to fit with node ones. Circular graphs provide thereby an explicit view of the similarities and differences between the cell clusters. In the context of large datasets, static representations can be difficult to interpret. The MDS and circular graph representations generated by CytoCompare through the D3. js library  [23]  are provided as interactive HTML files. In these MDS representations, if the user places the mouse over a dot then a tooltip is displayed to indicate the number of associated cells with this cell cluster.
Comparison of cell clustering results using different algorithms We first illustrate the capabilities of CytoCompare to compare the cell clustering results obtained with SPADE and viSNE/ ACCENSE algorithms. We used a public dataset of mass cytometry profiles obtained in the context of a macaque vaccine study  [10]. This dataset consists of 15 cytometry profiles obtained from a mass cytometry panel of 25 markers designed to target B-cell populations. Five cynomolgus macaques were vaccinated twice at 2 months apart with the modified vaccinia virus Ankara HIV-B, and B cell responses at baseline, and 8 and 28 days after a second immunization were compared. We performed in parallel a SPADE analysis and a viSNE/ ACCENSE analysis on this dataset. Our analyses were restricted to a set of 10 markers, consisting of CD20, CD21, CD22, CD23, CD27, CD40, CD80, IgM, IgG, and HLA-DR used as clustering markers.
Comparison of cell clustering results using different clustering parameters In a second illustration, we demonstrate that CytoCompare can be used to compare results obtained with different parameter sets using the same automatic gating algorithm. The purpose is here to check and compare the impact of cell clustering algorithm parameters on the ability to detect rare cell populations or cell populations of interest. To do so, we performed three independent SPADE analyses using different settings on the same dataset of macaque samples previously used. Three SPADE analyses were performed to identify 20, 70 and 80 cell populations using the same down-sampling parameter of 40%. We respectively named these analyses K20, K70, and K80. In this situation, the K20 analysis should capture a smaller diversity of cell populations compared to the K70 and K80 analyses.
Discussion CytoCompare constitutes a powerful pipeline for comparing the phenotypes of cell clusters with the purpose to benchmark cell clustering results generated by different approaches. This need is commonly met by researchers working in high-dimensional cytometry data analysis to understand the specificity of the cell clustering results generated and to interpret the identified cell populations properly. For each comparison of two cell clusters, CytoCompare computes a distance and a p-value to determine whether the cell clusters are statistically different or not. Multidimensional scaling and circular graph representations can be generated to make these comparison results easily interpretable and explicit. Our R package handles results provided by SPADE, viSNE/ACCENSE, and Citrus algorithms, which are predominant for the analysis of highdimensional cytometry data.
Moreover, CytoCompare has been developed to be easily used by non-bioinformatician experts but can be easily customized by users with more expertise. CytoCompare brings a precious help in the various contexts defined in this paper. The goal of our algorithm is mainly to benchmark the effects of different clustering approaches on the cell clustering results. No automatic gating algorithm is currently defined or will ever be defined as the gold-standard for the analysis of any high-dimensional cytometry data. All algorithms have their own specificities, and some can be more adapted for specific biological questions and datasets. CytoCompare offers thus a way to compare these different algorithms and associated parameters, or even to organize large quantities of identified cell clusters. To perform the comparisons of cell cluster phenotypes, we used an approach based on the Kolmogorov-Smirnov distance and a lefttailed binomial test.