dataesr/scientific-paragraphs-categorization huggingface dataset:
TAD loop We calculate the contact frequency between two boundaries of the TADs via the loop query mode: hicmaptools -in_map fly. bimap -in_bin fly. bins -loop epiTAD. bed -output resLoop. tsv Interestingly, PcG TADs show strong loop frequency, whereas active TADs have the lowest frequency (Fig. 3 , raw data in Additional file 2). Therefore, we assume that PcG TADs form a loop structure with high probability  [20].
TAD intra-density Furthermore, we check whether intra-TAD contact frequencies also show differences across epi-classes via the TAD query mode: hicmaptools -in_map fly. bimap -in_bin fly. bins -TAD epiTAD. bed -output resTADs. tsv HP1 TADs show a higher average intra-TAD contact density than the other three classes, whereas active TADs show the lowest contact density (Fig. 4 , raw data in Additional file 3). This is in agreement with previous studies which reported that active chromatin domains present a weaker inside contact density than inactive domains, PcG, and null epiTADs  [16].
Conclusion We present a C+ + package that provides an efficient way to query HiC maps. HiC-mapTools supports seven access options so that biologists can quantify the contact frequency of the interest sites. Furthermore, the frequency probability is estimated based on a null hypothesis that shuffles the query position. Finally, the frequency is visualized as an output plot: a vertical line in the density plot of the random samples. The authors will continue to develop new functions for comparative HiCs to pursue HiC quantitative analysis. (Project GENE-IGH), the INSERM, and the French National Cancer Institute (INCa). The funding bodies had no role in study design, data collection, experiment analysis, result interpretation, or manuscript preparation.
Acknowledgements We thank  Ming-Han Yang  and  Dr. Shih-Yu Chen  for generously providing mouse HiCs used in this study. We thank the  National Center for High-performance Computing (NCHC)  for providing computational and storage resources. We thank two anonymous reviewers for their suggestions that improved the quality of the manuscript. In addition, we would like to acknowledge  Dr. Aaron Heidel  for his assistance in polishing the manuscript.
Funding This work was supported by the  Taiwan Ministry of Science and Technology  [ 106-2813-C-004-036-E to W-T. C. , 108-2628-E-004-001-MY3  to J-M. C]. Research in the G. C. laboratory was supported by grants from the  European Research Council  (Advanced Grant  3DEpi , under grant agreement No. 788972 ), the  European Union 's  Horizon 2020 Research and Innovation Programme (MuG,  under grant agreement No. 676556 ), the  Agence Nationale de la Recherche  ( ANR-15-CE12-0006  EpiDevoMath), the  Fondation pour la Recherche Médicale  ( DEI20151234396 ), the  MSDAVENIR  foundation.
On the other hand, high-throughput techniques based on lipidomics provide accurate estimates of the composition of cellular membranes at the level of subcellular compartments. The community needs computational tools for lipidomics and simulation data effectively interacting to better understand how changes in lipid compositions impact membrane function and structure. Lipidomic data can be analyzed to design biologically relevant models of membranes for MD simulations. Similar applications easily result in a massive amount of simulation data where the bottleneck becomes the analysis of the data to understand how membrane properties and membrane-protein interactions are changing in the different conditions. In this context, we developed LipidDyn, an in silico pipeline to streamline the analyses of MD simulations of membranes of different compositions.
The most commonly used physical models, i. e. , force fields, for MD simulations, are Martini  [18, 19]  and the ones in the CHARMM  [20, 21]  and AMBER  [22, 23]  families. These force fields cover most of the biologically relevant lipids and allow an accurate description of membranes including various lipid species and interaction with proteins. However, for complex systems, other force fields, as Slipids  [24]  or FUJI  [25]  may represent valuable alternatives. Recent developments in software and hardware for biomolecular simulations allow access to the microsecondmillisecond timescale of large and complex biological systems  [26] [27] [28] , such as lipid bilayers of heterogeneous composition  [29]. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Overview on LipidDyn LipidDyn is a Python package for analyzing biophysical membrane properties and facilitating their interpretation in a non-time-consuming manner (Figure  1 ). LipidDyn allows to perform the analyses through an easy and practical Application Programming Interface (API) and implements full-fledged user programs accessible from the command line, including support for both analysis and plotting of the results. In this way, users can perform standard analyses of general interests on their molecular ensembles and write custom Python scripts that integrate several calculations seamlessly. LipidDyn is based on popular and well-maintained open-source packages, such as MDAnalysis  [43] , to handle trajectory files and other packages as back-ends  [44]. While using the provided API ensures the highest degree of flexibility, it also requires extra programming work and expects the user to be familiar with Python packages.
Nonetheless, the LipidDyn user scripts still allow for fine-tuning some aspects of the calculation either by command-line options or by using configuration files. This flexibility makes it possible to support simulations using different molecular mechanics physical models to represent the system under investigation. In addition, the workflow supports both the analyses of timeseries and average properties. LipidDyn applies to both full-atom and coarse-grained topologies and trajectories. We include configuration files in the package for both short trajectories with all-atom (CHARMM36) and coarse-grained topologies (Martini). We designed LipidDyn to process trajectory files in GROMACS format. It requires three input files: i) a configuration file (YAML format) including the definition (using the MDanalysis syntax) of each headgroup of the lipid species included in the system and the ratio of each lipid species to the total number of lipids in the system, ii) a topology file (.
Order Parameters The Order Parameter class implements the calculation of the order parameter for the acyl chain tails of each lipid moiety. This analysis gives insight into the overall order of the lipid bilayer and the conformations that the acyl chains assume  [49]. For the full-atom trajectories, the class includes a reimplementation of the algorithm in NMRlipids [https://github. com/NMRLipids] to calculate the carbon-hydrogen order parameter (S CH ) of the acyl chains. For the coarse-grained trajectories, the class includes the Lipyphilic SCC() class  [40]  to calculate the carbon-carbon order parameter (S CC ) of the acyl chains. For each lipid species, consecutive carbon atom pairs composing the sn-1 and sn-2 acyl chains are defined inside the configuration file. The Order Parameter class calculates S CH or S CC for the sn-1 and sn-2 acyl chains of each lipid species over trajectory time.
Comparison with other tools We compared the analyses provided by LipidDyn with other available tools (Figure  2 )  [40] [41] [42]. Each tool focuses on a group of analyses, with some classical ones in common, such as order parameter, thickness, and APL. LiPyphilic  [40]  and Memsurfer  [42]  provide mostly data on the bilayer's geometrical properties as in the case of domain registration, z-positions, and z-angles calculations or membrane surface and curvature. On the other hand, LOOS  [41]  includes tools for analyzing membranes and accounts for an embedded protein. We noticed that most of these tools are either a suite of different scripts for analyses or a collection of Python classes to be imported. They do not provide complete workflows to streamline analysis collection and visualization and ensure reproducibility.
Cell extracts were mixed with water (pH 9), and after brief vortexing, the samples were incubated for 1 hour at -20 ˚C. After centrifugation at 16,000 x g for 15 minutes, the organic phase was collected. We reconstituted the dried extracts in acetonitrile/isopropanol (50:50), resuspended them for 10 minutes, centrifuged (16,000 x g for 5 minutes), and transferred them to vials for UPLC-MS analysis. We used two quality controls described in  [72]. Randomized duplicate sample injections were performed. We performed pre-processing, normalization, and statistical analysis with TargetLynx application manager for MassLynx 4. 1 software (Waters Corp. , Milford, USA). The processing was executed using a set of predefined retention time, mass-to-charge ratio pairs, Rt-m/z, corresponding to metabolites included in the analysis.
From lipidomics to MD design We designed a Python script (see GitHub) to analyze the ATG9A-positive lipidomic dataset. We quantified the average lipid concentration from the raw data among the two starved samples. To design the lipid composition for the MD simulation, we associated the sphingomyelin species identified in the lipidomic dataset with those available in the CHARMM36 force field, querying the LipidDyn internal database. We designed a Python script (see GitHub) to analyze the lipidomics dataset of the endoplasmic reticulum from HeLa cells at the level of classes of lipids and to design the lipid composition of the bilayer for the coarse-grained MD simulations using Martini force field. In addition, we have been curating a more general dictionary to link lipid species to the corresponding available molecules in all-atom and coarse-grained force fields to assist the design of the lipid composition for MD simulations.
Availability and future perspective The package and test cases are available at https://github. com/ELELAB/LipidDyn. LipidDyn has been released in its first version to provide a well-organized workflow for analyses of lipid and protein-lipid simulations and streamline cases where many bilayers with different compositions need to be analyzed in parallel. We selected the most important parameters that often need to be scrutinized, with emphasis on supporting properties that can also be experimentally determined. Nevertheless, LipidDyn at the moment does not cover all the available portfolio of analyses of structural and biophysical properties that can be applied to membrane simulations. In the future, we will widen the range of analyses supported by the package and also find new visualization solutions.
For example, we will include tools to calculate the shapes and curvatures of lipid membranes. In terms of protein-lipid interaction, (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 14, 2022. ; https://doi. org/10. 1101/2022. 01. 04. 474788 doi: bioRxiv preprint we will implement analyses of occurrence, maximal occupancy and time life of contacts along the simulation time. We will provide LipidDyn outputs compatible with Pyinteraph2 [77,78] so that protein-membrane simulations can be analyzed using methods from graph theory. Furthermore, we plan to include in LipidDyn automated support to convert the information in the processed lipidomics data to design models of membranes for MD simulations that resemble experimentally observed lipid compositions.
Our focus is to provide a tool in LipidDyn that includes a dictionary to automatize the mapping and conversion of the lipid species in the lipidomics datasets to the corresponding molecules for which parameters are available in the commonly used force fields for MD simulations. LipidDyn. Most of the tools include the analysis of biophysical properties of lipid bilayers such as area per lipid, thickness, and order parameter. However, none of the tools currently cover all the possible analyses. Only LipidDyn has been designed as a workflow. Figure  3. Analyses of MD simulations of ATG9A-positive compartments. A-B) Boxplot of the area per lipid and membrane thickness calculated for the full-atom simulations of the bilayers with lipid ratio DOPC 100%, DOPC 59% sphingomyelins (SMs) 41%, and DOPC 35% cholesterol (CHOL) 24% SMs 41%.
Acknowledgments E. P. group is supported by  Danmarks Frie Forskningsfond , Natural Science, Project  1 (102517 ),  NovoNordisk Fonden Bioscience and Basic Biomedicine  ( NNF20OC0065262 ) and  Andreas og Grethe Gullev Hansens Fond  (to M. L. ). M. J. is supported by  NovoNordisk   Distinguished Investigator Grant -Endocrinology and Metabolism  ( 054296 ). M. J. and E. P. groups are part of the  Center of Excellence for Autophagy, Recycling, and Disease (CARD) , which is supported by  Danmarks Grundforskningsfond  ( DNRF125 ). K. M. is supported by  Danmarks Frie Forskningsfond ,  Sapere Aude  ( 6108-00542B ). D. J. and S. A. T. were supported by  The Francis Crick Institute  which receives its core funding from  Cancer Research UK  ( FC001187 ,  FC001999 ), the  UK Medical ResearchCouncil  ( FC001187 ,  FC001999 ).
At the same time, some limitations of "pure" simulation applications need to be ad-dressed, such as the nature of heuristic solutions due to fixed structural elements, behavior algorithms and parameters, as well as subjectivity in model development and result interpretation. We refer to the works by  Snyder et al. (2016) ,  Dolgui et al. (2018)  and  Hosseini et al. (2019)  for more recent state-of-the art reviews. Even if SC design with the help of mathematical optimization provides powerful decisionmaking support, it does not take into account some important dynamic restrictions such as inventory control or shipment policies for which simulation is considered to be a useful tool  (Bottani and Montanari 2010 , Ivanov 2017a , Schmitt et al. 2017 , Ivanov and Rozhkov 2020). Therefore, modern commercial software for SC simulation and optimization such as anyLogistix apply a multi-method technology that combines optimization and simulation  (Burgos and Ivanov 2021).
The purpose of the present study is to contribute to existing works by integration of planning and control decisions within a unified modelling-algorithmic framework. The combination of optimization and simulation was seen as a logical way of increasing the quality of the SC decision-support system (DSS), and different forms of such combinations have been developed  (Gao and Chen 2016, Frazzon et al. 2018 ). These combinations resulted in the SC DSS that contained simulation models, analytical models, a database (which is presently evolving into a knowledge base with artificial intelligence roots), and an integration system for alignment of data, analytical models, simulation models, and decision-making needs leading to development of digital SC twins  (Ivanov and Dolgui 2021b ). In such a complex DSS, the integration and 1.
At the same time, some limitations of "pure" simulation applications need to be ad-dressed, such as the nature of heuristic solutions due to fixed structural elements, behavior algorithms and parameters, as well as subjectivity in model development and result interpretation. We refer to the works by  Snyder et al. (2016) ,  Dolgui et al. (2018) and Hosseini et al. (2019)  for more recent state-of-the art reviews. Even if SC design with the help of mathematical optimization provides powerful decisionmaking support, it does not take into account some important dynamic restrictions such as inventory control or shipment policies for which simulation is considered to be a useful tool  (Bottani and Montanari 2010 , Ivanov 2017a , Schmitt et al. 2017 , Ivanov and Rozhkov 2020). Therefore, modern commercial software for SC simulation and optimization such as anyLogistix apply a multi-method technology that combines optimization and simulation  (Burgos and Ivanov 2021).
At the same time, some limitations of "pure" simulation applications need to be ad-dressed, such as the nature of heuristic solutions due to fixed structural elements, behavior algorithms and parameters, as well as subjectivity in model development and result interpretation. We refer to the works by  Snyder et al. (2016) ,  Dolgui et al. (2018)  and Hosseini et al. (  2019 ) for more recent state-of-the art reviews. Even if SC design with the help of mathematical optimization provides powerful decisionmaking support, it does not take into account some important dynamic restrictions such as inventory control or shipment policies for which simulation is considered to be a useful tool  (Bottani and Montanari 2010 , Ivanov 2017a , Schmitt et al. 2017 , Ivanov and Rozhkov 2020). Therefore, modern commercial software for SC simulation and optimization such as anyLogistix apply a multi-method technology that combines optimization and simulation  (Burgos and Ivanov 2021).
The purpose of the present study is to contribute to existing works by integration of planning and control decisions within a unified modelling-algorithmic framework. The combination of optimization and simulation was seen as a logical way of increasing the quality of the SC decision-support system (DSS), and different forms of such combinations have been developed  (Gao and Chen 2016, Frazzon et al. 2018 ). These combinations resulted in the SC DSS that contained simulation models, analytical models, a database (which is presently evolving into a knowledge base with artificial intelligence roots), and an integration system for alignment of data, analytical models, simulation models, and decision-making needs leading to development of digital SC twins  (Ivanov and Dolgui 2021b ). In such a complex DSS, the integration and Abstract: Optimal control is a convenient way to develop both supply chain process optimization models and describe the dynamics of process fulfillment.
The purpose of the present study is to contribute to existing works by integration of planning and control decisions within a unified modelling-algorithmic framework. The combination of optimization and simulation was seen as a logical way of increasing the quality of the SC decision-support system (DSS), and different forms of such combinations have been developed  (Gao and Chen 2016, Frazzon et al. 2018 ). These combinations resulted in the SC DSS that contained simulation models, analytical models, a database (which is presently evolving into a knowledge base with artificial intelligence roots), and an integration system for alignment of data, analytical models, simulation models, and decision-making needs leading to development of digital SC twins  (Ivanov and Dolgui 2021b ). In such a complex DSS, the integration and alignment of simulation and optimization models plays a crucial role.
Using the unified formal language of the dynamic control system analysis, SC design and control problems have been described in unified model-algorithmic complex with uncertainty factor considerations. This is a difference to typical approaches which either separately solve SC design problems with optimization and SC control problems with simulation or achieve some level of integration numerically. Moreover, the developed theoretical framework was exemplified by a combined optimization-simulation modeling of the SC design and planning problem with disruption risks consideration in anyLogistix. The results gained provide evidence that the modeling needs to be based on a combined methodology to achieve the adequacy, precision, and validity of models accuracy, closeness, and consistency of results.
EURAD's KM vision emphasises the principle that KM is part of every EURAD component: the Project Management Office (PMO, WP1), ten R&D WPs, two Strategic studies WPs and three KM WPs, Figure  1. Each work package contributes to knowledge production, knowledge management, networking or dissemination of results. Within this overall structure, the three colleges (WMO, TSO and RE) contribute through position papers sharing their view on KM with the Bureau and PMO that are brought forward together with opinions of the External Advisory Board and Chief Scientific Officer to guide the update of the EURAD roadmap and connect knowledge production, management, networking and/or dissemination of results. The EURAD roadmap gives a structure for all collected knowledge and functions as mainly explicit knowledge hub, incorporating knowledge from all EURADs WPs and by external experts.
The ten R&D WPs and the two strategic studies WP support the knowledge management as knowledge providers. This is done through writing of State-of-the-Art (SotA) documents, which on a fine granular scale address end-user need-defined research topic. The ten R&D and the two strategic studies WPs are; • WP2 -Assessment of Chemical Evolution of ILW and HLW Disposal Cells (ACED), involving a multiscale approach and process integration to improve long-term modelling and assessments;  • WP17 -Monitoring equipment and data treatment for safe repository operation and staged closure (MODATS), aims to evaluate, develop and describe methods and technologies, and to provide the means to measure, treat, analyse and manage data in a consistent manner. The R&D WPs are contributing also through creation of courses to disseminate their achievements.
Furin is involved in the endoproteolytic processing of various protein precursors implicated in many diseases such as diabetes, obesity, atherosclerosis, cancer, Alzheimer's disease and viral infection including COVID-19. Recently, cell entry of SARS-CoV-2 was found to require sequential cleavage of the viral spike glycoprotein (S protein) at the S1/S2 and the S2ʹ cleavage sites. The S1/S2 site (PRRAR) can be cleaved by the proprotein convertase furin that facilitates membrane fusion and viral spread. Here we summarized the recent findings on furin and S protein structures, the role of S protein cleavage by furin during SARS-CoV-2 infection. We analyzed 12 diverse representative inhibitors of furin using a chemoinformatic approach starting from a list of 628 compounds downloaded from the ChEMBL database.
FURIN 3D STRUCTURE, SUBSTRATES AND INHIBITORS Furin 3D Structure Twenty-four crystal structures of furin are available at the Protein Data Bank  (Burley et al. , 2021)  at the time of writing. Furin is a 794 amino acid calcium-dependent multidomain enzyme. The structure of furin in complex with meta-guanidinomethyl-Phac-RVR-Amba (PDB file 5JXH) at 2. 0 Angstrom resolution  (Dahms et al. , 2014 ) is shown as an example (Figure  3A ). Only the  catalytic and P domains are present in these experimental 3D structures (i. e. , the other domains: the cysteine-rich domain, the transmembrane domain and the C-terminal cytoplasmic domain are not visible). On the N-terminal side, the protein also contains a so-called prodomain that acts as an intramolecular chaperone, and seems important to assist proper folding of the enzyme.
Small Molecules Inhibitors It has been suggested that furin inhibitors would be very valuable to reduce but not completely abolish viral spread. Several small molecules have been shown to inhibit furin. Yet, most of the time, these involve peptides and peptide-like structures that are often not well suited for human administration due to short half-life, instability, degradation by proteases or difficulties to administrate via oral route. In fact, it seems that most furin preclinical candidates have been stopped thus far due to various absorption, distribution, metabolism, excretion and toxicity problems (ADME-Tox, e. g. , cellular toxicity, low cell permeability, chemical instability, etc. ) (see below). A search with furin as keyword at the https://www. clinicaltrials. gov/ website did not output ongoing clinical trials involving direct small molecule inhibitors.