dataesr/scientific-paragraphs-categorization huggingface dataset:
We then present UnityMol, developed with the video game engine Unity. Using this software, we upgraded the Umbrella Visualization from a 2D method giving results as density plots to an integrated tool allowing the visualization of the impact of glycans onto a protein structure during a MD trajectory. The Umbrella Visualization is fully implemented in the UnityMol interface in order to provide a user-friendly, fully automated tool able to accommodate a wide variety of Nglycan structures. We tested this method on a glycosylated insulin receptor and highlighted the differential impact of bi-antennary and triantennary glycans on the protein. This tool, in conjunction with insights of experimental studies, could help understand mechanisms involving N-glycans and design in vitro experiments aimed at understanding their impact on protein functions.
Glycosylation and modelling The first step needed to study glycosylated proteins is to identify the glycosylation sites. Most of the time, only the first saccharides from a glycan are present on the. pdb structure files available on the Protein Data Bank at www. rcsb. org [17]. This can help identify some glycosylation sites but most of the time, glycan structures are not available: because of their great flexibility, glycans are not sufficiently ordered to be detected by classical methods used to determine protein structures such as X-ray crystallography  [18]. It is thus necessary to use prediction tools in order to identify the glycosylation sites. Several tools are available as web-portals or standalone programs  [13, 19, 20]. Before running MD simulation, it is useful to analyze and understand the structure of glycosylated proteins.
Visualization Many of the main visualization software integrate the visualization of saccharides in all-atom representations but are often limited to licorice, balls and sticks or van der Waals (VdW) sphere representations. But, few of them propose ways of visualization that highlight the positions and structures of glycosylations on the proteins' surface. PyMOL includes a plugin called Azahar  [32]  which is able to build a glycan from a template list of saccharide structures. The plugin adds three specific display modes aimed at simplifying the representation of glycan structures: the cartoon and wire representations show the cycles as non-flat polygons linked by rods and the bead representation shows each cycle as a sphere. This plugin also gives the possibility to compute relevant structural values in order to analyze both static structures or MD simulations.
With the 3D-SNFG plugin  [33] , the VMD software implements its own graphical representation based on the nomenclature developed before the second edition of the Essentials of Glycobiology textbook  [34, 35]. Each monosaccharide is represented by a 3D object based on the corresponding symbol. UnityMol, an open source platform developed with the Unity engine (http://unity3d. com/) in C# language  [36]  is dedicated to the visualization of biological molecules and uses HyperBalls  [37]  representation. In the latest versions called SweetUnitymol  [38] , this software integrates display modes and representations dedicated to saccharides such as ribbons representation or the possibility to color saccharide depending on its nature: for each saccharide, the associated color is determined by the symbol found in the SNFG nomenclature.
2D Umbrella Visualization It is well known that sialic acids, charged saccharides often found at the extremities of glycan chains, are involved in protein function's regulation  [40]  or dysfunctions such as in type 2 diabetes  [41]. To better understand the influence of sialic acid on protein's function and structure, a molecular dynamics study on sialylated and non-sialylated glycan chains has been initiated. Trajectories for four glycan chains were computed, with glycans bearing two and three branches, with and without sialic acids  [14]. For each system, the starting carbohydrates structures were built using the Avogadro software. 500 ns of trajectories were generated with the OPLS-AA forcefield and the Gromacs package 4. 6. 3. NPT ensemble was used with a 310 K temperature and 1 bar pressure.
In order to plot and evaluate the relative flexibility of glycan chains with and without sialic acids, the Umbrella Visualization was developed. The Umbrella Representation considers the glycan acting as an umbrella protecting the protein surface so that an hypothetical molecule would not be able to interact directly with this part of the protein (Fig. 1A ). The glycan is divided into vectors representing its different components: two vectors describe the glycan core. The other vectors describe the branches of the glycan. The first step is to measure the distance between the centers of mass of the saccharide residues defining these vectors for every trajectory step. This is done thanks to the Gromacs  [42, 43]  software and the distance module. Files listing the coordinates of these vectors during the simulation are generated and analyzed.
UnityMol In order to better understand how glycans and sialylation impact proteins, it was then decided to transform this 2D-analysis method into a visualization method aimed at identifying which regions of the protein surface are impacted by a glycan during a MD trajectory. For this purpose, we have decided to work with an existing open source software, UnityMol, to implement this new functionality. By integrating the Umbrella Visualization to a software already used by the scientific community, we hope to make it user-friendly and broadly available. As stated previously, UnityMol is a molecular editor, viewer and prototyping platform, coded in C# with the Unity game engine. It already has all the classic visualization modes used in molecular modeling and is even able to automatically detect saccharide residues in order to provide user-friendly visualization modes dedicated to the representation and study of saccharide chains.
It also provides a plug-in reading MD trajectories. The Unity engine makes it easy to work either with built-in tools or to implement new features. The wide use of this platform also warrants support from an active community and provides online resources and tutorials. This engine supports different platforms such as Android, Windows, Linux or Mac platforms. Exporting the executable file for these platforms is done at compilation, and saves a great amount of time by avoiding further developments. The Unity graphical interface also displays a Game Mode that allows a user to run Fig. 1. A) Schematic principle of the Umbrella Visualization: The glycan here is shown in sticks representation and colored by residue type. The Umbrella Representation considers the glycan acting as an umbrella over the protein surface.
B) Implementation of the Umbrella Visualization process: The glycan is represented in sticks. The core of the Glycan, shown in green, is aligned along the z axis thanks to vectors describing the core (red arrows). The center of mass (red dots) of the last saccharide residue on the branches is projected on the xy plane. The final result is a density plot reflecting the relative flexibility of the glycan during a MD trajectory. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) its program without compiling it. This feature is especially useful in order to rapidly and easily test adjustments brought to the code. Unity is also known for being relatively user-friendly, partially thanks to its interface, but it is also important to note that every parameters and components are accessible via scripting in C# or Javascript languages, making it a very versatile tool.
General principle One of the many advantages of the Unity engine is the possibility to build prefabs that can be used for development purposes. Prefabs are assets storing GameObjects components and setting their properties to desired values. This way, when the prefab is instantiated into a scene, the GameObjects components are already positioned and oriented as desired. Among these components, C# or Javascript scripts can be assigned to a GameObject component and used in order to dictate a behavior to this component. Prefabs are very versatile: even if all the instances of a prefab will share its properties and ensure a certain homogeneity between the instances, individual instances are independently editable and it is thus possible to change some individual properties as needed. Building a prefab and assigning scripts to its component is a good way to build a template for an asset that would be instantiated several times into a Unity scene.
By using these built-in tools and components from the Unity engine, we designed a Unity prefab able to capture the glycan shadow and project it onto the protein surface. This prefab is composed of a camera, a projector and a directional light oriented perpendicular to a flat mesh defining a plane (Fig. 2A ). These components are organized together to project the shadow of a glycan on the protein. Cameras are also defined as GameObjects components and can be used into prefabs. Cameras usually capture and display a view to the user but can also render this view into a texture. To this end, a Render Texture is assigned to a camera. Unity also provides layers that allow for lights and cameras to not illuminate or not render elements from the layers defined in their respective "culling masks". The Projector prefab from the Unity Standard Assets package projects a desired material or texture onto all objects intersecting its frustum.
User interface One of the aim of the Umbrella Visualization is to make this method accessible to every UnityMol users and enable the production of pictures for analysis and illustration purposes. This implies to link the described process to the UnityMol interface and works on display modes highlighting clearly the covered parts of the protein surface. We expanded the UnityMol sugar menu, adding four new boxes. The first one (Enable box) starts the Umbrella Visualization once the data are loaded. Detection of the data type (single structure file or molecular dynamics trajectory) is automated so that no other input from the user is needed. Depending on the input data, the limits of the covered surface may be blurry. In order to have a clear view of this surface, we added a display mode available through the Contour button.
1). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) Fig. 5. Display modes available for the Umbrella Visualization. A) classical display, the glycan's shadow is very detailed but blurry. B) The Contour display mode gives a better idea of the covered surface by highlighting different zones. The darker parts correspond to the most covered area during the trajectory. The scale to the right gives the thresholds for each zones. These pictures are obtained averaging the data extracted from 12,500 frames of MD simulations of a glycosylated insulin receptor (for details, refer to Section 5. 1). extracted from the IR-dimer with two non-sialylated bi-antennary glycans. The results presented in the Section 5. 3 were computed on 12500 frames from a 250 ns trajectory (1 frame every 20 ps).
Performances The investigated system on this study is a dimer of the insulin receptor ectodomain with one glycan per monomer. This corresponds to a number of 26030 atoms, not counting the water molecules used to calculate the motions along the trajectory. The Umbrella Visualization analysis was performed with a 3. 7 GHz Intel Xeon W-2145 CPU machine with 64 Gb of DDR4 RAM and a Nvidia GeForce GTX 1080Ti graphic card. The performances of our methodology were calculated through the analysis of three different sets of frames: 250, 2500 and 10,000 frames. We used 512 pixels wide texture. Using a smaller texture saved time (18 min for a 256 pixels wide texture). The main limitation is the number of atoms contained in the structure. The dimer simulated here is a relatively large system, thus processing the data for every frame of the simulation requires more computational resources than a smaller system.
We were able to see significant results even with a small numbers of frames that allowed us to highlight the main conformational tendencies of the glycan of interest. The largest samples showed more details and hinted at less represented conformations. This emphasizes the high importance of the number of frames. Because of the statistical nature of the Umbrella Visualization, working with a low number of frames could lead to the overestimation of some conformations and the underestimation of others, meaning that we could miss relevant information. The choice of trajectory length is thus a key point regarding the significance of the Umbrella Visualization results. Nonetheless, this first experiment showed that this method has the potential to give informative results that could also either help design in vitro experiments and/or corroborate data from other calculations.
Correlating the results of the 2D graphical method and the results of the 3D visual method is a good way to provide quantifiable data to support the visualization results. To support this, we also plan to add methods to bring more informations to the Umbrella Visualization results, such as the occupied volume during the trajectory: as glycans are bulky, the space they explore and occupy during a MD trajectory could also inform us on their influence on molecular complex formation. More generally, correlating the Umbrella Visualization results with other calculations could bring new insight into the impact of glycans on proteins. Among the methods used in molecular modeling, free-energy calculations are used to characterized interactions between partners  [47] : it is possible to evaluate the contribution from each residue from both partners.
Experimental Data and Molecular Descriptors The experimental data of ILs' toxicity towards Escherichia coli were collected from the literature  [11]. The analyzed dataset contains 24 ionic liquids, in which six various types of cations (imidazolium, pyridinium, pyrrolidinium, piperidinium) and three anions (bromide, thiocyanate, bis(trifluoromethylsulfonyl)amide) can be distinguished. Antimicrobial activity was expressed as EC50 in mM unit. Detailed information can be found in the Table  S1  in Supplementary Materials. The structure of each ionic liquid has been described using molecular descriptors in two ways. The first set of descriptors contained those calculated after independent geometry optimization of each ion (A|B). The second one contained molecular descriptors calculated for the whole ionic liquid after geometry optimization of the ionic pair ([A+B]) by one of the DFT methods (B3LYP/6-311 + G*) with the Gaussian 09 software (Revision D.
01, Gaussian, Inc. , Wallingford, CT, USA)  [12]. In the case of all sets, the descriptors values were calculated with the DRAGON (v. 7) software  [13]. However, to increase efficiency of the benchmark study, the pool of descriptors was reduced to the following groups: constitutional descriptors, topological indices, ETA indices, walk and path counts, information indices, atom-centered fragments, WHIM, GETAWAY and Randic molecular profiles connected to molecular shape, and geometrical descriptors  [14].
Model Development The optimal, physically interpretable combination of the descriptors was selected by employing a stepwise selection algorithm in olsrr package implemented in R programming language  [15]. The algorithm starts with an empty model. Then, in each step the best model (according to a specific criterion, e. g. , lowest mean absolute error (MAE) value) is chosen from all models with one additional feature and from all models with one feature less. The algorithm was used for the sets of descriptors mentioned in previous section ((A|B) and [A + B]). The multiple linear regression (MLR) technique was used to find the relationship between the chemical structure of ionic liquids (described by molecular descriptors) and the modeled value (logEC50). Goodness-of-fit of the QSPR models was measured by using sets of measures such as the determination coefficient (R 2 ), root mean square error of calibration (RMSE C ), mean of absolute errors (MAE).
Validation Process According to requirements established by the Organization for Economic Co-operation and Development (OECD) referring to principles for the validation of QSAR models, we performed the internal and external validation of our models  [16]. The stability of the models was verified by leave-one-out cross-validation coefficient (Q 2 CV ) and root mean square error of cross-validation (RMSE CV ). We also estimate the predictive ability by calculating two external validation measures: external validation coefficient (Q 2 EXT ) and root mean square error of prediction (RMSE EXT ). It should be noticed that external measures are calculated only for chemicals from the validation set. Additional parameters have been also calculated in order to confirm quality of the developed QSPR models, namely: concordance correlation coefficient (CCC) and modified r 2 for whole dataset (r 2 (overall) )  [17].
It is worth to mention that our case study was based on the limited types of descriptors. The maximum number of descriptors would even reach several thousand, if all types of descriptors available in the modern software (e. g. , DRAGON, alvaDesc) were calculated. Preserving the highest possible ratio between the number of objects (here: ionic liquids) in the training set and variables (here: descriptors) is crucial for the efficient execution of the feature selection algorithms. Moreover, any feature selection algorithm (e. g. , genetic algorithm) will be more effective and time-competitive when working on a smaller set of possibly important descriptors  [28]. The study was performed in two groups of models, dependently on the way of description of the analyzed ionic liquids: (i) models in which ions are described separately (M1-M3) and (ii) models that utilized descriptors calculated for the whole ionic pair (M4-M6).
Obviously, when using descriptors calculated separately for the individual ions, the values of the descriptors are identical for all ionic liquids in the dataset. For example, thiocyanate anion has the same value of L1i A descriptor (equals to 1. 38) in the case of both: 1-butyl-3-methylpyridinium thiocyanate and 1-octyl-3-methylimidazolium thiocyanate. The same situation is in the case of cationic descriptors (Table  3 ). Therefore, the borders of the applicability domain are exact and easy to define. However, when the descriptors are calculated based on the structure of the whole ionic liquid the values of 3D descriptors are influenced by geometries of both ions. Thus, the same descriptors have a range of different values (Table  4 ). In consequence, the verification of AD is more challenging.
Other Practical Aspects of ILs Modeling with QSAR/QSPR There are several examples in the literature, where QSAR models for ILs use even more sophisticated approaches of calculating the molecular descriptors than those presented in Sections 3. 1-3. 3. For example, Bruzzone et al. [29]  developed a QSAR model for predicting toxicity (EC 50 ) for Vibrio fischeri based on for 33 ionic liquids. Because all 33 ILs contained a halide anion (chloride or bromide), only the molecular structure of the cations was optimized at the DFT level of theory and used for calculating constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors (with CODESSA software,). Similarly, Nekoeinia et al. [30]  developed a QSPR model for predicting the normalized polarity parameter (E T N ).
The model was developed based on a set of 52 ILs having the same anions: (CF 3 SO 2 )N 2-. The 2D and 3D descriptors such as topological and GETAWAY were calculated only for cations, after geometry optimization at the molecular mechanics level of theory (MM+ force field implemented in HyperChem software, v. 7). In the model of cytotoxicity to the leukemia rat cell line (IPC-81) developed by Torrecilla et al. [31]  the dataset included ionic liquids having various types of cation and anions. Therefore, the authors optimized molecular geometries of the cations and anions independently at the level of DFT (B3LYP/6-31++G**). Based on that, they derived the Sσ-profile molecular descriptors of counterions. A different approach was used by Wang et al. [32]. Although they modeled ionic liquids with the same anion (bromide), they were optimizing geometry of the whole structure (at the DFT level, B3LYP/6-311G (d, p)) in the case of each IL.
Introduction Understanding cellular differentiation, e. g. , the transition of immature cells into specialized types, is a central task in modern developmental biology. Recent advances in single-cell technologies, such as single-cell RNA-sequencing and mass cytometry, enabled important insights into these processes based on high-throughput cell measurements  [1] [2] [3] [4]. Computational methods to accurately discover and represent cell development processes from large datasets and noisy measurements are therefore in great demand. This is a challenging task, since methods are required to reveal the progression of cells along continuous trajectories with tree-like structures and multiple branches (e. g. , as in Waddington's classic epigenetic landscape  5  ). Multiple advances have been made towards this goal of discovering and analyzing hierarchical structures from single-cell measurements  6.
Monocle 2  15  forces a tree-like topology on the data using "reversed graph embedding" in a low-dimensional Euclidean space. However, similar to UMAP, such a representation might not exist for complex trees. SIMLR  10  is a multi-kernel learning designed to perform well on datasets with multiple clusters, making it a poor choice to model data with continuous trajectories. Finally, SAUCIE  10  is an autoencoder model, which is optimized through reconstruction error, therefore its properties for preserving local and global similarities are theoretically less understood. 1 2.
For this purpose, we employ multiple synthetic datasets generated from known dynamical systems and four single-cell RNA sequencing datasets varying in size, complexity (number of cell types and branches) and single-cell technology to acquire the data  2;3;31;32. We compare Poincaré maps with the canonical hematopoetic cell lineage tree  33  , and various state-of-the-art embeddings (Supplementary Note 2). First, we evaluate the capabilities of Poincaré maps for data visualization and dimensionality reduction. It is not possible for humans to comprehend visualizations in more than three dimensions, and a third dimension already adds additional challenges for interpretation. However, for existing methods, even three-dimensional embeddings are not sufficient to capture the underlying manifold structure on many complex hierarchies.
Here, we demonstrate that as few as two dimensions of Poincaré maps are enough to reconstruct the hierarchy and preserve global similarities on the datasets with a very high complexity. A common way to evaluate the quality of an embedding in labeled datasets is to use classification scores. However, this evaluation approach has limitations in the context of single-cell data, and specifically for recovering hierarchies and continuous developmental trajectories. First, quite often labels are assigned using some unsupervised learning approach, such as clustering. This could promote the dimensionality reduction method that better agrees with the label assignment method, rather than with the objective ground-truth. Second, discrete labels do not easily apply to datasets with continuous trajectories, where a clear-cut cutoff between cell types does not exist.