dataesr/scientific-paragraphs-categorization huggingface dataset:
The WebGL version of UnityMol designed for the MinOmics application communicates with the web page by sending messages to and from the game engine so that every UnityMol feature can be triggered from outside of the game engine by Javascript code. This communication is possible because the Unity game engine is embedded as a Javascript library. The module further offers access to immersive and stereoscopic WebVR visualization from within the web application. Recent advances in the WebVR library allow to experiment with immersive molecular visualization directly from the web browser. UnityMol is one of the first molecular viewers that can be used in a VR mode inside a web browser thanks to WebVR (Figure  6 ). Nowadays, the WebVR standard is still very young and we expect many evolutions in the near future.
The WebVR integration in Unity3D is implemented by adding Javascript functions compiled during the Unity3D build process. These functions provide a communication layer between the Unity3D game engine renderer and WebGL. This experimental yet working implementation was achieved by using sample code provided by an independent developer (https://github. com/gtk2k/Unity-WebVR-Assets/). Interaction devices yet need to be implemented and configured for a more efficient use of this functionality. The most obvious way to integrate UnityMol in MinOmics was by embedding the WebGL version as just described. The restrictions of Unity3D implementation of message sharing between Javascript code and in-game C# script still required to develop a specific set of functions exposed to any Javascript code. This mechanism only allows sending one parameter per function message.
Another issue to be addressed is the capture of input devices by the UnityMol WebGL component. Therefore UnityMol was embedded as an "iframe" in MinOmics, isolating it, thereby preventing mouse and keyboard input events from being captured. Through this mechanism, several instances of UnityMol WebGL can be created easily. This integration allows visualizing large datasets managed by MinOmics and displayed by UnityMol on a large high-resolution display wall, giving access to the easy filtering capability and high-quality rendering on the same screen. The analysis is then directly linked to the visualization to provide a visual analytics loop. A remaining issue with the embedded WebGL version is access to stereoscopic rendering (other than through fully immersive WebVR). As a workaround, we explored an alternative way for MinOmics and Uni-tyMol to communicate by using a standalone version of UnityMol outside a web context receiving data via a web socket or checking for new files to read in a folder.
Scenario 1: Full-Screen MinOmics with Embedded UnityMol WebGL in Monoscopy The first scenario we developed was the integration of UnityMol WebGL to depict and manipulate 3D objects in a MinOmics web page displayed on the wall-sized display (Figure  7 , panel 1). Everything is depicted in monoscopy. Only one UnityMol instance was used to display molecular data from the MinOmics database but the layout can be customized and several instances can be started to show or possibly compare side-by-side different types of information. In this particular setup, multiple users can benefit from the same view. A single user is interacting, for instance through a gyroscopic mouse, to perform both 2D and 3D tasks. Multiple users can watch and discuss with the main user to refine the analysis or change the camera point of view in UnityMol for instance.
Scenario 4: Full VR Context by Bringing MinOmics Inside UnityMol VR Using a standalone software instance that communicates with or even embeds the MinOmics server part enables to access the workstation's full performance and features, leveraging recent advances in the VR field. The VR implementation of UnityMol provides an immersive way to visualize 3D molecular data coming from the MinOmics workflow and to interact with suitable 3D interaction metaphors using a Leap Motion or common VR controllers (Figure  7 , panel 4 and Figure  9 ). To bring and integrate MinOmics within the VR context, a 2D web browser interface can be mapped to a customizable billboard placed anywhere in the scene. The user can interact with this interface using a VR controller to mimic a mouse with a pointing metaphor and process omics data using MinOmics features.
Protein Properties The three datasets correspond to a list of unique proteins, identified by their accession number (usually Uniprot ID) and represent proteins undergoing each modification (SS, SNO, SSG). The properties of the proteins in each dataset can be easily visualized using user-defined criteria based on the features determined by/through the MinOmics pipeline. These features are inferred by MinOmics from other external and public databases or prediction algorithms and are not included in the initial proteomic dataset files provided. They allow visualization of the data according to biological features (e. g. subcellular localization, functional annotation) or physico-chemical properties of the proteins (e. g. molecular weight, number of cysteines, hydrophobicity, …). This type of visualization allows user and data-driven exploration of the results.
The three redox PTMs studied are mainly triggered under stress conditions associated with the production of reactive oxygen and nitrogen species (ROS/RNS). The production of these species that drives redox PTMs mainly occurs in specific subcellular compartments, especially electron transfer chains of the mitochondria and chloroplasts. Therefore, one could wonder whether the proteins from these compartments are more susceptible to undergo redox PTMs due to increased local concentrations of ROS/RNS. This question can easily be answered using MinOmics. For example, after loading the largest dataset containing 1188 proteins harboring thioredoxin-regulated disulphides (SS), the user can represent the proteins according to their subcellular localization determined by MinOmics using the PredAlgo software, a multi-subcellular localization prediction tool dedicated to algae  [62].
The distribution can be visualized by the user as a pie chart that reveals that the 1188 proteins are distributed for 30 % to the chloroplast, 9 % to mitochondria, 6 % to the secretory pathway and 55 % to other compartments (see Figure  10  and Supplemental Material S2). For comparison, the user can load our control dataset containing an experimental total soluble proteome of C. reinhardtii, and visualize its subcellular distribution (unpublished data). This view immediately reveals that the distributions of the two datasets (SS and soluble proteome) are comparable in percentages. This result suggests that the propensity of proteins to undergo SS oxidoreduction is not influenced by their cellular localization. It is consistent with the established importance of thioredoxins in multiple subcellular compartments, organs and developmental stages of photosynthetic organisms  [42].
Use Case 1: Exploring the Protein Specificity of Multiple Redox PTMs Numerous proteins are known to be regulated by multiple redox PTMs such as the Escherichia coli transcription factor OxyR  [63]  or the 11 enzymes of the Calvin-Benson cycle in photosynthetic organisms  [64]. This feature may not be true for all redox modified proteins and we can wonder whether multiple redox PTMs occur on a limited number of proteins containing reactive cysteines or if each modification targets a distinct redox network. The first use case will address this question using the three datasets available for C. reinhardtii (SS, SSG, SNO). The aim is to explore the specificity of redox PTMs at the proteome scale. MinOmics allows fast and easy comparison of multiple proteomes generated in the same organism. After loading the datasets generated, the user can select the proteomes to be compared and activate the grouping function of MinOmics that will analyse the overlap between the datasets and provide a graphical representation of the resulting network for further visual analysis.
With the three Chlamydomonas datasets, the user obtains a comprehensive map of the algal redox network (Figure  11 ). Although some proteins are clearly targeted by multiple PTMs, the overlap appears limited since 68. 8 % appear regulated by a single type of modification. A similarly high specificity was observed when comparing 193 sulfenylated proteins with previously identified targets of SS formation, SNO and SSG  [65]. The limited overlap between the protein targets suggests that the different redox PTMs are specific toward different subsets of the proteome.
Use Case 2: Exploring the Cysteine Site Specificity of Multiple Redox PTMs Despite the specificity observed at the proteome scale, 100 of proteins are regulated by multiple redox PTMs. Nevertheless, the fact that these proteins undergo two or three distinct modifications does not necessarily imply that the same cysteine is targeted. In the second use case we will use MinOmics to explore the site specificity of the different redox PTMs at the proteome scale. After loading the datasets generated, the user can first select the proteomes to be compared and, after selecting redox-modified cysteines, can activate the grouping function of MinOmics that will analyse the overlap between the residues undergoing each redox PTM (SS, SSG, SNO). The visualization reveals a strikingly high specificity of each modification for specific cysteine residues.
Use Case 3: Exploring Structural Determinants of Redox-Modified Cysteines Bioinformatics tools to reliably predict redox modification sites are currently lacking, presumably because they depend on the environment of the target cysteine in the folded protein rather than on the primary sequence alone. The specificity of the different redox PTMs primarily depends on the biochemical properties of the target cysteine residue that are largely linked to its microenvironment within the folded protein, which can notably influence the accessibility, the acidity, and the nucleophilicity of the residue  [38] ,  [39] ,  [40]. The MinOmics framework coupled to UnityMol enables structural interpretation of proteomic datasets. The third study case will focus on the structural analysis of the microenvironment of redox-modified cysteines (Figure  13 ).
DE GRUYTER Maes et al. Figure  13 : Navigation through MinOmics datasets. To illustrate navigation through datasets available in MinOmics, we investigate the subset of nitrosylated proteins. A combination of filtering clauses permits to i) focus on nitrosylated proteins (filter 0), ii) located on a β-sheet (filter 4), iii) with a cysteine and thiol buried inside the protein surface (filter 1 and 2) and iv) located on a Rossman-like fold (filter 11). This part is shown on the right half split-screen. The left half splitscreen, a UnityMol stereoscopic view, allows to explore the molecular structures that were selected through the filtering process, here one of the proteins selected through the combined filters with highlighted cysteines. The user loads the Chlamydomonas SNO dataset, selects redox-modified cysteines and splits the nitrosylome network in two groups according to cysteine accessibility (buried vs.
Further inspection of these nine structures revealed that they all harbor a Rossman or sandwich fold and that the modified cysteine shares a similar position within the structure (Figure  14C ). The user can perform the same type of analysis on a distinct control dataset, such as one comprising cysteines involved in a TRX regulated disulphide (SS). This analysis yields results comparable to those obtained for nitrosylated cysteines, since 26 proteins out of 49 possess a modified cysteine buried on a β-strand in a Rossman or sandwich fold. These results suggest that buried cysteines are more likely to be redox modified if they are located on a β-strand within a Rossman fold protein. This fold may increase cysteine reactivity but does not appear to contribute to the specificity of the modification, at least not for SNO and SS.
Additional analyses with other datasets for different redox PTMs and from diverse organisms will need to be explored to confirm and generalize this observation. This use case illustrates how MinOmics can lead from the genome level to detailed molecular-level structural interpretations of proteomic data. Future developments of MinOmics will allow automated analysis of the structural environment of modified residues in order to perform statistical analysis of the physico-chemical environment of modified cysteines (charges, hydrophobicity, accessibility, nature of neighboring amino acids,. ). These data could be employed to train an algorithm, which would be able to predict potential redox modification sites for a given protein, based on its actual or predicted structure.
Conclusion and Perspectives In this paper we present MinOmics, an innovative analysis pipeline for multi-omics data that benefits from the latest data management, web and 3D technologies. The Python Django framework provides clean, fast, secure structure to the web interface and query builder while the efficient design of the PostgreSQL database allows retrieving the query results in a matter of milliseconds. UnityMol takes advantage of the latest progress in game engines to provide advanced 3D representations of molecular and network models embedded in a We-bGL version. Integrated in virtual reality environments, those tools provide an accessible and fully immersive comprehensive picture of multi-omics datasets. MinOmics combined with UnityMol offers a web-based tool using a visual analytics approach to manage and analyse numerous omics data types, providing an accessible and clear tool to achieve in-depth insight of complex and interconnected scientific data.
Here, we have presented the main concepts and design principles. The full-fledged application is still in development and requires further stabilization of the different elements to achieve the robustness expected for everyday use. Some parts are already quite advanced in that respect, such as the database server backend. The link with the visualization components and the full implementation of the visual analytics features are still work in progress. Some general challenges in the field concern MinOmics as all other applications of that type, for instance in terms of Human Computer Interaction capacities. Efficiently interacting with a large-scale, high-resolution, stereoscopic display wall is generally still a largely unsolved problem.
Funding This work was supported by the  Paris Ile-de-France Region  and by the  Fondation pour la Recherche Médicale , grant number  FRM DBI20141231801 , to SL. XM and MB thank  UCB Biopharma  for support. We further acknowledge support by the " Initiative d'Excellence" program  from the  French State (Funder Id :  10. 13039/501100001665 , Grant " DYNAMO ",  ANR-11-LABX-0011-01  and Funder Id:  10. 13039/501100001665 , Grant " CACSICE ",  ANR-11-EQPX-0008 )".
Hence, many research papers were focused on finding the most appropriate location that lead to cost and weight reduction  [16]  presented an approach based on the Rayleigh-Ritz (RR) assumed mode shape method to predict the behavior of a thin plate with piezoelectric patches bonded to its surface. [21]  proposed optimization technique of the placement and size and the feedback control gains of the control system for vibration damping of beam structures. [22]  studied the optimization of the size and location of piezoelectric actuators/sensors, and the controller parameters of composite beam by using the particle swarm optimization algorithm. Moreover, extra research papers are used commercially software such APDL which has the ability to model piezoelectric materials and perform the investigation of the active vibration control.
[13]  proposed a performance criterion for the optimization of piezoelectric patch actuator locations on flexible plate structures using APDL and genetic algorithm. [11]  dealt with the active control of a smart laminate composite structure (SLCS) by using type of controllers direct velocity feedback (DVF) and displace-ment feedback (DF) controls with constant gain are used. [18]  proposed a prototype developed in APDL for the active vibration control of lightly damped cantilever aluminium using a linear quadratic controller (LQ). [9]  coupled APDL and Matlab to study the active vibration control of a cantilever beam by means piezoelectric patch and PID controller. Unfortunately, the use of certain control algorithm such as LQR requires a state space mathematical model of the smart structure.
Several work dealt with the extraction of state space model using the system identification technique. [12]  used known Observer/Kalman filter Identification (OKID) to obtain an explicit state space model for control law based on the output of a commercial finite element code APDL. [20]  introduced an experimental device for the identification and validation of active vibration control of lightly damped cantilever aluminium beam with piezoelectric patch. [4]  presented a methodology to use the software APDL in modelling and active vibration control of a functionally graded (FGM) plate with upper and lower surface bonded piezoelectric layers. The problematic of using controllers as LQR and LQG in active vibration control is that they required a good knowledge of the structures behaviour which means the necessity of a mathematical model of the structures such as state space representation.
In the present paper, a methodology is presented using system identification technique based on the input-output of the structure to derive a space state model for the structure under consideration. The aim is to carry out a numerical investigation of the active vibration control of composite beam using LQG. First the smart structure is modelled in the commercial finite element software APDL, after the system identification toolbox in Matlab is used to get the state space model based on the input/output time histories of the structure. Final the LQG control law is designed and integrated into APDL to perform closed loop simulations.
The following section describes in detail the steps involved in using the software for analysis of the smart structure. First the geometric solids of the composite beam and the actuator are created using the block command, then the mechanical properties needed for the composite part are inserted using MP command, while the piezoelectric matrices are specified using TB and TBDATA commands. The meshing is performed using two types of three dimensional structural element, (SOLID45) for the composite beam and (SOLID5) for the piezoelectric patch, after that the degrees of freedom, VOLT, are coupled for the nodes at the top and bottom surfaces of the parch by CP command. Finally the appropriate boundary conditions were applied at the fixed end.
Input/output time histories data is then loaded into Matlab System Identification Toolbox for the extraction of the state space equation which can be derived as: ẋ(k + 1) = [A] x(k) + [B] u(k) (3) Y(k) = [ Ĉ]x(k) Where {x} is the state vector, {Y}is the output vector, [A] is the system matrix, [B] is the control matrix, and [ Ĉ] is the output matrix.
The input-output time history obtained from Ansys is now used to obtain the final state-space model using system identification toolbox in Matlab  [10]. Using the space state model, an LQG control algorithm is designed and implemented in APDL for the closed loop simulation, The deflection of the beam due to the applied PRBS voltage is plotted in  To test the effectiveness of the present methodology, first the free end of the beam is subjected to a transverse load of 1N magnitude for 0. 01s. Figure  5  presents the tip    coupled with system identification technique to obtain the state space equation. LQG control law is designed and implemented into APDL to calculate the voltage gain. Numerical results demonstrate that the control algorithm is efficient in reducing the vibration which encourages using the presented technique for more complex problems.
After defining the random force, a transient analysis is performed; this process can be implemented in ANSYS using this code: Specifies the time step sizes ! Sets time to Tstop second ! Set the transient analysis ! Specifies transient analysis options ! Lumped mass set to zero ! Controls the solution data written to the database ! Specifies the time step sizes to be used for load steps ! Integration parameters, by defaultThe final step is applying the random voltage; this can be done by selecting a value from the vector force that has been already created for each time step as: *DO,t,2*Tstep,Tstop,.
! After defining the random force, a transient analysis is performed; this process can be implemented in ANSYS using this code: Specifies the time step sizes ! Sets time to Tstop second ! Set the transient analysis ! Specifies transient analysis options ! Lumped mass set to zero ! Controls the solution data written to the database ! Specifies the time step sizes to be used for load steps ! Integration parameters, by defaultThe final step is applying the random voltage; this can be done by selecting a value from the vector force that has been already created for each time step as: *DO,t,2*Tstep,Tstop,.
Predictive microbial modelling and quantitative microbiological risk assessment, two important and complementary areas within the food safety community, are generating a variety of scientific knowledge (experimental data and mathematical models) and resources (databases and software tools) for the exploitation of this knowledge. However, the application and reusability of this knowledge is still hampered as the access to this knowledge and the exchange of information between databases and software tools are currently difficult and time consuming. To facilitate transparent and consistent knowledge access and exchange new tools and community resources are needed. These resources will promote the creation of a public microbiological food safety knowledge repository encompassing available data and models.
Introduction Microbiological food safety is a major challenge for the increasingly globalized food sector  [1]. Therefore, the microbiological food safety community (including people working within food authorities, food industries, consultancy companies and food research institutes) has invested great research efforts in the fields of predictive microbial modelling  [2 ]  and quantitative microbial risk assessment (QMRA)  [3 ]. This led to new technologies for obtaining and processing experimental data, open databases to compile and share data, new mathematical models, and software tools capable to generate and apply these models  [4, 5]. In the present decade, the way of making use of scientific information is changing. Despite of the fact that, there are still some barriers that hamper the open science (e.
Therefore, greater efforts are needed to facilitate knowledge exchange between the existing and future resources like software tools and databases. This paper initially reviews the recent developments in the field of predictive microbial modelling and QMRA, focusing mainly on available databases and mathematical models as well as software tools and resources that facilitate the exchange of knowledge. Then this paper, focuses on its main objectives that are firstly, to discuss the recent challenges related to transparency and consistency, deployment and reusability, as well as information exchange between software tools and secondly, to highlight future developments that should be carried out to overcome these current challenges.