dataesr/scientific-paragraphs-categorization huggingface dataset:
The user can then adjust the amplitude and direction of the forces applied to the selected particles according to their requirements. The interactive application of external forces to particles can be viewed as a two-step paradigm. The first step is the manual selection of one or more particles, and the second is the use of a force model  [39]  to calculate the forces acting on these selected particles. The forces are used simultaneously for visual and sensory feedback and then sent back to the simulation engine. This approach is fairly ubiquitous and can be adapted to most molecular simulation programs such as BioSpring or Gromacs. This functionality was implemented as an independent software library to provide a TCP/IP network connection between a graphical molecular representation with a user interface and molecular simulation.
VR To add VR capabilities to our digital twin experiments, we used the open-source software UnityMol  [40]  as the basis for development. The molecular graphics and interactive tasks were designed in Unity, and the VR capabilities took advantage of the available vendor SDKs for popular HMD models such as Oculus Rift, Quest, and HTC Vive. For all the experiments described here, we used UnityMol. The software can be downloaded and it works in desktop (2D), display wall (3D), and VR headset (immersive 3D with headtracking) modes. Special visualization features were added for VR, as described in [41]. UnityMol was connected to the original MDDriver library for interactive simulations and alternatively, to an early prototype of the Artemis software library with equivalent functionality. The rendering performance was so good that no latency was observed in the systems described in this work, and high frame rates, which are required for a good VR experience, were achieved.
Molecular builder's workshop: tools for integrative modeling Integrative modeling is often used to describe the creation of 3D molecular models based on the integration of various experimental data sources, including cryo-Electron Microscopy, Small Angle X-Ray Scatteriing, Fluorescence Resonance Energy Transfer, mutagenesis, Nuclear Magnetic Resonance, mass spectrometry, and cross-linking experiments. A digital twin provides a useful platform for integrating such diverse information into structural models because the human operator may select the best strategy to evolve the current design. As this building task is intimately linked to the 3D architecture of a molecular object, VR enables an adapted environment for manipulation. A typical example is shown in Figure  2. The molecular object under study is the MutS protein complex.
The first molecular structure can be found in the protein database  [43]  ; here, the crystal structure 1W7A is described in [44]. This structure does not agree with reported measurements of the active conformation when the complex interacts with partner proteins in a realistic environment. Here, we targeted the distance limits from the FRET fluorescence and crosslinking experiments. Therefore, we explored the possible deformation geometries of the MutS protein complex to generate configurations compatible with these available measurements. Two distances -D1 and D2 -were monitored with the constraint that D1 was larger than 28Å and D2 was smaller than 17Å. Initially, D1 was too small and D2 was too large. The manipulation was delicate because, as shown schematically in Figure  1 , the two distances were interconnected and the protein domains were linked in a circular arrangement.
Often, the structures of individual parts can be found in databases or can be predicted quite reliably using tools such as AlphaFold  [45]. However, it is a delicate task to establish a connection between two such parts, as this connection must not stress the molecule or otherwise sabotage its functionality. Immersive digital twins of the individual parts assembled in a framework, where they can be carefully connected, are an excellent application for VR. We applied this approach to model the influenza virus  [46]  and assembled a hemagglutinin model in which a helical connection had to be made. Hemagglutinin, a trimeric glyco-membrane protein on the outer envelope, is an essential component of the virus. The 1MQM crystal structure contained only the part of the extracellular domain that protruded from the membrane but not the inner half.
From twins to triplets: digital, physical, and tangible siblings using smart hardware Here, we want to connect virtual drug screening, for which there are digital twins, with a tangible "sibling" in the real world. The goal is to simplify the screening process in which millions of molecules can be considered. To achieve this, a pharmacophore model is needed to assess a large database of molecules. One approach is to use a 3D pharmacophore that is a 3D representation of molecular properties. Pharmacophores can either be ligand-based-derived from a set of known ligands, or structure-based-derived from the information of the target. With an interactive 3D pharmacophore design, the user can take advantage of virtual screening without ignoring the interaction of the drug candidate with its environment.
Optical sensors allow us to identify components and detect events that occur between them during the manipulation and construction of the physical model. The sensors embedded in the flexible model allow us to obtain all the information required to create a virtual 3D molecular representation of the physical model in real time. This information is transmitted via a special wireless network protocol. The virtual 3D representation of this molecular physical model can then be visualized using UnityMol software. Once a model is created, its numerical counterpart and thus, the pharmacophore, can be varied by modifying the tangible interface. Limitations arise from the fact that the tangible parts must be produced using a 3D printing process. This requires a certain amount of preparation time before an experiment can be performed, and thus, thorough planning is necessary to avoid disrupting the creative design process.
Discussion and conclusion Our results provide guidelines for the design of digital twins and triplets equipped with VR. Globally, molecular simulations provide a mature foundation for the creation of digital twin models. Transferring this experience to the virtual dimension is very easy because of the inherent 3D focus of molecular objects. We present UnityMol as an existing software tool for which VR porting has been successful and experiments can now be routinely performed in VR with enhanced digital twin models. Several use cases were presented to demonstrate this, starting with the creation of molecular models and integrative models in particular. An intuitive VR tool could help this thriving field by bringing molecular simulations to scientists from other fields who are not experts in these methods.
DNA isolation, PCR and Sanger sequencing DNA was isolated from intravenous blood samples using PureGene DNA Purification Kit (Gentra Systems, Inc. Minneapolis, MN, US). The quality and quantity were determined using NanoDrop® ND-1000 (NanoDrop Inc. , Wilmington, DE, US). Primers designed by Primer3 web-based tool were optimized on human control DNA. PCR was performed according to standard protocols. PCR products were sequenced using the Sanger DNA sequencing protocol using the 3730 XL Analyzer (Applied Biosystems, Foster City, CA, USA). The sequencing results were analyzed using ChromasPro (Technelysium Pty Ltd. , South Brisbane, Australia) and SeqMan software (DNASTAR Inc. , Madison, WI, USA).
Genome-wide SNP genotyping and autozygome analysis J o u r n a l P r e -p r o o f SNP genotyping was performed using GeneChip Human Genome-wide SNP Axiom Arrays according to manufacturer's protocols and guidelines (Affymetrix Inc. , Santa Cruz, CA, USA). Generated SNP calls were used for autozygosity mapping using AutoSNPa software as previously described  (34, 35).
A 1:1000 dilution DAPI antibody (ThermoFisher Scientific, Waltham, MA, USA) was incubated in blocking buffer for 30 min at room temperature to label the nucleus. We also performed immunocytochemistry control experiments in which we omitted the primary antibody specific for the GlyR β-subunit, or where constructs with no fluorescent fused protein were expressed (Fig. S3 ). For laser scanning confocal microscopy, images were acquired with a ZEISS LSM 710 confocal microscope using a x40 (1. 4 NA) objective and immersion oil. Images were obtained from three independent transfections and staining using the ZEN software (Carl Zeiss AG, Oberkochen, Germany). Cell counting was performed using the Image J Fiji plugin on maximum fluorescence intensity projections of Z-stacks. The number of DAPI-positive nuclei was used as the total number of cells per image.
Data analysis and statistical tests Data acquisition and analysis were performed using customized virtual instruments programmed in LabVIEW (V8. 0, National Instruments, Austin, TX, USA). Results from the analysis were exported into Origin Pro (2019, OriginLab Corporation, Northampton, MA, USA) for figures production. To calculate the reversal potential for J o u r n a l P r e -p r o o f GlyRs (ECl) the intercept between the linear fit of the I-V relation and the x-axis was used. Junction potentials were not corrected. Current density, which provides an estimate of the number of functional GlyRs per µm2 of cell surface membrane area, was calculated by dividing the amplitude of the mean glycinergic current (Vholding = -60 mV) by whole-cell capacitance. The decay times of glycine currents were determined by fitting a single exponential to the (90-10%) decay phase.
Concentration-response curves were fit using the Hill equation, I/Imax = A1 + (A2 -A1)/(1 + 10(logEC50log[agonist]) × Hill slope), where A1 and A2 refer to top and bottom asymptotes, I is the current amplitude activated by a given concentration of glycine, Imax is the maximum response of the cell, and EC50 is the concentration eliciting a half-maximal response. In pharmacological experiments, the amplitude of glycinergic currents was averaged over three successive trials and normalized to pre-drug amplitude level to obtain a time-course. Hypothesis testing and statistical inferences from the data were performed using IBM SPSS Statistics 26 (IBM, Armonk, NY, USA). Data were subjected to normality distribution tests prior to performing a statistical analysis using parametric or non-parametric tests.
Computational structural analysis of the A455P variant The three-dimensional structure of the GlyR β-subunit in the context of the native 4α1:1β pentameric assemble was retrieved from the protein data bank (PDB)  (37)  based on the cryo-electron microscopy structure of native GlyR oligomers (PDB ID 7MLY)  (12). RaptorX  (38)  was used for predicting secondary structure and disorder. The mutation was evaluated using the Pymol program (http://www. pymol. org). J o u r n a l P r e -p r o o f autozygosity mapping, NGS, gene filtering and gene variant hunting: GA; cell culture: GA; immunohistochemistry: GA, FA, AR; electrophysiology: GA, AR; biochemistry: SA, KH, AR; manuscript drafting: GA, SA, NK, MA, KH, AR with input from all authors.
Acknowledgements We are very grateful to the family for their participation in this study. We thank colleagues at  King Faisal Specialist Hospital and Research Center (KFSHRC)  and staff at Genotyping and  Sequencing Core Facilities of the Genetics Department, Research Advisory Council Committees and Saudi Human Genome Program. We thank the  National Plan for Science, Technology and Innovation program under King Abdulaziz City for Science and Technology , and the  King Salman Center for Disability Research , for supporting  Dr Namik Kaya  and  Dr Dilek Colak. We are grateful to  Prof Robert Harvey  for early mentorship of  Ghada Aboheimed  at  UCL School of Pharmacy  and for his role in liaising with  Dr Mustafa Salih. We also thank  Prof Dimitri Kullmann  for the providence of Labview routines.
Concrete is a widespread material all over the world. Due to this material's heterogeneity and structural complexity, predicting the behavior of concrete structures under extreme environmental conditions is a very challenging task. High temperatures lead to microstructural changes which affect the macrostructural performance. In this context, computational tools that allow the simulation of structures may assist the analysis, by reproducing varied situations of thermal and mechanical loading and boundary conditions. In order to contribute to this scenario, this study proposes a numerical methodology to simulate the thermomechanical behavior of concrete under temperature gradients, through inverse analyses and a user subroutine implemented in Abaqus software. Thermal loading effects were considered as loading data for a damage model.
Overview This work employed a set of experimental data provided by Razafinjato  [23]  who studied the thermomechanical behavior of concretes of varied compositions. According to Razafinjato  [23] , the experimental data were obtained from cylindrical specimens with a proportion of 40% of aggregates. These samples were molded covered with a damp cloth and stored in plastic bags for 90 days. Then the specimens were subjected to heating/cooling cycles, in a furnace, at a rate of 0. 50K/min till temperatures of 573. 15K, 723. 15K, 873. 15K and 1023. 15K, maintained for two hours. After the heating/cooling cycles, at room temperature (293. 15K), the specimens were taken to the press so that, from the uniaxial compression test, the residual concrete Young's modulus could be determined. Figure  1  shows the resulting Young's modulus and temperature relation, for 293.
Then, the temperature field is applied as thermal loading and a damage model is adopted so as to evaluate the material's deterioration. To evaluate the concrete deterioration process, opted to use the Mazars'  [2]  damage model, which is unavailable in the library of the Abaqus  [25]. So, a UMAT subroutine  [26]  was implemented for the software to perform the analysis considering the desired model. However, Mazars'  [2]  parameters for the material were unknown and, therefore, it was necessary to use another numerical adjustment. Finally, from the results of the damage model, it was possible to construct a damage curve as a function of temperature, from which we carried out the analysis of the behavior of the concrete under high thermal gradients. We emphasize that the strategy used, which is summarized in a weak coupling, has the advantage of low computational cost.
Linear elastic model mesh From mechanical properties, by Razafinjato  [23] , we had the Young's modulus of mortar and concrete. However, the Young's modulus of aggregate as well as the Poisson's ratio of the aggregate and the mortar were unknown and, to determine them, an inverse procedure was used. The numerical procedure consisted of simulating a 1/4 of the longitudinal section of a cylindrical specimen with 150mm in diameter and 300mm in height, composed by two phases, mortar and aggregate, under increasing axial loading, through a displacement applied at the top of the sample as represented in Figure  5. Flowchart 1, in Figure  2  represents the applied inverse methodology, based on a trial and error procedure implemented in Python language, applying the differential_evolution function, from the optimize package of the SciPy library  [27] , assuming as objective function the error between the experimental Young's modulus and numerical.
CONCLUSIONS The present work achieved the proposed objectives: the Mazars'  [2]  damage model was implemented in the Abaqus  [25]  software through the UMAT user subroutine, making it possible to observe how the increase in temperature influences the concrete damage and, consequently, in the material Young's modulus, obtaining results consistent with the theory. Also, the proposed computational model was able to represent the results obtained experimentally by Razafinjato  [23]  with an error of the order of 0. 03%, which is relatively small. Besides, the data available were referents to only four temperatures, while by the curve obtained numerically it was possible to evaluate Young's modulus at any point, between the maximum (803. 15K) and minimum (293. 15K) temperatures considered. It is noteworthy the performance of the proposed model, which, with little experimental data, is able to contribute to the elucidation of the mechanism of damage generated in concrete subjected to high temperatures.
Social networks give free access to their services in exchange for the right to exploit their users' data. Data sharing is done in an initial context which is chosen by the users. However, data are used by social networks and third parties in different contexts which are often not transparent. In order to unveil such usages, we propose an approach which focuses on the effects of data sharing in impactful real-life situations. Focus is put on visual content because of its strong influence in shaping online user profiles. The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation.
These components are combined in LERV U P , a method which learns to rate visual user profiles in each situation. LERV U P exploits a new image descriptor which aggregates object ratings and object detections at user level and an attention mechanism which boosts highly-rated objects to prevent them from being overwhelmed by low-rated ones. Performance is evaluated per situation by measuring the correlation between the automatic ranking of profile ratings and a manual ground truth. Results indicate that LERV U P is effective since a strong correlation of the two rankings is obtained. A practical implementation of the approach in a mobile app which raises user awareness about shared data usage is also discussed.
Photos are in focus because they constitute a large part of shared data and contribute strongly to shaping user profiles  [2]. The main technical contribution is a method that rates visual user profiles and individual photos in a given situation by exploiting situation models, visual detectors and a dedicated photographic profiles dataset. The proposed method, named LERV U P from LEarning to Rate Visual User Profiles, learns a ranking of user profiles which attemps to reproduce human profiles ranking. LERV U P exploits a new descriptor which combines object impact ratings and object detections in a compact form. The contributions of objects with high ratings are boosted in order to mimic the way humans assess photographic content. We compare manual and automatic rankings of user profile ratings and obtain a positive correlation between them.
For instance, the introduction of deep architectures brought important progress in classification  [8, 26] , but accuracy is still affected by internal representation limitations  [32]. A second bias is due to the variable accuracy of predictions due to the inherent difficulty of visual objects  [7]  and/or the availability of skewed  [47]  and imbalanced data  [21]. While affected by biases, approaches like ours are needed to make AI-powered decision-making more transparent to users. Notably, impact and profile ratings are potentially biased toward the opinions of the persons involved in the experiments. Object detection is biased because the detection dataset is incomplete and detectors are imperfect. Below, we illustrate contexts in which users' lives are influenced by their online activity.
The understanding of the effects of visual content sharing was pioneered by  [2] , with the introduction of disclosure dimensions such as security, identity and convenience. The study concludes that user feedback should provide warnings to prevent mistakes, inform about the effects of data aggregation and estimate the appropriate audience. The authors of  [54]  used hand-crafted visual features to predict the privacy status of an image with encouraging results but far from practical usability. Transfer learning from generalist deep models as a way to improve privacy prediction was proposed in  [43]. An important step forward was made in  [33] , with the creation of a taxonomy of privacy-related attributes and of a dataset dedicated to privacy prediction. Interestingly, the resulting model provided more consistent predictions compared to users' judgments, indicating that users might fail to follow their own privacy-related preferences.
Preliminary experiment We hypothesize that linking feedback about the effects of personal data sharing to real-life situations improves its efficiency compared to existing approaches. We designed an experiment to test this hypothesis using four real-life situations: bank loan, accommodation search, IT job search and waiter job search. A set of 20 photos that depict objects with potentially negative effects is selected. Objects with different negative ratings for each situation as obtained in Subsection 3. 3 are kept. They include: casino, knife, cannabis leaf, rifle, etc. Participants assume that they are about to share a photo and that an AI-driven assistant advises against sharing. They see the following messages which implement: • existing feedback  [33, 43] : "The app predicts that the image should not be shared because it automatically detected object X in the image.
Crowdsourcing Situation Ratings The interpretation of a object might vary between contexts, and so would the effects of sharing its images. Situations are modeled by crowdsourcing visual objects ratings. Impactful situations were selected: accommodation search (ACC below), bank credit demand (BANK), job search as IT engineer (IT) and job search as a waitress/waiter (WAIT). ACC and BANK are applicable to a large part of the population. IT and WAIT are relevant for population segments, but the respective job searches require different profiles. Detectable objects from the OpenImages  [27] , ImageNet  [40]  and COCO  [29]  datasets were rated to boost detector coverage. A limitation here is that task-relevant objects are missing and D could be enriched. A rating interface is created which includes for each situation: the object name, illustrative thumbnails and a 7points Likert scale with ratings between -3 (strongly negative influence) to +3 (strongly positive influence).
There were 56 participants in total, with 14 rating sets per situation. The final rating r is obtained by averaging their contributions. The resulting detection dataset D includes 269 objects with r = 0 for at least one situation. Interrater agreement, which is important for tasks prone to bias such as the one proposed here, is computed using the average deviation index (AD)  [6]. The obtained AD varies between 0. 48 for IT and 0. 65 for W AIT. These values are well below AD ≤ 1. 2, the maximum acceptable value for a 7-points Likert scale defined in  [5]. Beyond rater agreement, the qualification of annotators is important. We define expertise as having working experience in BANK, IT and WAIT and having a landlord experience for ACC. Depending on the situation, the number of annotators with expertise in each situation was: two for BANK, six for IT, four for WAIT and four for ACC.
The relatively low number of strong ratings indicates that the dataset should be enriched with highly rated objects in order to allow a finer-grained computation of visual profile ratings. More details about objects crowdsourcing are given in the supp. material. Figure  1. Rating patterns (columns) obtained by clustering objects with similar ratings across situations (lines). The pattern name and the corresponding number of objects are provided under each column. Rating colors go from red (strongly negative) to green (strongly positive) with stronger intensity indicating a higher absolute value of rating. The object details of the patterns are shown in Table  4  of the supplementary material.