dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paragraph, the first studies that have demonstrated the feasibility and usefulness of multi-omics approaches for MD will be described. Kremer et al. decided  to integrate WES data with RNA-seq data to identify variants responsible for MD for a cohort of 48 patients  (Kremer et al. , 2017). They have developed a pipeline to detect three main causes responsible for variants: aberrant transcript expression, aberrant splicing and MAE. Thanks to their bioinformatics approach created ad hoc, they found one aberrantly expressed gene, five aberrant splicing events and six mono-allelically expressed variants. This approach resulted in the diagnosis of 5 patients from the 48-patient cohort with undiagnosed MD and the identification of a candidate gene for 36 other patients. To improve the results and design a tool that can be used on other datasets, most of the same authors as this pioneer work, developed the OUTRIDER (OUTlier in RNA-seq fInDER) method that identifies "outlier" genes that are aberrantly expressed in the entire cohort  (Brechtmann et al.
, 2018). OUTRIDER is based on the use of auto-encoders, a deep learning approach. However, OUTRIDER does not yet allow the integration of multi-omics data but only RNA-seq data from different platforms (e. g. , patients vs. Genotype Tissue Expression database). Further development of this promising method would allow the integration of multi-omics data for MD in the future. Stenton et al. (2019)  also used an ad hoc bioinformatics approach. They analyzed the inconclusive cases in the WES samples by running transcriptomics and proteomics analyses in parallel. They tracked the impact of a variant on the abundance of transcripts and their sequences during translation and vice versa by tracing the aberrant expression and splicing back to the responsible protein. Gonorazky et al. (2019)  developed a pipeline based on transcriptome analysis.
Another challenge is to develop databases specific to MD. For rare diseases, very few patients are affected, so few data are available and especially very few patients share the same pathogenic variant. Data from a single hospital are not sufficient and the establishment of interoperable national and European clinical-biological databases would allow us to expand the available cohorts and accelerate the knowledge of these diseases. Several initiatives have been set up, including the RD-Connect project. Funded by the European Union since 2012, RD-Connect is developing data sharing mechanisms and tools for omics and bioinformatics analysis that are incorporated into an integrated platform linking patient registries, biobanks and clinical bioinformatics data into a central resource for rare disease research  (Johnston et al.
First, more comparative analyses will be needed to assess the performance of tools in contexts other than cancer, as this will allow the selection of the right tool based on the dataset, even if one tool may not always be preferred. Reference data sets should also be developed using simulation tools, which will allow for more accurate estimation of false positives and false negatives. Two other points need to be developed. On one hand, the development of tools that can be used on small cohorts and capable of managing more variables than patients. On the other hand, the development of databases for healthy individuals that will be used as a control to calibrate the tools. Collaboration between scientists from different fields is also essential for the integration of multiple layers of information.
cancer. gov/about-nci/organization/ccg/research/ structural-genomics/tcga 1000 genomes Healthy ( Diseases 26 populations 2,504 https://www. internationalgenome. org/homeFor each database, we report the type of data (healthy or Diseases), the number of site (tissues, populations, experiments) available, the number of human samples available, and the links to web databases. Numbers are only given for organism Homo Sapiens. CCLE, Cancer Cell Line Encyclopedia; CPTAC, Clinical Proteomic Tumor Analysis Consortium; dbGAP, database of Genotypes and Phenotypes; ENCODE, Encyclopedia of DNA Elements; GDC, Genomic Data Commons; GEO, Gene Expression Omnibus; gnomAD, genome aggregation database; GTEx, Genotype-Tissue Expression; HMDB, The Human Metabolome Database; ICGC, International Cancer Genome Consortium; METABRIC, Molecular Taxonomy of Breast Cancer International Consortium; PCAWG, Pan Cancer Analysis of Whole Genomes; PDB, Protein Data Bank; SRA, Sequence Read Archive; TARGET, Therapeutically Applicable Research To Generate Effective Treatments; TCGA, The Cancer Genome Atla.
Indeed, we wanted to know if they would lead to the same results and to assess if one of them was quantitatively more robust. The first one was to detect and specifically segment the aggresome itself in each cell, which leads to having three masks, one for the nuclei, one for the cytoplasm, and one for the aggresome. We performed this with the predefined building blocks available in Columbus software (PerkinElmer, Courtaboeuf, France). The second method used both texture measurements in the cytoplasm and a machine learningbased method with no need to specifically segment the aggresome. The texture measurements allowed us to detect cells with and without aggresome. We performed this with the machine learning-based Phenoglyph module available in IN Carta software (GE Healthcare Life Sciences, Vélizy-Villacoublay, France).
Image Acquisition and Analysis Images were acquired on a high-content screening platform (Equipex Imaginex BioMed; Institut Pasteur de Lille, Lille, France) automated by Agilent Technologies (Santa Clara, CA) with the IN Cell Analyzer 6000 microscope (GE Healthcare Life Sciences) at 60× magnification in the nonconfocal mode with a DAPI filter set (excitation 405 nm/ emission 455 nm) and Texas Red filter set (excitation 488 nm/emission 605 nm). Six to 12 images per well were collected. A screening 384-well plate was totally acquired in approximatively 2 h 30 min. Images were analyzed either with Columbus software or with IN Carta software.
Data Analysis Z′ factors were calculated according to Zhang et al. using means and standard deviations from DMSO and 60 nM carfilzomib controls. 11 Plates were validated if their respective Z′ factors were ≥0. 4 and if the percentage of positive cells with 60 nM carfilzomib was ≥70%. EC 50 values of compounds were calculated from concentration-response curves by a nonlinear regression analysis of four parameters with GraphPad Prism 7 (La Jolla, CA).
Hit Analysis The 1280 screened compounds were clustered by chemical similarity. First, the molecular similarities were computed for each molecule pair using the Fingerprint ECFP4. These fingerprints were calculated using the Maya Chem Tools package. 12 Then, we generated a similarity matrix that we used to cluster the compounds using a hierarchical cluster analysis using the "complete" method to build the dendrogram (shown in Suppl. Fig. S2 ). The clustering was achieved using the R statistical language. 13 Finally, compounds were arbitrarily split into 50 distinct clusters. Different physicochemical properties were calculated: molecular weight, cLogP, pKa, total surface area, relative polar surface area, number of H-bond donors, number of H-bond acceptors, number of rotatable bonds, number of aromatic rings, number of aromatic atoms, and number of rings.
Image Analysis for Aggresome Quantification To analyze images, we have used two different software programs for high-content screening to generate statistically significant quantitative and multiparametric data in a fast and automated way. First, we created a script with the Columbus software developed by PerkinElmer. In this system, we used predefined building blocks to perform image segmentation and analyze the cell phenotype. As described in Figure  1 , the script starts by identifying nuclei using the Hoechst 33342 channel (e. g. , emission 405 nm/excitation 455 nm). Different nuclei parameters were measured at this step because they are indicators of cell viability: for instance, nuclei count and nuclei area decrease with apoptosis and death, when Hoechst intensity generally increases.
A second time, we defined the cytoplasm using the Proteostat channel (e. g. , emission 488 nm/excitation 605 nm) and measured the mean intensity of Proteostat reagent in the cells. However, we observed that the variation of the mean fluorescence intensity between positive cells (treated with proteasome inhibitors at concentration and time that induce aggresome formation) and negative control cells (treated with vehicle DMSO) was low. In consequence, the mean fluorescence intensity in cells would not be a robust discriminating parameter for screening applications, and we went further in the script development using another Columbus functionality to detect spots of aggregated proteins in the cells. By comparing Proteostat intensity in positive and negative control cells, we have considered that an intense and wide fluorescent spot localized at the perinuclear level should correspond to an aggresome.
So, we placed two thresholds based on spot intensity and spot area, to select and count specific aggresome spots. In particular, we used the corrected spot intensity provided by the Columbus software, which corresponds to the difference between mean spot intensity and spot background intensity. By dividing the number of aggresomes by the number of nuclei, we obtained the percentage of cells with aggresome, which was a better parameter for the screening application than the mean fluorescence intensity in cells value. It is possible that this improvement is related to the fact that the spot detection is based on corrected spot intensity, which takes into account the background intensity of the spot. Most of the time, the script found one aggresome per cell, but it happened that two spots were selected in the same cell.
That explains why sometimes we obtained a value for the percentage of cells with aggresome that was slightly higher than 100%. Alternatively, a plate with cells treated or not with various concentrations of carfilzomib have been analyzed with IN Carta software using the Phenoglyph module. Each treatment was performed in triplicate wells and six images containing from 31 (for the high concentration of carfilzomib) to 197 total nuclei (for vehicle treatment) were acquired per well. Phenoglyph is a machine learning-based classification combining several features that allows the integrating and addressing of phenotypic cell complexity. In this case, only nuclei and the cytoplasm were detected and all possible features including texture measurement were extracted to be processed by this module.
2B ). In the following experiments presented, images were analyzed with the Columbus software because of its availability in our laboratory. To evaluate our method, we performed several separate dose response experiments using carfilzomib and tunicamycin as reference compounds because they were expected to induce aggresome formation. It is known that the inhibition of proteasome by some compounds, like MG132 or carfilzomib, leads to the accumulation of misfolded proteins in the cytoplasm. These misfolded, and possibly toxic, proteins can then be transported toward the MTOC to form the aggresome before their degradation via the autophagic pathway. 2 Figure  3  shows that aggresome detection in our miniaturized image-based assay leads to a fine quantification of positive cells that allows the determination of robust EC 50 values.
Twelve images were acquired per well and analyzed with the script developed on the Columbus software. A Z′ factor of 0. 57 and an average percentage of aggresome-positive cells of 96. 6% ± 1. 3% for the reference compound carfilzomib were obtained (Fig. 4A ). Two thresholds were set to discriminate primary hits. We selected compounds that induced more than 50% of aggresome positive cells but discarded those that reduced the number of nuclei to an extent higher than carfilzomib. Indeed, the robustness of the image analysis and calculation would decrease if the number of analyzed objects became too low. So, 59 compounds were selected as primary hits and advanced in confirmatory sequential steps, including (1) repeat single-dose testing in triplicate and (2) dose-responsive effect and confirmation of identity and purity of the library samples.
Acknowledgments We thank  Misha Bashkurov  and  Prashanth Dumpuri ,  GE Healthcare , for assistance with IN Carta. We thank  Enzo Life Sciences, Inc. for advice about Proteostat use and commercial support. We thank  Alexandre Vandeputte  for its help with the automated HCS ImaginEx platform (supported by the  Feder 12001407 D-AL  and the  Agence Nationale de la Recherche   ANR-10-EQPX-04-01 ).
Materials and Methods A systematic review method was conducted using the PRISMA protocol  [11]  (Figure  1 ). Three scientific databases were chosen: ScienceDirect, PubMed, and IEEE. In more details, a focus on human soft tissues like upper/lower limb muscles, facial muscles, livers, and skins was done. A special attention was also given on the contributions related to the improvement of computational methods and/or employing effective hardware/software system architectures for realtime medical simulation systems. Finally, other articles focused on analyzing applications of real-time soft-tissue models for system validation, user acceptability and safety requirements were included. Note that in this present review, the method refers to the development strategy of mathematical constitutive formulations of soft-tissue deformations based on a specific computational approach.
The eyeball model includes 1,970 nodes and 8,638 elements. Each muscle model includes 1,100 nodes and 2,673 elements. The computation time needed to solve the model was 20 ms. The precomputation-based FEM is the most popular variation of FEM. This method uses the relationship between the mechanical forces and the deformations precomputed from the accurate FEM with full physical and biomechanical characteristics to train an approximate model. To achieve this goal, a database of the accurate FE simulation outcomes needs to be constructed a priori. The computational accuracy and speed of the simulated model depend on the types of employed approximate techniques such as linear/nonlinear regression functions and machine learning (ML). By using this strategy, Cotin et al. developed a liver surgical simulation system  [12].
Basically, GPUs are comprised of highly parallel architectures. Each separate GPU contains numerous processors and memory segmentations, and each processor works independently on its own data distribution. Consequently, although the clock frequencies of GPUs are often smaller than CPUs, the overall computation speed of GPUs are much faster than CPUs, even when CPUs can be composed of multiple processing cores up to now. Furthermore, various programming frameworks supported for model implementations have been improved in an easier and flexible ways. Two classical interfaces have been employed for programming on GPUs have been OpenGL, application programming interfaces (APIs), DirectX, CUDA from NVIDA, and CTM from ATI. These frameworks have been written in high-level C-programming language which bring many benefits for modelers to implement their developed methods executing on GPU effectively  [17].
The experimental evaluations showed that when the size of the model increased from 125 to 91,125 elements, the computational time was from 1 s to 1 h 39 min 37 s running on Abaqus commercial software, from 0. 149 s to 34. 143 s running on the most powerful GPU of GTX980. Courtecuisse et al. proposed an implementation, called linearized FEM (L-FEM), which was the combination between the linear elastic material with a FEM  [27]. Mafi et al. deployed a model using element-by-element preconditioned conjugate gradient (EbE PCG) FEM method in the GTX470 GPU platform for speeding up the deformation computation in real time  [32]. The implicit time integration of a nonlinear FEM on the GPU platform was also performed by Courtecuisse et al. [33].
System Frameworks. System development frameworks must be selected carefully so that the system could be developed both in high productivity and short time-to-market. Generally speaking, a software framework is a generalization software structure in which programmers can contribute their written codes to modify this structure to a specific application. Taking advantages of available configurations and prebuilt libraries, simulation systems could be developed much more flexibly and faster than in traditional development procedures. The system frameworks can be divided into four groups: the input/output interaction frameworks, the graphic interaction frameworks, the modelling frameworks, and the hybrid frameworks. Regarding the input/output interaction frameworks, haptic devices have been commonly used in the literature, and they are often interfaced with computer systems by GHOST  [3, 19, 44]  and PHANTOM  [44]  input interaction framework.
These input interaction frameworks are all free and open source. Moreover, other standard input interaction devices, such as keyboards, PC mouse, web cameras, and microphones, can also interface with computer systems through application programming interfaces (APIs) supported by Microsoft Windows systems  [42, 45, 48]. Regarding the graphic interaction frameworks, the most employed graphic framework was OpenGL in which 2D and 3D vector graphics can be rapidly rendered by GPU-platform boards. The rendering tasks can be executed on a separate computer system or on a local thread  [3, 6, 18, 19, 38, 44, 45, 59]. In particular, the OpenGL framework can be embedded in multiple types of operating systems such as Android, iOS, Linux, Windows, and various embedded operating systems. Moreover, it can also support for writing in multiple programming languages (e.
g. , C++, Python, C#, and Cg). In addition, the CUDA™ graphic framework was developed by Nvidia Corporation. There have been numerous studies using CUDA framework for implementing their simulation system on of-the-shelf 22 Applied Bionics and Biomechanics graphic GPU boards and achieving great benefits from parallel execution structure in real-time computations  [17, 23, 24, 27, 32, 34]. Another general graphic framework called OpenCL™ was also developed for flexible parallel implementation. An image processing framework called Virtual Place (AZE Co. ) was also used for converting 3D deformations to stereo video frames for creating depth feeling on human visions  [31]. Regarding the modelling frameworks, GHS3D  [26] , TetGen  [47] , and CDAJ-Modeler  [31]  were employed for generating mesh models from CT/MRI images.
Additionally, Maxilim software could also support for boundary condition simulations  [43]. Moreover, the CHOLMOD open source library could also be used for solving the linear systems in real time  [50]. Finally, the combinational frameworks have been developed to provide a much more flexible and multifunctional environment for developing a whole system. MATLAB is a powerful combinational framework including a facial analysis toolbox used for facial expression analysis  [42] ; a toolbox called iso2mesh was used to generate a tetrahedral mesh of a brain and its tumor  [7] ; an artificial neuro network toolbox was employed to train the force-deformation data  [7] ; an optimization toolbox was used to obtain optimal parameters of simulation models that represent for simulated physical quantities; the OpenGL graphic library could be supported in the MATLAB environment for simulating interaction between soft-tissue model and surgical tools  [47].
An Android programming platform was also used  [52]. Additionally, supporting for FEM physical modelling, the Fast FE Modelling Software Platform  [14]  and GetFEM++  [3]  were also employed. For parallel threading, the RTAI-patched Linux was used for satisfying the hard real-time requirements  [18]. Other powerful and more multifunctional system frameworks are the SOFA  [13, 26, 29, 33]  and CHAI3D  [6]  frameworks. In fact, they support various libraries and modules for implementing a complete simulation system including input/output interaction device drivers, geometrical model libraries, modelling algorithms libraries, and graphic rendering modules.
Due to expensive processing time and resources, this scheme is just suitable for offline geometrical validation. For example, real data from patients under maxillofacial surgeries were stored and compared with the predicting appearances for improving the reproducibility capacity of the simulation system  [43]. Moreover, the soft-tissue phan-tom could be used to give the validating data for the geometrical validation  [7]. Regarding the model behavior validations, the physical characteristics of the simulated models must be assessed with the real physical data at different deforming states. One of the most popular schemes is to use the calculated data from a standard commercial simulation software as baseline data. For example, a model using linear viscoelastic FEM was validated through a compression test solved by both the proposed computation approach and the ANSYS finite element software package.
Obtained results showed that the maximum error of displacement was less than 1%  [19]. The ANASYS software package was also used for validating a model based on a machine learning-based FEM method  [36]. Recently, the Marquardt-based model has also been validated by ANSYS in a liver simulation system  [56]. The Abaqus software was also used for validation purpose  [17, 24, 32]. Other used FE packages relate to MSC NASTRAN 2003 which was used by Yarnitzky et al. [58]  and LS-DYNA™ which was used by Joldes et al. [15]. Note that open source packages were also employed. The SOFA framework was the execution environment for performance evaluation between the developed method and the previous modelling methods  [26]. In addition to geometrical and model validations, user acceptability/safety validation needs to be performed to evaluate the quality of interfaces between the system and its users in real clinical applications.
Among various tools for analysing tomographic data, TauFactor, developed by Cooper et al. 30  , is an open-source MATLAB application that calculates tortuosity factors directly from segmented image stacks, as well as volume fractions, surface areas, and other microstructural properties. To calculate the tortuosity factor, TauFactor uses an over-relaxed iterative approach to solve the steady-state diffusion equation of species in an infinitely dilute solution between two fixed value boundaries in a porous medium, i. e. , similar to eRDM method. However, it is worth stating that this simple definition of the tortuosity factor does not account for the multiphysics processes occurring in a real electrochemical system. For example, there are no contributions to ionic transport from electric migration or convection and no double-layer formation at the solid/liquid interface  31.
Numerical methods-simulation TauFactor is an open-source MATLAB application developed by Cooper et al. 31  for characterizing microstructure based on image data; including determining the tortuosity factors. In line with the conventional definition of the tortuosity factor, only simple Fickian diffusion is currently solved in TauFactor, which is only valid in certain cases, e. g. , diffusion of species in an infinitely dilute solution within pores having electrically insulating walls (no double-layer formation). TauFactor uses an over-relaxed iterative approach to numerically solve Fick's diffusion equation at steady-state in order to calculate the flux and hence the associated tortuosity factor. A diffusion simulation can be used to model effective steady-state electronic or thermal conductivity instead, by conceptually replacing the concentration gradient with a potential or temperature gradient respectively, as the mathematics is identical.