dataesr/scientific-paragraphs-categorization huggingface dataset:
After processing the ROI according to the parent matrix or filter method, statistical features such as coarseness, business, correlation, entropy, and energy are calculated. There are multiple open-source, in-house developed, and commercial software solutions that facilitate the exploration and development of radiomics in head and neck cancer. Beyond open-source software tools, a number of groups have also developed in-house tools for radiomic analyses, often in the MATLAB environment; however, they are not publicly available to our knowledge  [14, [42] [43] [44] [45] , thus cannot be validated. One such example is a modified version of Computational Environment for Radiotherapy Research (CERR) used for texture analysis.
7%) with larynx cancer were included. The authors considered nine discriminant features. Their results suggest that the most homogenous lesions at baseline were associated with better prognosis  [4]. From the group of Bogowicz, M. et al. we identified three papers. In one of them the authors tried to show the association of post-radiochemotherapy (RCT) PET radiomics with local tumor control. To this effect, the authors evaluated the models against two radiomics software implementations, 11 out 128 (9%) patients with larynx cancer were included, and the study showed the potential of post-RCT FDG-PET radiomics for early identification of patients with a high risk of local tumor recurrence  [57]. In another study, the authors tried to predict local tumor control after RCT of head and neck squamous cell carcinoma and Human Papilloma Virus (HPV) status using CT radiomics.
Precision Medicine, Big Data, and Machine Learning in Larynx Cancer The aim of precision medicine is to accurately define diseases in order to find personalized and individual therapies. This approach should improve healing, reducing the spectrum of side effects  [3]. Large data volumes are acquired during the disease evolution, thus providing various options for the definition of appropriate biomarkers or marker patterns in different stages of the disease, on different steps of the analysis, and of most diverse materials and data sources. To exploit this big amount of retrospective data, using mathematical algorithms, a quantitative high-throughput extraction of radiological features based on meta-datasets (DICOM format) should be carried out to evaluate all those image features that cannot be perceived with the human eye against the clinical outcome as the final objective reference for predictive analysis, thus creating a place for radiomics and texture analysis in the spectrum of precision medicine  [62]  (Figure  2 ).
amount of retrospective data, using mathematical algorithms, a quantitative high-throughput extraction of radiological features based on meta-datasets (DICOM format) should be carried out to evaluate all those image features that cannot be perceived with the human eye against the clinical outcome as the final objective reference for predictive analysis, thus creating a place for radiomics and texture analysis in the spectrum of precision medicine  [62]  (Figure  2 ). The development of "big data" and "machine learning" techniques have increased our possibilities to identify the different characteristics of a disease containing information about prognosis and diagnosis with regard to the status, the outcome, and the therapeutic response. Those variables can be analyzed in the context of clinical data and the course of the disease while the clinical data still serve as diagnostic and prognostic parameters, helping clinicians to improve decisionmaking protocols looking for precision medicine.
According to the definition proposed in "Big data in health research: An EU action plan", "Big data in health" encompasses high volume, high diversity, biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points  [63]. However, big data come from a variety of heterogeneous information sources, such as clinical trials, electronic health records, patient registries, or clinical databases, posing big problems for interoperability and fusion of the information towards building decision support systems (DSS) with clinical value. Machine learning can be broadly defined as the collection of computational methods using data to improve performance or make accurate prediction.
However, big data come from a variety of heterogeneous information sources, such as clinical trials, electronic health records, patient registries, or clinical databases, posing big problems for interoperability and fusion of the information towards building decision support systems (DSS) with clinical value. Machine learning can be broadly defined as the collection of computational methods using data to improve performance or make accurate prediction. These programmable methods can learn from the data, and hence automate and improve the prediction process. Recently Parmar et al. assessed a large panel of machine-learning methods for overall survival prediction of head and neck cancer patients. They investigated 12 machine-learning classifiers belonging to the 12 classifier families: Bagging (BAG), Bayesian (BY), boosting (BST), decision trees (DT), discriminant analysis (DA), generalized linear models (GLM), multiple adaptive regression splines (MARS), nearest neighbors (NN), neural networks (Nnet), partial least square and principle component regression (PLSR), random forests (RF), and support vector machines (SVM).
Image Segmentation Image segmentation represents one of the most important steps during radiomic workflow. It can be performed manually, semiautomatically or automatically. Manual segmentation represents a time-consuming procedure. This also needs to be carried out by a specialist, and a high interobserver variability can be expected. Automatic segmentation represents a more reproducible and faster way to do our segmentation and is very useful for large imaging datasets. Semiautomatic segmentation needs an interaction between the observer and software. Most often, the observer needs to define the VOI and may refine the semiautomatic segmentation results manually. Although some automatic and semiautomatic segmentation methods exist, they are not suitable for every VOI and thus need to be upgraded for certain problems and used with standardized adjustments when they are applied  [7].
Feature Extraction Imaging features are extracted from the VOI areas defined by the segmentation. Every feature represents one part from the whole radiomic concept. The tumor intensity is quantified by an intensity histogram that displays the three-dimensional fractional volume data for the range of voxel values. Shape data contain values like the total volume, surface area, compactness, and actual form of the lesion. Texture-based features are compiled by mathematical algorithms that deliver second-order statistics or co-occurrent matrix features. Hundreds of values may be generated containing information about additional qualities like densities, homogeneity, grey shades, clusters, correlations between those in different settings, and much more. A common challenge in radiomics is to define a non-redundant set of imaging biomarkers from the vast number of extracted features, and to improve the radiomic performance, redundancies should be excluded  [7].
Image Processing To make a successful image processing procedure, an appropriate bioinformatics approach must be chosen that is able to cope with big data, to obtain a reproducible and solid biomarker signature. However, this make it necessary to perform careful data management to improve the study results and diminish the influence of outliers, blurring values, interobserver variability, and diffuse readings  [8]. Hundreds of radiomic features can be extracted from each tumor; as many patients as possible should ideally be integrated into the study. This is of course often limited-even in multicenter studies. Commonly, those relevant markers are determined in retrospective cohort studies. When a radiomic signature has been identified, it should be verified in an independent cohort in a prospective study  [6].
The parameter η is determined by an educational expert and was set to 0. 7. In the simulations each algorithm was run with a group of 1000 students, and was required to sequence 200 questions for each student. Each question belonged to one of 10 possible skills, uniformly distributed. We generated questions according to an absolute difficulty level, uniformly distributed between 1 (easiest), and 5 (hardest). Students' initial competency level in each skill were also uniformly distributed between 0 (no skill knowledge) and 1 (full knowledge of skill). All the sequencing algorithms had access to "historical" data generated by the simulation engine in a pre-simulation step so they can build their internal models. This data contained 1000 students each solving 500 randomly selected questions. We now describe key simulation results.
The numerical model needs to save the computational efforts to ease the work of building designers and engineers. It is also relevant in the research context of sensitivity analysis or parameter estimation problem, where a large number of direct model computations is required. Surprisingly, despite the widespread use of models in research and practice, the efficiency of the numerical models have received little attention in the literature. Models are implemented in software such as EnergyPlus  Crawley et al. (2001) ,  ESP-r Clarke (2013) , BSim  Rode and Grau (2003) , etc. The governing equations are well detailed but the description of the numerical methods is generally brief and no discussion on their efficiency are provided. Moreover, the validity of the assumption that the numerical error can be negligible has never been verified.
Moreover, the floating point numbers have the highest density within the interval ( 0, 1 ) and their density decays exponentially when we move further away from zero. Figure  1  shows the accuracy of the floating points in Matlab TM environment. It is generated using the eps( x ) function in the Matlab environment. For a numerical model written using dimensionless equations, the accuracy of the floating points scales with O( 10 -17 ). The potential commonly used in the physical model of heat and mass transfer is usually the temperature T in [ K ], the vapor pressure P v or the capillary pressure P c both pressures in [ Pa ]. According to Figure  1 , if the numerical model is written using the temperature or the vapor pressure with their physical dimension, the accuracy of the floating points scales between 10 -14 and 10 -13 , respectively.
The standard finite-differences method The standard semi-discrete scheme based on central finite-differences can be written as: du j dt = F o u j-1 -2 u j + u j+1 ∆x 2 , j = 1,. , N -1 , n 0 , ( 7 ) whose starting value is directly obtained from the initial condition: u j ( 0 ) = 1. Many approaches can be used for the temporal discretisation of Eq. (  7 ). Here the algorithm is implemented in Matlab TM environment using the function ode45 providing an efficient explicit Runge-Kutta scheme. In whole figures, this approach will be referenced as FDM.
(1928) : ∆t 1 2 F o ∆x 2. ( 10 ) The algorithm is implemented in the Matlab TM environment. Interested readers are invited to consult  Davies (2004) ;  Fraisse et al. (2002)  for more details on this approach and Deconinck and Roels (2016);  Jimenez et al. (2009) ;  Reynders et al. (2014)  for examples of applications in building physics.
Methods implementation and metrics of their efficiency All the numerical algorithms for the classic and advanced schemes are written using dimensionless variables, while for the RC model, the physical dimensional variables are used. The numerical solutions are computed using an adaptive time step ∆t using Matlab TM function ode45  Shampine and Reichelt (1997)  with an absolute and relative tolerances set to 10 -4. The efficiencies of the method are evaluated in terms of three criteria: (i) the global error of the numerical solution, (ii) the significant digits of the solution and (iii) the computational run time to compute the solution. To evaluate the error, a reference solution u ref ( x , t ) is computed using a numerical pseudo-spectral approach obtained with the Matlab TM open source toolbox Chebfun  Driscoll et al.
The significant correct digits of the solution are evaluated according to  Soderling and Wang (2006) : scd ( u ) def := -log 10 u ( x , τ ) -u ref ( x , τ ) u ref ( x , τ ) ∞. As the RC approach computes directly the fields in their physical dimension, a scaling transformation is performed to compute the errors. The last criteria is the computational (CPU) run time required by the numerical model to compute the solution. It is measured using the Matlab TM environment with a computer equipped with Intel i7 CPU and 32 GB of RAM. We define the ratio: R cpu def := t cpu τ , where t cpu [ s ] is the CPU time and τ is the final physical time of the simulation.
The error remains stable at O( 10 -4 ) corresponding to the tolerance set in the ode45 solver of the Matlab TM environment. Although, if one is interested in the heat flux density, the minimum number is r = 30 to reach an error lower than 10 -2. Table  1. Efficiency of the numerical models for linear heat diffusion. Numerical Model scd [ -] ε ∞ [ -] for T ε ∞ [ -] for q R cpu [ ms/h ] R2C.
Acknowledgments The authors acknowledge the  Brazilian Agencies CAPES of the Ministry of Education , the  CNPQ of the Ministry of Science, Technology and Innovation , for the financial support for the project  CAPES-COFECUB Ref. 774/2013  as well as the support of  CNRS/INSIS (Cellule énergie)  under the program "Projets Exploratoires -2017". The authors also acknowledge the  Junior Chair Research program  "Building performance assessment, evaluation and enhancement" from the  University of Savoie Mont Blanc  in collaboration with The  French Atomic and Alternative Energy Center (CEA)  and  Scientific and Technical Center for Buildings (CSTB).
3D Reconstruction and Conception of the Shoulder Prosthesis The model used was constructed by image processing of an MRI file obtained on a subject without a pathology in the shoulder complex, using the technique of 3D reconstruction through several stages. The data conversion method used to develop the finite element model of the shoulder is illustrated in Figure  1. This method is based on tomographic sections taken from radiographs of the shoulder complex performed in a repetitive manner at regular intervals. From these, we can reconstruct a numerical volume model closer to reality. Indeed, the first step is to recover the data from tomographic sections (CT-scan), namely a sequential image file with a 0. 5 mm interval, type "DICOM". X-ray images are transferred to the Mimics image processing software.
Indeed, the smoother a surface is, without any alterations and distortions, the better the mesh is. However, the surface generated by Mimics software is not perfect. It is common to find anomalies, most often in the form of folds, where the surface gets wrapped around itself to create a bead. These invisible irregularities disrupt the generation of mesh by creating an infinite number of superimposed elements or elements of almost zero size. For this reason, RapidForm software is used to perform a smoothing operation by filling gaps and rebuilding a regular automatic mesh using NURBS (Non-Uniform Rational Basis Splines) surfaces. The three-dimensional geometric data obtained are converted into the data format of type Parasolid x_t. After three-dimensional regeneration of the model using Mimics, the same file is run in the Remesh module to eliminate any debris and correct some of the shape defects (asperities) caused by the segmentation procedure.
The resulting model is then exported to an "STL" file. The content of the file is a mesh composed of several triangular facets. However, this mesh cannot be used as is for a finite element calculation. Indeed, segmentation sometimes generates gaps or overlays of elements (discontinuity). In addition, it is performed automatically without any possible control of dimensional parameters of the elements, and does not necessarily respect the constraints necessary for building a mesh of good quality. It is possible to mesh a surface provided that it is very regular. Indeed, the smoother a surface is, without any alterations and distortions, the better the mesh is. However, the surface generated by Mimics software is not perfect. It is common to find anomalies, most often in the form of folds, where the surface gets wrapped around itself to create a bead.
These invisible irregularities disrupt the generation of mesh by creating an infinite number of superimposed elements or elements of almost zero size. For this reason, RapidForm software is used to perform a smoothing operation by filling gaps and rebuilding a regular automatic mesh using NURBS (Non-Uniform Rational Basis Splines) surfaces. The three-dimensional geometric data obtained are converted into the data format of type Parasolid x_t. In this work, the model of Delta3 inverted shoulder prosthesis was chosen for its wide use  [30]. Figure  2  illustrates the 3D drawing of this prosthesis using CAD software Solidworks. In this work, the model of Delta3 inverted shoulder prosthesis was chosen for its wide use  [30]. Figure  2  illustrates the 3D drawing of this prosthesis using CAD software Solidworks.
Then, a numerical model was developed into the MATLAB software representing the joint glenoid-humeral by a spherical connection and muscles by eleven fibers. An algorithm was developed to calculate the contact reaction force of the glenoid, the strength of the muscles and the contact position of the glenoid, using the kinematic measures of the first part  [34]. Bioengineering 2019, 6, x FOR PEER REVIEW 5 of 14.
Then, a numerical model was developed into the MATLAB software representing the joint glenoid-humeral by a spherical connection and muscles by eleven fibers. An algorithm was developed to calculate the contact reaction force of the glenoid, the strength of the muscles and the contact position of the glenoid, using the kinematic measures of the first part  [34]. The action of contact of the cup on the glenosphere was modeled by pressure. In fact, the contact force was the compressive force between the humerus head and the glenoid cavity undergone by the arm during abduction movements. The surface of the applied pressure, to get closer to reality, varied depending on the position of the arm by subdividing the glenosphere into four parts as shown in Figure  5. The resulting force of the applied pressure was chosen using the direction shown in Figure  6.
Conclusion The main objective of this study was to propose a solution to the problem of bone graft regeneration in the case of revision of the implanted inverted shoulder prosthesis using the BIO-RSA method. In order to achieve this goal, we started by designing the prosthesis using Solidworks software. In the second phase, we reconstructed the anatomical model of the shoulder in 3D using Mimics and RapidForm software. ANSYS Workbench software was used to conduct a finite element analysis of different efforts applied to the prosthetic shoulder with different biomaterials of the graft. Emphasis was placed on the abduction motion of the arm because it is this gesture that most solicits the articulation of the shoulder. This movement was studied in three precise positions: 30 • , 60 • and 90 • for three individuals.
Bioinformatic protocol ARResT/Interrogate  [32]  was the main bioinformatics platform used in this study. Both Vidjil  [37]  and IMGT  [38]  resources are available through ARResT/Interrogate as built-in tools and were employed for specific aspects of this work, mainly analysis of rearrangements with unclear annotation. Data are deposited at EMBL/EBI European Nucleotide Archive (ENA), accession code PRJEB32668. Demultiplexing was performed accepting no mismatches. Reads were annotated with EuroClonality-NGS primer sequences (to trim non-amplicon sequences, and for the cPT-QC-based quality control), paired-end joined, dereplicated, immunogenetically annotated  [39] , and eventually classified into rearrangement types (complete and incomplete, and other special types like intron-Kde rearrangements), or 'junction classes'.
Reads without rearrangement were excluded from the total read count used for relative abundances. cIT-QC sequences described above and elsewhere (see accompanying manuscript by Knecht et al. [30] ), were identified in the data through their immunogenetic annotation. Their counts served both as 'in-tube' control and for normalization per primer set: total cIT-QC cells are divided by cIT-QC total reads, the resulting factor used to convert rearrangement reads to cells, and those cells then further divided by total input cells (15,000 in this study). Identified IG/TR sequences were defined as index sequences if their abundance after cIT-QC normalisation exceeded 5%. ARResT/Interrogate can track the DNJ 3′ stem of a junction, the sequence remaining stable during IGH or TRB clonal evolution in case of V replacement or ongoing V to DJ rearrangements.
In 16/ 19 q/ddPCR evaluated cases the rearrangement was confirmed by ASO-PCR, in three of these on a subclonal level. Conversely, 52/311 clonal IG/TR rearrangements were detected by Sanger sequencing only, when applying the 5% NGS threshold: for 5/52 sequences (1 TRG, 2 TRB-VJ and were spiked to each sample as library-specific quality control and calibrator. Pipetting was performed in a 96-well format. The data analysis was performed using ARResT/Interrogate 2 IGH-DJ) the relevant primer was not present in the NGS primer set, in 12/52 cases no explanation was found for the discrepancy. However, in most discordant cases (35/52) the Sanger identified sequences (7 TRD, 8 TRB-VJ, 6 TRG, 4 TRB-DJ, 2 IGK-VJ-Kde, 5 IGH-VJ and 3 IGH-DJ) that were also detectable by NGS, but with an abundance below 5%.
In 36/39 q/ddPCR evaluated cases the rearrangement was confirmed by ASO-PCR (including all low NGS positive sequences), in 14 of these on a subclonal level. The overall concordance between Sanger and NGS, including negative libraries, was 78%. Interestingly, in 12/29 B-lineage ALL samples the evolution of the dominant clonal IGH sequence was identified employing a special tool in ARResT/Interrogate. The evolved clonotypes shared the DNJ stem with the dominant one, but the VND part of the rearrangement differed (example in Fig. 4 ). Assay performance was also analysed by standardized evaluation of QC samples (cIT-QC and cPT-QC, see accompanying manuscript by Knecht et al. [30] ) and showed high intra-and inter-lab consistency without statistically significant differences between the five labs.
In contrast, the EuroClonality-NGS assays do not require specific workflows for particular targets, thus enormously streamlining the process of MRD marker identification. This becomes increasingly important in times of MRD-based treatment requiring early patient assignment to the respective MRD risk group. Critically, our assays provide ways to evaluate primer performance and overall quality of the whole NGS run (primers in the cPT-QC) and of each tube (spike-ins in the cIT-QC, see accompanying manuscript by Knecht et al. [30] ). Such functionalities are embedded in the ARResT/ Interrogate pipeline, further standardizing the whole workflow. A challenge for correct MRD marker identification in NGS data is the phenomenon of accompanying lymphoid clones that might be mixed up with the leukaemia-specific ones.