dataesr/scientific-paragraphs-categorization huggingface dataset:
9°-2. 9°) but less for Miranda-Tibia (up to 7°). Larger differences were observed in the medio-lateral axis (range: 0. 5°-11. 5°) and anterior-posterior axis (range: 1. 9°-11. 7°). The workflow of  Modenese et al. (2018)  connects the tibia to the tibiofemoral axis identified in the femur in the segmented pose, hence when the knee joint assumes the neutral position with zero flexion/extension angle, offsets due to standard supine imaging  (Hirschmann et al. , 2015)  could still be present. The automatic algorithms could inform procedures to adjust the tibiofemoral alignment, but further work is required in this respect. A current limitation of the study is that our evaluation datasets were only four, and despite their heterogeneity, did not include bone geometries presenting abnormalities, deformities and partial geometries due to imaging  (Henckel et al.
, 2006)  or amputations that could be encountered clinically. This limitation is mitigated by the availability of multiple algorithms for the long bones, some of which specifically developed for incomplete geometries  (Miranda et al. , 2010; Renault et al. , 2018) , but addi-tional validation is undoubtedly required. Also, the quality of the bone geometries can vary depending on the medical image modality and their specifications and resolution, potentially affecting the automatic algorithms. This possibility was not systematically investigated in this study, although we can report that, when processing low quality bone models, algorithms based on bone global geometrical features (Kai-algorithms) were generally more robust than those relying on the identification of the articular surfaces (GIBOC-algorithms), while the Mirandaalgorithms seemed the most affected and failed to process some of the datasets.
The importance of a fully automatic approach as a step towards clinical application of modelling can be fully appreciated considering the reduction in total processing time for generating a skeletal model, from segmentation of the medical images to the final Open-Sim model. For the ipsilateral models considered in this study, the bone segmentation and manual modelling steps required a comparable time (between two and four hours each, depending on the experience of the operator). For example, an expert operator segmented the ICL-MRI dataset, the only one that we processed directly, in around two hours using the semi-automatic functionalities of  ITK-Snap (Yushkevich et al. , 2006)  and created an ipsilateral skeletal model in roughly the same amount of time. Based on the TLEM2 segmentation time (Table  3 ), our previous experience  (Renault et al.
, 2018)  and literature reports  (Matsiushevich et al. , 2019) , we estimated similar processing times also for CT datasets. STAPLE executes modelling workflows in seconds, therefore practically halving the total processing time to generate a skeletal model and reducing it essentially to an image segmentation task. This means, for example, that when radiological scans are collected and segmented for planning musculoskeletal surgical interventions, obtaining further model-based biomechanical analyses becomes a quick and inexpensive option. Considering that deep learning techniques are reducing also the segmentation time by order of magnitudes, e. g. Noguchi et al. (2020)  reported ~12 s for segmenting a full-body CT scan of ~600 slices, the generation of personalised lower limb models in a number of clinical applications appears technically feasible.
The STAPLE toolbox is intentionally modular and can automate the generation of entire or partial lower limb models (see supplementary materials for examples). Moreover, the GIBOC and STAPLE algorithms provide a large amount of anatomical information such as articular surfaces (Fig. 6-A ) and bone profiles that can be used for implementing more advanced joint models than those proposed here, e. g. contact models  (Brandon et al. , 2017; Conconi et al. , 2015)  or parallel mechanisms  (Sancisi and Parenti-Castelli, 2011). Extending skeletal models with models of muscle anatomy can also be completely automated  (Modenese and Kohout, 2020). In a previous contribution  (Modenese et al. , 2020)  we have used a non-rigid iterative closest point registration  (Audenaert et al. , 2019)  to map the muscle attachment areas from a cadaveric dataset to the ICL-MRI participant's bones, generating highly-discretized, personalised muscle representations from segmented muscle geometries and simulating their kinematics during gait.
Acknowledgements LM was supported by an  Imperial   College Research Fellowship  granted by  Imperial College London  and by an  Academy of Medical   Sciences Springboard Grant  [ SBF004\1056 ] supported by the  Academy of Medical Sciences , the  British Heart Foundation ,  Diabetes UK , the  Global Challenges Research Fund , the  Government Department for Business, Energy and Industrial Strategy  and the  Wellcome Trust. The authors want to thank  Dr Michael Rainbow  for making available the MATLAB code employed in the Miranda-Femur and  Miranda-Tibia  algorithms. LM wants to thank  Metin Bicer ,  Arnault Caillet ,  Bobby Zhang ,  Clement Favier  and  Andrew Phillips  for the feedback on the manuscript.
We thus created the Det. Belt server, which provides a tool for biochemists to determine the amount of amphipathic compounds surrounding the membrane protein of interest. This tool is available to the community as a web server: https://www. detbelt. ibcp. fr (manuscript version released 24 March 2021). Here we report the creation of the Det. Belt server to represent detergent belts around membrane proteins. This server is a tool to evaluate the gross amount of detergent that surrounds any membrane protein for which a PDB code can be provided, and can be used to predict the amount of detergent around a new target. The amount of detergent estimated using Det. Belt is useful for example in reconstitution assays where knowing the amount of detergent is key to estimate liposome swell and detergent saturation values.
Server Input The server takes as input a 3D structure in the PDB format. The structure must be positioned in the lipid membrane by the PPM server  [22, 23]. Alternatively, a pre-oriented membrane protein database can be queried by keywords or PDB codes. This database was created by combining information coming from two databases of membrane proteins. The first resource is the XML release of the Membrane Protein Topology (MPTOPO) Database ( https://blanco. biomol. uci. edu/mptopo/) (accessed on 15 December 2020), which provides a detailed description and classification of membrane proteins. The second resource is the Orientations of Proteins in Membranes (OPM) database (http://opm. phar. umich. edu) (accessed on 15 December 2020), which provides a large collection of membrane protein structures positioned in a lipid bilayer.
The PDB structure files initially fetched from the OPM database were sorted and annotated according to the classification of the MPTOPO database. Structures are thus oriented with the z axis normal to the membrane plane and coordinate origins at the center of the membrane. The server also takes as input the number of detergent molecules around the protein. Several detergent types can be selected. The number of detergent molecules is used to compute the total volume of detergent, using nominal detergent volumes obtained from the program VOIDOO  [24]. SMILES string for each detergents were retrieved from 2D chemical structures using the NIH web server (https://cactus. nci. nih. gov/translate/index. html) (accessed on 15 December 2020) and 3D structures were created and energy minimized using the program Elbow in PHENIX  [25].
Server Implementation The protein of interest can be interactively visualized in 3D, both prior to submission and after the computation of its solvent belt. The 3D representation of the protein is carried by the NGL library, while solvent volumes are drawn with the ThreeJS library ( https://threejs. org/) (accessed on 15 December 2020). The computation of the detergent belt is performed by a set of Python and R scripts freely available at https://github. com/ MMSB-MOBI/detbelt_coreScripts (accessed on 20 June 2021). All of the metrics described above (h, r, R, V) are available on display and for download. A Pymol  [27]  script is also available for download.
Det. Belt Web Interface In order to represent the detergent belt around a membrane protein, a structure file coming from the Protein Data Bank  [28]  or a typical refinement or model building software must be provided. The membrane insertion of the protein is used for all downstream calculations, which is provided by the "Orientation of Protein in the Membrane" server  [22]. Investigators can query a pre-processed local database, where we downloaded the structure files for integral membrane proteins, and pre-oriented them. Investigators can search for either the PDB identifier or keywords relevant to their proteins (Figure  1A ). Users can also upload their own PDB file, which is required to be pre-oriented by the "Orientation of Protein in the Membrane" server  [22]. The protein is represented in cartoon with a color per monomer.
The red and blue spheres correspond to the membrane insertion; the blue spheres correspond to the cytoplasmic side (Figure  1B ). The membrane insertion for each protein is then approximated as a cylinder. The volume of the belt corresponds to the amount of detergent input by the user multiplied by the volume occupied by each detergent and is represented as a hollow cylinder (Figure  1C ). A color was assigned to each class of detergent to distinguish them easily. The result is displayed using OpenGL and is interactive; users can then move and zoom on their protein in the web browser window and check the result of the data submitted. Metrics are available to users for downstream processes in the window below the graphic representation. A Pymol  [27]  script is downloadable to create high resolution figures, and the metrics are generated in the download tab (Figure  1C ).
The stacking of detergents is not physiological, it is a representation of the volume occupied by each detergent. For a more physiological representation of the detergent mixture, investigators are encouraged to explore this behavior using molecular dynamics simulations. [36]. The ratios define the detergent's molar ratio used during the purification. (B) The measured number of detergents is depicted using Det. Belt. Note that the measured amounts differ completely from the detergent ratio in purification buffers  [36]. PDB code used in this figure: 2hyd.
Detergent and Lipids Database All of the detergents and lipids displayable in Det. Belt were assembled into a database. For each entry, the database contains the full name and chemical formula, the molecular weight, the critical micellar concentration, the reference from where these information were derived, the calculated volume, and the SMILES string, so that it is easy to retrieve structural information for downstream studies. This information appears in a box once the detergent has been selected by the user, with a 3D view of its chemical structure (Figure  4 ). If the investigator asks to visualize several detergents, a tab appears for each detergent queried. The database includes detergents from the maltoside, glucoside, neopentyl glycol, cholesterol derivative, fos-choline, and amine oxide families, as well as thio-derivatives of some of these detergents.
Discussion Detergents are ghosts to biochemical studies of membrane proteins, always present but seldom seen. Investigators know how much they put into buffers and all the purification steps, but do not have immediate feedback on what is left around the membrane protein. Additionally, detergents can have side effects, varying from mild loss of activity to complete denaturation in extreme cases  [1, 3, [43] [44] [45]. Since the effect of detergents is hard to grasp, false impression can originate and lead to misleading data interpretation. The Det. Belt server is a tool that was created to rationalize detergent use by giving investigators an idea of how many detergents are riding along with their protein. Using this server, it is possible to vary the number of detergent monomers, and the type of detergent, thereby virtually screening within seconds hundreds of conditions and visualizing the belt around any membrane protein.
Whole-exome sequencing Exome sequencing was carried out by Eurofins Genomics (Ebresberg 85,560, Germany) using a HiSeq2500 sequencer (Illumina, San Diego, CA 92,122 USA) and DNA libraries made with Agilent's SureSelect Exome V6 + UTR Capture Library Kit, according to the manufacturer's instructions. Sequence reads were trimmed to remove read-through adaptors and low-quality sequences (using fastp  [16] ) and aligned against the GRCh38 build of the human genome (using the bwamem algorithm  [17] ). After the removal of PCR duplicates using Picard tools (https://broadinstitute. github. io/ picard/), the bases were recalibrated using the GATK (v4) tool Baserecalibrator (https://gatk. broadinstitute. org/hc/en-us), as recommended by the Broad Institute Genome Team  [18]. Variants were then called with GATK haplotype Caller (https://gatk.
jcvi. org/), Polyphen2 (http://genetics. bwh. harvard. edu/pph2/), and M-CAP (http://bejerano. stanford. edu/mcap/). Lastly, in order to identify variants potentially associated with MA, we only considered those in genes strongly or exclusively expressed in the testis or described as being essential for spermatogenesis and meiosis in the literature or in the Gene-Tissue Expression (https://gtexportal. org/home/), Human Protein Atlas (https://www. proteinatlas. org), PubMed or Ensembl databases.
Sanger sequencing The variants observed in our analysis were confirmed with Sanger sequencing (carried out by Eurofins Genomics). Primers were designed using Primer 3 Plus software (http://primer3. ut. ee/), and 4 Peaks software (https:// nucleobytes. com/4peaks/) was used to read the chromatogram files generated by Eurofins. For each mutation, primers are reported in supplementary material and data (Supplemental Table  1 ).
E115D, rs761700747) that had not previously been reported in the ExAC database (https://gnomad. broadinstitute. org). Given (i) the absence in the patient 2 of clinical signs of the immunodeficiency-centromeric instability-facial anomalies syndrome 1 (OMIM 242,860) associated with deleterious DNMT3B SNVs, and (ii) the high predicted variability associated with this SNV in the various databases (Table  1 ), we decided to screen the entire exome and to focus on genes that are strongly expressed in the testis. Three homozygous SNVs (p. T4A (rs8100709), p. R74W (rs142862960) and p. T1044I (rs72522121)) were identified in TMPRSS9 gene. Two of the 3 SNVs were considered to be pathogenic and had a low frequency in general population (at 0. 0011 and 0. 0032, respectively, according to gnomAD). The data are summarized in Table  1.
Discussion The SNVs' impact on spermatogenesis We identified three rare variants for patient 2 and then developed a hypothesis for the possible link between consanguinity, maturation arrest during spermatogenesis, and gene defects. The three TMPRSS9 mutations all occurred in the isoform ENST00000332578. 7, which is strongly expressed in testis. Both TMPRSS9 and DNMT3B proteins are strongly expressed in testes, according to the Gene-Tissue Expression database and the Human Protein Atlas. Although TMPRSS9 mRNA expression has also been reported in the liver and spleen, there are no data on protein levels in these organs. High levels of DNMT3B mRNA expression are also observed in testes. The DNMT3B protein appears to be present in many tissues, with the highest levels in testis and placenta. An IHC assessment of a testicular biopsy from a control subject with a positive TESE and complete spermatogenesis revealed strong TMPRSS9 protein staining in the nuclei of spermatogonia A and B and strong DNMT3B protein staining in spermatocytes.
These results were in line with the expression data recorded in the Human Protein Atlas and indicated that TMPRSS9 is expressed earlier than DNMT3B in the seminiferous tubules during normal spermatogenesis. If a defect in the TMPRSS9 gene does indeed cause MA, it would presumably occur at the spermatogonia stage (i. e. before DNMT3B expression). More generally, the absence of TMPRSS9 is associated with the absence of all the proteins expressed downstream (e. g. DNMT3B); this might explain the absence of protein expression in patient 2's testicular biopsy. Furthermore, many pathogenic allelic variants in DNMT3B have been identified  [22, 23] , all of which were associated with the immunodeficiency-  Staining: the antigen-antibody complex was visualized with the DAB detection system, and slides were counterstained with haematoxylin centromeric instability-facial anomalies syndrome 1 (ICF1) (OMIM 242,860).
The variants spanned the entire sequence, and there were no hotspots. Taking into account the great variability in the SNV's impact predicted by the various databases and in the absence of clinical manifestations of ICF1 syndrome, we conclude that the DNMT3B SNV did not have an effect on spermatogenesis. We therefore hypothesize that TMPRSS9 gene defects (caused here by 2 rare pathogenic SNVs) may impair meiosis and lead to a spermatogenic defect. Further experiments will be necessary to refine our results TMPRSS9 and to characterize its putative functional role during spermatogenesis; this role has never been studied, even though the gene is known to be mainly or exclusively expressed in the testis. To date, TMPRSS9 variants have been only reported in a single patient with autism spectrum disorders  [24] , who was a compound heterozygote for 2 frameshift mutations.
This hypothesis was strengthened by the absence of a pathological variant in the WES analysis. In contrast, the reciprocal translocation alone could not explain the MA for patient 2. However, the combination of the reciprocal translocation with the 3 rare variants of TMPRSS9 (2 of which were considered to be deleterious in predictive databases) could probably explained the spermatogenesis phenotype. These observations strengthen the "two genetic defects" hypothesis for spermatogenesis failure, as has been seen for several other pathologies  [34]. In view of the present two cases, the possible impact of a reciprocal translocation on spermatogenesis should be discussed more widely during genetic counselling. Should TESE be suggested, therefore, in patients with a reciprocal translocation? In such a case, one must take account of additional genetic abnormalities.
In 2019, the New York University Langone Health and Facebook AI Research consortium created a fast MRI challenge  [8]  to investigate the use of DL to make MRI scans faster while maintaining high image quality. The dataset released contained k-space data, and the idea was to simulate acquisition matrix subsampling by applying a mask in the Fourier domain and learn the mapping between subsampled images and fully sampled images. Thanks to the advances of DL algorithms, manufacturers now sell MRI machines equipped with DL algorithms to accelerate scan times combining them for example with compressed sensing algorithms  [9]  or with DL denoising algorithms that work directly on the final image, reducing the number of measurements of the signal or number of excitation (NEX)  [10]. Finally, in this constantly progressive research field, numerous algorithms have been developed.
Dataset, Resampling and Denoisning DL Models To reduce the acquisition time in MRI, acquisitions can be made with half the acquisition matrix or half the NEX. However, this leads to undersampled and noisy images. Therefore, a supervised learning approach was used to "learn" a function that maps low quality images (acquired rapidly) to high quality images (acquired slowly). As it was not possible to obtain true downsampled and noisy images owing to the limited availability of the MRI, downsampled and noisy images were simulated by halving the acquisition matrix using linear interpolation to decrease spatial resolution and adding Rician noise in the MRI image, respectively. The NEX was halved by transforming the image in the Fourier domain then adding an additive random Gaussian noise both to the real part and the imaginary part.
The magnitude of the noisy complex image was then computed before finally transforming it back to the spatial (pixel) domain  [18, 19]. The flow diagram of the method used in this study is presented in Figure  S1. DL models were developed with a total of 14,243 unique post-Gd T1 brain images obtained from 85 acquisitions which were split into 9756, 2438 and 2049 images for training, validation, and testing, respectively. DL models were developed using the Keras python library  [20]  which is based on a U-Net architecture  [21] , described in Figure  1. The loss function used to train the model is described below: MixE Y, Ŷ = MSE Y, Ŷ + 0, 1 MGE G Y , Ĝ Ŷ + 0, 1 MS_SSI M Y, Ŷ (1) With: MSE Y, Ŷ = 1 mn m ∑ i=1 n ∑ j=1 ( Ŷ(i, j) -Y(i, j)) 2 (2) MGE G Y , Ĝ Ŷ = 1 mn m ∑ i=1 n ∑ j=1 ( Ĝ Ŷ(i, j) -G Y (i, j)) 2 (3) G Y (i, j) = G Y 2 x (i, j) + G Y 2 y (i, j) (4) G Y x = Y *   -1 -2 -1 0 0 0 1 2 1   (5) G Y y = Y *   -1 0 1 -2 0 2 -1 0 1   ( 6 ) SSI M P Ŷ, P Y = 1 N ∑ P Ŷ P Y 2µ P Ŷ µ P Y + c 1 2σ P Ŷ σ P Y + c 2 µ 2 P Ŷ + µ 2 P Y + c 1 σ 2 P Ŷ + σ 2 P Y + c 2 (7) where: The quality of the model was then evaluated comparing the peak signal-to-noise ratio (PSNR, formula  (8) ) and the structural similarity method between input and output images with the original MRI image as reference.
Image Processing, Radiomics Extraction and Analysis All image processing was performed using a 3D slicer version 4. 10  [23]  and ImageJ software  [24]. A total of 40 BM contours from 11 patients were obtained using the 3D slicer segmentation program designed for brain tumors  [25]. Volumes of interest (VOIs) obtained from the original images were also used on DL input and output images. Radiomic feature values were extracted from BM VOI using the Pyradiomics python library  [26]  as previously described  [27]. Through radiomics extraction in accordance with the Imaging Biomarker Standardization Initiative (IBSI)  [28] , up to seven classes of features can be obtained. (1) The First-order intensity class describes the distribution of pixel values. (2) The Gray Level Co-occurrence Matrix (GLCM) class describes the occurrence of similar pixel values in the image.
Statistical Analysis Data are presented using boxplots with minimum, maximum, 1st quartile and 3rd quartile. p-values < 0. 05 were considered statistically significant. A paired student's t-test was used to compare features in original and DL images. The correlation between original, fast and DL images was analysed with a Pearson test and the Concordance Correlation Coefficient (CCC)  [31]. CCC values of ± 1 describe a perfect positive/negative correlation respectively and a value of 0, no correlation. Features with a minimum CCC of 0. 85 were considered as statistically reproducible and concordant and the radiomic values stable  [32]. All the statistical analysis were performed using python  [33]  and SciPy library. All python codes used in the analysis are available on https://github. com/AurelienCD/Resampling_ Denoising_Deep_Learning_MRI (accessed on 21 December 2021).
Number of batch over which SSIM has been averaged µ P Y and µ P Ŷ : Mean of patches P Y and P Ŷ, respectively σ P Y and σ P Ŷ : Deviation of patches P Y and P Ŷ, respectively c 1 and c 2 : Constants : Mean Squared Error MGE: Mean Gradient Error MS_SSIM: Multi-Scale SSIM corresponding to multiple SSIM image evaluations at different image scales [22]. //github. com/AurelienCD/Resampling_Denoising_Deep_Learning_MRI (accessed on N: The Python code for the resampling and denoising DL model is available at: https: 21 December 2021).