dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and PET Characteristics Sixty-three patients were referred for the staging of breast carcinoma from April 2009 to June 2012. Twenty-five patients were excluded from the analysis, leading to a final database of 38 patients. The causes of exclusion were as follows: PET-CT not performed prior to surgery (n = 8), metastatic tumors on initial staging (n = 4), missing data (n = 1), breast lesions not 18 F-FDG avid (n = 3), hormonal receptors (ER and PR) negative tumors (n = 7), IHC slide unusable (n = 1), and volume of interest too small to be analyzed with LifeX software (n = 1). Patient characteristics are displayed in Table  1. Thirty-four tumors were ER+/PR+ and 4 tumors were ER+/PR-. All patients underwent an adjuvant treatment: radiotherapy and hormonotherapy in 10 patients (26. 3%) or chemotherapy, radiotherapy and hormonotherapy +/-trastuzumab in case of HER2+ tumors in 28 patients (73.
Survival Data Analysis The statistical process for this specific part is summarized in Figure  1. The median follow-up was 6. 5 years (range: 2. 5-9. 1 years) and with 11 recorded events, the 8y-EFS was 71. 0% in the entire population. Among the 11 recorded events, 8 were metastatic recurrences, 2 were contralateral recurrences, and 1 was a local recurrence. The median time to recurrence from the date of diagnosis was 78 months ranging from 21 to 96 months. Of note, 4 deaths were recorded over the 8-year follow-up. Random forest classification found age, clinical stage, SUV max , skewness_ ER , kurtosis_ ER , entropy_ HISTO , and uniformity_ HISTO to be variables of importance to predict the 8y-EFS (Supplemental Figure  1 ). The OOB estimate was equal to 28. 9%. Mean skewness_ ER and mean entropy_ HISTO were significantly higher (p = 0.
RESULTS We compared the patterns of connectivity between lower motor neurons and muscle fibers in 31 individual muscles from 21 mice, ranging from newborn to adult. These datasets include a serial-section electron microscopy (EM) analysis of the entire connectional map (connectome) of the interscutularis muscle at birth (postnatal day 0, P0; Fig. 1A ), brainbow-based reconstructions of omohyoid and lumbrical muscle connectomes at P6-9 (Fig. 1B ), and two fluorescent protein (XFP)-based reconstructions of the interscutularis muscle connectome at P60 (Fig. 1C ). For P3-5, P10-12, and P30, we reconstructed partial connectomes. As serial-section EM takes an extraordinary amount of time and effort, we only used it for the P0 reconstruction and obtained full connectomes at later ages with confocal microscopy instead.
expansion and mitochondria classification, Tien Tran for axon reconstruction in the EM dataset, John Brady for assistance with manual reconstruction of axons from the EM datasets and inspiring discussions on multiple-endplate fibers early in this study, Marta Montero Crespo for insightful discussions and precious assistance with the identification and reconstruction of the endplates in the EM dataset, Marco Badwal for many discussions and his early contribution to the research, including reconstruction of the extra-muscular dataset, Siyan Zhou for help with the initial analysis of the confocal image with the EM and reconstruction of Schwann cells in the nerve, Xupeng Chen for helping with the reconstruction of part of the extra-muscular bundle and assistance with Schwann cell reconstruction.
A low-resolution (3. 57 m/pixel) optical image was acquired from each wafer mounted on the wafer holder, which µ identified the position of each section relative to the wafer holder fiduciaries  (Hayworth et al. 2014). A six-sided polygonal region of interest (ROI) was defined and superimposed onto each section in the optical image of each wafer using the Zen software package (Zeiss Microscopy), to target high-resolution imaging using MultiSEM 505.
Image Stitching and Alignment of the ssSEM sample Stitching and alignment of the dataset presented some challenges due to the large spatial section area, the sparsity of features in many of the tiles and sections, the arc-like structure of the tissue, and the wrinkles that appear in random places throughout the sections. These issues were addressed by the techniques described below. The overall stitching and alignment was similar to Saalfeld et al. (Saalfeld et al. 2012) , where each step was parallelized over image tiles and sections. Stitching. To stitch the tiles of the sections, point correspondences were obtained between overlapping tiles, followed by an optimization process that estimates the tile position and rotation while minimizing the matching correspondences' sum of squared errors.
Muscle fiber reconstruction An expert annotator enumerated and seeded all myofibers that intersected the right side of the volume. This approach required to scroll through the z-axis, identify the rightmost extreme of a myofiber, name it and volumetrically segment part of the fiber to assist visual search of other myofibers. This resulted in the identification of 208 muscle fibers which were skeletonized by a second annotator. Subsequently, the remaining 32 fibers were identified by backtracking fibers after all the initially seeded fibers were completely skeletonized in the volume. This backtracking was initiated both from identified endplates that were not associated with a skeletonized fiber and by a third annotator by exhaustive search for missing fibers in the horizontal middle plane of the dataset.
All of the 32 fibers that were identified in the second step were: secondary fibers that did not reach the right side of the dataset, a small number of damaged fibers (~5) or fibers at the periphery of the muscle that exited the volume at a slightly more medial point relative to the initial saturated box. An additional set of cellular structures with a topology similar to myofibers was identified as being composed of myoblasts, given the absence of sarcomere structure, their shorter extent and irregular sarcolemma. Interestingly, all the secondary fibers possessed an endplate close to the location of the endplate of the primary fibers to which they were morphologically associated (Fig. S6 ) as reported by  (Duxson, Usson, and Harris 1989).
e. (Volume of Fiber)/(Number of Nuclei in Fiber) in the newborn sample, we approximated it by Constant*(Fiber CSA) / (Number of Nuclei in Fiber) under the assumption that fibers maintain a relatively fixed CSA and that all fibers have similar lengths. We reconstructed all the synaptic and nonsynaptic myonuclei of 52 (25 MEFs and 27 SEFs) randomly selected muscle fibers in the newborn EM sample. In total we reconstructed 930 nuclei. To do so, one annotator placed skeleton nodes in each of the nuclei. We then trained a neural network using the software mEMbrain to classify pixels based on three classes: nucleoplasm, nuclear membrane, and pixels that do not belong to the two first categories. The output predictions of such a network were used by a second annotator, who expanded each seeded nucleus leveraging VAST's flood-filling tool and using the classified pixels as constraints.
, Berger, D. R. , Morozova, O. , Drescher, B. , Bidel F. , Kang K. , Lichtman J. W. , Meirovitch Y. 2019 ) and adopting a similar approach to the fiber reconstruction, as explained above. Axon bundles. The 21 axons identified in the nerve reconstruction were traced throughout the muscular tissue by seven annotators (three experts and four extensively trained undergraduate students). The estimated total number of annotation hours exceeded 3500 human annotation hours. The first task was to skeletonize the axonal bundles as deeply as possible iteratively across all possible bifurcations using the annotation software VAST. Once the procedure was saturated, three annotators commenced tracing from identified endplates, connecting anonymous axon segments to identified fascicles, until all axonal branches were either associated with a synapse (the vast majority of cases) or rarely reached specialized zones containing disorganized axonal swellings surrounded by intricate glial processes close to the sarcolemma of a muscle fiber.
Identification of presynaptic innervation in the confocal brainbow stacks To assess the identity of motor axons at each junction, each image stack was individually evaluated using the 'section view' panel in the Imaris software. This panel displays a maximum intensity projection of a re-sizable subvolume of the data stack and allows real-time, user-specified gamma and threshold adjustment. By re-sizing and scrolling a small sub-volume maximum intensity projection of the data stack through the Z-dimensions, the color of each non-overlapping axonal terminal at or near the synapse could be compared to other colors present in the surrounding volume. When no non-overlapping region was available or the structure size was too small to accurately judge the color of the process, the neurite was followed back along its path to see more samples of its color and branches to which it connected.
5nm, 561nm and 633nm, respectively). For each muscle (p1 to p60), we acquired tiled stacks of the entire endplate band, simultaneously scanning YFP and Alexa-647 and sequentially scanning Alexa-594. The voxel size was isotropic in X, Y and Z (e. g. , p1: 0. 31 m, p10: 0. 42 m and p30: 0. 83 m) to facilitate orthogonal-and µ µ µ off-axis slicing in subsequent analysis to count/track the mapping of endplates to muscle fibers. The Zeiss tiled datasets were montaged using the stitch function of the LSM StitchArt module. The Olympus tiled datasets were montaged in Fiji  (Schindelin et al. 2012 ) using the Grid/Collection stitching plugin  (Preibisch, Saalfeld, and Tomancak 2009). For p60 interscutularis muscle, we also imaged YFP at very high resolution on the Olympus system using the 60x objective (0.
Axon tracing Tracing the axonal arbors of two adult interscutularis muscles (P60) was accomplished in VAST  (Berger, Seung, and Lichtman 2018)  using manual segmentation by one expert annotator aided by a second annotator. Each annotator focused on a different part of the dataset. This was made possible due to the very high resolution YFP-based confocal images at 138 nm/pixel. The tracing strategy that we found most convenient was to trace axons in their transverse sections which in most cases required using an orthogonal plane annotation plane in VAST. When tracing became difficult (rarely in these high-quality stacks), to avoid ambiguity, the two annotators raised concerns and attempted to reconcile contradicting opinions. Several regions required postponing the decision for a difficult axonal tracing until the companion axons, at the vicinity of the ubiquitous axon reconstruction, were reconstructed.
Moreover, even when a large motor unit, such as motor unit 10, is not selective to muscle fiber size, it is still part of the periphery of the linear order. In the newborn dataset we found several secondary fibers with immature contractile properties that were attached to other more mature primary fibers. In all cases the secondary fiber possessed endplates near the location of endplates on the primary fibers, as reported by  (Duxson, Usson, and Harris 1989).
ACKNOWLEDGEMENTS We would like to thank the  Computational Connectomics Group (CCG)  of  Prof. Nir Shavit  at  MIT CSAIL  for insightful discussions on input synchronization. We would also like to thank several individuals who greatly contributed to this work. Jincheng Tian  for his enormous efforts to reconstruct the muscle fibers in the newborn sample,  Vikram Norton  for inspiring discussions and hard work on the reconstruction of several axonal arbors,  Emma Yang  for multifaceted contribution to the reconstruction of the newborn sample,  Sophia Laskaris  for insightful discussions, animal care and preparation of the machine learning datasets for the muscle fiber.
Specifically designed procedure then permit to assemble the domains to the linker conformations and to optimize the tandem domain conformations with respect to two sets of NMR measurements: residual dipolar couplings and paramagnetic resonance enhancements. The relative populations of optimized conformations are finally determined by fitting small angle X-ray scattering (SAXS) data. The most populated conformation of the tandem domain is a semi-closed one, fully closed and more extended conformations being in minority, in agreement with previous observations. The SAXS and NMR data show different influences on the determination of populations. During the last two decades, interest in the involvement of disordered regions of proteins in biological processes has steadily increased  1. The influence of disordered regions of proteins has been demonstrated in many phenomena such as: moonlighting in the network of interactions between proteins 2 or in post-translational modifications  3  , short linear patterns (SLiMS)  4, 5  and molecular recognition functionalities (MoRF)  6.
Consequently, the approaches traditionally adopted to determine the structural set of proteins for disordered regions rely on two steps: firstly, the generation of a set of conformations, secondly the fitting of these conformations to various biophysical measurements to determine their relative populations. The conformations are classically generated from databases built in advance from the statistical analysis of protein structures  [8] [9] [10]. Here, we propose a new point of view where all possible conformations are systematically enumerated within limits determined from measurements of chemical shifts. This enumeration problem is addressed as an interval branch-and-prune (iBP) problem  [11] [12] [13] [14] [15] [16]  in which the protein structure is described as a tree and the available experimental measurements permit tree branching and pruning.
2A ). In that way, a group of conformations was progressively built starting from conformation 13, then adding conformations 55, 20 and 150. RMS, R and Q values of 0. 3, 0. 6 and 0. 4 were observed for this group. The plot of correlation factor R, of RMS and Q factors (Fig. 2A ) along the size of conformation set shows that the minima of R, RMS and Q were observed for a group size of 4. Thus, the conformations 13, 20, 55 and 150 (Fig. 2B ) were selected as closed conformations for further analysis. Determining the populations of conformations by fitting the SAXS curve. Theoretical SAXS curves were calculated on the P1LnkP2 conformation with the tool CRYSOL  35  available in the package ATSAS 3. 0. 3 36 using the procedure described in Section "Determination of the P1LnkP2 populations" of Supplementary Materials.
The software BioEn 0. 1. 1  37  and Mesmer 1. 0. 0  38  were then used to determine the sets of populated conformations and their corresponding populations fitting the experimental SAXS curve. as described in the Section "Determination of the P1LnkP2 populations" in the Supplementary Materials. The set of processed conformations includes: (1) the 83 -10 = 73 P1LnkP2 conformations for which the distance between the geo- metric centers of P1 and P2 is larger than 32 Å; (2) various sets of conformations from the subset of closed conformations 13, 20, 55 and 150 previously selected after the PRE refinement step. Two series of BioEn calculations were performed using the whole SAXS curve (Table  2 ) or using the SAXS curve measured for scattering vectors q up to 3. 5 nm -1 (Table  S7 ). The first conclusion which can be drawn from the BioEn calculations (Tables  2  and S7 ) is that whatever is the set of included closed conformations, only one of them displays a non-null population except for the calculation conf13,20,55,150 in Table  S7.
In addition, the superpositions of experimental and theoretical SAXS curves are quite similar in all Bioen calculations (Figure  S2 ). The software Mesmer  38  was used in parallel to produce an independent estimation of populations. Two sets of Mesmer calculation were performed using different numbers of randomly chosen initial conformations (the so-called components) equal to 40 and 60. The same sets of conformations were selected by the two calculations, with very similar populations (Table  S2 ). Interestingly, these conformations are also the ones selected by BioEn, except that the closed selected conformation is 150. The populations of conformations determined by Mesmer are also similar to the ones obtained by BioEn. One can notice a slight decrease of the population of 176 from about 60% to about 50%, and a change of population balance between conformations 12 and 24.
The fitting of SAXS curve using the software BioEn 0. 1. 1  37  Mesmer 1. 0. 0 38 and ATSAS 3. 0. 3  36 select the same set of conformations and produce similar results for the populations of conformation. Nevertheless, the SAXS curve fitting is more reliable for small than for large scattering vectors. Unsurprisingly, it was not possible to determine the relative population of each closed conformations, as all closed conformations display similar global shapes. The conformational landscape of P1LnkP2. The previous analyses allow us to propose the following set of conformations for describing the conformational landscape of P1LnkP2 in solution (Fig. 3 ). The most populated conformation 176, with semi-closed configuration of P1 and P2 displays a population of 60% or 50% depending on the algorithm used for population determination.
For most spin labels and protein conformations, reasonable R correlations were obtained. The RDC values have been recalculated on the conformations 12, 13, 24, 140, 176 using the software PALES and averaged according to populations displayed in Fig. 3. The comparison of calculated and observed RDC values (Fig. 4 ) reveals a reasonable agreement between the two sets of values, with a RMS of 4. 1 Hz, a R factor of 0. 8 and a Q factor of 0. 5. The conformation indexes are given along with the populations obtained by SAXS curve fitting in bold, and with the gyration radius REV2NUMB6 ( R g ). The most closed P1LnkP2 conformations, for which it was not possible to determine the individual populations from the fitting of SAXS data, are plotted within a frame. on the conformations 12, 13, 24, 140, 176 and were averaged using the populations determined in Fig.
0  39. The chemical shifts were averaged according to the population of individual conformations (Fig. 3 ), assuming fast equilibrium between the conformations. The obtained averaged chemical shifts were then analyzed using TALOS-N and the obtained likelihood maps M theo compared to the initial maps M obs. The contour plots of maps M theo and M obs are superimposed for each Lnk residue using black color for M obs and red color for M theo (Figure  S3 ). Inspection of the plots reveals a good agreement between M obs and M theo maps. Indeed, over the 61 residues considered, only 17 display discrepancies for the peak positions in the two maps. Two glycines, G228 and G256, display a displacement of the peak located in the region of positive φ val- ues. As glycine residues display a tendency to populate regions of positive φ values, this may bias the TALOS-N database and consequently the predicted likelihood map.
In addition, several residues display peak variations in the α and β regions of the maps. The residues Y235, T236, Q253 and E278 display on maps M obs (black) two peaks in the regions of α helix and β strand, and the α peak disappears in maps M theo (red). At the contrary, the residues S249, Q253, H255, as well as the residues 260-264 and 269-272, display also two peaks on maps M obs (black) in the regions of α helix and β strand, and the β peak disappears in maps M theo (red). One should notice that only one or two chemical shifts are assigned in H233, V238 and H255, and that the of residues W237, P247, and 252-254 are unassigned: the lack of measured chemical shifts makes the map M obs to rely mainly on sequence alignment with the database of TALOS-N and could hamper the precision of M obs prediction.
Nevertheless, the small quantity of residues displaying discrepancies is of good hope for the quantitative analysis of TALOS-N likelihood maps. Beside, it should be noticed that cross-correlated spin relaxation was recently proposed  40, 41  for directly determining probability distributions of φ and ψ angles: these distributions could be also used for determining φ and ψ boxes for enumeration of conformations. In addition, backbone angle information determined from J couplings could be used as inputs for the TAiBP calculation. The refinement of P1LnkP2 conformations with respect to the NMR and SAXS measurements put in evidence the different influence of global (SAXS) and more local (RDC, PRE) parameters. In particular, the fitting from SAXS data is much less sensitive to closed conformations than the fitting to PRE data.
Methods The P1LnkP2 conformations have been determined in two steps: first using the TAiBP approach for systematically enumerating Lnk conformations, then applying a specifically designed procedure to assemble P1 and P2 to the Lnk conformations. The scripts and programs required to run TAiBP are available at: https:// github. com/ tmall iavin/. Enumeration of Lnk conformations. The Lnk conformations have been enumerated using the threading-augmented interval branch-and-prune (TAiBP) approach recently proposed  20  to take advantage of the systematic enumeration permitted by the iBP approach  [11] [12] [13] [14] [15]  while overcoming the combinatorial barrier arising from the enormous space of protein conformations  44. The approach efficiency was proved for proteins up to 60 residues  20  and TAiBP is thus relevant for the Lnk calculation, which contains 61 residues.
The assembled conformation was then checked to verify whether there is no C α atoms closer than 1 Å. The assembled conformations not verifying this condition were pruned from the calculation. The fragment assembly was implemented using python scripting based on the MDAnalysis 0. 1  47, 48  and numpy 1. 7. 1 49 packages. To scale down the combinatorial explosion of the calculation, a clustering approach, the Self-Organizing Map (SOM)  32, [50] [51] [52]  , which is an artificial neural network (ANN) trained using unsupervised learning, was used to reduce the number of conformations. SOM displays the advantage with respect to the k-means clustering approach that it does not require the predetermined knowledge of the number of clusters. The SOM approach was used after each iBP calculation or assembly step as soon as the number of saved conformations was larger than 1000.
The P1-Lnk protein was then generated by merging the atomic coordinates of P1 for residues 136-238 and the atomic coordinates of Lnk for residue 239-280. Sidechains were generated for the linker in the selected solutions and molecular dynamics trajectories of 100 ps were recorded using the protocol described in Supplementary Material (Section "Molecular dynamics refinement in implicit solvent") with positional restraints on the backbone heavy atoms for residues 225-280. At the end, conformations for which the total energy was higher than -2000 kcal/mol were discarded, which produced 153 conformations of P1-Lnk. The domain P2 was then assembled to P1Lnk. During the previous structural work on P1LnkP2 27 , a model of the complex P1/P2, in which the two PDZ domains were not covalently bound, was built using the HADDOCK server  [54] [55] [56]  from the RDC restraints and the previously determined structures of the human whirlin PDZ domains (PDB entries 1UEZ and 1UF1).
Selection of patients Patients with at least one visit to the gastroenterology department of the University hospital of Rouen in 2014 were screened with the code K50. 9 (Crohn's disease, unspecified) of the International Classification of Diseases 10th edition (ICD-10). Patients with an uncertain diagnosis of CD or undefined colitis were excluded. All visits to the emergency department of the hospital since the beginning of the follow-up in the gastroenterology department were identified using the hospitalization database of the hospital.