dataesr/scientific-paragraphs-categorization huggingface dataset:
For the quantification of minimum Feret diameter (MFD), we set up a pipeline primarily aimed at better defining the edges of myofibers. The workflow of the first part of the MyoProfiler pipeline is reported in Figure  1A  and downloadable in the Supplementary Materials section (Supplementary File S1). The first module of the pipeline was Re-scaleIntensity, which stretches the intensity of the image to the full intensity range, which is particularly useful for 16-bit images (Figure  1B ). The next steps aimed at making the boundaries of muscle fibers sharper, and to this end, we set up a custom "unsharp mask". Briefly, we used GaussianFilter to blur the input image and reduce its background noise (Figure  1C ). We then used two ImageMath modules, which perform simple mathematical operations on image intensities, as follows: with the first one, we subtracted the blurred image, obtained with GaussianFilter, from the rescaled laminin image in order to sharpen the edges of muscle fibers; secondly, we added the sharpened laminin image to the original one (Figure  1C , ImageMath 1+2).
As the name implies, the EnhanceOrSuppressFeatures module is designed to enhance or suppress specific image features of interest. In this case, we enhanced "Line structures" from original rescaled images in order to recover and highlight low-intensity linear structures (Figure  1D , EnhanceFeatures 1). With another EnhanceOrSuppressFeatures module, we made muscle fiber boundaries sharper and clearer by enhancing the "Neurites" feature and using "Tubeness" as an enhancing method (Figure  1D , EnhanceFeatures 2). The signal of the output image was then enhanced using an ImageMath module. Afterwards, another ImageMath module took the average of the two enhanced laminin output images ("Enhance_Line_Laminin_09" and "Enhance_Laminin_11") to further reconstruct the laminin signal without increasing the background noise (Figure  1D , ImageMath 3+4).
Accordingly, we chose to assign the middle-intensity class to the background in order to exclusively select the truepositive signal. The output image is a binary image in which the negative signal is set to 0, while the positive signal is set to 1 (Figure  1G ). In order to ensure the correct performance of the segmentation, we also added a Morph module, which further closed the gaps between muscle fibers and filled small holes, such as capillaries and nerve bundles (Figure  1H ). We then inverted the pixel values of the binary input image by using an ImageMath module (with the "Invert" operation). This step is necessary to ensure that the muscle fibers are segmented correctly, as explained in the next step (Figure  1I , ImageMath 5). The IdentifyPrimaryObjects module received the inverted thresholded image as input and identified muscle fibers as objects by using a global minimum cross-entropy threshold  [29].
FilterObjects (followed by a MeasureOb-jectSizeShape module) is a very important module, since it allows the exclusion of objects that do not meet the criteria for specific parameters determined by the user. For our pipeline, we decided to discard all objects below 40 pixels in area and objects with a minimum value of 0. 32 for the form factor and a minimum of 0. 75 for solidity (Figure  1J ; magentacolored arrows point to discarded objects). Nevertheless, we found that a minimal number of elongated muscle fibers (which is rarely present only in mdx mice or in poor histological sections) could be discarded. Overall, this setting was solid and successful in all image sets used for this paper. We also decided to add an optional module (EditObjectsManually #22, followed by a MeasureObjectSizeShape #23 module), which can be enabled and used in the analysis.
As previously mentioned, we took advantage of the MaskObjects module to mask and remove all one-point nuclei outside shrunken muscle fibers, thus considering only nuclei inside myofibers (Figure  2G , Shrink+MaskObjects). Afterwards, the RelateObjects module assigned the relationship between previously identified objects. In this case, "child objects" are all objects (nuclei) inside "parent objects" (shrunken fibers). Shrunken fibers were then classified into CNFs and PNFs by the ClassifyObjects module. Interestingly, as output values, it is also possible to quantify the number of nuclei per CNF. Finally, we added an OverlayOutlines to highlight myofiber boundaries (red), nuclei (yellow), shrunken fibers (cyan) and nuclei of CNFs (outlined as pink dots; Figure  2G , Re-lateObj+Overlay).
Indeed, F4/80 is a membrane marker, and this module prevents objects with the DAPI signal juxtaposed to the F4/80 signal from being discarded (Figure  2L , ExpandOrShrinkObj). With the MaskObjects module, we were able to mask (remove) objects or regions outside the region of interest. The objects that were partially masked were removed on the basis of the overlap fraction. Mask objects (macrophages) will keep an object (expanded nuclei generated by the previous module) only if the overlap is at least 0. 1, meaning that 1/10 of an object must be in the masking region (expanded nuclei are outlined in purple; Figure  2M , MaskObjects). Afterwards, the RelateObjects module assigned the relationship between previously identified objects (Figure  2M , RelateObjects). In this case, "child objects" are all objects (nuclei) inside "parent objects" (macrophages).
Our data showed that MFD values quantified with MyoProfiler were extremely similar to those obtained with Fiji (WT: p = 0. 8117; mdx: p = 0. 8188). Consistently, fiber size distributions were also almost equal when comparing the two approaches (Figure  3B ). Moreover, the numbers of detected and segmented nuclei were also comparable between the two methods (WT: p = 0. 4880; mdx: p = 0. 5724; Figure  3C ). Consistency in detecting nuclei and segmenting myofibers was also observed for the quantification of CNFs (WT: p = 0. 8906; mdx: p = 0. 7162) and PNFs (WT: p = 0. 9413; mdx: p = 0. 6020; Figure  3D ). Interestingly, MyoProfiler also allowed the quantification of the number of nuclei per myofiber. As expected, we observed a significant increase in the number of nuclei per myofiber in mdx mice compared to their WT littermates (Figure  3E ).
We applied a global Otsu thresholding method since it allows the division of a grayscale input image into three classes. Indeed, the intensity of Sirius red appeared to be composed of high-and middle-intensity signals. Therefore, we assigned pixels in the middle-intensity class to the foreground (Figure  4D ). We also added two optional modules to perform a quality check of the quantification. Briefly, the thresholded image is converted into objects (ConvertImageToObjects), and then this output image is used to display the outlines (in red) of the selected area in the original image. The positive area is then quantified and multiplied by a conversion factor (0. 2289), calculated starting from the pixel/μm ratio value, in order to convert the calculated area from pixel to μm 2. Output data are generated as a ".
This is probably because in CellProfiler analysis, we excluded objects touching the borders. The identified nuclei were then shrunk to one point (ExpandOrShrinkObjects) and masked with 5-pixel-shrunken fibers (MaskObjects) in order to detect CNFs and PNFs with RelateObjects and ClassifyObjects modules. The strategy of identification and quantification of CNFs and PNFs, as with myofiber and nuclei, worked efficiently. We also used the identified nuclei for the detection and quantification of resident (in WT mice) and infiltrating (in mdx mice) macrophages. Once the background noise had been reduced (MedianFilter) and the F4/80 signal had been enhanced (EnhanceOrSup-pressFeatures), we once again used an adaptive minimum cross-entropy threshold for macrophage segmentation. Since F4/80 is a membrane surface marker, we chose to expand nuclei dimensions by 2 pixels and then expanded segmented nuclei with macrophages in order to better detect them.
To finely select true macrophages, we decided to discard all objects that were partially masked (10% of masking region). Despite the challenge in detecting macrophages, especially in mdx cross-sections, we found overall comparable values in CellProfiler vs. Fiji analysis. The SiriusProfiler pipeline, in contrast to MyoProfiler, works on brightfield images. This pipeline consists of a few crucial steps: the splitting of the input image into red, yellow and blue components and then the application of a Threshold module to the image corresponding to the red component. In this case, we used the three-class Otsu method  [27] , in which we assigned the middle-class intensity to the foreground, since Sirius red staining always shows a bright red signal and a less intense one. For Fiji analysis, we basically used the same approach, and we found no differences between the two methods, demonstrating the effectiveness of this method.
Moreover, isolated muscles can undergo poor inclusion or inappropriate storage before the sectioning. Finally, histological artifacts can occur during the sectioning of muscles, or they can have an uneven or irregular signal pattern due to errors occurring during the staining. Even one of these events can make the identification of objects and cellular components difficult, thus also affecting the quantification. This identification is allowed by CellProfiler thanks to the IdentifyPrimaryObjects module, which relies on a thresholding method that needs to be finely tuned in order to realize correct image segmentation. Fortunately, CellProfiler provides a test mode that makes it possible to test the pipeline on selected image sets and correct or change specific parameters once the output image has been generated.
Identification of differential CTCF binding In order to identify differential CBSs on a genome-wide scale, genomics data for CTCF binding should be generated or obtained for different conditions (e. g. ChIP-seq or CUT&Tag data from cancer cells and matching healthy control cells). An outline of the exper-imental assays to obtain such data is provided in  [123]. Data to control for sequencing biases (e. g. from input material without enrichment or from material after enrichment using a control antibody) should be included as well. Particularly in cancer cells, these controls will help to correct for sequencing biases due to copy number variations (CNVs). To improve the reliability of identified CBS, the experiments in both the cancer cells and their matching controls should be replicated. After high-throughput sequencing of the different samples, significant CBS will first be identified in the individual data sets.
After validation of the quality of sequencing quality, the sequencing reads should be aligned to the reference genome (e. g. available from https://www. ncbi. nlm. nih. gov/genome/guide/human/) (Fig. 5 ). Widely-used mapping algorithms for short-read sequencing data are bowtie2  [124]  and BWA  [125]. Each of the resulting data files (BAM format) contains the genome-wide alignments of the sequencing reads for one individual sample. As most sequencing library preparation protocols include a PCR-amplification step, duplicate alignments should be removed from these individual files. Tools for such filtering are included in the Picard suite (https://broadinstitute. github. io/picard) (Fig. 5 ). Significant CBSs in each data set can subsequently be identified by using peak calling algorithms (e.
g. MACS2;  [126] ). In such an analysis, data on CTCF binding is compared to controls to identify regions where signal is significantly enriched only in the CTCF binding data set. Reproducible lists of CBSs can subsequently be obtained by filtering for regions that are identified in all or multiple replicates. To identify differentially bound CBSs between conditions, it is preferred to perform a dedicated differential binding analysis. Such analyses can be further refined using corresponding control samples. Moreover, to compensate for differences in immunoprecipitation efficiency between samples, signal can be normalized by spiking-in a DNA reference of known concentration. Practically, prior to immunoprecipitation each spiked-in sample is supplemented with a fixed amount of control chromatin from a different species.
One option for differential CBS calling is to use the DiffBind package after independent peak calling using the MACS2 algorithm  [126, 128]. In a first step, DiffBind can perform different normalizations, including spike-in normalization. Next, two approaches for differential analysis that were originally developed for the identification of differentially expressed genes: DESeq2 and edgeR  [129, 130]  can be used in Diffbind(Fig. 5 ). A consensus binding motif, either from all CBSs or from differentially bound CBSs can be identified by extracting enriched sequences within the population of CBSs. Available tools for such analysis are the MEME suite  [131]  or the RSAT software  [132]  (Fig. 5 ). Such analysis can be particularly useful to detect sequence biases due to ZF mutations or other modifications that change CTCF binding specificity in the genome.
More computationally efficient file formats have been developed that store only informative bins, with efficiency further improved by storing in a binary manner. Among the most used and adapted interaction matrix formats is the Cooler format  [139]. Cool files can be stored in both binary and computationally-efficient formats. These files can be used as input in the ''cooltools ecosystem", a suite of command line tools and python3 libraries for the analysis of 3D genome organization  [139]. The hicConvertFormat tool from the hicexplorer toolbox permits efficient conversion of various other file formats into the Cooler format  [138]. After this conversion, the computationally efficient interaction matrix must be normalized to correct for experimental biases that stem from variation in GC content, mappability and restriction fragment length (Fig.
However, this finer-scale 3D organization level requires a Hi-C matrix at a high resolution of 2. 5 -10 kb. Hi-C interaction matrices can be visualized and interactively explored using the HiGlass tool  [146] , which allows the highlighting of identified 3D genome features and the addition of external data as well, at different resolutions from a single mcool file (multi-resolution cool files). Subsequent identification of sites where changes in CBSs correlate with 3D genome reorganization can be done by intersection of genomics features. Juicer can identify CTCF motifs at loop anchors, Cooltools can calculate the enrichment of CTCF binding at TAD boundaries and HOMER can annotate loops with CTCF binding peaks. Currently, we are not aware of dedicated tools for systematic differential analysis of combined CTCF binding and 3D organization.
Acknowledgments We apologize to our colleagues whose work was excluded due to space constraints. We thank  Benoit Moindrot  and  Francois Charon  for critical reading of the manuscript and the members of the  Noordermeer lab  for useful discussion. We thank  Wolfgang Heymes  for help with compiling the figures. This work has been supported by funds from  PlanCancer  ( 19CS145-00 ), the  Agence Nationale de la Recherche  ( ANR-18-CE12-0022-02  and  ANR-21-CE12-0034-01 ) and the  Fondation Bettencourt Schueller  to D. N. J. S. is supported by a PhD grant from the  Université Paris-Saclay  ( Ecole Doctorale Structure et dynamique des systèmes vivants ). The funders had no influence on the design of the manuscript or the decision to publish.
2010)  which offers the study of the physical effects of minor merger of galaxies, encompassing a wide range of cosmologically motivated initial conditions; thus fulfilling the goal of this paper. The rest of the paper is organised as follows: Section 2 provides a brief description of the GalMer database and the minor merger models used here. Section 3 provides the details of the disc-DM halo off-set configuration arising in minor merger models. Section 4 quantifies the  = 1 lopsided distortions present in the stellar disc of the host galaxy whereas section 5 provides pattern speed measurement and the location of resonance points associated with the  = 1 lopsidedness. Section 6 provides the details of the kinematic lopsidedness present in the models. Section 7 compares the properties of the  = 1 lopsidedness, as presented here, with the past literature.
MINOR MERGER MODELS -GALMER DATABASE The publicly available GalMer 1  library offers a suite of -body+smooth particle hydrodynamics (SPH) simulations of galaxy mergers that can be used to probe the details of galaxy formation through hierarchical merger process. It offers three different galaxy interaction/merger scenarios with varying mass ratio -the 1:1 mass ratio mergers (giant-giant major merger), 1:2 mass ratio mergers (giant-intermediate merger), and 1:10 mass ratio mergers (giantdwarf minor merger). An individual galaxy model is comprised of a non-rotating spherical dark matter halo, a stellar and a gaseous disc (optional), and a central non-rotating bulge (optional). The bulge (if present) and the dark matter halo are modelled using a Plummer sphere  (Plummer 1911 ) and the baryonic discs (stellar, gaseous) are represented by the Miyamoto-Nagai density profiles  (Miyamoto & Nagai 1975).
The simulation models also include recipes for the gas phase metallicity evolution as well the supernova feedback. The simulations are run using a TreeSPH code by  Semelin & Combes (2002). For calculating the gravitational force, a hierarchical Table  1. Key parameters for the selected minor merger models from GalMer library. model (1)   peri (2)  1,peri tree method  (Barnes & Hut 1986 ) with a tolerance parameter  = 0. 7 is employed which includes terms up to the quadrupole order in the multipole expansion. The gas evolution is achieved by means of smoothed particle hydrodynamics (e. g. Lucy 1977). A Plummer potential is used to soften gravitational forces. The adopted softening length () varies with different merger scenarios, with  = 200 pc for the giant-intermediate and giant-dwarf merger models and  = 280 pc for the giant-giant merger models.
ACKNOWLEDGEMENT The authors acknowledge support from an  Indo-French CEFIPRA  project (Project No. :  5804-1 ). CJJ thanks the  DST, Government of India  for support via a  J. C. Bose  fellowship ( SB/S2/JCB-31/2014 ). This work makes use of the publicly available GalMer library of galaxy merger simulations which is a part of  HORIZON  project (http://www. projet-horizon. fr/rubrique3. html).
Due to the high sequence identity (~70%) between the proteins, we intentionally did not build a homology model for one or the other protein. This is justified, as our main aim is to characterize the global opening and closing mechanism rather than to look into atomistic details such as protonation states. Moreover, we now avoid additional uncertainties about details of the structure as would be inevitably introduced during the homology modelling process. Our composite scheme using coarse-grained molecular dynamics with common constraints elastic network 'ComDYN', that keeps part of the unchanged position constraints between the two protein structures, is explained in the approach below and further details are supplied in the supporting methods, available in the deposited code  (Feenstra, 2019b).
Molecular dynamics simulations In this study, we employed molecular dynamics (MD) simulations using the GROMACS 4. 0. 5 programme package  (Hess et al. , 2008). For efficiency reason, we investigated the applicability of the MARTINI coarse-grained (CG) force field  (Arnarez et al. , 2015; de Jong et al. , 2013; Hsu et al. , 2017; Monticelli et al. , 2008; Periole et al. , 2009) , which is about 500-fold faster than the full-atomistic GROMOS  (May et al. , 2014). This speed-up is obtained at the expense of explicit description of hydrogen bonds in the protein, which necessitates the addition of an elastic network to maintain secondary structure and other tertiary contacts and thus overall protein stability; the elastic network is thus tailed to a particular conformational state of the protein.
Analysis of transporter dynamics Essential dynamics analysis was performed on the GLUT1 and GLUT3 simulations using the built-in analysis tools of GROMACS. To allow this comparison between these two homologous proteins, and allow for focusing on overall motions of the transporter region, we selected the structurally conserved helical segments, as summarized in Supporting Table  S2 , available as extended data  (Feenstra, 2019a). Then, the covariance and eigenvalue calculation were performed on the ensemble of both wild-type systems, using the coordinates of the C-alpha atoms for the full atomistic (AT) simulations, and the backbone particle at the C-alpha position for the coarse-grained (CG) and ComDYN simulations. To analyse the transporter dynamics from the ComDYN, we defined several order parameters as previously proposed by  Nagarathinam et al.
'Overlap' is the fraction of overlap between both distributions, as the integral of the minimum of both functions. 'Shift' quantifies the direction of change, and is obtained by taking the difference in the position of the peak of the two distributions from the ensemble of the simulations (typically, mutation version wildtype). Negative indicates a 'closing' motion, positive is 'opening'. This analysis was performed using the script 'calc_overlap. py', which may be found in the extended data.
Results and discussion Prediction of mutation impact Table  1  lists the mutants of GLUT1 and GLUT3 that we will consider here, which are situated in the conserved RXGRR-motif distal to the transporter region. The table lists the predicted impact upon mutation obtained from  SIFT (Sim et al. , 2012)  and PolyPhen-2  (Adzhubei et al. , 2010). Most mutations are classified as likely pathogenic by both methods, with the exception of GLUT1 R93Q, and GLUT3 R91C and R91H. However, these methods are trained on the dbSNP database which also includes these known mutations, so this should be no surprise. Moreover, these predictions do not allow us to gain any insights into the mechanism by which these mutations may affect transporter function. We performed essential dynamics (ED) analysis  (Amadei et al.
Lucie Delemotte Department of Applied Physics, Science for Life Laboratory (SciLifeLab), KTH Royal Institute of Technology, Stockholm, Sweden The manuscript is improved and describes accurately the insights made possible by the newly developed ComDYN method. I noticed two small issues that should be resoved: In the abstract, a reference is made to "both states of the protein". However, it should be clarified which two states given that this transporter cycles through more than 2 states during its conformational cycle. 1. In the conclusion, "human glutamate transporter proteins" should be replaced by "human glucose transporter proteins".
This does indeed not exclude that pathogenicity linked to a specific mutant might arise from a dysfunction of the binding mechanism. In this study we focus on the first part of the transport mechanism of SLC transporters, and introduce the ComDYN method, which is designed to capture conformational changes between different states. We have now added this point to the manuscript and explicitly mention that, to guarantee the study of all the relevant effects, simulations including the ligand-bound states might yield additional insight on the structure and dynamic of different mutants. However, this would only be feasible using full-atomistic simulations, since sugars are not well parametrized in the Martini force field. As this would pose strong limits on the time scales we could practically attain in our simulations, it is out of the scope of the current study.