dataesr/scientific-paragraphs-categorization huggingface dataset:
Note: Step d has to be performed anytime a new Matlab session is started. Alternatively, in the Matlab ''Home'' tab, section ''Environment'', press "Set Path", select "Add folder," and browse to the Grafeo package folder. Press ''Save''. This way, Matlab will locate the Grafeo package automatically any time you start a new Matlab session. A simplified Grafeo manual is posted with each new package release (see key resources table ). The extensive knowledge of MATLAB is not necessary to use Grafeo and to follow this protocol. However, we strongly recommend familiarizing oneself with the basic Matlab functionalities using online Matlab tutorials. Grafeo version 3 was developed and tested through Matlab 2019a to Matlab 2021a on both Mac and Windows 64-bit operating systems. The majority of Grafeo functionalities will work on Matlab 2017-2018.
However, few functions used previously were replaced with new functions in the 2019 release (some examples are provided in the following sections). For better performance, Grafeo requires a computer with sufficient processor power and RAM. However, it was tested on a portable computer with an i5 1. 70 GHz processor and 8 Gb of RAM. Higher specifications will increase the analysis time and speed up the visualization. Grafeo will run on any processor compatible with Matlab (https://fr. mathworks. com/ support/requirements/matlab-system-requirements. html ), including dual core (I3). However, its performance will depend on the generation of the processor. The minimum of 8 Gb of RAM is recommended. Grafeo is under constant development; please consult the GitHub repository regularly to download the latest version.
See troubleshooting 2. Note: You can upload all ''. txt'' files in a given folder at once, including first-level subfolders, by choosing the ''batch processing  '' option, Figure 2A(i). After the data is uploaded to Matlab, the corresponding Voronoi Diagram is calculated to link each localized molecule to a Voronoi polygon (VP). The local molecule density is estimated as an inverse of VP size and can be used to efficiently filter the data (see step 3). The zr value is 1 if the Z coordinate fit (refer to Nikon   6 ) frame index at which the molecule was detected, (7) column index with the channel tag (in the Nikon NIS elements software, the name for each channel can be set, e. g. , 488, then the same name will be used in the molecular list file), (8) trace length (the number of subsequent image frames the single molecule appeared), (9) Z coordinate column, (10) a flag for the molecules for which Z position fit failed (in the Nikon file it is 'Z Rejected', and this tag replaces the channel tag), (11) a binary tag specifying whether to import all the data (set to 0) or only the molecules with a successful Z position fit (set to 1), (  12 ) the two element vector specifying the minimum number of photons and the maximum localization precision (the molecules with fewer number of photons or greater localization precision will be discarded from the subsequent analyses), (  13 ) the number of header lines preceding the column data, and finally (  14  manual) was successful given the calibration and the constraints on fit parameters.
Note: The alignment algorithm uses Matlab build-in function fminunc and minimizes the distance between two sets of points (channel 1 to channel 2, and then channel 3 to channel 2). To learn more, please refer to Matlab documentation on fminunc and the optimization parameters (optimset, TolX, StepTol, FunTol). The alignment can also be done on the fiducial markers or the subpart of the data (see the last parameter in Figure  3B ), which substantially speeds up the alignment process. For this, either plot ROI (Region of Interest) around a fiducial marker (see next section to learn how to draw ROI in the main Grafeo axes), or filter the data by setting very stringent parameters, e. g. , VD $0. 01, so that almost all the data except the fiducial markers are filtered out. Use the "Poly roi draw'' pushbutton in the "Create ROI'' panel (see below) to plot a polygonal ROI that would include mostly the fiducial markers or relevant data.
mat''). a. Before applying the alignment to the currently loaded dataset, first adjust the filtering parameters, Figure  2D. b. To load and apply the alignment on the currently open file in Grafeo, go to the ''Manual alignment panel'', press ''Load align" and selected the file with the results of alignment, and then press ''Apply align'' Figure  3E. To refresh the plot, press ''Scatter'' in the panel above the main Grafeo axes. 6. Manual alignment. If the automatic alignment results are not satisfactory (or if you do not have Matlab optimization toolbox), you can perform the manual alignment. In the ''Manual alignment'' panel, select the step size (in nm), Figure  3E. As in the automatic alignment, during the manual alignment, channel 2 stays unchanged. You can align channel 1 to channel 2 (column 1), and channel 3 to channel 2 (column 2), by pressing ''Step UP'', ''Step DOWN'', ''Step LEFT'', ''Step RIGHT'', ''Z up'', ''Z down''.
d. To save a twin polygonal ROI and the analyzed data, press "Save twin poly roi". You can load a previously saved twin polygonal ROI using ''Load twin poly roi''. 10. Visualizing the data in the polygonal and rectangular ROI. To visualize the data in the polygonal or rectangular ROI using a scatter plot press the "3d scatter'' pushbutton in the ''ROI plot'' panel Figure  5A (i). For the 3D data, you can rotate the plot manually or change the view by taping view(az, el) in the Matlab command window, where az is an azimuth angle (0<az<180) and el is elevation angle (0<el<360). 11. Generating 3D animated scatter plot. To animate your scatter plot, press ''3D animate'' in the ''ROI plot'' panel, Figure  5A (ii). You will be prompted to provide several input parameters, including initial and final azimuth and elevation angles, the number of rotation steps, and the output file format (''.
Timing: variable time, few to tens of minutes This section describes the spatial statistics functions, such as Ripley's (K, L) and Pair Correlation Functions (PCF) that permit evaluating spatial distribution and spatial correlation between biomolecules. These functions are commonly used to analyses spatial point patterns, such as 2D or 3D dSTORM datasets, composed of the point types (colors) and their coordinates  (Sengupta et al. , 2011; Veatch et al. , 2012; Sengupta, Jovanovic-Talisman and Lippincott-Schwartz, 2013; Shivanandan et al. , 2014; Shivanandan, Unnikrishnan and Radenovic, 2016; Nicovich, Owen and Gaus, 2017; Peters et al. , 2017 , Peters et al. , 2018). For the three-color data set, these functions are computed pairwise. 13. Ripley/PCF cluster analyses and colocalization. a. First, draw a polygonal ROI as described in the ''data visualization section''.
The ROI should be a convex polygon. To test your ROI, press the ''Test roi'' pushbutton, Figure  6A. If the blue points lie outside the red polygon, then the polygon is not convex (compare Figure  6C , a concave polygon, randomized data fall outside of the ROI) with Figure  6D  (a convex polygon, the randomized data fall inside the ROI). The polygonal ROI can be adjusted by dragging and repositioning its vertices. This can be applied only to the latest drawn polygonal ROI. b. Press ''Ripleys'' in the ''Voronoi, Ripley, Graph in ROI'' panel. You will be prompted to provide several input parameters, as described in Figure  6B. c. When the operation is finished, you will be prompted to save the data to a file. See troubleshooting 3. Note: Ripley's analysis can be lengthy for a large dataset and many randomization steps.
molecule density (r) should be estimated prior to fitting (see ''model fitting to ACF and PCF'' in the ''materials and equipment'' section). The uncertainty s can be estimated from the localization precision. However we recommend to perform the fit (see step 15b,i below). i. Fitting the ACF model to Point Spread Function (PSF). Grafeo permits fitting the model to recover s. This requires dataset where the molecule of interest is distributed randomly and does not form any clusters with more than one molecule. We recommend imaging the secondary antibodies on the cover glass in the same imaging conditions as your experiment. First, calculate the PCF as described in the step 13 or load the previously obtained data (''Ripley's analysis'' -> 'Load randomized''). To fit the model press ''Fit PSF'' pushbutton located in the ''Voronoi, Ripley, Graph in ROI'' panel.
Graph-based cluster analysis Timing: variable time, few seconds to tens of minutes This section describes undirected graph-based cluster analysis on the data in the active ROI,  Figure 10 (Shivanandan et al. , 2014; Levet et al. , 2015; Khater, Nabi and Hamarneh, 2020). The graph is computed using 2D or 3D Delaunay triangulation converted to an undirected graph consisting of nodes (data points) connected by undirected edges (Delaunay triangulation DT edges)  (Yang and Cui, 2010). The graph-based analysis permits segmenting objects (clusters) by tresholding the maximum inter point distance (ipd, or graph edge length), the minimum number of points in a cluster (area, or the number of detections/points), and a node degree (the number of connections with other points). These functions are located in the panel ''Voronoi, Ripley, Graph in ROI'', and can be applied to a 2D or 3D dataset.
Note: You can load previously analyzed graph data using the "Load graph" pushbutton. It will also load ROI that was used for the graph segmentation. 18. Voronoi Tessellation visualization. To plot 2D or 3D Voronoi diagrams from the data in a small ROI, press ''3d Voronoi''. This function is not performing a full cluster segmentation. Thus, the Voronoi diagram may be continues. However, it lets you adjust the minimum/maximum displayed VD to suppress the large/small VPs and to disconnect the clusters, Figure  13. When using ''3d Voronoi'' function, the average VD in a ROI will be displayed in the Matlab command window both before and after suppressing the large/small VPs. The last input parameter permits the control of the individual VP plot, as either calculated VP or the convex hull of a VP (the smallest convex polygon/polyhedron in 2D/3D that enclose the vertices defining a Voronoi Polygon).
ACKNOWLEDGMENTS A. P. has received the support of the  French National Research Agency (ANR)  GoodVibration  ANR-17-CE13-0007  and of the EU in the framework of the  Marie-Curie   FP7 COFUND People Program , through the award of an  AgreenSkills+ fellowship  (under grant agreement n  201310 ). The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
spatial statistics functions are Ripley's K function (and its derivatives) and Point Correlation Function (PCF), Figure9. Both K and PCF are functions of the inter point distance d and can be calculated for the univariate (single color) or bivariate (two color) point patterns. The univariate Ripley's K function probes whether points are distributed randomly, are dispersed or cluster at a given scale in the studied area (ROI). The bivariate Ripley's K function determines whether two sets of points are distributed independently, segregate or aggregate at a given scale in the studied area (ROI). REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data Multicolor 3D dSTORM (Peaucelle et al. , https://data. mendeley. com/ sample data sets 2020) datasets/mj4hdpwyb8/1 Experimental models: organisms/strains Arabidopsis thaliana Na Na Software and algorithms InPolygon third-party MATLAB function Guillaume Jaquenot https://uk.
mathworks. com/ matlabcentral/fileexchange/20754- fast-inpolygon-detection-mex MATLAB MathWorks https://www. mathworks. com/ products/matlab. html Grafeo MATLAB script package (Peaucelle et al. , 2020) https://github. com/inatamara/ (Haas et al. , 2018) Grafeo-dSTORM-analysis- (Haas et al. , 2020b) (Haas et al. , 2020a) used.
3, 4 KMT2D is located on chromosome 12, and encodes lysine (K)-specific methyltransferase 2D, which catalyzes the trimethylation of the lysine 4 on histone 3 (H3K4), promoting the expression of its target genes. 5 n two girls and a boy with KS-like features, Lederer et al. 6  identified de novo X-chromosome deletions that encompassed KDM6A (formerly known as UTX) (KS2, MIM 300867). Miyake et al. 7  and Banka et al. 8  subsequently identified pathogenic point variants in KDM6A by targeted sequencing in cohorts of patients clinically suspected to have KS and showed that KDM6A variants account for ~5% cases of KS. KDM6A partially escapes Xinactivation. 6, 9 The canonical transcript (GenBank NM_021140. 3; Ensembl ENST00000377967. 8) has 29 exons and encodes a H3K27 demethylase of 1,401 amino acids (UniProtKB entry O15550).
" Articles on KS1 or papers that did not provide the phenotype of affected individuals were excluded following title and abstract review. Full texts of the remaining articles were reviewed. The criteria for including published patients in the present study were (1) availability of clinical details, (2) unambiguous description of the KDM6A variant, and (3) not duplicated from any previous report. Exclusion criteria were (1) patients with copy-number losses that encompassed additional known developmental disorder related genes, and (2) patients with additional known genetic developmental disorders. Variant analysis. All detected point variants were harmonized according to the canonical KDM6A transcript NM_021140. 3 using MutationTaster2 (http://www. mutationtaster. org/). InterVar (http://wintervar.
wglab. org/) was used to apply the 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for variant interpretation. 19 Protein altering variants (PAVs) were analyzed by the Variant Effect Predictor (http://grch37. ensembl. org/Homo_sapiens/ Tools/VEP) to obtain minor allele frequencies (MAFs) in controls, exon location, in silico predictions, previous reports, and evolutionary conservation. Alamut® Visual 2. 11 (Interactive Biosoftware, France) and UniProtKB (https://www. uniprot. org/uniprot/O15550) were used for exon-skipping analyses and determining affected domains, respectively. PAVs with decreased in vitro demethylation were obtained from the work of Shpargel et al. 20  To analyze copy-number variants (CNVs) encompassing KDM6A, the University of California-Santa Cruz (UCSC) Genome Browser (GRCh37) was used.
Statistical analyses For calculation of frequencies of individual features, we excluded individuals for whom that feature was coded as "UNKNOWN" (which includes instances where presence or absence of a particular feature was not clearly documented or where a feature may not be applicable due to the individual's sex or age) in the clinical proforma (Supplementary Table  S1 ). Absolute and relative frequencies (expressed as n[%]) were used for describing categorical variables, whereas medians (m), interquartile ranges (IQR) and minimum (min) and maximum (max) were used for describing continuous variables. Chi-square/Fisher's exact and Wilcoxon-Mann-Whitney test was applied to study categorical and continuous variables respectively. Two-tailed/adjusted p value <0. 05 was considered as significant for all statistical analyses, which were carried out using the IBM© SPSS© Version 25 software.
ACKNOWLEDGEMENTS We are thankful to all the individuals and their families for taking part in the study. V. F. acknowledges to  CONICYT, Chile's National Commission for Scientific and Technological Research , for its scholarship support (grant number  72160007 ). S. B. acknowledges the Kabuki Research Fund 629396 at Manchester University NHS Foundation Trust. The DDD study presents independent research commissioned by the  Health Innovation Challenge Fund  (grant number  HICF-1009-003 ). This study makes use of DECIPHER (http://decipher. sanger. ac. uk), which is funded by the  Wellcome Trust. See Nature PMID: 25533962 or www. ddduk. org/access. htmlfor full acknowledgement. The research team acknowledges the support of the  National Institute for Health Research , through the  Comprehensive Clinical Research Network, UK.
H. T. B. is supported by a grant by the  Louma G. Foundation. We thank  AKABE (Belgian Kabuki association)  for their funding for systematic panel gene analysis of patients with suspected Kabuki syndrome. We are also grateful to  Kabuki Syndrome Network (kabukisyndrome. com) for the connection with new families affected by KDM6A variants. This work is also supported in part by  AMED  under grant numbers  JP20ek0109280 ,  JP20dm0107090 ,  JP20ek0109301 ,  JP20ek0109348 , and  JP20kk0205012 , by  JSPS KAKENHI  under grant numbers  JP17H01539  and  JP19H06321 , and by  Ministero della Salute  ( RC2020 , to M. L. D. ).
Data and code availability statements The code for TractLearn is available on https://github. com/ geodaisics/TractLearn The code for UMAP  ( McInnes et al. , 2018 )  is also freely available on https://github. com/lmcinnes/umap and the TractSeg pre-trained DWI algorithm, openly available at https://github. com/MIC-DKFZ/TractSeg ( Wasserthal et al. , 2018a ).
Data acquisition 20 healthy males (mean age 20. 9 [SD 3. 3] years) were recruited for the study. Informed written consent was obtained from all subjects in accordance with ethical approval from the local human research ethics committee of Bordeaux university  (France, IRB number 2016-A00765-46). MRI data were acquired on a 3 T Siemens Prisma scanner using a 48channel head coil. High angular resolution diffusion imaging (HARDI) datasets were acquired for each subject over two sessions with a delay of 12 months. Five age-matched subjects referred with mild traumatic brain injury (mTBI) were also included with the same MR protocol and ethical statements, for one unique MR session acquired between the time of the test and retest session for the controls. Diffusion-weighted imaging (DWI): single-shot spin-echo sequence; TE/TR: 80/4700 ms; voxel size: 1.
, 2014 ). We derived the SH peaks from the FOD maps. Spatial correspondence was achieved by first generating a group-specific population template with an iterative registration and averaging approach  ( Raffelt et al. , 2011 )  using FOD images from 45 MR scans (5 mTBI, 20 healthy control subjects acquired in a test session, and 20 healthy control subjects acquired in a retest session). Each subject's FOD image was then registered to the template via a FOD-guided non-linear registration  ( Raffelt et al. , 2011 ( Raffelt et al. , , 2012a)). All preprocessing steps were conducted using commands either implemented within MRtrix3  ( Tournier et al. , 2019 )  ( www. mrtrix. org ), or using MRtrix3 scripts that interfaced with external software packages.
e. the standard deviation of the mean)  ( Tilquin et al. , 2019 ; Vik et al. , 2007 ). The choice to work on a manifold (either Riemannian or using a more classical linear principal component analysis) is to shorten the standard deviation value so that  mainly represents the potential disease effect. A visual comparison of standard deviation between the GLM and TractLearn is provided in supplementary figure  1. (i) To ensure robustness in our dataset,  was estimated using a leaveone-out (LOO) strategy, by randomly selecting inter and intraindividual distances in the manifold from the test session. We consider that the residual  is representative of the abnormalities present in a new subject when it is greater than the model variability learned during the LOO on the healthy control group. A total of 1360 permutation tests were computed in the control group using the LOO.
Tilquin et al. (2019 )  have already proposed to learn the manifold spanned by the normal controls using non-linear dimensionality reduction techniques. In their work, based on T1-weighted imaging, the image of a subject is projected on the control group manifold allowing a com-parison of the reconstruction with the subject's original neuroimaging data. The objective of these projection techniques is to detect abnormal patterns by way of statistical tests on the residuals. In this prior work, the importance of non-linear modeling of the manifold in the reduceddimension subspace was highlighted, as well as robustness to abnormalities detection on a larger dataset, yet without retest session to assess the reliability, as we were able to include in our study. Manifold learning has also been previously used in DWI.
Regardless of the learned manifold chosen, the resulting number and location of abnormalities showed good correlation using the zero-one-loss test. It should be noted that Cohen's Kappa should be avoided as performance measure due to the unbalanced nature of our dataset  ( Delgado and Tibau, 2019 )  (i. e. an important mismatch between the high number of bundles with no voxel abnormalities and the low number of altered bundles). This suggests that, in our dataset, the local effect due to local axonal shear lesion or bleeding (associated with mTBI) is larger than the global effects related to the use of either the test or retest session to compute the learned manifold. It could be speculated that this may also suggest that TractLearn might be more lenient to be able to detect local (disease related) effects over global (acquisition related) effects when using data from various acquisition or scanning protocols.
Riemannian versus Euclidean statistical analysis The GLM, based on a Euclidean framework, has been widely used in the medical and neurosciences communities, and has been implemented in popular software tools, such as Statistical Parametric Mapping (SPM, https://www. fil. ion. ucl. ac. uk/spm/ ). While being effective in the case of low-dimensional data comparison for group study, its main limitation relies on its inability to properly model the data distribution without using a smoothing step. The smoothing of Diffusion-weighted data usually decreases the standard deviation of all voxels metrics coming from the GLM, yet here we have shown that it leads to standard deviation much higher than in our framework (as illustrated in the supplemental material). It could be argued that smoothing still occurs within the TWI calculation; this however can be seen as a 'smart-type' (streamline-informed) smoothing  ( Calamante, 2017 ).
Acknowledgments The computation server used benefitted from the  ERATRANIRMA  project, by  Agence Nationale de la Recherche  [grant  ANR-12-EMMA-0056 ]. We would like to acknowledge the funding from the  Alain Rahmouni grants of the French Society of Radiology. This work was also supported by funding from the  National Health and Medical Research Council of Australia  and the  Australian Research Council.
Data and code availability statements The code for TractLearn is available on https://github. com/ geodaisics/TractLearn. The code for UMAP  ( McInnes et al. , 2018 )  is also freely available on https://github. com/lmcinnes/umap and the TractSeg pre-trained DWI algorithm, openly available at https://github. com/MIC-DKFZ/TractSeg ( Wasserthal et al. , 2018a ).
Coherent genomic groups are frequently used as a proxy for bacterial species delineation through computation of overall genome relatedness indices (OGRI). Average nucleotide identity (ANI) is a widely employed method for estimating relatedness between genomic sequences. However, pairwise comparisons of genome sequences based on ANI is relatively computationally intensive and therefore precludes analyses of large datasets composed of thousands of genome sequences. In this work we proposed a workflow to compute and visualize relationships between genomic sequences. A dataset containing more than 3,500 Pseudomonas genome sequences was successfully classified with an alternative OGRI based on k-mer counts in few hours with the same precision as ANI. A new visualization method based on zoomable circle packing was employed for assessing relationships among the 350 groups generated.