dataesr/scientific-paragraphs-categorization huggingface dataset:
Image quality measurement To quantify image quality, we implemented a plugin in Vaa3D (RRID:SCR_002609, version 3. 497, http://vaa3d. org) that computes the features described in detail in  (Bray and Carpenter 2018). A brief description of the features measured with the plugin can be found in the Supplementary Materials. The Vaa3D plugin used is available at: https://github. com/Vaa3D/vaa3d_tools/tree/master/hackathon/linus/image_quality. To obtain image quality measurements associated with each individual reconstruction, we also computed these features within the 3D data only in the image voxels belonging to SWC nodes of the trees. Specifically, we extended the "image_quality" plugin, allowing us to specify a SWC file associated with an image volume and compute the image quality metrics only using the intensity information of those voxels.
Image preprocessing Due to varying properties of image acquisition pipelines among institutions, it is challenging to define a universal data preprocessing protocol for reconstruction. We performed Brainbow color separation, 8-bit data type conversion and color inversion for brightfield images as preprocessing tasks prior to automatic reconstruction algorithm bench-testing, using Vaa3D and its plugins. A brief description of the preprocessing steps can be found in the Supplementary Materials. Figure  S1  shows examples of the effect of each preprocessing step in the imaging datasets.
Generation of gold standard reconstructions A total of 14 labs contributed 17 datasets spanning a broad diversity of species, brain regions, neuron types, labeling methods, microscopy techniques, and imaging resolution (Supplementary Materias Table  1 ). Gold standard annotations were produced in the BigNeuron Neuron Annotation Workshop at Allen Institute, Seattle, June 15-17, 2015. Each reconstruction entry was manually validated by at least 6 annotators working collaboratively. Notably, due to limited image quality, some reconstructions can be ambiguous. Annotators were asked to make their best judgment in such cases and vote to maximize the agreement between them. Thus, we are confident the final reconstructions reflect the best possible reconstruction generated by human experts from the respective image given the limited image quality, time, and resources; we thus adopted this dataset as the gold standard for the automated reconstruction algorithms.
Development of automatic reconstruction algorithms We ported 44 implementations of 32 methods for automatic tracing as BigNeuron plugins to Vaa3D (Supplementary Materials Table  2 ), including 16 already existing tracing algorithms and 16 novel ones specifically developed within the scope of this project and not previously published (for these latter ones, we provide a brief description in the Supplementary Materials). Of the 44 implementations, 7 used a two-step process of inclusion of filamentary processes  (Gu and Cheng 2015)  to improve the results of several algorithms and were not included to provide a fair comparison among all base algorithms. Two variants were found too slow or did not generate results (PSF and LCMboost_2). We therefore considered only the remaining 35 base implementations (called "algorithms" hereafter) for bench testing on the BigNeuron image data (Supplementary Materials Table  2 ).
Algorithm bench-testing To bench-test the algorithms, we ran all algorithms on the image stacks of gold standard reconstructions after preprocessing. Given that the testing of 35 algorithms in the Gold166 release image volumes (163 unique 3D stacks) implied running 5705 automatic reconstruction processes, those were parallelized in High-Performance Computing (HPC) facilities. The processing was distributed using the TITAN supercomputer at Oak Ridge National Laboratory (USA), as well as supercomputers at Lawrence Berkeley National Laboratory (USA) and Human Brain Project (Europe). Processes that took longer than 1 hour of computing time were terminated and did not provide results for the algorithm-dataset pair being tested. As a result, 5571 automatic reconstructions were generated (https://github.
com/BigNeuron/Data/releases/tag/gold166_bt_v1. 0). example of a bench testing script for calling the tested algorithms can be found at: https://github. com/Vaa3D/vaa3d_tools/blob/d8e434c93708ab2a5bd349a79d9093d11aecf9d1/bigneuron_ported/benc h_testing/ornl/script_MPI/gen_bench_job_text_scripts_short. sh.
Morphological analysis of neuronal reconstructions To consistently compare morphological features that are dependent on the size of the trees, we first scaled both gold standard and automatic reconstructions using the pixel size information of each dataset (available at: https://github. com/lmanubens/BigNeuron/blob/main/scaling_gold. csv). Subsequently, to ensure consistent distance measurements in the 3D space and generation of consensus trees, we resampled the reconstruction nodes so that all the reconstruction segments had a length of 2 µm. To avoid loops, disconnected subtrees, and inconsistent hierarchies, we sorted the reconstructions using the "sort_neuron_swc" plugin (using the gold standard soma location as the root) in Vaa3D. Some of the tested algorithms did not perform radius estimation by default.
Interactive data analysis app The datasets collected in this project are large, and the exploration is time-consuming. To allow fast interaction with the data, we developed an interactive analysis web app in Shiny (https://www. shinyapps. io/). Shiny allows the development of web applications that build on the R programming language utilities for statistical analysis and data plotting. Within the server, users can analyze dataset images, gold standard annotations, automatic reconstructions, and metadata associated with each dataset. The app allows users to interactively choose the image quality and tree morphology metrics used for dimensionality reduction and clustering analyses and perform reconstruction quality benchmarking. The Shiny app source code can be found at: https://github. com/lmanubens/BigNeuron/tree/main/shiny_app.
Generation of consensus trees Next, we developed an algorithm to iteratively merge the reconstructions obtained by different automatic algorithms. The aim of the algorithm is to conserve tree regions reliably retrieved by different algorithms and discard possible algorithm-dependent artifacts in order to obtain a consensus reconstruction that is potentially closer to the ground truth. A brief description of the steps of the algorithm can be found in the Supplementary Materials. An implementation of the algorithm can be found as a Vaa3D plugin called "consensus_skeleton_2" and the source code is publicly available at: https://github. com/Vaa3D/vaa3d_tools/tree/master/hackathon/xiaoxiaol/consensus_skeleton_2. We did statistical tests for errors in morphological metrics of automatic reconstructions using the "stat_compare_means" function of the ggpubr package (version 0.
Statistical comparisons among groups were performed using the Wilcoxon test (two-sided); *** indicates p<0. 001, **** indicates p<0. 0001. The number of reconstructions in each group are: All algorithms=1113, True best=20, Predicted best=20, and Predicted worst=20. D Pair plot showing the percentage of difference between automatic reconstructions and gold standard annotations for the true best algorithms and the predicted best algorithms. Colors indicate different neuron datasets in the testing set. E Representative images of the gold standard annotation, the true best automatic reconstruction algorithm, and various predictions using the regression results. Red lines represent neuron tree branches, blue dots indicate root points, red dots indicate roots of the trees, and green dots indicate terminal points.
E Predicted percentage of different structure for automatic reconstructions of fMOST axon image blocks. F Maximum intensity projection of a representative fMOST axon image block. G-H 2D projections of the neutube (G) and Advantra (H) algorithms automatic reconstructions in the image volume represented in B, predicted to provide best and low accuracy results respectively. I Percentage of different structure between automatic reconstructions and gold standard trees for the Zebrafish larvae RGC neurons. Mean and Standard Errors of percentage of different structure are shown as bar plots. J Principal Component Analysis of gold standard datasets accounting for their image quality metrics. Each point is one gold standard image volume, and the color indicates the dataset it comes from. Arrows represent the direction of each variable in the PCA space.
Before WES all patients underwent routine clinical examination, hormonal tests and instrumental diagnostics (including assessment of bone age, ultrasound (US) and/or MRI if necessary). For patients who undergone gonadectomy, additional data included -age at gonadectomy, indication for this procedure and histology result. All patients had a karyotype determined by standard methods, and for eight 46,XX boys, molecular cytogenetic studies (FISH) was done to determine a Y-X translocation of the SRY locus. The main inclusion criteria included the followingambiguous external genitalia (female genitalia with an enlarged clitoris, posterior labial fusion, or an inguinal/labial mass and/or inguinal hernia or male genitalia with bilateral undescended testes, micropenis, isolated perineal hypospadias, or mild hypospadias with undescended testis), delayed or incomplete puberty, virilization with typical female external genitalia, primary amenorrhea, breast development in a typical male, a discordance between the genital appearance and the karyotype and family history of DSD  (1).
Genetic Testing Exome sequencing of genomic DNA was performed for 79 patients. Enrichment for WES was generated with Agilent SureSelect Human All Exon V4, followed by paired-end sequencing on the Illumina HiSeq2000 platform with TruSeq v3 chemistry. Data analysis was performed from the sequencing platform using manufacturer's proprietary software. All reads were aligned against the human reference genome (NCBI, GRCh37/hg19 or GRCh38/hg38) via Burrows-Wheeler aligner. Single-nucleotide variants and small insertions and deletions (InDel) were selected with GATK version 1. 6. Picard version 1. 62 (http://broadinstitute. github. io/picard/) and SAMtools version 0. 1. 18 were used to mark duplicate reads and to process the BAM files manipulations, respectively. For each case, singlenucleotide polymorphism (SNP) and indel variants were annotated to dbSNP 138 identifiers using the Genome Analysis Toolkit (GATK) Unified Genotyper.
The SNP Effect Predictor bioinformatics tools on the Ensembl website (http://www. ensembl. org/homosapiens/userdata/uploadvariations), gnomAD (https:// gnomad. broadinstitute. org/) and ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) were used to annotated the novel variants, followed by manual screening of all variants by using the Human Gene Mutation Database Professional Biobase (http:// www. biobaseinternational. com/product/hgmd/). Potentially pathogenic variants were confirmed by Sanger sequencing.
46,XY DSD Cohort The main genetic causes of DSD in the XY cohort that underwent WES are pathogenic variants in the AR and NR5A1 genes (16/79, 20. 2%; Tables  1, 2  and Figure  1B ). Eleven patients from 10 families carried hemizygous pathogenic variants in the AR gene. Of the nine AR variants associated with XY DSD, eight have not been reported to cause either PAIS or CAIS and they are absent from public SNP databases (Table  2 ). Eight of the variants are missense and one is an essential splice donor site. Of the eleven patients, five were classified as CAIS and seven registered as female. Two girls were defined as PAIS due to clitoromegaly. Six females had a gonadectomy between 6 and 16 years old, and of these, one girl was diagnosed with a sertolioma at 14 years (Table  1 ). Four of the eleven patients were registered as male with a PAIS phenotype.
ACKNOWLEDGMENTS We would like to thank all Ukrainian regional pediatric endocrinologists and gynecologists for referring patients:  Pogadaeva N, Enhovatova V, Horoshaya O, Shevchenko I, Behytova T, Malashonok V, Siryk N, Slepyan E, Ivanenko L, Bachinska I, Gavrilova I, Kolesnik N, and cytogeneticists Kulbalaeva S, Kurakova V.
FUNDING This work is funded in part by a research grant from the  European Society of Pediatric Endocrinology , and by the  Agence Nationale de la Recherche  ( ANR ),  ANR-10-LABX-73   REVIVE ,  ANR-17-CE14-0038-01 ,  ANR-19-CE14-0022  and  ANR-19-CE14-0012  and by the  Ministry of Health of Ukraine  ( 0117U003036 ).
DATA AVAILABILITY STATEMENT The data presented in the study are deposited in the ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) repository, accession numbers (VCV000492789, VCV000974911, VCV000658124, VCV001202584, VCV001202585, VCV001202591. 1, VCV00 1205840. 1, VCV001202603. 1, VCV000935575, VCV001202587, VCV001202586, VCV001202588, VCV001202589, VCV00 0929443, VCV000981464, VCV000631595. 5, VCV001199399. 1 and VCV001199405. 1).
INTRODUCTION Functional MRI (fMRI) is a non-invasive imaging technique that indirectly probes brain function by providing a measure of the metabolic activity consequent to an increased neural activation. Two experimental setups are commonly used to acquire fMRI data, task-fMRI, and resting-state fMRI (rs-fMRI). In the former, the subject is asked to follow an experimental paradigm, whereas in the latter the subject is asked to rest in the scanner and not do or think of anything in particular. Standard approaches for the analyses of task-fMRI data are based on the well-known general linear model (GLM) adapted by  Friston and colleagues in 1998  in the context of fMRI data analyses  (Friston et al. , 1998). This approach requires prior knowledge of the task parameters and timing of events, as well as assumptions about the neural and hemodynamic responses.
The A4D-fMRI acts concurrently in space and time, thus overcoming the limitation of previous deconvolution approaches that consider the two problems of spatial and temporal regularization as decoupled processes. In our method, the regularization flow is performed according to the time and to the local geometry of the image to evaluate the presence of an edge and its local strength. The rest of the article is organized as follows. We first introduce the PDEs regularization theoretical framework, illustrate the mathematical problem, and how we solved it. Next, we validate the A4D-fMRI on phantom data and on task-fMRI data from 51 subjects using the experimental paradigm as ground truth and positively compare its performance to the TA approach. We also applied the A4D-fMRI approach to rs-fMRI data.
, 2011) : x(v, t) = u n (v, t) * h(t). Real time series acquired using the fMRI technique is corrupted by different kinds of noise and artifacts given by mechanisms that do not reflect any neurophysiological function, such as heart rate, respiratory fluctuations, motion artifacts, thermal noise, and scanner drifts  (Lund et al. , 2006). For this reason, we added noise to x(t) thus obtaining the acquired fMRI signals y(v, t) = x(v, t) + ǫ a = u n (v, t) * h(t) + ǫ a 1 gitlab. inria. fr/cobcom/a4dfmri where ǫ a is the additive random Gaussian noise with zero mean and standard deviation σ a. A scheme representing the model of the phantom fMRI data is shown in Figure  1.
Validation of Synthetic Data To test and validate the A4D-fMRI, we scaled a 3D activation map computed with the FMRIB Software Library (FSL 2  ) Physics-Oriented Simulated Scanner for Understanding MRI (POSSUM 3  ) in the range [0, 3], with a 2-mm isotropic resolution (Figure  2A ). We multiplied it by a piece-wise constant signal u(t) of 100 s, with one onset of 40 s, from 20 to 60 s (Figure  2B ). Starting from u(t), we simulated the acquired fMRI timecourses y(t) as explained in the previous section. We tested the A4D-fMRI on several simulated images obtained by adding different amount of noise for each experiment. We regularized the whole image using the A4D-fMRI as shown in Section 2, and we recovered the voxel-wise activity-inducing signals û(t). To evaluate the results, we compared the simulated activityinducing signal u(t) with the recovered û(t).
Task-fMRI Data In addition to validating the A4D-fMRI on simulated data, we evaluated its performance on real data. Although our approach was conceived to be applied to rs-fMRI data, or in any situation where no experimental paradigm is given, we test it here on task-fMRI data. This testing strategy provides a ground truth, i. e. , the task timing, which can be used to assess the performance of the algorithm. The study was conducted on the motor task-fMRI data from 51 subjects from the Human Connectome Project (HCP) database  (Van Essen et al. , 2013). The data underwent a minimal pre-processing pipeline  (Glasser et al. , 2013) , which includes correction of gradient-non linearity-induced distortions, registration of each image frame to the signalband reference image to achieve motion correction, phaseencoding distortion correction, EPI image distortion correction, registration of the fMRI volumes to the structural data, coregistration of the fMRI data to the Montreal Neurological Institute (MNI) space, masking and fMRI image intensity normalization to the 4D whole global mean of 10 3.
One is very small converging toward zero and a second one stabilizing at the variance of the noise in the data. This stopping criteria was chosen because it was the minimal number of iterations required for the Pearson correlation to stabilize for all tasks fMRI datasets. To highlight the ability of the A4D-fMRI to recover brain activations without knowledge of the experimental paradigm, we qualitatively compared brain regions recovered using the A4D-fMRI to those recovered using the GLM as implemented in the FSL library. The GLM model requires as input the exact occurrence of the tasks that the subject is asked to perform. We run the A4D-fMRI on the whole brain thus blindly recovering brain activations, without prior knowledge on the intervals of the evoked stimuli. To estimate the results obtained using the A4D-fMRI, we computed the voxel-wise correlation maps, by estimating the Pearson correlation coefficient (r) between the recovered activations and the five tasks.
The tasks were simulated as a piece-wise constant signal with unit amplitude when the subject is performing the task and zeros elsewhere. As for the GLM, we included the five tasks in a design matrix and we estimated the regressors' weights with FSL. Results showing differences and similarities of both approaches were qualitatively assessed. Subsequently, we quantitatively compared the results obtained using the A4D-fMRI with the ones given by the TA, on the sample data composed by 51 subjects. We first defined four ROIs located in brain regions that are involved in the five considered tasks. The ROI related to the tongue was bilateral, whereas for the hands and the feet we defined separate ROIs for the left and the right side of the brain. To do this, we started by defining the ROIs from the work proposed by  Roux et al.
Resting-State fMRI Data Finally, we applied the A4D-fMRI on the rs-fMRI image of one subject (100307) from the HCP database. The data were acquired with a SIEMENS MAGNETOM Connectome Syngo MR D11 using a gradient-echo EPI sequence (TR = 720 ms; TE = 33. 1 ms; flip angle = 52 • ; FOV = 208 × 180 mm; slice thickness 2. 0 mm; number of slices = 72; 2. 0 mm isotropic voxels; multiband factor = 8). The subject was asked to lay in the scanner without thinking about anything in particular. The number of acquired frames was 1,200 and the duration of the acquisition was 14:33 min. In the case of rs-fMRI data, the task paradigm is unavailable since the subject does not perform any task in the scanner. The data underwent the same minimal preprocessing of the task fMRI data as proposed in the HCP pipeline  (Glasser et al.
Task-fMRI Data Figure  6  shows the structure tensors D (Equation  8 ) in a coronal brain slice for a representative subject of the HCP database. Note that the image refers to the spatial maps, and the temporal dimension is not represented. The presence of ellipsoids oriented in different directions rather than spheres shows the anisotropic nature of the regularization. As for the real data analyses, and specifically the comparison between the A4D-fMRI and the GLM, we show that the correlation maps related to each task computed with the A4D-fMRI were well overlapped to the values of the regressors coefficients obtained using the GLM as shown for one illustrative subject (100307) in Figure  7. The GLM shows results that follow the GM, while the activations found with the A4D-fMRI, which again were performed across the whole brain, and not masked with the GM mask, cover also voxels across the white matter.
This is not the case if a threshold is applied on the TA output, because for a low threshold both regions would be considered active, whereas for a high threshold both would result inactive. We compared the performance of the implementations of TA and A4D-fMRI by running both algorithms on the same hardware. We used the fMRI motor task data analysis with a size of 109 × 91 × 109 × 284 where 284 is the number of volumes and 109 × 91 × 109 is the MNI space dimension. TA, as implemented in the TA toolbox, and constrained in the GM voxels, ran in for approximately 9-9. 5 h while our algorithm processed the whole dataset in 7 min per iteration, with a total computation time of 4. 5-5 h for 40 iterations.
The A4D-fMRI could help in the recovery of time series and spatial maps that could be post-processed afterwards to perform statistical analyses. Another application of A4D-fMRI is the detection of innovation-driven co-activation patterns (iCAPs)  (Karahanoglu and Van De Ville, 2015). Indeed, iCAPs identifies spatially overlapping activation maps that reveal transients in spontaneous neural temporal activity on rs-fMRI data. It has been demonstrated that decomposing rs-fMRI data using iCAPs reveals the spatiotemporal dynamics below the socalled resting-state networks. Within this context, A4D-fMRI could be used to recover the innovation signal, which is derived from the recovered brain activations. A second application of interest is structure-function mapping  (Deslauriers-Gauthier et al. , 2020b)  where A4D-fMRI would serve as a preprocessing step.
ACKNOWLEDGMENTS Data were provided [in part] by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research  and by the  McDonnell Center for Systems Neuroscience at Washington University.
Introduction The discovery of position effect variegation in Drosophila melanogaster paved the way towards revealing the importance of chromatin contexts in the regulation of gene expression  (1, 2). Since then, cytogenetic and molecular profiling of the epigenome, as well as topological analyses of chromatin architecture, have allowed the mechanisms involved in partitioning the gene-rich euchromatic fraction from the repeat-rich heterochromatic fraction to be elucidated. Large protein complexes specific to insulator DNA sequences contribute to partitioning chromatin domains with distinct identity at multiple scales. These complexes maintain the identity of adjacent genomic loci with antagonistic chromatin marks and functionality, and more globally influence the formation of long-range chromosomal interactions  (3).