dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the fixed time point constraint of our dataset, confirming the source of the unequal ratio of prometaphase and IC-connected cells is challenging. However, the fact that we observed neither cells interconnected as syncytia (i. e. , more than two connected cells in a row) nor microtubule-bare ICs, both of which are features observed in IBs  (6, 44) , suggests that many of the ICs we observed differ from IBs previously described in gonad maturation  (6). Our 3D reconstructions revealed three specific features that differentiate ICs from classical CBs, each of which could give clues about the nature of ICs. First, IC-connected GCs that are connected at locations other than their somas may indicate a time lag that gave CBs the opportunity to shift in position (Fig. 3A ). Second, ICs were wrapped by membranous, lamellipodia-like sheets that stemmed from both connected GCs (Fig.
P3 and P7 The P3 and P7 datasets used in this study were obtained from the medial part of the cerebellar vermis in lobule VIII of unsexed, CD1 wild-type P3 and P7 mice pups, previously published (and available online at https://bossdb. org/project/wilson2019). The aligned and stitched image volumes contained sections cut in the sagittal plane and imaged at 4 nm 2 /px resolution (or a anisotropic voxel size of 4 × 4 × 30 nm 3 ). The preparation of this volume was carried out as described above, and as previously published  (14). Note that for all calculations of morphological features, the downsampled data at lower resolution (i. e. , 32 × 32 × 30 nm 3 per voxel) was obtained using the VAST software package to reduce computational cost.
Checks on dataset usability Initial identification of cells in the EGL was performed manually via VAST (https://lichtman. rc. fas. harvard. edu/vast/) by annotating the nucleus of every cell (or cell soma for cells with disassembled nuclei). Slight shifts in x and y during image acquisition of the dataset prevented exhaustive use of cells located at the top of the EGL, including all cells bordering the pia layer and cells on both side edges. To accurately estimate the number of cells in the volume, we employed the following rule: (i) classify cells as either (a) containing a fully visible nucleus ITV and (b) partially visible OOV; (ii) all cells ITV within a distance range of 7. 5 μm from the center of the cell (the average thickness of a GCP) were flagged as likely OOV. Of 2042, this method yielded an estimate of 932 GCPs.
CellWalker The software CellWalker is scripted in Python (tested on version 3. x) and is available freely under the GNU General Public License (version 3. 0) (https://github. com/utraf-pasteur-institute/ CellWalker). Dependencies are described in the user manual (https://github. com/utraf-pasteur-institute/CellWalker). Cell-Walker supports image sequences of segmented microscopy images in PNG format. CellWalker exports various types of outputs depending on the analysis performed, which include skeleton. swc files,. csv files for results of characterization, and Wavefront OBJ files for 3D visualization. These OBJ files can be directly imported in 3D rendering software Blender.
Calculation of elongation of cross sections Elongation value was calculated for each cross section along the connection to confirm the circularity of the cross section. Elongation of a 2D shape can be calculated as follows Elongation ¼ 1 À ðminor diameter=major diameterÞ The elongation ranges between 0 and 1, with values close to zero indicating circular shapes (see fig. S5 ). The ICs in our data showed elongation values ranging between 0. 14 and 0. 3 (average value, 0. 22), indicating that the ICs were fairly circular in cross section. Therefore, we chose major diameter as the diameter of the ICs.
Cross sectioning of IC along length The ICs were cross sectioned along their lengths using in the Blender software, which works on the OBJ meshes exported from VAST or CellWalker. The cross sectioning was assisted by a skeleton built using Kimimaro algorithm. The Kimimaro skeleton was built for an IC by loading its segmentation (exported from VAST) into CellWalker. CellWalker's skeletonization module helps applying the Kimimaro algorithm on selected segments in the loaded segmented image stack. The skeleton was visualized inside the Cell-Walker to manually identify the nodes on the skeleton that would form an axis approximately along the length of the connection. Cell-Walker's ability to export skeleton as OBJ file allowed us load it into Blender and confirm that the selected nodes indeed formed an axis along the length of the IC.
This axis (also called "centerline") was also exported by CellWalker as an OBJ file. Next, the OBJ file of the IC (from VAST) and OBJ file for the centerline (from Cell-Walker) were loaded in Blender. A Blender-Python script (provided in the GitHub repository above) was written to slice the mesh of the IC along the centerline at each point on the centerline (50-nm spacing decided by CellWalker). The Blender-Python script also calculates the properties of the slices (or cross sections), including major axis, minor axis, and cross-section area (more features can be found in the script).
A CNN classifier was trained to detect cytological features to differentiate cells in the EGL from parallel fibers in the ML and was then applied to uniformly sampled image tiles (300 × 300 pixel 2 ) from the entire ssSEM volume (fig. S1 ). The training dataset consisted of randomly chosen and manually labeled EGL and ML tiles. In the inference phase, the CNN was applied to uniformly sampled tiles from the full ssSEM volume. We used the coordinates of image tiles that were identified by the CNN as ML to create a point cloud and applied a ball-pivoting algorithm to construct a mesh demarcating the EGL|ML boundary. We then applied a cubic spline interpolation to remesh this coarse-grained surface and obtained a dense 3D surface (shown in light yellow in Fig. 2A ). The detailed procedure is as follows.
Training data The resolution (mip) level 3 data were used for training the CNN. At mip level 3, the pixel resolution is 32 × 32 nm 2 per pixel. Fourhundred tiles of size 300 × 300 pixel 2 were randomly chosen from the ssSEM volume representing both EGL (200 tiles) and ML (200 tiles) classes. These tiles were subjected to augmentation to generate a larger training dataset as follows. Train/test split Each selected region was used to generate several augmented tiles. We split-training images into train and test datasets before augmentation to ensure that augmented images from the same tile do not end up in both the train and test datasets. We split the 200 tiles of each EGL and ML class as 160 tiles in the train dataset and 40 tiles in the test dataset, i. e. , 80:20 proportion.
Manual filtering These 5000 tiles belonging to each class are manually filtered to remove blank and partially out-of-volume tiles as well as ML tiles that contain portions of GCs. For EGL class after filtering: 4875 tiles (train: 3900 and test: 975) For ML class after filtering: 3400 tiles (train: 2800 and test: 600) The total number of manually filtered tiles is thus 8275 with 6700 (81%) tiles in the train dataset and 1575 (19%) tiles in the test dataset.
Implementation of the CNN pipeline The CNN pipeline was designed using TensorFlow-keras (version 2. 4). Training was performed on a Kaggle (https://kaggle. com/) kernel with Tesla P100 GPU. The training process was fast (9 s per epoch). Training batch size was set to 64 for optimal use of the GPU. Inference was performed on a local computer (Dell Precision 3000 workstation with Intel Core i9-9900 processor and equipped with Nvidia Quadro RTX4000 GPU). Prediction on 15,636 tiles sampled from the ssSEM took 6. 8 s. The batch size was set to 64. Our code is available as IPython notebooks on GitHub (https:// github. com/utraf-pasteur-institute/ssSEM-EGL-ML-analysis). Datasets are available as indicated in the Data and materials availability section.
Reconstruction of EGL|ML boundary surface using CNN predictions The center coordinates of the tiles identified as ML tiles by the CNN classifier were used to create a point cloud using a Python Open3D library. The normals were assigned to each point in the point cloud such that they oriented toward the EGL. The ball-pivoting algorithm (as implemented in the Open3D library) was then applied to generate a mesh from the point cloud. The assignment of normals facing in the direction of the EGL was necessary to ensure that the ball-pivoting algorithm identifies the required orientation in the point-cloud. The assignment of normals took care of the correct facing of the surface mesh such that it was created at the EGL|ML boundary. This mesh was then exported as a. ply file (Stanford format) and loaded in the 3D rendering software Blender for visualization and manual fine-tuning.
The fine-tuning was necessary because of the anomalies in the ssSEM data such as badly imaged regions (scratches, dark/light patches, etc. ), GCs passing through the ML, and also the nature of the EGL|ML boundary, which is extremely uneven in certain regions. These anomalies led to the generation of holes in the EGL|ML boundary surface that needed manual attention. The fine-tuning step also involved deleting all other points in the point cloud that did not contribute to the EGL|ML boundary. After closing the holes in the reconstructed EGL|ML boundary and removing unwanted points, we performed a remeshing operation using cubic-spline interpolation implemented in 3DSmax software. The resulting dense mesh (that contained approximately 54,000 points) representing the EGL|ML boundary was used for all further analysis.
Acknowledgments: We thank members of the Lichtman lab:  R. Schalek  for P0 volume curation training and support,  A. Suissa-Peleg  for P0 volume stitching and alignment, and  D. Berger ,  N. Dhanyasi ,  X. Lu , and  X. Wang  for fruitful discussions;  J. Livet ,  D. DiGregorio , and  L. Cathala  for critical discussions; all members of the  Zurzolo lab  for discussions and support, especially  M. Rakotobe  for critical discussions on cerebellum biology;  W. Silversmith  for early help during our initial skeletonizations;  J. B. Masson  and  C. Godard  for visualization of electron density using DIVA; data annotators:  Ö. Özen ,  M. M. Trottmann ,  O. Mulhern ,  N. Borak ,  M. Bogan ,  R. McLellan ,  A. Mancevski ,  E. Sun ,  A. Atkin ,  S. O'Shea ,  N. Sullivan ,  A. Vaca ,  C. Vargas ,  D.
Pacheco ,  A. P. Lastra ,  P. Chavan ,  C. Beltran ,  J. Mamani ,  A. Flores ,  A. Meruvia ,  F. O. Salinas , and T. Chrysostomou. C. Z. is indebted to late  M. Michel , whose bequest to Institut Pasteur has contributed to support part of this project; C. Z. is grateful to the  Radcliffe Institute for Advanced Study at Harvard  that allowed the establishment of the collaboration with the  Lichtman lab  through the Fellowship program 2018-2019. Funding: To C. Z. :  Inception program  ( Investissement d'Avenir grant )  ANR-16-CONV-0005 ,  ANR-17-CONV-0005  Projet Q-life,  Cancéropôle Ile-de-France   2018-1-PL BIO-03-IP-1 ,  Big Brain Theory Program  cofunded by  Institut Pasteur and Paris Brain Institute , SRA3-SPAIS seed grant from  Institut Pasteur Paris , and Equipe FRM -EQU202103012630; to J.
W. L. :  U19 NS104653 ,  U24 NS109102-01 , and  P50MH094271  Conte Center; C. Z. was supported by the  Radcliffe Institute for advanced study at Harvard  (fellowship program  2018-2019 ). D. C. C. was supported by the  PPU program  at IP. Author contributions: Conceptualization: C. Z. , J. W. L. , and D. C. C. Datasets: A. M. W. and D. C. C. Methodology: A. M. W. , D. C. C. , and  H. K. Training  of annotators: D. C. C. Software: H. K. Investigation: D. C. C. , H. K. , and O. C. -M. Visualization: D. C. C. , H. K. , and N. D. M. Funding acquisition: C. Z. and D. C. C. Project administration: C. Z. Supervision: D. C. C. and C. Z. Writing-original draft: D. C. C. and H. K. Writing-review and editing: C. Z. , D. C. C. , H. K. , O. C. -M. , N. D. M. , A. M. W. , and  J. W. L. Competing  interests: The authors declare that they have no.
Consciousness is comprised of arousal (i. e. , wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 µm resolution.
Each set of diffusion images (diffusion-weighted images [DWI], apparent diffusion coefficient [ADC] map, fractional anisotropy [FA] map, color FA map, and non-diffusion weighted images [b=0]) was compared to its corresponding stained sections to ensure that radiologic dAAN nodes shared the same location and morphology as the dAAN nuclei identified by histology and immunohistochemistry (Figs. 1, S4, S5). For specimens 2 and 3, the brainstem dAAN nuclei were traced manually on each diffusion MRI dataset with reference to selected histologic and immunohistochemistry sections and canonical atlases  (48, 49). All histology and immunostaining data are available at https://histopath. nmr. mgh. harvard. edu. To facilitate reproduction of our methods, we detail the neuroanatomic boundaries of all candidate dAAN nodes in Tables  S2-S6.
In addition, we provide a comprehensive summary of our anatomic approach in the Supplementary Methods, including a discussion of how the neuroanatomic borders of our candidate dAAN nodes relate to those defined in canonical atlases  (50) (51) (52)  and how they differ from those of the nodes distributed in version 1. 0 of the Harvard Ascending Arousal Network Atlas  (9). Based on the updated neuroanatomic nomenclature and nodal anatomic characteristics reported here, we release version 2. 0 of the Harvard Ascending Arousal Network Atlas (Video S1) as a tool for the academic community (doi:10. 5061/dryad. zw3r228d2; download link: https://datadryad. org/stash/share/aJ713eXY12ND56bzOBejVG2jmOFCD2CKxdSJsYWEHkw). Specimen 1 was scanned at 600 µm resolution as a dissected specimen, consisting of the rostral pons, midbrain, hypothalamus, thalamus, basal forebrain, and part of the basal ganglia (  9 ).
Specimens 2 and 3 were scanned at 750 µm resolution as whole brains. Diffusion data from all three specimens were processed for deterministic fiber tracking for qualitative assessment of tract trajectories, and diffusion data from specimens 2 and 3 were processed for probabilistic fiber tracking for quantitative assessment of connectivity. Probabilistic data for Specimen 1 were not analyzed because differences in scanning parameters and specimen composition -(i. e. , dissected versus whole brain) precluded quantitative comparison. We performed deterministic tractography to assess qualitative anatomic relationships of dAAN tracts, and probabilistic tractography to measure quantitative dAAN connectivity properties.
Criteria for structural connectivity and anatomic classification of dAAN pathways Structural connectivity was defined using a two-step process that incorporated the quantitative probabilistic data and qualitative deterministic data. First, we performed a quantitative assessment for the presence of a connection linking each node-node pair using the probabilistic data for specimens 2 and 3 (see statistical analysis section of the Materials and Methods). A significant quantitative connection requires a connectivity probability measure that exceeds the 95% confidence interval of connectivity with two control regions that are unlikely to have connectivity with dAAN nodes based on prior animal labeling studies (e. g. , (  53 )): basis pontis and red nucleus. If this a priori criterion was met in the probabilistic analysis, we then qualitatively assessed the deterministic tractography data for all three specimens for visual confirmation of tracts connecting each pair of candidate nodes.
Projection pathways of the dAAN Deterministic tractography analysis of the three specimens revealed that tracts emanating from all dAAN candidate brainstem nuclei formed well-defined projection pathways that coursed through the rostral brainstem, diencephalon, and forebrain. Each pathway contained intermingled tracts from reticular nuclei (i. e. , pontine and mesencephalic reticular formations) and extrareticular nuclei (e. g. , monoaminergic, cholinergic, and glutamatergic nuclei), though the distributions of reticular and extrareticular tracts within each pathway differed (Fig. 3 ). Abbreviations are described in Table  1. All candidate brainstem nodes connected with at least one candidate hypothalamic, thalamic, or basal forebrain node based on confidence interval testing (Table  2 ). Quantitative connectivity data are provided in Tables  S7   VTA tracts, to the MPFC via the MFB (Fig.
DISCUSSION In this multimodal brain imaging study, we map the connections of a proposed subcortical network that sustains resting wakefulness in human consciousness. We provide evidence for a human default ascending arousal network (dAAN) comprised of interconnected nodes linking brainstem, hypothalamic, thalamic, and basal forebrain nuclei that are known from prior animal and human studies to sustain resting wakefulness. Further, we identified patterns of extensive structural and functional interconnectivity between subcortical dAAN nodes and cortical DMN nodes, providing a putative architecture for the integration of arousal and awareness in human consciousness. Consistent with prior human  (61)  and animal  (16, 18, 19, 62)  studies, convergent structural connectivity data from ex vivo diffusion MRI tractography and functional connectivity data from in vivo 7 Tesla resting-state fMRI revealed that a midbrain dAAN node -the dopaminergic VTA -is a widely connected hub node at the nexus of subcortical arousal and cortical awareness networks.
Yet connectivity maps also revealed variable relationships between structural and functional connectivity of dAAN nodes, with serotonergic and noradrenergic nodes showing higher levels of functional connectivity with the DMN than would be expected based on their structural connectivity. These observations provide a basis for further investigation of the structural and functional brainstem properties that modulate the human DMN. We release all imaging data, immunohistochemistry data and network-based autopsy methods, including an updated atlas of dAAN brainstem nodes, to support efforts to map the connectivity of human consciousness.
Ex vivo MRI data acquisition For Specimen 1, we dissected the hemispheres from the diencephalon and basal forebrain, and dissected the cerebellum from the brainstem to fit the specimen into the small bore of a 4. 7 Tesla MRI scanner, as previously described  (9). The dissected specimen consisted of the rostral pons, midbrain, hypothalamus, thalamus, basal forebrain, and part of the basal ganglia. Specimens 2 and 3 were scanned as whole brains on a large-bore 3 Tesla MRI scanner. Immediately prior to scanning, the specimens were transferred from 10% formaldehyde to Fomblin (perfluropolyether, Ausimont USA Inc. , Thorofare, NJ), which reduces magnetic susceptibility artifacts (166).
Diffusion MRI of whole brain specimens Specimens 2 and 3 were scanned as whole brains on a 3 Tesla Tim Trio MRI scanner (Siemens Medical Solutions, Erlangen, Germany) using a 32-channel head coil. To increase diffusion sensitivity on the large-bore 3 Tesla MRI clinical scanner that was required to scan a whole brain specimen, diffusion data were acquired using a 3D diffusion-weighted steady-state freeprecession (DW-SSFP) sequence  (47, 167). DW-SSFP is an SNR efficient approach that can achieve diffusion weighting without requiring long TE, making it well suited for ex vivo samples characterized by short T2 (168). We acquired 90 diffusion-weighted volumes and 12 b=0 volumes at 750 µm isotropic spatial resolution. Total diffusion scan time was 30 hours and 31 minutes per specimen. In contrast to the classic DW-SE-EPI sequence utilized for specimen 1, the diffusion-weighting for a DW-SSFP sequence is not defined by a single global b value, as it depends on other tissue and imaging properties, such as the T1 relaxation time, the T2 relaxation time, the TR, and the flip angle (167).
Cortical surface parcellation of whole brain specimens We parcellated the cortical nodes of the DMN in whole brain specimens 2 and 3 for the dAAN-DMN connectivity analysis. DMN nodes were parcellated according to the canonical Yeo 7network atlas (170), as instantiated in the 1000-node Schaefer version of the atlas (171) available at https://github. com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefe r2018_LocalGlobal/Parcellations/project_to_individual. First, to generate cortical surfaces for identification of DMN nodes, we acquired multiecho flash (MEF) data at 1 mm isotropic resolution on the 3 Tesla Tim Trio MRI scanner during the same scanning session during which the diffusion data were acquired. Key MEF sequence parameters were TR = 23. 00 msec, TE = 2. 64 msec, and 6 flip angles of 5˚ to 30˚ were collected at 5˚ increments.
e. , posterior cingulate cortex and precuneus), medial prefrontal cortex (MPFC), inferior parietal lobule (IPL), lateral temporal lobe (LT), and medial temporal lobe (MT). To translate the surface-based MEF labels into volumetric diffusion space we used the boundarybased registration (175) method available through FreeSurfer. Using the transformation matrix obtained through coregistration, we then translated our atlas labels from MEF surface space to volumetric diffusion space for further analysis. We then projected the Yeo atlas labels onto those surfaces using a fixed 2 mm distance into the surface via a nonlinear spherical transform (176). Detailed processing parameters are provided in our code, which we release at https://github. com/ComaRecoveryLab/Network-based_Autopsy. All data were processed using a standard release of FreeSurfer 7.
Since brainstem nuclei change shape, size and contour along the rostro-caudal axis, histological guidance of dAAN node tracing was performed for every axial diffusion image using its corresponding histological section, yielding a 3-dimensional model of brainstem arousal nuclei. For specimens 2 and 3 the brainstem dAAN nuclei were also traced manually on each diffusion MRI dataset, but with reference to canonical atlases and selected immunostains, not serial immunostains or comprehensive histological data. Specifically, the neuroanatomic boundaries of brainstem nodes were visually confirmed with respect to two reference templates: 1) the ex vivo template of histologically guided nodes generated for specimen 1, which we release here as part of the Harvard Disorders of Consciousness Histopathology Collection (http://histopath.
Histology-guided neuroanatomic localization of diencephalic and forebrain dAAN nodes Similar to the brainstem nucleus segmentation, we used histological guidance via coronal sections through the diencephalon and basal forebrain to identify the borders of thalamic, hypothalamic, and basal forebrain dAAN nodes on the diffusion MRI dataset for Specimen 1. To segment the thalamic, hypothalamic, and basal forebrain nuclei for specimens 2 and 3, we manually traced each node based on the Allen Institute human brain atlas  (52).