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  6. Massive black holes (BHs) at the centres of massive galaxies are ubiquitous. The population of BHs within dwarf galaxies, on the other hand, is evasive. Dwarf galaxies are thought to harbour BHs with proportionally small masses, including intermediate mass BHs, with masses 102
    Free, publicly-accessible full text available August 31, 2023
  7. Abstract We present multiwavelength observations of the Type II SN 2020pni. Classified at ∼1.3 days after explosion, the object showed narrow (FWHM intensity <250 km s −1 ) recombination lines of ionized helium, nitrogen, and carbon, as typically seen in flash-spectroscopy events. Using the non-LTE radiative transfer code CMFGEN to model our first high-resolution spectrum, we infer a progenitor mass-loss rate of M ̇ = ( 3.5 – 5.3 ) × 10 − 3 M ⊙ yr −1 (assuming a wind velocity of v w = 200 km s −1 ), estimated at a radius of R in = 2.5 × 10 14 cm. In addition, we find that the progenitor of SN 2020pni was enriched in helium and nitrogen (relative abundances in mass fractions of 0.30–0.40 and 8.2 × 10 −3 , respectively). Radio upper limits are also consistent with dense circumstellar material (CSM) and a mass-loss rate of M ̇ > 5 × 10 − 4 M ☉ yr − 1 . During the initial 4 days after first light, we also observe an increase in velocity of the hydrogen lines (from ∼250 to ∼1000 km s −1 ), suggesting complex CSM. The presence of dense and confinedmore »CSM, as well as its inhomogeneous structure, indicates a phase of enhanced mass loss of the progenitor of SN 2020pni during the last year before explosion. Finally, we compare SN 2020pni to a sample of other shock-photoionization events. We find no evidence of correlations among the physical parameters of the explosions and the characteristics of the CSM surrounding the progenitors of these events. This favors the idea that the mass loss experienced by massive stars during their final years could be governed by stochastic phenomena and that, at the same time, the physical mechanisms responsible for this mass loss must be common to a variety of different progenitors.« less
    Free, publicly-accessible full text available February 1, 2023
  8. A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework’s utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
  9. As connectomic datasets exceed hundreds of terabytes in size, accurate and efficient skeleton generation of the label volumes has evolved into a critical component of the computation pipeline used for analysis, evaluation, visualization, and error correction. We propose a novel topological thinning strategy that uses biological constraints to produce accurate centerlines from segmented neuronal volumes while still maintaining bio- logically relevant properties. Current methods are either agnostic to the underlying biology, have non-linear running times as a function of the number of input voxels, or both. First, we eliminate from the input segmentation biologically-infeasible bubbles, pockets of voxels incorrectly labeled within a neuron, to improve segmentation accuracy, allow for more accurate centerlines, and increase processing speed. Next, a Convolutional Neural Network (CNN) detects cell bodies from the input segmentation, allowing us to anchor our skeletons to the somata. Lastly, a synapse-aware topological thinning approach produces expressive skeletons for each neuron with a nearly one-to-one correspondence between endpoints and synapses. We simultaneously estimate geometric properties of neurite width and geodesic distance between synapse and cell body, improving accuracy by 47.5% and 62.8% over baseline methods. We separate the skeletonization process into a series of computation steps, leveraging data-parallel strategies to increasemore »throughput significantly. We demonstrate our results on over 1250 neurons and neuron fragments from three different species, processing over one million voxels per second per CPU with linear scalability.« less
  10. Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. However, current datasets for neuronal nuclei usually contain volumes smaller than 0.01 cubic mm with fewer than 500 instances per volume, unable to reveal the complexity in large brain regions and restrict the investigation of neuronal structures. In this paper, we have pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 cubic mm electron microscopy (EM) volume containing nearly the entire zebra sh brain with around 170,000 nuclei; and one 0.25 cubic mm micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei. With two imaging modalities and significantly increased volume size and instance numbers, we discover a great diversity of neuronal nuclei in appearance and density, introducing new challenges to the  eld. We also perform a statistical analysis to illustrate those challenges quantitatively. To tackle the challenges, we propose a novel hybrid-representation learning model that combines the merits of foreground mask, contour map, and signed distance transform to produce high-quality 3D masks. The benchmark comparisons on the NucMM datasetmore »show that our proposed method significantly outperforms state-of- the-art nuclei segmentation approaches. Code and data are available at« less