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Here, we introduce cellular morphology neural networks CMNs , based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings Neuron2vec of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors.
Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions. Introduction Advances in volume electron microscopy VEM have led to increasingly large three-dimensional 3D images of brain tissue, making manual analysis infeasible 1.
Multi-beam scanning electron microscopes 2 and transmission electron microscopes equipped with fast camera arrays can now generate data sets exceeding TB 3 , a development that was fortunately accompanied by substantial progress in neuron reconstruction 4 , 5 , 6 , 7 , 8 , 9 and the automatic analysis of synapses 10 , 11 , 12 , These advances have enabled automatic morphology analyses on the neuron fragment scale, which were previously restricted to direct segmentation error detection 5 , 14 or the use of manual skeletons with data-specific hand-crafted features 11 , 15 , Cell types and other biological properties that can be inferred from the morphology of a neuron are required for the interpretation of a connectome 11 and can also constrain automatic neuron reconstruction itself Outside of the field of connectomics, many approaches have been developed for automated 3D shape analysis, including multi-view two-dimensional 2D projection-based neural network models 18 , 19 as well as voxel- and point cloud-occupancy-based 3D networks 20 , Interestingly, projection-based models often appear to outperform 3D architectures, possibly because of higher-resolution input due to decreased model complexity Here we present cellular morphology neural networks CMNs , which use multi-view projections to enable the supervised and unsupervised analysis of cell fragments of arbitrary size while retaining high resolution.
First, we demonstrate that CMNs can be used to automate morphology feature extraction itself by inferring low-dimensional embeddings, dubbed Neuron2vec, through unsupervised triplet-loss training 22 , Second, we apply CMNs to the supervised classification of glia cells and use these data to demonstrate the feasibility and effectiveness of a simple top—down false merger resolution strategy.
Third, we identify neuronal cell types and compartments, outperforming methods with hand-crafted features based on skeleton representations on the same data, and finally perform high-resolution cell surface segmentation to identify dendritic spines. Results Cellular morphology learning networks CMNs are convolutional neural networks CNNs optimized for the analysis of multi-channel 2D projections of cell reconstructions, inspired by multi-view CNNs for the classification of objects fitting into projections from one rendering site 18 , Su et al.
The neuron reconstructions were taken from a songbird basal ganglia data set and consisted of flood-filling network FFN -created supervoxels 25 SVs , which were agglomerated to sets of super-SVs SSVs 8 , each corresponding to a single neuron.
A rectangular field of view FoV was chosen for the projection to capture the elongated shape of most neuronal arbors more effectively. Additionally, we incorporated image channels beyond the cell shape here represented through depth-map projections of the same rendering perspective. This extension allows the CMN to analyze the geometry and density of objects contained in a cell, such as mitochondria and other organelles Fig.
The models were trained using standard loss optimization procedures Adam or stochastic gradient descent on various supervised and unsupervised tasks see Fig. Neuron2vec embedding Supervised models often require hard-to-obtain manual ground truth, making alternative objective functions and models based on intuitive isomorphisms of the underlying data desirable. During training, the views were sampled from cell or cell fragment reconstructions We explored the information content of the embedding through inspection of clusters in a 2D t-SNE 26 projection Fig.
Insets show close-ups of an axonal bouton and a spiny dendrite. Axons, or thin processes in general, and soma regions could be readily identified based on a color projection of the embedding using principal component analysis PCA; Fig.
Interestingly, views did not only group by compartment type but also formed sub-clusters separating views with cell objects such as mitochondria and synaptic junctions from those without first and second column in Fig.
We next took a supervised end-to-end approach and examined ground truth-based CMN models. Eldritch Spear When you cast eldritch blast , its range is feet. Eyes of the Rune Keeper You can read all writing. Fiendish Vigor You can cast false life on yourself at will as a 1st-level spell, without expending a spell slot or material components.
Gaze of Two Minds You can use your action to touch a willing humanoid and perceive through its senses until the end of your next turn. As long as the creature is on the same plane of existence as you, you can use your action on subsequent turns to maintain this connection, extending the duration until the end of your next turn.
Mask of Many Faces You can cast disguise self at will, without expending a spell slot. Master of Myriad Forms You can cast alter self at will, without expending a spell slot. Misty Visions You can cast silent image at will, without expending a spell slot or material components.
Thief of Five Fates You can cast bane once using a warlock spell slot. Visions of Distant Realms You can cast arcane eye at will, without expending a spell slot. Whispers of the Grave You can cast speak with dead at will, without expending a spell slot. Pact Boon At 3rd level, your otherworldly patron bestows a gift upon you for your loyal service.
You gain one of the following features of your choice. Pact of the Blade You can use your action to create a pact weapon in your empty hand. You are proficient with it while you wield it. This weapon counts as magical for the purpose of overcoming resistance and immunity to nonmagical attacks and damage.
Your pact weapon disappears if it is more than 5 feet away from you for 1 minute or more. It also disappears if you use this feature again, if you dismiss the weapon no action required , or if you die.
You can transform one magic weapon into your pact weapon by performing a special ritual while you hold the weapon. You perform the ritual over the course of 1 hour, which can be done during a short rest.
You can then dismiss the weapon, shunting it into an extradimensional space, and it appears whenever you create your pact weapon thereafter.
The weapon ceases being your pact weapon if you die, if you perform the 1-hour ritual on a different weapon, or if you use a 1-hour ritual to break your bond to it. The weapon appears at your feet if it is in the extradimensional space when the bond breaks. Pact of the Chain You learn the find familiar spell and can cast it as a ritual.
When you cast the spell, you can choose one of the normal forms for your familiar or one of the following special forms: imp , pseudodragon , quasit , or sprite. Additionally, when you take the Attack action, you can forgo one of your own attacks to allow your familiar to make one attack with its reaction. Pact of the Tome Your patron gives you a grimoire called a Book of Shadows. While the book is on your person, you can cast those cantrips at will.
If you lose your Book of Shadows, you can perform a 1-hour ceremony to receive a replacement from your patron. This ceremony can be performed during a short or long rest, and it destroys the previous book. The FAQs here, while not as pretty, are more useful, and free! I mostly use the Prima guide because the pictures look nice.
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I tell you not to eat any. You eat a cookie. I respond by locking you in the basement until you starve to death. Thank you for the reply, KasaiAisu1. How much do you think it's worth? Because I'm thinking of downloading it from this site.
Just a paper weight for Skyrim. Harmony of Despair PS3. Someone help me out!