SummaryDrosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience.Video Abstract
The JHU turbulence database [1] can be used with a state of the art visualisation tool [2] to generate high quality fluid dynamics videos. In this work we investigate the classical idea that smaller structures in turbulent flows, while engaged in their own internal dynamics, are advected by the larger structures. They are not advected undistorted, however. We see instead that the small scale structures are sheared and twisted by the larger scales. This illuminates the basic mechanisms of the turbulent cascade. THE JHU TURBULENCE DATABASEIn [1] a database containing a solution of the 3D incompressible Navier-Stokes (NS) equations is presented. The equations were solved numerically with a standard pseudo-spectral simulation in a periodic domain, using a real space grid of 1024 3 grid points. A large-scale body force drives a turbulent flow with a Taylor microscale based Reynolds number R λ = 433. Out of this solution, 1024 snapshots were stored, spread out evenly over a large eddy turnover time. More on the simulation and on accessing the data can be found at http://turbulence.pha.jhu.edu. In practical terms, we have easy access to the turbulent velocity field and pressure at every point in space and time. VORTICES WITHIN VORTICESOne usual way of visualising a turbulent velocity field is to plot vorticity isosurfaces -see for instance the plots from [3]. The resulting pictures are usually very "crowded", in the sense that there are many intertwined thin vortex tubes, generating an extremely complex structure. In fact, the picture of the entire dataset from [3] looks extremely noisy and it is arguably not very informative about the turbulent dynamics.In this work, we follow a different approach. First of all, we use the alternate quantityfirst introduced in [4]. Secondly, the tool being used has the option of displaying data only inside clearly defined domains of 3D space. We can exploit this facility to investigate the multiscale character of the turbulent cascade. Because vorticity is dominated by the smallest available scales in the velocity, we can visualize vorticity at scale ℓ by the curl of the velocity box-filtered at scale ℓ. We follow a simple procedure:• we filter the velocity field, using a box filter of size ℓ 1 , and we generate semitransparent surfaces delimitating the domains D 1 where Q > q 1 ;• we filter the velocity field, using a box filter of size ℓ 2 < ℓ 1 , and we generate surfaces delimitating the domains D 2 where Q ≥ q 2 , but only if these domains are contained in one of the domains from D 1 ;and this procedure can be used iteratively with several scales (we use at most 3 scales, since the images become too complex for more levels). Additionally, we wish sometimes to keep track of the relative orientation of the vorticity vectors at the different scales. For this purpose we employ a special coloring scheme for the Q isosurfaces: for each point of the surface, we compute the cosine of the angle α between the ℓ 2 filtered vorticity and the ℓ 1 filtered vorticity: cos α = (∇ × u 1 ) · (∇ × u ...
2 SUMMARY (150 words) 21Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-22 neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only 23 electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; 24 however, the fly brain is too large for conventional EM. We developed a custom high-throughput 25 EM platform and imaged the entire brain of an adult female fly. We validated the dataset by 26 tracing brain-spanning circuitry involving the mushroom body (MB), intensively studied for its 27 role in learning. Here we describe the complete set of olfactory inputs to the MB; find a new cell 28 type providing driving input to Kenyon cells (the intrinsic MB neurons); identify neurons 29 postsynaptic to Kenyon cell dendrites; and find that axonal arbors providing input to the MB 30 calyx are more tightly clustered than previously indicated by light-level data. This freely available 31 EM dataset will significantly accelerate Drosophila neuroscience. 32 33 KEYWORDS 34Electron microscopy, connectomics, neural circuits, Drosophila melanogaster, mushroom body, 35 olfaction, image stitching 36 37 HIGHLIGHTS 38 -A complete adult fruit fly brain was imaged, using electron microscopy (EM) 39 -The EM volume enables brain-spanning mapping of neuronal circuits at the synaptic level 40 -Olfactory projection neurons cluster more tightly in mushroom body calyx than expected from 41 light-level data 42
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