The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

The neural circuits responsible for animal behavior remain largely unknown. We 31 summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly 32 Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, 33 segment, find synapses in, and proofread such large data sets. We define cell types, refine 34 computational compartments, and provide an exhaustive atlas of cell examples and types, many of 35 them novel. We provide detailed circuits consisting of neurons and their chemical synapses for 36 most of the central brain. We make the data public and simplify access, reducing the effort needed 37 to answer circuit questions, and provide procedures linking the neurons defined by our analysis 38 with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs 39 on different scales, electrical consequences of compartmentalization, and evidence that 40 maximizing packing density is an important criterion in the evolution of the fly's brain. 41 1 of 57 53 Producing this data set required advances in sample preparation, imaging, image alignment, ma-54 chine segmentation of cells, synapse detection, data storage, proofreading software, and protocols 55 to arbitrate each decision. A number of new tests for estimating the completeness and accuracy 56 were required and therefore developed, in order to verify the correctness of the connectome. 57 These data describe whole-brain properties and circuits, as well as contain new methods to 58 classify cell types based on connectivity. Computational compartments are now more carefully 59 defined, we identify actual synaptic circuits, and each neuron is annotated by name and putative 60 cell type, making this the first complete census of neuropils, tracts, cells, and connections in this 61 2 of 57 Manuscript submitted to eLife Figure 2. Brain regions contained and defined in the hemibrain, following the naming conventions of (Ito et al., 2014) with the addition of (R) and (L) to specify the side of the soma for that region. Gray italics indicate master regions not explicitly defined in the hemibrain. Region LA is not included in the volume. The regions are hierarchical, with the more indented regions forming subsets of the less indented. The only exceptions are dACA, lACA, and vACA which are considered part of the mushroom body but are not contained in the master region MB.portion of the brain. We compare the statistics and structure of different brain regions, and for 62 the brain as a whole, without the confounds introduced by studying different circuitry in different 63 animals. 64 All data are publicly available through web interfaces. This includes a browser interface, Ne-65 uPrint (Clements et al., 2020), designed so that any interested user can query the hemibrain con-66 nectome even without specific training. NeuPrint can query the connectivity, partners, connection 67 strengths and morphologies of all specified neurons, thus making identifica...

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

We report airborne measurements of total and nonvolatile (at T < 290°C) fine and ultrafine aerosol emission indices (EI's) generated by a variety of jet aircraft. The data were obtained using an instrumented jet aircraft, flown repeatedly through aircraft wakes. These aircraft were observed to produce 0.5hyphen;10 × 1015 nonvolatile particles kg−1 of fuel burned. Their numbers varied as a function of aircraft type, age, and engine operating parameters, but less with atmospheric conditions. Large numbers of volatile aerosols were measured in all cases. Volatile EI's ranged from 0.1 to 40×1016 kg−1 fuel burned. The observed soot emissions are estimated to have only a minor impact on atmospheric aerosol loading, but a future fleet producing such high concentrations of implied sulfate aerosols could perturb cloud formation and heterogeneous chemical processes in the upper troposphere/lower stratosphere.