Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain, and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based 3D interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analysing the connectome of mechanosensory neurons.

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.

Due to advances in automated image acquisition and analysis, new whole-brain connectomes beyond C. elegans are finally on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a fly brain, and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based 3D interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants, and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analysing the connectome of mechanosensory neurons.

Skeletal muscle is comprised of a heterogeneous population of muscle fibers which can be classified by their metabolic and contractile properties (fiber "types"). Fiber type is a primary determinant of muscle function along with fiber size (cross-sectional area). The fiber type composition of a muscle responds to physiological changes like exercise and aging and is often altered in disease states. Thus, analysis of fiber size and type in histological muscle preparations is a useful method for quantifying key indicators of muscle function and for measuring responses to a variety of stimuli or stressors. These analyses are nearubiquitous in the fields of muscle physiology and myopathy, but are most commonly performed manually, which is highly labor-and time-intensive. To offset this obstacle, we developed Myosoft, a novel method to automate morphometric and fiber type analysis in muscle sections stained with fluorescent antibodies. MethodsMuscle sections were stained for cell boundary (laminin) and myofiber type (myosin heavy chain isoforms). Myosoft, running in the open access software platform FIJI (ImageJ), was used to analyze myofiber size and type in transverse sections of entire gastrocnemius/soleus muscles. ResultsMyosoft provides an accurate analysis of hundreds to thousands of muscle fibers within 25 minutes, which is >10-times faster than manual analysis. We demonstrate that Myosoft is capable of handling high-content images even when image or staining quality is suboptimal, which is a marked improvement over currently available and comparable programs.

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify and characterize the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we focus on changes in the behavioral state of Drosophila females that persist for minutes following optogenetic activation of a single class of central brain neurons termed pC1. We find that female pC1 neurons drive a variety of persistent behaviors in the presence of males, including increased receptivity, shoving, and chasing. By reconstructing cells in a volume electron microscopic image of the female brain, we classify 7 different pC1 cell types and, using cell type specific driver lines, determine that one of these, pC1-Alpha, is responsible for driving persistent female shoving and chasing. Using calcium imaging, we locate sites of minutes-long persistent neural activity in the brain, which include pC1 neurons themselves. Finally, we exhaustively reconstruct all synaptic partners of a single pC1-Alpha neuron, and find recurrent connectivity that could support the persistent neural activity. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.

The goal of this article is to provide guidance for those who have decided to apply to graduate school with the plan to obtain a PhD in biomedical science. Choosing an appropriate graduate school and program can seem like a daunting choice. There are numerous graduate training programs that offer excellent training with multiple specific program choices at any given institution. Thus, identifying a program that provides an optimal training environment, which aligns with the applicant's training and career goals, can be daunting. There is no single training program that is ideal for all applicants, and, fortunately, there is no sole perfect place for any individual applicant to obtain a PhD. This article presents points to consider at multiple phases of this process as collected from the authors, including a senior faculty member, a junior faculty member, and four current graduate students who all made different choices for their graduate training (Fig. 1). In Phase I of the process, the vast number of choices must be culled to a reasonable number of schools/programs for the initial application. This is one of the most challenging steps because the number of training programs is very large, and most applicants will rely primarily on information readily available on the internet. Phase II is the exciting stage of visiting the program for an interview where you can ask questions and get a feel for the place. Finally, Phase III suggests information to collect following the interview when comparing choices and making a final decision. While the process may feel long and can be stressful, the good news is that making informed decisions along the way should result in multiple options that can support excellent training and career development.

Background:Skeletal muscle is comprised of a heterogeneous population of muscle fibers which can be classified by their metabolic and contractile properties (fiber "types"). Fiber type is a primary determinant of muscle function along with fiber size (cross-sectional area). The fiber type composition of a muscle responds to physiological changes like exercise and aging and is often altered in disease states. Thus, analysis of fiber size and type in histological muscle preparations is a useful method for quantifying key indicators of muscle function and for measuring responses to a variety of stimuli or stressors. These analyses are nearubiquitous in the fields of muscle physiology and myopathy, but are most commonly performed manually, which is highly labor-and time-intensive. To offset this obstacle, we developed Myosoft, a novel method to automate morphometric and fiber type analysis in muscle sections stained with fluorescent antibodies. Methods:Muscle sections were stained for cell membrane (laminin) and myofiber type (myosin heavy chain isoforms).Myosoft, running in the open access software platform FIJI (ImageJ), was used to analyze myofiber size and type in transverse sections of entire gastrocnemius/soleus muscles. Results:Myosoft provides accurate analysis of muscle histology >50-times faster than manual analysis. We demonstrate that Myosoft is capable of handling high-content images even when image or staining quality is suboptimal, which is a marked improvement over currently available, comparable programs. Conclusions:Myosoft is a reliable, accurate, high-throughput, and convenient tool to analyze high-content muscle histology. Myosoft is freely available to download from Github at https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub.

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