SummaryBackgroundSex-specific behavior may originate from differences in brain structure or function. In Drosophila, the action of the male-specific isoform of fruitless in about 2000 neurons appears to be necessary and sufficient for many aspects of male courtship behavior. Initial work found limited evidence for anatomical dimorphism in these fru+ neurons. Subsequently, three discrete anatomical differences in central brain fru+ neurons have been reported, but the global organization of sex differences in wiring is unclear.ResultsA global search for structural differences in the Drosophila brain identified large volumetric differences between males and females, mostly in higher brain centers. In parallel, saturating clonal analysis of fru+ neurons using mosaic analysis with a repressible cell marker identified 62 neuroblast lineages that generate fru+ neurons in the brain. Coregistering images from male and female brains identified 19 new dimorphisms in males; these are highly concentrated in male-enlarged higher brain centers. Seven dimorphic lineages also had female-specific arbors. In addition, at least 5 of 51 fru+ lineages in the nerve cord are dimorphic. We use these data to predict >700 potential sites of dimorphic neural connectivity. These are particularly enriched in third-order olfactory neurons of the lateral horn, where we provide strong evidence for dimorphic anatomical connections by labeling partner neurons in different colors in the same brain.ConclusionOur analysis reveals substantial differences in wiring and gross anatomy between male and female fly brains. Reciprocal connection differences in the lateral horn offer a plausible explanation for opposing responses to sex pheromones in male and female flies.

SummaryNeural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish.Video Abstract

SummaryThe Drosophila sex pheromone cVA elicits different behaviors in males and females. First- and second-order olfactory neurons show identical pheromone responses, suggesting that sex genes differentially wire circuits deeper in the brain. Using in vivo whole-cell electrophysiology, we now show that two clusters of third-order olfactory neurons have dimorphic pheromone responses. One cluster responds in females; the other responds in males. These clusters are present in both sexes and share a common input pathway, but sex-specific wiring reroutes pheromone information. Regulating dendritic position, the fruitless transcription factor both connects the male-responsive cluster and disconnects the female-responsive cluster from pheromone input. Selective masculinization of third-order neurons transforms their morphology and pheromone responses, demonstrating that circuits can be functionally rewired by the cell-autonomous action of a switch gene. This bidirectional switch, analogous to an electrical changeover switch, provides a simple circuit logic to activate different behaviors in males and females.

Neural circuit mapping is generating datasets of 10,000s of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches.We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types including searching neurons against transgene expression patterns. Finally we show that NBLAST is effective with data from other invertebrates and zebrafish.

The cyclin-dependent kinase inhibitor protein, p27(Kip1), is necessary for the timing of cell cycle withdrawal that precedes terminal differentiation in oligodendrocytes of the optic nerve. Although p27(Kip1) is widely expressed in the developing central nervous system, it is not known whether this protein has a similar role in neuronal differentiation. To address this issue, we have examined the expression and function of p27(Kip1) in the developing retina, a well-characterized part of the central nervous system. p27(Kip1) is expressed in a pattern coincident with the onset of differentiation of most retinal cell types. In vitro analyses show that p27(Kip1) accumulation in retinal cells correlates with cell cycle withdrawal and differentiation, and when overexpressed, p27(Kip1) inhibits proliferation of the progenitor cells. Furthermore, the histogenesis of photoreceptors and Müller glia is extended in the retina of p27(Kip1)-deficient mice. Finally, we examined the adult retinal dysplasia in p27(Kip1)-deficient mice with cell-type-specific markers. Contrary to previous suggestions that the dysplasia is caused by excess production of photoreceptors, we suggest that the dysplasia is due to the displacement of reactive Müller glia into the layer of photoreceptor outer segments. These results demonstrate that p27(Kip1) is part of the molecular mechanism that controls the decision of multipotent central nervous system progenitors to withdraw from the cell cycle. Second, postmitotic Müller glia have a novel and intrinsic requirement for p27(Kip1) in maintaining their differentiated state.

IMPORTANCE Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not to have been studied. OBJECTIVESTo assess accuracy and real-world outcome from implementation of a machine learning model that predicts surgical case duration. DESIGN, SETTING, AND PARTICIPANTSThis randomized clinical trial was conducted on 2 surgical campuses of a cancer specialty center. Patients undergoing colorectal and gynecology surgery at Memorial Sloan Kettering Cancer Center who were scheduled more than 1 day before surgery between April 7, 2018, and June 25, 2018, were included. The randomization process included 29 strata (11 gynecological surgeons at 2 campuses and 7 colorectal surgeons at a single campus) to ensure equal chance of selection for each surgeon and each campus. Patients undergoing more than 1 surgery during the study's timeframe were enrolled only once. Data analyses took place from July 2018 to November 2018. INTERVENTIONSCases were assigned to machine learning-assisted surgical predictions 1 day before surgery and compared with a control group. MAIN OUTCOMES AND MEASURESThe primary outcome measure was accurate prediction of the duration of each scheduled surgery, measured by (arithmetic) mean (SD) error and mean absolute error. Effects on patients and systems were measured by start time delay of following cases, the time between cases, and the time patients spent in presurgical area.RESULTS A total of 683 patients were included (mean [SD] age, 55.8 [13.8] years; 566 women [82.9%]); 72 were excluded. Of the 683 patients included, those assigned to the machine learning algorithm had significantly lower mean (SD) absolute error (control group, 59.3 [72] minutes; intervention group, 49.5 [66] minutes; difference, −9.8 minutes; P = .03) compared with the control group. Mean start-time delay for following cases (patient wait time in a presurgical area), dropped significantly: 62.4 minutes (from 70.2 minutes to 7.8 minutes) and 16.7 minutes (from 36.9 minutes to 20.2 minutes) for patients receiving colorectal and gynecology surgery, respectively. The overall mean (SD) reduction of wait time was 33.1 minutes per patient (from 49.4 minutes to 16.3 minutes per patient). Improved accuracy did not adversely inflate time between cases (surgeon wait time). There was marginal improvement (1.5 minutes, from a mean of 70.6 to 69.1 minutes) in time between the end of cases and start of to-follow cases using the predictive model, compared with the control group. Patients spent a mean of 25.2 fewer minutes in the facility before surgery (173.3 minutes vs 148.1 minutes), indicating a potential benefit vis-à-vis available resources for other patients before and after surgery.CONCLUSIONS AND RELEVANCE Implementing machine learning-generated predictions for surgical case durations may improve case duration accura...

Clonal analysis with the MARCM (mosaic analysis with a repressible cell marker) system can be used for studying cell lineage, development, and anatomy in the Drosophila olfactory system and other parts of the fly brain. To compare confocal images of labeled neurons in different brains, it may be desirable to register them to a template or standard brain. There are various image registration approaches available. Some depend on manually specifying landmarks on the brains to be registered. Others depend only on the grayscale intensity value of one of the channels in the confocal image. Another important difference between registration approaches is whether they apply linear or nonlinear (warping) transformations. Linear transformations typically include translation, rotation, and scaling along each axis. Nonlinear transformations are much more computationally intensive, but are required to register brains with different shapes. Here we describe the practical steps required for an intensity-based nonlinear registration that has been used to map the higher olfactory centers of the Drosophila brain using the staining for the presynaptic marker Bruchpilot (nc82). This registration is in fact a two-step process. The first step is a linear transformation that roughly aligns the two brains, followed by a second nonlinear step that allows different parts of the brain to move in slightly different directions.

Mapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derived from the same neural stem cell (neuroblast clones) are functionally related and morphologically highly stereotyped across animals. However identifying these neuroblast clones (approximately 180 per central brain hemisphere) manually remains challenging and time consuming. Here, we take advantage of the stereotyped nature of neural circuits in Drosophila to identify clones automatically, requiring manual annotation of only an initial, smaller set of images. Our procedure depends on registration of all images to a common template in conjunction with an image processing pipeline that accentuates and segments neural projections and cell bodies. We then measure how much information the presence of a cell body or projection at a particular location provides about the presence of each clone. This allows us to select a highly informative set of neuronal features as a template that can be used to detect the presence of clones in novel images. The approach is not limited to a specific labeling strategy and can be used to identify partial (e.g., individual neurons) as well as complete matches. Furthermore this approach could be generalized to studies of neural circuits in other organisms.