Animals can perform complex and purposeful behaviors by executing simpler movements in flexible sequences. It is particularly challenging to analyze behavior sequences when they are highly variable, as is the case in language production, certain types of birdsong and, as in our experiments, flies grooming. High sequence variability necessitates rigorous quantification of large amounts of data to identify organizational principles and temporal structure of such behavior. To cope with large amounts of data, and minimize human effort and subjective bias, researchers often use automatic behavior recognition software. Our standard grooming assay involves coating flies in dust and videotaping them as they groom to remove it. The flies move freely and so perform the same movements in various orientations. As the dust is removed, their appearance changes. These conditions make it difficult to rely on precise body alignment and anatomical landmarks such as eyes or legs and thus present challenges to existing behavior classification software. Human observers use speed, location, and shape of the movements as the diagnostic features of particular grooming actions. We applied this intuition to design a new automatic behavior recognition system (ABRS) based on spatiotemporal features in the video data, heavily weighted for temporal dynamics and invariant to the animal’s position and orientation in the scene. We use these spatiotemporal features in two steps of supervised classification that reflect two time-scales at which the behavior is structured. As a proof of principle, we show results from quantification and analysis of a large data set of stimulus-induced fly grooming behaviors that would have been difficult to assess in a smaller dataset of human-annotated ethograms. While we developed and validated this approach to analyze fly grooming behavior, we propose that the strategy of combining alignment-invariant features and multi-timescale analysis may be generally useful for movement-based classification of behavior from video data.

Advances in neuro-technology for mapping, manipulating, and monitoring molecularly defined cell types are rapidly advancing insight into neural circuits that regulate appetite. Here, we review these important tools and their applications in circuits that control food seeking and consumption. Technical capabilities provided by these tools establish a rigorous experimental framework for research into the neurobiology of hunger.

The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find minima.

Intracellular levels of the amino acid aspartate are responsive to changes in metabolism in mammalian cells and can correspondingly alter cell function, highlighting the need for robust tools to measure aspartate abundance. However, comprehensive understanding of aspartate metabolism has been limited by the throughput, cost, and static nature of the mass spectrometry (MS)-based measurements that are typically employed to measure aspartate levels. To address these issues, we have developed a green fluorescent protein (GFP)-based sensor of aspartate (jAspSnFR3), where the fluorescence intensity corresponds to aspartate concentration. As a purified protein, the sensor has a 20-fold increase in fluorescence upon aspartate saturation, with dose-dependent fluorescence changes covering a physiologically relevant aspartate concentration range and no significant off target binding. Expressed in mammalian cell lines, sensor intensity correlated with aspartate levels measured by MS and could resolve temporal changes in intracellular aspartate from genetic, pharmacological, and nutritional manipulations. These data demonstrate the utility of jAspSnFR3 and highlight the opportunities it provides for temporally resolved and high-throughput applications of variables that affect aspartate levels.

The golden age of DNA: We describe a strategy for engineering bifunctional proteins that simultaneously associate with metals and DNA to create self-assembled nanostructures. A DNA binding protein engineered with a gold binding peptide arranges colloidal gold particles along a DNA guide by virtue of its introduced peptide motif. These self-assembled complexes represent a step toward constructing nanoarchitectures with potential in nanoelectronic and photonic devices.

Ends-out gene targeting allows seamless replacement of endogenous genes with engineered DNA fragments by homologous recombination, thus creating designer "genes" in the endogenous locus. Conventional gene targeting in Drosophila involves targeting with the preintegrated donor DNA in the larval primordial germ cells. Here we report G: ene targeting during O: ogenesis with L: ethality I: nhibitor and C: RISPR/Cas (Golic+), which improves on all major steps in such transgene-based gene targeting systems. First, donor DNA is integrated into precharacterized attP sites for efficient flip-out. Second, FLP, I-SceI, and Cas9 are specifically expressed in cystoblasts, which arise continuously from female germline stem cells, thereby providing a continual source of independent targeting events in each offspring. Third, a repressor-based lethality selection is implemented to facilitate screening for correct targeting events. Altogether, Golic+ realizes high-efficiency ends-out gene targeting in ovarian cystoblasts, which can be readily scaled up to achieve high-throughput genome editing.

To establish functional connectivity between two candidate neurons that might form a circuit element, a common approach is to activate an optogenetic tool such as Chrimson in the candidate pre-synaptic neuron and monitor fluorescence of the calcium-sensitive indicator GCaMP in a candidate post-synaptic neuron. While performing such experiments in Drosophila, we found that low levels of leaky Chrimson expression can lead to strong artifactual GCaMP signals in presumptive postsynaptic neurons even when Chrimson is not intentionally expressed in any particular neurons. Withholding all-trans retinal, the chromophore required as a co-factor for Chrimson response to light, eliminates GCaMP signal but does not provide an experimental control for leaky Chrimson expression. Leaky Chrimson expression appears to be an inherent feature of current Chrimson transgenes, since artifactual connectivity was detected with Chrimson transgenes integrated into multiple genomic locations. While these false-positive signals may complicate the interpretation of functional connectivity experiments, we illustrate how a no-Gal4 negative control improves interpretability of functional connectivity assays. We also propose a simple but effective procedure to identify experimental conditions that minimize potentially incorrect interpretations caused by leaky Chrimson expression.

Anaplastic lymphoma kinase (Alk) is a gene expressed in the nervous system that encodes a receptor tyrosine kinase commonly known for its oncogenic function in various human cancers. We have determined that Alk is associated with altered behavioral responses to ethanol in the fruit fly Drosophila melanogaster, in mice, and in humans. Mutant flies containing transposon insertions in dAlk demonstrate increased resistance to the sedating effect of ethanol. Database analyses revealed that Alk expression levels in the brains of recombinant inbred mice are negatively correlated with ethanol-induced ataxia and ethanol consumption. We therefore tested Alk gene knockout mice and found that they sedate longer in response to high doses of ethanol and consume more ethanol than wild-type mice. Finally, sequencing of human ALK led to the discovery of four polymorphisms associated with a low level of response to ethanol, an intermediate phenotype that is predictive of future alcohol use disorders (AUDs). These results suggest that Alk plays an evolutionary conserved role in ethanol-related behaviors. Moreover, ALK may be a novel candidate gene conferring risk for AUDs as well as a potential target for pharmacological intervention.

The paradigm that the secretory pathway consists of a stable endoplasmic reticulum and Golgi apparatus, using discrete transport vesicles to exchange their contents, gained important support from groundbreaking biochemical and genetic studies during the 1980s. However, the subsequent development of new imaging technologies with green fluorescent protein introduced data on dynamic processes not fully accounted for by the paradigm. As a result, we may be seeing an example of how a paradigm is evolving to account for the results of new technologies and their new ways of describing cellular processes.