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.

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, 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 three different genomic locations (two insertions tested in larvae; a third insertion tested in the adult fly). 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.

The use of fluorescent sensors for functional imaging has revolutionized the study of organellar Ca2+ signaling. However, understanding the dynamic interplay between intracellular Ca2+ sinks and sources requires bright, photostable and multiplexed measurements in each signaling compartment of interest to dissect the origins and destinations of Ca2+ fluxes. We introduce a new toolkit of chemigenetic indicators based on HaloCaMP, optimized to report Ca2+ dynamics in the endoplasmic reticulum (ER) and mitochondria of mammalian cells and neurons. Both ER-HaloCaMP and Mito-HaloCaMP present high brightness and responsiveness, and the use of different HaloTag ligands enables tunable red and far-red emission when quantifying organelle Ca2+ dynamics, expanding significantly multiplexing capacities of Ca2+ signaling. The improved brightness of ER-HaloCaMP using either red or far-red HaloTag ligands enabled measuring ER Ca2+ fluxes in axons of neurons, in which the ER is formed by a tiny tubule of 30-60 nanometers of diameter that impeded measurements with previous red ER Ca2+ sensors. When measuring ER Ca2+ fluxes in activated neuronal dendritic spines of cultured neurons, ER-HaloCaMP presented increased photostability compared to the gold-standard ER Ca2+ sensor in the field, ER-GCaMP6-210, while presenting the same responsiveness. On the other hand, Mito-HaloCaMP presented higher responsiveness than current red sensors, and enabled the first measurements of mitochondrial Ca2+ signaling in far-red in cell lines and primary neurons. As a proof-of-concept, we used 3-plex multiplexing to quantify interorganellar Ca2+ signaling. We show that effective transfer of Ca2+ from the ER to mitochondria depends on the ER releasing a critical amount of Ca2+. When this threshold is not met, the mobilized Ca2+ is diverted to the cytosol instead. Our new toolkit provides an expanded palette of bright, photostable and responsive organellar Ca2+ sensors, which will facilitate future studies of intracellular Ca2+ signaling.

Zebrafish larvae are used to model the pathogenesis of multiple bacteria. This transparent model offers the unique advantage of allowing quantification of fluorescent bacterial burdens (fluorescent pixel counts: FPC) in vivo by facile microscopical methods, replacing enumeration of bacteria using time-intensive plating of lysates on bacteriological media. Accurate FPC measurements require laborious manual image processing to mark the outside borders of the animals so as to delineate the bacteria inside the animals from those in the culture medium that they are in. Here, we have developed an automated ImageJ/Fiji-based macro that accurately detect the outside borders of Mycobacterium marinum-infected larvae.

Zebrafish larvae are used to model the pathogenesis of multiple bacteria. This transparent model offers the unique advantage of allowing quantification of fluorescent bacterial burdens (fluorescent pixel counts [FPC]) by facile microscopical methods, replacing enumeration of bacteria using time-intensive plating of lysates on bacteriological media. Accurate FPC measurements require laborious manual image processing to mark the outside borders of the animals so as to delineate the bacteria inside the animals from those in the culture medium that they are in. Here, we have developed an automated ImageJ/Fiji-based macro that accurately detects the outside borders of -infected larvae.

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ’taxonomy to tree’ approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.