SmY RNAs are a family of approximately 70-90 nt small nuclear RNAs found in nematodes. In C. elegans, SmY RNAs copurify in a small ribonucleoprotein (snRNP) complex related to the SL1 and SL2 snRNPs that are involved in nematode mRNA trans-splicing. Here we describe a comprehensive computational analysis of SmY RNA homologs found in the currently available genome sequences. We identify homologs in all sequenced nematode genomes in class Chromadorea. We are unable to identify homologs in a more distantly related nematode species, Trichinella spiralis (class: Dorylaimia), and in representatives of non-nematode phyla that use trans-splicing. Using comparative RNA sequence analysis, we infer a conserved consensus SmY RNA secondary structure consisting of two stems flanking a consensus Sm protein binding site. A representative seed alignment of the SmY RNA family, annotated with the inferred consensus secondary structure, has been deposited with the Rfam RNA families database.

Online monitoring and quantification of neural signals has tremendous value both for neurofeedback experiments and for brain-computer interfaces. Unfortunately, established methods of online monitoring primarily involve the use of thresholded neural activity rather than sorted single-neuron spikes. The recent introduction of large-scale, high-density electrophysiology has enabled the recording of activity from hundreds of neurons simultaneously in both model organisms and human participants. This development highlights the need for a robust and easily implementable system for sorting spikes during data collection for ‘live’ analyses of neuronal signals. Here, we describe a system for live sorting of neuronal activity (LSS) based on the widely used Kilosort platform. The LSS workflow utilizes an initial period of recorded neural data to identify waveform templates using Kilosort 4. LSS then interfaces with the SpikeGLX API to retrieve small batches (e.g. 50 ms) of data and for processing online. We measured the similarity of single-neuron activity sorted live by LSS to that sorted offline in neurophysiological recordings from macaque visual cortex using Neuropixels probes. We show that LSS closely replicates the post-stimulus time histograms and visual response tuning curves of single-neurons obtained using offline sorting. Furthermore, we show that decoding neural signals online with LSS consistently outperforms online decoding of thresholded activity, and that LSS can achieve the same performance as that obtained with offline sorting.

Drosophila melanogaster is an established model for neuroscience research with relevance in biology and medicine. Until recently, research on the Drosophila brain was hindered by the lack of a complete and uniform nomenclature. Recognizing this, Ito et al. (2014) produced an authoritative nomenclature for the adult insect brain, using Drosophila as the reference. Here, we extend this nomenclature to the adult thoracic and abdominal neuromeres, the ventral nerve cord (VNC), to provide an anatomical description of this major component of the Drosophila nervous system. The VNC is the locus for the reception and integration of sensory information and involved in generating most of the locomotor actions that underlie fly behaviors. The aim is to create a nomenclature, definitions, and spatial boundaries for the Drosophila VNC that are consistent with other insects. The work establishes an anatomical framework that provides a powerful tool for analyzing the functional organization of the VNC.

Insect nervous systems are proven and powerful model systems for neuroscience research with wide relevance in biology and medicine. However, descriptions of insect brains have suffered from a lack of a complete and uniform nomenclature. Recognising this problem the Insect Brain Name Working Group produced the first agreed hierarchical nomenclature system for the adult insect brain, using Drosophila melanogaster as the reference framework, with other insect taxa considered to ensure greater consistency and expandability (Ito et al., 2014). Ito et al. (2014) purposely focused on the gnathal regions that account for approximately 50% of the adult CNS. We extend this nomenclature system to the sub-gnathal regions of the adult Drosophila nervous system to provide a nomenclature of the so-called ventral nervous system (VNS), which includes the thoracic and abdominal neuromeres that was not included in the original work and contains the neurons that play critical roles underpinning most fly behaviours.

Despite the importance of the insect nervous system for functional and developmental neuroscience, descriptions of insect brains have suffered from a lack of uniform nomenclature. Ambiguous definitions of brain regions and fiber bundles have contributed to the variation of names used to describe the same structure. The lack of clearly determined neuropil boundaries has made it difficult to document precise locations of neuronal projections for connectomics study. To address such issues, a consortium of neurobiologists studying arthropod brains, the Insect Brain Name Working Group, has established the present hierarchical nomenclature system, using the brain of Drosophila melanogaster as the reference framework, while taking the brains of other taxa into careful consideration for maximum consistency and expandability. The following summarizes the consortium’s nomenclature system and highlights examples of existing ambiguities and remedies for them. This nomenclature is intended to serve as a standard of reference for the study of the brain of Drosophila and other insects.

To survive, animals must be able quickly infer the state of their surroundings. For example, to successfully escape an approaching predator, prey must quickly estimate the direction of approach from incoming sensory stimuli. Such rapid inferences are particularly challenging because the animal has only a brief window of time to gather sensory stimuli, and yet the accuracy of inference is critical for survival. Due to evolutionary pressures, nervous systems have likely evolved effective computational strategies that enable accurate inferences under strong time limitations. Traditionally, the relationship between the speed and accuracy of inference has been described by the "speed-accuracy tradeoff" (SAT), which quantifies how the average performance of an ideal observer improves as the observer has more time to collect incoming stimuli. While this trial-averaged description can reasonably account for individual inferences made over long timescales, it does not capture individual inferences on short timescales, when trial-to-trial variability gives rise to diverse patterns of error dynamics. We show that an ideal observer can exploit this single-trial structure by adaptively tracking the dynamics of its belief about the state of the environment, which enables it make more rapid inferences and more reliably track its own error but also causes it to violate the SAT. We show that these features can be used to improve overall performance during rapid escape. The resulting behavior qualitatively reproduces features of escape behavior in the fruit fly Drosophila melanogaster, whose escapes have presumably been highly optimized by natural selection.

To survive, animals must be able quickly infer the state of their surroundings. For example, to successfully escape an approaching predator, prey must quickly estimate the direction of approach from incoming sensory stimuli and guide their behavior accordingly. Such rapid inferences are particularly challenging because the animal has only a brief window of time to gather sensory stimuli, and yet the accuracy of inference is critical for survival. Due to evolutionary pressures, nervous systems have likely evolved effective computational strategies that enable accurate inferences under strong time limitations. Traditionally, the relationship between the speed and accuracy of inference has been described by the “speed-accuracy tradeoff” (SAT), which quantifies how the average performance of an ideal observer improves as the observer has more time to collect incoming stimuli. While this trial-averaged description can reasonably account for individual inferences made over long timescales, it does not capture individual inferences on short timescales, when trial-to-trial variability gives rise to diverse patterns of error dynamics. We show that an ideal observer can exploit this single-trial structure by adaptively tracking the dynamics of its belief about the state of the environment, which enables it to speed its own inferences and more reliably track its own error, but also causes it to violate the SAT. We show that these features can be used to improve overall performance during rapid escape. The resulting behavior qualitatively reproduces features of escape behavior in the fruit fly Drosophila melanogaster, whose escapes have presumably been highly optimized by natural selection.

Our aim is to develop quantitative single-molecule assays to study when and where molecules are interacting inside living cells and where enzymes are active. To this end we present a three-camera imaging microscope for fast tracking of multiple interacting molecules simultaneously, with high spatiotemporal resolution. The system was designed around an ASI RAMM frame using three separate tube lenses and custom multi-band dichroics to allow for enhanced detection efficiency. The frame times of the three Andor iXon Ultra EMCCD cameras are hardware synchronized to the laser excitation pulses of the three excitation lasers, such that the fluorophores are effectively immobilized during frame acquisitions and do not yield detections that are motion-blurred. Stroboscopic illumination allows robust detection from even rapidly moving molecules while minimizing bleaching, and since snapshots can be spaced out with varying time intervals, stroboscopic illumination enables a direct comparison to be made between fast and slow molecules under identical light dosage. We have developed algorithms that accurately track and co-localize multiple interacting biomolecules. The three-color microscope combined with our co-movement algorithms have made it possible for instance to simultaneously image and track how the chromosome environment affects diffusion kinetics or determine how mRNAs diffuse during translation. Such multiplexed single-molecule measurements at a high spatiotemporal resolution inside living cells will provide a major tool for testing models relating molecular architecture and biological dynamics.

Temperature dependent sex determination (TSD) is the process by which the environmental temperature experienced during embryogenesis influences the sex of an organism, as in the red-eared slider turtle Trachemys scripta elegans. In accord with current paradigms of vertebrate sex determination, temperature is believed to exert its effects on sexual development in T. scripta entirely within the middle third of development, when the gonad is forming. However, whether temperature regulates the transcriptome in T. scripta early embryos in a manner that could influence secondary sex characteristics or establish a pro-male or pro-female environment has not been investigated. In addition, apart from a handful of candidate genes, very little is known about potential similarities between the expression cascade during TSD and the genetic cascade that drives mammalian sex determination. Here, we conducted an unbiased transcriptome-wide analysis of the effects of male- and female-promoting temperatures on the turtle embryo prior to gonad formation, and on the gonad during the temperature sensitive period. We found sexually dimorphic expression reflecting differences in steroidogenic enzymes and brain development prior to gonad formation. Within the gonad, we mapped a cascade of differential expression similar to the genetic cascade established in mammals. Using a Hidden Markov Model based clustering approach, we identified groups of genes that show heterochronic shifts between M. musculus and T. scripta. We propose a model in which multiple factors influenced by temperature accumulate during early gonadogenesis, and converge on the antagonistic regulation of aromatase to canalize sex determination near the end of the temperature sensitive window of development.

Gene expression patterns can be useful in understanding the structural organization of the brain and the regulatory logic that governs its myriad cell types. A particularly rich source of spatial expression data is the Allen Brain Atlas (ABA), a comprehensive genome-wide in situ hybridization study of the adult mouse brain. Here, we present an open-source program, ALLENMINER, that searches the ABA for genes that are expressed, enriched, patterned or graded in a user-specified region of interest.