BACKGROUND: Logos are commonly used in molecular biology to provide a compact graphical representation of the conservation pattern of a set of sequences. They render the information contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for each position, where the height of the stack corresponds to the conservation at that position, and the height of each letter within a stack depends on the frequency of that letter at that position. RESULTS: We present a new tool and web server, called Skylign, which provides a unified framework for creating logos for both sequence alignments and profile hidden Markov models. In addition to static image files, Skylign creates a novel interactive logo plot for inclusion in web pages. These interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position. CONCLUSION: Skylign is available as a website, a scriptable web service with a RESTful interface, and as a software package for download. Skylign’s interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at http://skylign.org.

The growing channel count of silicon probes has substantially increased the number of neurons recorded in electrophysiology (ephys) experiments, rendering traditional manual spike sorting impractical. Instead, modern ephys recordings are processed with automated methods that use waveform template matching to isolate putative single neurons. While scalable, automated methods are subject to assumptions that often fail to account for biophysical changes in action potential waveforms, leading to systematic errors. Consequently, manual curation of these errors, which is both time-consuming and lacks reproducibility, remains necessary. To improve efficiency and reproducibility in the spike-sorting pipeline, we introduce here the Spike-sorting Lapse Amelioration System (SLAy), an algorithm that automatically merges oversplit spike clusters. SLAy employs two novel metrics: (1) a waveform similarity metric that uses a neural network to obtain spatially informed, time-shift invariant low-dimensional waveform representations, and (2) a cross-correlogram significance metric based on the earth-movers distance between the observed and null cross-correlograms. We demonstrate that SLAy achieves 85% agreement with human curators across a diverse set of animal models, brain regions, and probe geometries. To illustrate the impact of spike sorting errors on downstream analyses, we develop a new burst-detection algorithm and show that SLAy fixes spike sorting errors that preclude the accurate detection of bursts in neural data. SLAy leverages GPU parallelization and multithreading for computational efficiency, and is compatible with Phy and NeuroData Without Borders, making it a practical and flexible solution for large-scale ephys data analysis.

Although the function of sleep remains elusive, several lines of evidence suggest that sleep has an important role in learning and memory. In light of the available data and with the prevalence of sleep deprivation (SD), we sought to determine the effect of SD on neuronal functioning. We found that the exposure of rats to 72 hr of primarily rapid eye movement SD impaired their subsequent performance on a hippocampus-dependent spatial learning task but had no effect on an amygdala-dependent learning task. To determine the underlying cellular level mechanisms of this hippocampal deficit, we examined the impact of SD on several fundamental aspects of membrane excitability and synaptic physiology in hippocampal CA1 pyramidal neurons and dentate gyrus granule cells. We found that neuronal excitability was severely reduced in CA1 neurons but not in granule cells and that the production of long-term potentiation of synaptic strength was inhibited in both areas. Using multiple SD methods we further attempted to differentiate the effects of sleep deprivation from those associated with the nonspecific stress induced by the sleep deprivation methods. Together these data suggest that failure to acquire adequate sleep produces several molecular and cellular level alterations that profoundly inhibit hippocampal functioning.

Although the function of sleep remains elusive, there is compelling evidence to suggest that sleep plays an important role in learning and memory. A number of studies have now shown that sleep deprivation (SD) results in significant impairment of long-term potentiation (LTP) in the hippocampus. In this study, we have attempted to determine the mechanisms responsible for this impairment. After 72 h SD using the multiple-platform technique, we observed a reduction in the whole-cell recorded NMDA/AMPA ratio of CA1 pyramidal cells in response to Schaffer collateral stimulation. This impairment was specific to sleep deprivation as rats placed over a single large platform, which allowed sleep, had a normal NMDA/AMPA ratio. mEPSCs evoked by local application of a high osmolarity solution revealed no differences in the AMPA receptor function. NMDA currents recorded from outside-out patches excised from the distal dendrites of CA1 cells displayed a reduction in amplitude after SD. While there were no alterations in the glutamate sensitivity, channel open probability or the single channel conductance of the receptor, a crosslinking assay demonstrated that the NR1 and NR2A subunits of NMDA receptors were preferentially retained in the cytoplasm after SD, indicating that SD alters NMDAR surface expression. In summary, we have identified a potential mechanism underlying SD-induced LTP impairment. This synaptic alteration may underlie the cognitive deficits seen following sleep deprivation and could represent a target for future intervention studies.

Prolonged wakefulness leads to persistent, deep recovery sleep (RS). However, the neuronal circuits that mediate this process remain elusive. From a circuit screen in mice, we identified a group of thalamic nucleus reuniens (RE) neurons activated during sleep deprivation (SD) and required for sleep homeostasis. Optogenetic activation of RE neurons leads to an unusual phenotype: presleep behaviors (grooming and nest organizing) followed by prolonged, intense sleep that resembles RS. Inhibiting RE activity during SD impairs subsequent RS, which suggests that these neurons signal sleep need. RE neurons act upstream of sleep-promoting zona incerta cells, and SD triggers plasticity of this circuit to strengthen their connectivity. These findings reveal a circuit mechanism by which sleep need transforms the functional coupling of a sleep circuit to promote persistent, deep sleep.

Neuronal physiology depends on a neuron’s ion channel composition and unique morphology. Variable ion channel compositions can produce similar neuronal physiologies across animals. Less is known regarding the morphological precision required to produce reliable neuronal physiology. Theoretical studies suggest that moraphology is tightly tuned to minimize wiring and conduction delay of synaptic events. We utilize high-resolution confocal microscopy and custom computational tools to characterize the morphologies of four neuron types in the stomatogastric ganglion (STG) of the crab \textitCancer borealis. Macroscopic branching patterns and fine cable properties are variable within and across neuron types. We compare these neuronal structures to synthetic minimal spanning neurite trees constrained by a wiring cost equation and find that STG neurons do not adhere to prevailing hypotheses regarding wiring optimization principles. In this highly modulated and oscillating circuit, neuronal structures appear to be governed by a space-filling mechanism that outweighs the cost of inefficient wiring.

The conventional view of neurons is that synaptic inputs are integrated on a timescale of milliseconds to seconds in the dendrites, with action potential initiation occurring in the axon initial segment. We found a much slower form of integration that leads to action potential initiation in the distal axon, well beyond the initial segment. In a subset of rodent hippocampal and neocortical interneurons, hundreds of spikes, evoked over minutes, resulted in persistent firing that lasted for a similar duration. Although axonal action potential firing was required to trigger persistent firing, somatic depolarization was not. In paired recordings, persistent firing was not restricted to the stimulated neuron; it could also be produced in the unstimulated cell. Thus, these interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites.

Glycans are one of the fundamental biopolymers encountered in living systems. Compared to polynucleotide and polypeptide biosynthesis, polysaccharide biosynthesis is a uniquely combinatorial process to which interdependent enzymes with seemingly broad specificities contribute. The resulting intracellular cell surface, and secreted glycans play key roles in health and disease, from embryogenesis to cancer progression. The study and modulation of glycans in cell and organismal biology is aided by small molecule inhibitors of the enzymes involved in glycan biosynthesis. In this review, we survey the arsenal of currently available inhibitors, focusing on agents which have been independently validated in diverse systems. We highlight the utility of these inhibitors and drawbacks to their use, emphasizing the need for innovation for basic research as well as for therapeutic applications.

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.