Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

The relationship between fasting-induced torpor, sleep, and wakefulness in laboratory mice.

STUDY OBJECTIVES: Torpor is a regulated and reversible state of metabolic suppression used by many mammalian species to conserve energy. Whereas the relationship between torpor and sleep has been well-studied in seasonal hibernators, less is known about the effects of fasting-induced torpor on states of vigilance and brain activity in laboratory mice. METHODS: Continuous monitoring of electroencephalogram (EEG), electromyogram (EMG), and surface body temperature was undertaken in adult, male C57BL/6 mice over consecutive days of scheduled restricted feeding. RESULTS: All animals showed bouts of hypothermia that became progressively deeper and longer as fasting progressed. EEG and EMG were markedly affected by hypothermia, although the typical electrophysiological signatures of non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wakefulness enabled us to perform vigilance-state classification in all cases. Consistent with previous studies, hypothermic bouts were initiated from a state indistinguishable from NREM sleep, with EEG power decreasing gradually in parallel with decreasing surface body temperature. During deep hypothermia, REM sleep was largely abolished, and we observed shivering-associated intense bursts of muscle activity. CONCLUSIONS: Our study highlights important similarities between EEG signatures of fasting-induced torpor in mice, daily torpor in Djungarian hamsters and hibernation in seasonally hibernating species. Future studies are necessary to clarify the effects on fasting-induced torpor on subsequent sleep.

An in vitro tumorigenesis model based on live-cell-generated oxygen and nutrient gradients.

The tumor microenvironment (TME) is multi-cellular, spatially heterogenous, and contains cell-generated gradients of soluble molecules. Current cell-based model systems lack this complexity or are difficult to interrogate microscopically. We present a 2D live-cell chamber that approximates the TME and demonstrate that breast cancer cells and macrophages generate hypoxic and nutrient gradients, self-organize, and have spatially varying phenotypes along the gradients, leading to new insights into tumorigenesis.

Functional and phenotypic characterization of a protein from Lactobacillus acidophilus involved in cell morphology, stress tolerance and adherence to intestinal cells.

Structural components of the cell surface have an impact on some of the beneficial attributes of probiotic bacteria. In silico analysis of the L. acidophilus NCFM genome sequence revealed the presence of a putative cell surface protein that was predicted to be a myosin cross-reactive antigen (MCRA). As MCRAs are conserved among many probiotic bacteria, we used the upp-based counterselective gene replacement system, designed recently for use in L. acidophilus, to determine the functional role of this gene (LBA649) in L. acidophilus NCFM. Phenotypic assays were undertaken with the parent strain (NCK1909) and deletion mutant (NCK2015) to assign a function for this gene. The growth of NCK2015 (ΔLBA649) was reduced in the presence of lactate, acetate, porcine bile and salt. Adhesion of NCK2015 to Caco-2 cells was substantially reduced for both stationary-phase (∼45 % reduction) and exponential-phase cells (∼50 % reduction). Analysis of NCK2015 by scanning electron microscopy revealed a longer cell morphology after growth in MRS broth compared to NCK1909. These results indicate a role for LBA649 in stress tolerance, cell wall division and adherence to Caco-2 cells.