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High activity of upper airway dilator muscles is thought to be critical in preventing sleep-related upper airway collapse. To date, most of the research regarding upper airway dilator muscles has focused on the genioglossus muscle, which protrudes the tongue and opens the retroglossal airway. However, collapse commonly occurs in the retropalatal region. We, therefore, aimed to examine the motor control of the palatoglossus muscle as well as investigate breathing route-related changes in genioglossus and palatoglossus motor units. Single motor unit recordings of the genioglossus and palatoglossus were made simultaneously in healthy individuals during wakefulness while breathing through the nose with the mouth closed (NMC), nose with mouth open (NMO), or orally (OMO). The palatoglossus was found to have all five motor unit firing patterns that have been observed in other upper airway dilator muscles, but during nasal breathing had a higher proportion of tonically active but inspiratory modulated motor units as compared with the genioglossus (67% vs. 30%). When still breathing nasally but with the mouth open, the units with an expiratory firing pattern in genioglossus, and all firing patterns in palatoglossus, increased their firing rates compared with nasal breathing with the mouth closed [genioglossus (GG): 17.8 ± 4.9 vs. 23.1 ± 4.8 Hz, palatoglossus (PG): 17.0 ± 4.0 vs. 19.3 ± 4.0 Hz]. Finally, oral breathing resulted in dramatic reductions in the number of palatoglossal motor units that were firing (35 units vs. 92 during nasal breathing). Palatoglossal activity may contribute importantly to airway collapsibility and may provide an alternate pathway for preventing sleep-related airway collapse. NEW & NOTEWORTHY The firing patterns of motor units in the palatoglossus have until now not been investigated, and how they and motor units in the genioglossus change with breathing route alteration was not known. This study has shown that the palatoglossus contains motor units with all the firing patterns observed in the genioglossus but in different proportions. Furthermore, breathing route changes alter units with different firing patterns differentially in the two muscles.

Demonstrating inter-device reliability is essential to use devices interchangeably, and accurately integrate, interpret, or compare data from different actigraphs. Despite this, there is a paucity of comparative literature over a timeframe exceeding one night. The aims of this study were to determine an optimal wake threshold for GENEActiv and to evaluate the concordance between Actiwatch-2 and GENEActiv using a common algorithm (Phillips Respironics). Data were collected from 33 individuals (20 female) aged 20-35 years ( M = 25.33, SD = 4.69) across a total 213 nights. Participants wore both devices simultaneously and continuously for seven days. The sleep parameters of interest were: total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Exploratory analyses of sensitivity, specificity, overall accuracy, mean bias, and paired samples t-tests indicated an optimal wake threshold of 115 for GENEActiv, compared with Actiwatch-2 at the 40 (medium, default) threshold. Using these thresholds, sensitivity, and overall accuracy of GENEActiv were both good (86% and 78%, respectively), however specificity was relatively low (40%). There were no significant inter-device differences for any sleep parameters, and all absolute mean biases were small. Overall, the findings from this study provide the first empirical evidence to support the reliability of GENEActiv against Actiwatch-2 over multiple nights using a common algorithm with device-specific wake thresholds.

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore TM , for automated wake-sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons ( n = 5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. F -measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.

Lanthanum-based adsorbents are ideal candidates for phosphate removal because of their excellent affinity to phosphate. However, their application in the removal of trace-levels of phosphate from sewage is still unsatisfactory due to the limited adsorption capacity and inadequate optimization of the operational parameters. To overcome these drawbacks, we have developed a novel lanthanum hydroxide (LH), using a facile precipitation and hydrothermal process that involves a nanorod-like structure with the lengths ranging from 124 to 1700 nm, depending on the La/OH molar ratio. The phosphate adsorption capacity of the developed LH is up to 170.1 mg-P g -1 in synthetic water, while a slightly lower adsorption capacity of 111.1 mg-P g -1 is observed in a sewage sample. A polynominal model consisting of three variables (i.e. dosage, reaction time and initial phosphate concentration) for predicting efficiency of phosphate removal has been successfully developed using a face-centred central composite design (CCD)-based methodology. The results also suggest a strong interactive effect of the dosage with the phosphate concentration, and reaction time, which can significantly affect the optimization of the phosphate removal by LH. Both X-ray photoelectron spectroscopy and X-ray diffraction studies indicate that the inner sphere complexation of phosphate with LH is probably the major mechanism governing phosphate removal., (Copyright © 2017 Elsevier Ltd. All rights reserved.)