Cloud cover amplifies the sleep-suppressing effect of artificial light at night in geese

In modern society the night sky is lit up not only by the moon but also by artificial light devices. Both of these light sources can have a major impact on wildlife physiology and behaviour. For example, a number of bird species were found to sleep several hours less under full moon compared to new moon and a similar sleep-suppressing effect has been reported for artificial light at night (ALAN). Cloud cover at night can modulate the light levels perceived by wildlife, yet, in opposite directions for ALAN and moon. While clouds will block moon light, it may reflect and amplify ALAN levels and increases the night glow in urbanized areas. As a consequence, cloud cover may also modulate the sleep-suppressing effects of moon and ALAN in different directions. In this study we therefore measured sleep in barnacle geese (Branta leucopsis) under semi-natural conditions in relation to moon phase, ALAN and cloud cover. Our analysis shows that, during new moon nights stronger cloud cover was indeed associated with increased ALAN levels at our study site. In contrast, light levels during full moon nights were fairly constant, presumably because of moonlight on clear nights or because of reflected artificial light on cloudy nights. Importantly, cloud cover caused an estimated 24.8% reduction in the amount of night-time NREM sleep from nights with medium to full cloud cover, particularly during new moon when sleep was unaffected by moon light. In conclusion, our findings suggest that cloud cover can, in a rather dramatic way, amplify the immediate effects of ALAN on wildlife. Sleep appears to be highly sensitive to ALAN and may therefore be a good indicator of its biological effects.ISSN:0269-7491ISSN:1878-2450ISSN:1873-642

Seasonal variation in sleep homeostasis in migratory geese:A rebound of NREM sleep following sleep deprivation in summer but not in winter

Sleep is a behavioral and physiological state that is thought to serve important functions. Many animals go through phases in the annual cycle where sleep time might be limited, for example, during the migration and breeding phases. This leads to the question whether there are seasonal changes in sleep homeostasis. Using electroencephalogram (EEG) data loggers, we measured sleep in summer and winter in 13 barnacle geese (Branta leucopsis) under semi-natural conditions. During both seasons, we examined the homeostatic regulation of sleep by depriving the birds of sleep for 4 and 8 h after sunset. In winter, barnacle geese showed a clear diurnal rhythm in sleep and wakefulness. In summer, this rhythm was less pronounced, with sleep being spread out over the 24-h cycle. On average, the geese slept 1.5 h less per day in summer compared with winter. In both seasons, the amount of NREM sleep was additionally affected by the lunar cycle, with 2 h NREM sleep less during full moon compared to new moon. During summer, the geese responded to 4 and 8 h of sleep deprivation with a compensatory increase in NREM sleep time. In winter, this homeostatic response was absent. Overall, sleep deprivation only resulted in minor changes in the spectral composition of the sleep EEG. In conclusion, barnacle geese display season-dependent homeostatic regulation of sleep. These results demonstrate that sleep homeostasis is not a rigid phenomenon and suggest that some species may tolerate sleep loss under certain conditions or during certain periods of the year.ISSN:1550-9109ISSN:0161-810

Right Ventricular Strain and Dyssynchrony Assessment in Arrhythmogenic Right Ventricular Cardiomyopathy: Cardiac Magnetic Resonance Feature-Tracking Study

BACKGROUND: Analysis of right ventricular (RV) regional dysfunction by cardiac magnetic resonance (CMR) imaging in arrhythmogenic RV cardiomyopathy (ARVC) may be inadequate because of the complex contraction pattern of the RV. Aim of this study was to determine the use of RV strain and dyssynchrony assessment in ARVC using feature-tracking CMR analysis. METHODS AND RESULTS: Thirty-two consecutive patients with ARVC referred to CMR imaging were included. Thirty-two patients with idiopathic RV outflow tract arrhythmias and 32 control subjects, matched for age and sex to the ARVC group, were included for comparison purpose. CMR imaging was performed to assess biventricular function; feature-tracking analysis was applied to the cine CMR images to assess regional and global longitudinal, circumferential, and radial RV strains and RV dyssynchrony (defined as the SD of the time-to-peak strain of the RV segments). RV global longitudinal strain (-17\ub15% versus -26\ub16% versus -29\ub16%; P-23.2%, SD of the time-to-peak RV longitudinal strain >113.1 ms, and SD of the time-to-peak RV circumferential strain >177.1 ms allowed correct identification of 88%, 75%, and 63% of ARVC patients with no or only minor CMR criteria for ARVC diagnosis. CONCLUSIONS: Strain analysis by feature-tracking CMR helps to objectively quantify global and regional RV dysfunction and RV dyssynchrony in patients with ARVC and provides incremental value over conventional cine CMR imaging

Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

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, SomnivoreTM, 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

‘Somnivore’ a user-friendly platform for automated scoring and analysis of polysomnography data

© 2017 Dr. Giancarlo AlloccaThe low-throughput nature of manual scoring of polysomnography (sleep) data, both in terms of speed and consistency, is a major factor preventing sleep research from reaching its full efficiency and potential. Automated approaches developed previously have generally failed to provide sufficient accuracy or 'usability' for sleep scientists lacking specialist-engineering expertise. Moreover, all earlier approaches have only been validated using baseline data, suggesting a failure to embed in the algorithm the robustness to remain effective when used to analyse the effect on sleep of treatment or disease. Finally, no single approach has been validated for mouse, rat and human data. Therefore, the aim of my research was to develop a user-friendly platform for real-time automated scoring and analysis of polysomnography data. The program is known as ‘Somnivore’ (from Latin somnus, ‘sleep’, and vorare, ‘to devour’), and was developed using state of the art supervised machine learning technology, with support vector machine (SVM) at its core, and coded as a graphical user interface (GUI)-based solution in the Matlab™ ambient. Somnivore learns, in parallel, by surveying features from a variety of different inputs (including EEG, EMG, EOG and ECG) and outputs data into the various sleep stages (wake, NREM, N1, N2, N3, REM). The classifier is trained for each subject via a brief session of manual scoring. Design and development strategies were built around both theoretical and heuristic approaches. This led to a multi-layered system capable of learning from extremely limited training sets, using large input space dimensionalities from a rich variety of polysomnography inputs, and with rapid computational times. Validation was pursued to approach the numerous contentious dynamics that have led to the demise of previous solutions. Somnivore generalisation was evaluated at the level of canonical classifier evaluation metrics such as F-measure, as well as experimental end-measures more germane to traditional biological sleep research. Somnivore, generated superior generalisation, with high power, on both murine (n = 54) and human (n = 52) recordings. These included multiple rat strains (Sprague-Dawley, Wistar) and mouse phenotypes (wild type, orexin neuron-ablated transgenic), various pharmacological interventions (placebo, alcohol, muscimol, caffeine, zolpidem, almorexant), and in humans, both genders, younger and older subjects, and subjects with mild cognitive impairment (MCI). Somnivore’s generalisation was also evaluated in conditions of signal challenged data, and provided excellent performance in all conditions using only one EEG channel for learning. Remarkable results were also reported for learning undertaken using only one EMG channel or two EOG channels. Furthermore, validation studies highlighted that a substantial part of the disagreement between manual and automated hypnograms was located within transition epochs. As Somnivore has several features geared towards the management of transition epochs, further control over generalisation is also possible. Comprehensive inter-scorer agreement analysis was conducted on human data, showcasing how inter-scorer agreement between manual hypnograms and their automated counterparts provided by Somnivore is comparable to the gold-standard of the inter-scorer agreement between two experts trained in the same laboratory. Results also highlighted critical problems within the scoring of stage N1. However, inter-scorer agreement validation studies also confirmed what has already been reported in the literature, that N1 is a volatile stage that systematically produces inadequate agreement even between trained experts, both within or outside the same laboratory. Accordingly, Somnivore performed as well on N1 as reported in the literature for manually scored data. Due to the high-throughput nature of Somnivore’s analyses of experimental end-measures, several novel, cautionary findings were extracted from the recordings provided by external laboratories for this research evaluations. Additionally, as Somnivore is also capable of scoring real-time during polysomnography recordings, it will facilitate the development of more advanced protocols such as biofeedback sleep-deprivation protocols and integrated optogenetics. In conclusion, Somnivore, has been comprehensively validated as an accurate, reliable, high-throughput solution for scoring and analysis of polysomnography data, in a range of experimental situations including studies of normal physiology and tests related to drug discovery for the improved treatment of sleep disorders and psychiatric diseases

Seasonal variation in sleep homeostasis in migratory geese: a rebound of NREM sleep following sleep deprivation in summer but not in winter

Sleep is a behavioral and physiological state that is thought to serve important functions. Many animals go through phases in the annual cycle where sleep time might be limited, for example, during the migration and breeding phases. This leads to the question whether there are seasonal changes in sleep homeostasis. Using electroencephalogram (EEG) data loggers, we measured sleep in summer and winter in 13 barnacle geese (Branta leucopsis) under semi-natural conditions. During both seasons, we examined the homeostatic regulation of sleep by depriving the birds of sleep for 4 and 8 h after sunset. In winter, barnacle geese showed a clear diurnal rhythm in sleep and wakefulness. In summer, this rhythm was less pronounced, with sleep being spread out over the 24-h cycle. On average, the geese slept 1.5 h less per day in summer compared with winter. In both seasons, the amount of NREM sleep was additionally affected by the lunar cycle, with 2 h NREM sleep less during full moon compared to new moon. During summer, the geese responded to 4 and 8 h of sleep deprivation with a compensatory increase in NREM sleep time. In winter, this homeostatic response was absent. Overall, sleep deprivation only resulted in minor changes in the spectral composition of the sleep EEG. In conclusion, barnacle geese display season-dependent homeostatic regulation of sleep. These results demonstrate that sleep homeostasis is not a rigid phenomenon and suggest that some species may tolerate sleep loss under certain conditions or during certain periods of the year

Seasonal variation in sleep time: jackdaws sleep when it is dark, but do they really need it?

Sleep is an important behavioural and physiological state that is ubiquitous throughout the animal kingdom. Birds are an interesting group to study sleep since they share similar sleep features with mammals. Interestingly, sleep time in birds has been shown to vary greatly amongst seasons. To understand the mechanisms behind these variations in sleep time, we did an electro-encephalogram (EEG) study in eight European jackdaws (Coloeus monedula) in winter and summer under outdoor seminatural conditions. To assess whether the amount and pattern of sleep is determined by the outdoor seasonal state of the animals or directly determined by the indoor light-dark cycle, we individually housed them indoors where we manipulated the light-dark (LD) cycles to mimic long winter nights (8:16 LD) and short summer nights (16:8 LD) within both seasons. Jackdaws showed under seminatural outdoor conditions 5 h less sleep in summer compared to winter. During the indoor conditions, the birds rapidly adjusted their sleep time to the new LD cycle. Although they swiftly increased or decreased their sleep time, sleep intensity did not vary. The results indicate that the strong seasonal differences in sleep time are largely and directly driven by the available dark time, rather than an endogenous annual clock. Importantly, these findings confirm that sleep in birds is not a rigid phenomenon but highly sensitive to environmental factors.ISSN:0174-1578ISSN:1432-136

A Study on REM Sleep Homeostasis in the Day-Active Tree Shrew (<i>Tupaia belangeri</i>): Cold-Induced Suppression of REM Sleep Is Not Followed by a Rebound

The function and regulation of rapid-eye-movement (REM) sleep is a topic of ongoing debate. It is often assumed that REM sleep is a homeostatically regulated process and that a need for REM sleep builds up, either during prior wakefulness or during preceding slow wave sleep. In the current study, we tested this hypothesis in six diurnal tree shrews (Tupaia belangeri), small mammals closely related to primates. All animals were individually housed and kept under a 12:12 light-dark cycle with an ambient temperature of 24 °C. We recorded sleep and temperature in the tree shrews for 3 consecutive 24 h days. During the second night, we exposed the animals to a low ambient temperature of 4 °C, a procedure that is known to suppress REM sleep. Cold exposure caused a significant drop in brain temperature and body temperature and also resulted in a strong and selective suppression of REM sleep by 64.9%. However, contrary to our expectation, the loss of REM sleep was not recovered during the subsequent day and night. These findings in a diurnal mammal confirm that the expression of REM sleep is highly sensitive to environmental temperature but do not support the view that REM sleep is homeostatically regulated in this species

Human α-L-fucosidase-1 attenuates the invasive properties of thyroid cancer

Glycans containing α-L-fucose participate in diverse interactions between cells and extracellular matrix. High glycan expression on cell surface is often associated with neoplastic progression. The lysosomal exoenzyme, α-L-fucosidase-1 (FUCA-1) removes fucose residues from glycans. The FUCA-1 gene is down-regulated in highly aggressive and metastatic human tumors. However, the role of FUCA-1 in tumor progression remains unclear. It is speculated that its inactivation perturbs glycosylation of proteins involved in cell adhesion and promotes cancer. FUCA-1 expression of various thyroid normal and cancer tissues assayed by immunohistochemical (IHC) staining was high in normal thyroids and papillary thyroid carcinomas (PTC), whereas it progressively decreased in poorly differentiated, metastatic and anaplastic thyroid carcinomas (ATC). FUCA-1 mRNA expression from tissue samples and cell lines and protein expression levels and enzyme activity in thyroid cancer cell lines paralleled those of IHC staining. Furthermore, ATC-derived 8505C cells adhesion to human E-selectin and HUVEC cells was inhibited by bovine α-L-fucosidase or Lewis antigens, thus pointing to an essential role of fucose residues in the adhesive phenotype of this cancer cell line. Finally, 8505C cells transfected with a FUCA-1 containing plasmid displayed a less invasive phenotype versus the parental 8505C. These results demonstrate that FUCA-1 is down-regulated in ATC compared to PTC and normal thyroid tissues and cell lines. As shown for other human cancers, the down-regulation of FUCA-1 correlates with increased aggressiveness of the cancer type. This is the first report indicating that the down-regulation of FUCA-1 is related to the increased aggressiveness of thyroid cancer

Validation of \u2018Somnivore\u2019, a machine learning algorithm for automated scoring and analysis of polysomnography data

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\u2122, 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, cognitive-impairment, 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\ub10.01; N1 0.57\ub10.01; N2 0.81\ub10.01; N3 0.86\ub10.01; REM 0.87\ub10.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95\ub10.01; NREM 0.94\ub10.01; REM 0.91\ub10.01) and pigeon (wake 0.96\ub10.006; NREM 0.97\ub10.01; REM 0.86\ub10.02) 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