Studies on sleep patterns and sleep homeostasis in birds:An ecological approach

Sleep is a complex phenomenon that consists of two completely different and alternating states, slow-wave sleep (SWS) and rapid-eye-movement sleep (REM sleep). Each of these two states is thought to play an important role in supporting brain and bodily functions. Yet, how exactly sleep fulfills these functions is a topic of ongoing research and debate. Most of what is known about sleep is derived from studies that were done in mammals under strictly controlled laboratory conditions. However, sleep is not restricted to mammals but is thought to be present in all living animals. Moreover, studies in a laboratory setting may not provide a complete picture of the regulatory processes and functions of sleep under natural conditions. For that reason, I measured sleep in three bird species under both laboratory conditions and semi-natural conditions: the European jackdaw (Coloeus monedula), the European starling (Sturnus vulgaris) and the barnacle goose (Branta leucopsis). The results provide evidence for homeostatic regulation of SWS in birds similar to what has been reported for mammals, but also produced unexpected findings. For example, the geese only showed a rebound of SWS after brief sleep deprivation in summer but not in winter. Also, both geese and starlings displayed strong seasonal variation in the overall amount of sleep. The starling in particular slept 5h per day less in summer than they did in winter. Moreover, both geese and starlings slept about 2h less during full moon nights than new moon nights. Another intriguing finding was the strong variation in REM sleep between the 3 species, which ranged from hardly any REM sleep in starlings to a much higher, mammalian-like amount of REM sleep in jackdaws. Such findings are difficult to reconcile with current theories in the function of REM sleep that are largely based on studies in mammals. Together, these findings in birds indicate that sleep is highly sensitive to environmental factors and suggest a great deal of flexibility in the regulation of sleep under natural conditions

Exploration via Epistemic Value Estimation

How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions - for instance to compute an exploration bonus or upper confidence bound. Unfortunately the required uncertainty is difficult to estimate in general with function approximation. We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators. It equips agents with a tractable posterior over all their parameters from which epistemic value uncertainty can be computed efficiently. We use the recipe to derive an epistemic Q-Learning agent and observe competitive performance on a series of benchmarks. Experiments confirm that the EVE recipe facilitates efficient exploration in hard exploration tasks

Seasonal variation in rest-activity patterns in barnacle geese:Are measurements of activity a good indicator of sleep-wake patterns?

Sleep is a widely spread phenomenon in the animal kingdom and is thought to serve important functions. Yet, the function of sleep remains an enigma. Studies in non-model animal species in their natural habitat might provide more insight into the evolution and function of sleep. However, polysomnography in the wild may not always be an option or first choice and some studies may need to rely on rest–activity recordings as a proxy for sleep and wakefulness. In the current paper, we analyzed how accelerometry-based activity data correlate with electroencephalogram (EEG)-based sleep–wake patterns in barnacle geese under seminatural conditions across different seasons. In winter, the geese had pronounced daily rhythms in rest and activity, with most activity occurring during the daytime. In summer, activity was more spread out over the 24 h cycle. Hourly activity scores strongly correlated with EEG-determined time awake, but the strength of the correlation varied with phase of the day and season. In winter, the correlations between activity and waking time were weaker for daytime than for night-time. Furthermore, the correlations between activity and waking during daytime were weaker in winter than in summer. During daytime in winter, there were many instances where the birds were awake but not moving. Experimental sleep deprivation had no effect on the strength of the correlation between activity scores and EEG-based wake time. Overall, hourly activity scores also showed significant inverse correlation with the time spent in non-rapid eye movement (NREM) sleep. However, correlation between activity scores and time spent in REM sleep was weak. In conclusion, accelerometry-based activity scores can serve as a good estimate for time awake or even the specific time spent in NREM sleep. However, activity scores cannot reliably predict REM sleep and sleep architecture

Factorized Q-Learning for Large-Scale Multi-Agent Systems

Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics between the environment and the agents. In this paper, we propose to make the computation of multi-agent Q-learning tractable by treating the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional tensor and then approximate it with factorized pairwise interactions. Furthermore, we utilize a composite deep neural network architecture for computing the factorized Q-function, share the model parameters among all the agents within the same group, and estimate the agents' optimal joint actions through a coordinate descent type algorithm. All these simplifications greatly reduce the model complexity and accelerate the learning process. Extensive experiments on two different multi-agent problems demonstrate the performance gain of our proposed approach in comparison with strong baselines, particularly when there are a large number of agents.Comment: 7 pages, 5 figures, DAI 201

A comparison of continuous and intermittent EEG recordings in geese:How much data are needed to reliably estimate sleep-wake patterns?

Recent technological advancements allow researchers to measure electrophysiological parameters of animals, such as sleep, in remote locations by using miniature dataloggers. Yet, continuous recording of sleep might be constrained by the memory and battery capacity of the recording devices. These limitations can be alleviated by recording intermittently instead of continuously, distributing the limited recording capacity over a longer period. We assessed how reduced sampling of sleep recordings affected measurement precision of NREM sleep, REM sleep, and Wake. We analysed a dataset on sleep in barnacle geese that we resampled following 12 different recording schemes, with data collected for 1 min per 5 min up to 1 min per 60 min in steps of 5 min. Recording 1 min in 5 min still yielded precise estimates of hourly sleep-wake values (correlations of 0.9) while potentially extending the total recording period by a factor of 5. The correlation strength gradually decreased to 0.5 when recording 1 min per 60 min. For hourly values of Wake and NREM sleep, the correlation strength in winter was higher compared with summer, reflecting more fragmented sleep in summer. Interestingly for hourly values of REM sleep, correlations were unaffected by season. Estimates of total 24 h sleep-wake values were similar for all intermittent recording schedules compared to the continuous recording. These data indicate that there is a large safe range in which researchers can periodically record sleep. Increasing the sample size while maintaining precision can substantially increase the statistical power, and is therefore recommended whenever the total recording time is limited

Characteristics of children requiring admission to neonatal care and paediatric intensive care before the age of 2 years in England and Wales: A data linkage study

\ua9 Author(s) (or their employer(s)) 2024. Objective: To quantify the characteristics of children admitted to neonatal units (NNUs) and paediatric intensive care units (PICUs) before the age of 2 years. Design: A data linkage study of routinely collected data. Setting: National Health Service NNUs and PICUs in England and Wales Patients: Children born from 2013 to 2018. Interventions: None. Main outcome measure: Admission to PICU before the age of 2 years. Results: A total of 384 747 babies were admitted to an NNU and 4.8% (n=18 343) were also admitted to PICU before the age of 2 years. Approximately half of all children admitted to PICU under the age of 2 years born in the same time window (n=18 343/37 549) had previously been cared for in an NNU. The main reasons for first admission to PICU were cardiac (n=7138) and respiratory conditions (n=5386). Cardiac admissions were primarily from children born at term (n=5146), while respiratory admissions were primarily from children born preterm (<37 weeks\u27 gestational age, n=3550). A third of children admitted to PICU had more than one admission. Conclusions: Healthcare professionals caring for babies and children in NNU and PICU see some of the same children in the first 2 years of life. While some children are following established care pathways (eg, staged cardiac surgery), the small proportion of children needing NNU care subsequently requiring PICU care account for a large proportion of the total PICU population. These differences may affect perceptions of risk for this group of children between NNU and PICU teams

Unraveling the Effects of Acute Inflammation on Pharmacokinetics: A Model-Based Analysis Focusing on Renal Glomerular Filtration Rate and Cytochrome P450 3A4-Mediated Metabolism

Background and Objectives Acute inflammation caused by infections or sepsis can impact pharmacokinetics. We used a model-based analysis to evaluate the effect of acute inflammation as represented by interleukin-6 (IL-6) levels on drug clearance, focusing on renal glomerular filtration rate (GFR) and cytochrome P450 3A4 (CYP3A4)-mediated metabolism. Methods A physiologically based model incorporating renal and hepatic drug clearance was implemented. Functions correlating IL-6 levels with GFR and in vitro CYP3A4 activity were derived and incorporated into the modeling framework. We then simulated treatment scenarios for hypothetical drugs by varying the IL-6 levels, the contribution of renal and hepatic drug clearance, and protein binding. The relative change in observed area under the concentration-time curve (AUC) was computed for these scenarios. Results Inflammation showed opposite effects on drug exposure for drugs eliminated via the liver and kidney, with the effect of inflammation being inversely proportional to the extraction ratio (ER). For renally cleared drugs, the relative decrease in AUC was close to 30% during severe inflammation. For CYP3A4 substrates, the relative increase in AUC could exceed 50% for low-ER drugs. Finally, the impact of inflammation-induced changes in drug clearance is smaller for drugs with a larger unbound fraction. Conclusion This analysis demonstrates differences in the impact of inflammation on drug clearance for different drug types. The effects of inflammation status on pharmacokinetics may explain the inter-individual variability in pharmacokinetics in critically ill patients. The proposed model-based analysis may be used to further evaluate the effect of inflammation, i.e., by incorporating the effect of inflammation on other drug-metabolizing enzymes or physiological processes

The European starling (<i>Sturnus vulgaris</i>) shows signs of NREM sleep homeostasis but has very little REM sleep and no REM sleep homeostasis

Most of our knowledge about the regulation and function of sleep is based on studies in a restricted number of mammalian species, particularly nocturnal rodents. Hence, there is still much to learn from comparative studies in other species. Birds are interesting because they appear to share key aspects of sleep with mammals, including the presence of two different forms of sleep, i.e. non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. We examined sleep architecture and sleep homeostasis in the European starling, using miniature dataloggers for electroencephalogram (EEG) recordings. Under controlled laboratory conditions with a 12:12 h light–dark cycle, the birds displayed a pronounced daily rhythm in sleep and wakefulness with most sleep occurring during the dark phase. Sleep mainly consisted of NREM sleep. In fact, the amount of REM sleep added up to only 1~2% of total sleep time. Animals were subjected to 4 or 8 h sleep deprivation to assess sleep homeostatic responses. Sleep deprivation induced changes in subsequent NREM sleep EEG spectral qualities for several hours, with increased spectral power from 1.17 Hz up to at least 25 Hz. In contrast, power below 1.17 Hz was decreased after sleep deprivation. Sleep deprivation also resulted in a small compensatory increase in NREM sleep time the next day. Changes in EEG spectral power and sleep time were largely similar after 4 and 8 h sleep deprivation. REM sleep was not noticeably compensated after sleep deprivation. In conclusion, starlings display signs of NREM sleep homeostasis but the results do not support the notion of important REM sleep functions

Community health center efficiency: The role of grant revenue in health center efficiency

Abstract: Objective: To test the relationship between external environments, organizational characteristics, and technical efficiency in federally qualified health centers (FQHCs). We tested the relationship between grant revenue and technical efficiency in FQHCs. Principal Findings: Increased grant revenues did not increase the probability that a health center would be on the efficiency frontier. However, increased grant revenues had a negative association with technical efficiency for health centers that were not fully efficient. Data Conclusion: If all health centers were operating efficiently, anywhere from 39 to 45 million patient encounters could have been delivered instead of the actual total of 29 million in 2007. Policy makers should consider tying grant revenues to performance indicators, and future work is needed to understand the mechanisms through which diseconomies of scale are present in FQHCs