Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird

The diet of an individual animal is subject to change over time, both in response to short-term food fluctuations and over longer time scales as an individual ages and meets different challenges over its life cycle. A metabarcoding approach was used to elucidate the diet of different life stages of a migratory songbird, the Eurasian reed warbler (Acrocephalus scirpaceus) over the 2017 summer breeding season in Somerset, the United Kingdom. The feces of adult, juvenile, and nestling warblers were screened for invertebrate DNA, enabling the identification of prey species. Dietary analysis was coupled with monitoring of Diptera in the field using yellow sticky traps. Seasonal changes in warbler diet were subtle, whereas age class had a greater influence on overall diet composition. Age classes showed high dietary overlap, but significant dietary differences were mediated through the selection of prey; (i) from different taxonomic groups, (ii) with different habitat origins (aquatic vs. terrestrial), and (iii) of different average approximate sizes. Our results highlight the value of metabarcoding data for enhancing ecological studies of insectivores in dynamic environments.

Molecular dynamics simulations explore effects of electric field orientations on spike proteins of SARS-CoV-2 virions

Emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its current worldwide spread have caused a pandemic of acute respiratory disease COVID-19. The virus can result in mild to severe, and even to fatal respiratory illness in humans, threatening human health and public safety. The spike (S) protein on the surface of viral membrane is responsible for viral entry into host cells. The discovery of methods to inactivate the entry of SARS-CoV-2 through disruption of the S protein binding to its cognate receptor on the host cell is an active research area. To explore other prevention strategies against the quick spread of the virus and its mutants, non-equilibrium molecular dynamics simulations have been employed to explore the possibility of manipulating the structure–activity of the SARS-CoV-2 spike glycoprotein by applying electric fields (EFs) in both the protein axial directions and in the direction perpendicular to the protein axis. We have found out the application of EFs perpendicular to the protein axis is most effective in denaturing the HR2 domain which plays critical role in viral-host membrane fusion. This finding suggests that varying irradiation angles may be an important consideration in developing EF based non-invasive technologies to inactivate the virus.

Impact of climate change on the elevational and latitudinal distributions of populations of Tipulidae (Diptera) in Wales, United Kingdom

As dominant features of most ecosystems, insects are responsive to changes in climate, both over short temporal scales (e.g. seasonal fluctuations in abundance) and over longer evolutionary scales (e.g. decade-scale changes in patterns of biodiversity). One such taxonomic group that is sensitive to changing climate are the craneflies (Diptera: Tipulidae). Here, we used aggregated biodiversity data to examine elevational and latitudinal distributions of adult Tipulidae between 1976 and 2019 in Wales, UK, and we related these distributions to climatic patterns. Our analyses showed that species with earlier-emerging adults were most affected by weather conditions in the year before observation. Specifically, as temperature increased, observed elevation increased in high-precipitation conditions, remained stable in average-precipitation conditions and decreased in low-precipitation conditions. For species with later-emerging adults, associations were seen between elevation and weather conditions in the year of observation. Observed latitude generally exhibited a negative association with maximum temperature in the year before observation, with observations of Tipulidae trending southwards during the 43-year study period. Our results support consideration of emergence phenology, weather and habitat data when predicting species distributional changes attributable to climate change, which is vital in understanding the selection pressures that species face in a changing environment.

Association of Obstructive Sleep Apnea With White Matter Integrity and Cognitive Performance Over a 4-Year Period in Middle to Late Adulthood

IMPORTANCE

Obstructive sleep apnea (OSA) is associated with cognitive impairment and brain structural alterations, but longitudinal outcomes are understudied.

OBJECTIVE

To examine the associations of OSA with cognition and white matter (WM) integrity over a 4-year period.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study was conducted in a community-based adult population among participants who had both baseline (2011-2014) and 4-year follow-up (2015-2018) polysomnography, diffusion tensor imaging, and cognitive assessment data. Participants with neurological disorders, anomalous findings on brain magnetic resonance imaging, or inadequate quality of the evaluations were excluded. Data were analyzed from March to November 2021.

EXPOSURES

Participants were categorized depending on the presence vs absence of OSA at baseline and follow-up polysomnographic analysis.

MAIN OUTCOMES AND MEASURES

The main outcomes were proportional changes over a 4-year period in neuropsychological performance and WM integrity. The neuropsychological assessment battery included verbal and visual memory, verbal fluency, Digit Symbol-coding, Trail Making Test-A, and Stroop Test. WM integrity was assessed by fractional anisotropy, axial, and radial diffusivity. To examine interactions with age and sex, participants were subgrouped by age older than 60 years vs 60 years or younger and men vs women.

RESULTS

A total of 1998 individuals were assessed for eligibility, and 888 were excluded based on exclusion criteria, leaving 1110 participants (mean [SD] age, 58.0 [6.0] years; 517 [46.6%] men) for analysis, including 458 participants grouped as OSA-free, 72 participants with resolved OSA, 163 participants with incident OSA, and 417 participants with persistent OSA. Incident OSA was associated with altered WM integrity and with concomitant changes in sustained attention compared with participants without OSA (eg, change in Digit Symbol-coding test score, -3.2% [95% CI, -5.2% to -1.2%]). Participants with resolved OSA showed better visual recall at the follow-up (change in Visual Reproduction-immediate recall test, 17.5% [95% CI, 8.9% to 26.1%]; change in Visual Reproduction-delayed recall test, 33.1% [95% CI, 11.3% to 54.9%]), with concordant changes in diffusion parameters at the relevant anatomic areas. In the older group only (age >60 years), persistent OSA was associated with altered WM integrity and cognition (eg, Visual Reproduction-recognition test: β = -24.2 [95% CI, -40.7 to -7.7]). Sex also was associated with modifying the association of OSA with WM integrity of the left posterior internal capsule, the left genu of corpus callosum, and the right middle cerebellar peduncle only in men and with cognition only in women (eg, Visual Reproduction-immediate recall test: β = 33.4 [95% CI, 19.1 to 47.7]).

CONCLUSIONS AND RELEVANCE

These findings suggest that dynamic changes in OSA status were significantly associated with WM integrity and cognition, which varied by age and sex. It is possible that adequate interventions for OSA could better preserve brain health in middle to late adulthood.

Exploring and Expanding Supererogatory Acts: Beyond Duty for a Sustainable Future

Supererogation has gained attention as a means of explaining the voluntary behaviours of individuals and organizations that are done for the benefit of others and which go above what is required of legislation and what may be expected by society. Whilst the emerging literature has made some significant headway in exploring supererogation as an ethical lens for the study of business there remain several important issues that require attention. These comprise, the lack of primary evidence upon which such examinations have been made, attention has been given to only singular pro-social acts of organizations, and the focus has been upon the actions of large organizations. Furthermore, Heyd's (Supererogation, Cambridge University Press, 1982) original taxonomy of six supererogatory acts, comprising Moral Heroism, Beneficence, Volunteering, Favour, Forgiveness and Forbearance, has been considered to be complete and other forms of supererogatory acts have not yet been explored. In order to address these gaps this study poses the research questions: First, it studies how a single, contemporary SME performs multiple supererogatory acts in its attempts to address its social and environmental goals that go beyond CSR. Second, it seeks to gain a deeper theoretical understanding of Heyd's (Supererogation, Cambridge University Press, 1982) taxonomy of six forms of supererogation through the capture of primary data. This research makes a three-year case study examination of a single SME that has been formally recognized for its work in addressing social and environmental issues at local, national and global levels. Primary data are acquired of the supererogatory acts that it performs through a three-year participant observation case study, utilizing 61 interviews and 3 focus groups with internal and external stakeholders. In doing so, it addresses the empirical limitations of the extant research, substantiates each of the forms that supererogatory acts may take, and makes a contribution to the theory of supererogation by identifying a further class of act that is 'Sharing'.

Optimal spindle detection parameters for predicting cognitive performance

STUDY OBJECTIVES

Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition.

METHODS

Adult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores.

RESULTS

Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings.

CONCLUSIONS

Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.

Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.

Systematic design, generation, and application of synthetic datasets for flow cytometry

Application of synthetic datasets in training and validation of analysis tools have led to improvements in many decision-making tasks in a range of domains from computer vision to digital pathology. Synthetic datasets overcome the constraints of real-world datasets, namely difficulties in collection and labelling, expense, time and privacy concerns. In flow cytometry, real cell-based datasets are limited by properties such as size, number of parameters, distance between cell populations and distributions, and are often focused on a narrow range of disease or cell types. Researchers in some cases have designed these desired properties into synthetic datasets, however operators have implemented them in inconsistent approaches and there is a scarcity of publicly available, high-quality synthetic datasets. In this research, we propose a method to systematically design and generate flow cytometry synthetic datasets with highly controlled characteristics. We demonstrate the generation of two-cluster synthetic datasets with specific degrees of separation between cell populations, and of non-normal distributions with increasing levels of skewness and orientations of skew pairs. We apply our synthetic datasets to test the performance of a popular automated cell populations identification software, SPADE3, and define the region where the software performance decreases as the clusters get closer together. Application of the synthetic skewed dataset suggests the software is capable of processing non-normal data. We calculate the classification accuracy of SPADE3 with robustness not achievable with real-world datasets. Our approach aims to advance research towards generation of high-quality synthetic flow cytometry datasets, and to increase their awareness among the community. The synthetic datasets can be utilised in benchmarking studies that critically evaluate cell population identification tools and help illustrate potential digital platform inconsistencies. These datasets have the potential to improve cell characterisation workflows that integrate automated analysis in clinical diagnostics and cell therapy manufacturing.

Severity of Daytime Sleepiness and Parkinsonian-Like Symptoms in Korean Adults Aged 50-64 Years

Background and Purpose

While excessive daytime sleepiness can predate Parkinson’s disease in late-life, its association with parkinsonian-like (P-L) symptoms in middle age are unknown. Since neurodegeneration can appear decades before a diagnosis of Parkinson’s disease, identifying clinical features associated with this early progression is important. The purpose of this study was to determine the association of daytime sleepiness with P-L symptoms in a population-based sample of middle-aged Korean adults.

Methods

During 2013 and 2014, daytime sleepiness and P-L symptoms were assessed in 2,063 males and females aged 50–64 years who were participating in the Korean Genome and Epidemiology Study. The severity of daytime sleepiness was quantified by the score on the Epworth Sleepiness Scale (ESS). Self-reported P-L symptoms included nine motor disorders commonly associated with Parkinson’s disease. Participants with parkinsonism and related conditions are excluded.

Results

The prevalence of excessive daytime sleepiness (ESS score >10) was 7.0%. The frequencies of P-L symptoms ranged from 0.5% (for “trouble buttoning buttons”) to 18.4% (for “handwriting smaller than it once was”). After adjustment for covariates and multiple testing, the relative odds of P-L symptoms comparing the 80th and 20th percentiles of ESS scores was 1.6 (p=0.001) for “voice is softer than it once was,” 2.1 (p<0.001) for “balance when walking is poor,” and 1.5 (p=0.002) for “loss of facial expression.” The prevalence of excessive daytime sleepiness increased from 6.3% to 19.8% when the number of symptoms increased from zero to three (p=0.004).

Conclusions

In Korean adults aged 50–64 years, daytime sleepiness is significantly associated with P-L symptoms. Whether coexisting daytime sleepiness and P-L symptoms predate extrapyramidal and other impairments in later life warrants further investigation.

Cardiopulmonary Coupling

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Discerning a smile - The intricacies of analysis of post-neck dissection asymmetry

INTRODUCTION

Iatrogenic facial nerve palsy is distressing to the patient and clinician. The deformity is aesthetically displeasing, and can be functionality problematic for oral competence, dental lip trauma and speech. Furthermore such injuries have litigation implications. Marginal mandibular nerve (MMN) palsy causes an obvious asymmetrical smile. MMN is at particular risk during procedures such as rhytidoplasties, mandibular fracture, tumour resection and neck dissections. Cited causes for the high incidence are large anatomical variations, unreliable landmarks, an exposed neural course and tumour grade or nodal involvement dictating requisite nerve sacrifice. An alternative cause for post-operative asymmetry is damage to the cervical branch of the facial nerve or platysmal dysfunction due to its division. The later tends to have a transient course and recovers. Distinction between MMN palsy and palsy of the cervical branch of the facial nerve or platysma division should therefore be made. In 1979 Ellenbogen differentiated between MMN palsy and "Pseudo-paralysis of the mandibular branch of the facial nerve". Despite this, there is paucity in the literature & confusion amongst clinicians in distinguishing between these palsies, and there is little regarding these post-operative sequelae and neck dissections.

METHOD

This article reflects on the surgical anatomy of the MMN and cervical nerve in relation to danger zones during lymphadenectomy. The authors review the anatomy of the smile. Finally, case studies are utilised to evaluate the differences between MMN palsy and its pseudo-palsy to allow clinical differentiation.

CONCLUSION

Here we present a simple method for clinical differentiation between these two prognostically different injuries, allowing appropriate reassurance, ongoing therapy & management.

Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks

OBJECTIVE

Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning.

METHODS

Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings.

RESULTS

For binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.417.8 and a r² of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas.

CONCLUSION

Our fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation.

SIGNIFICANCE

The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.

Cardiopulmonary coupling and serum cardiac biomarkers in obesity hypoventilation syndrome and obstructive sleep apnea with morbid obesity

STUDY OBJECTIVES

The main cause of death in patients with obesity hypoventilation syndrome (OHS) is cardiac rather than respiratory failure. Here, we investigated autonomic-respiratory coupling and serum cardiac biomarkers in patients with OHS and obstructive sleep apnea (OSA) with comparable body mass index (BMI) and apnea-hypopnea index (AHI).

METHODS

Cardiopulmonary coupling (CPC) and cyclic variation of heart rate (CVHR) analysis was performed on the electrocardiogram signal from the overnight polysomnogram. Cardiac serum biomarkers were obtained in patients with OHS and OSA with a BMI > 40kg/m². Samples were obtained at baseline and after 3 months of positive airway pressure (PAP) therapy in both groups.

RESULTS

Patients with OHS (n=15) and OSA (n=36) were recruited. No group differences in CPC, CVHR and serum biomarkers were observed at baseline and after 3 months of PAP therapy. An improvement in several CPC metrics, including the sleep apnea index, unstable sleep (low frequency coupling and elevated low frequency coupling narrow band [e-LFC_NB]) and CVHR were observed in both groups with PAP use. However, distinct differences in response characteristics were noted. e-LFC_NB coupling correlated with highly sensitive troponin (hs-troponin-T, p<0.05) in the combined cohort. Baseline hs-troponin-T inversely correlated with awake oxygen saturation in the OHS group (p<0.05).

CONCLUSIONS

PAP therapy can significantly improve CPC stability in obese patients with OSA or OHS, with key differences. e-LFC_NB may function as a surrogate biomarker for early subclinical cardiac disease. Low awake oxygen saturation could also increase this biomarker in OHS.

CLINICAL TRIAL REGISTRATION

Registry: Australian New Zealand Clinical Trials Registry; Name: Obesity Hypoventilation Syndrome and Neurocognitive Dysfunction; URL: https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367492; Identifier: ACTRN12615000122550.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

Understanding cell culture dynamics: a tool for defining protocol parameters for improved processes and efficient manufacturing using human embryonic stem cells

Standardization is crucial when culturing cells including human embryonic stem cells (hESCs) which are valuable for therapy development and disease modeling. Inherent issues regarding reproducibility of protocols are problematic as they hinder translation to good manufacturing practice (GMP), thus reducing clinical efficacy and uptake. Pluripotent cultures require standardization to ensure that input material is consistent prior to differentiation, as inconsistency of input cells creates end-product variation. To improve protocols, developers first must understand the cells they are working with and their related culture dynamics. This innovative work highlights key conditions required for optimized and cost-effective bioprocesses compared to generic protocols typically implemented. This entailed investigating conditions affecting growth, metabolism, and phenotype dynamics to ensure cell quality is appropriate for use. Results revealed critical process parameters (CPPs) including feeding regime and seeding density impact critical quality attributes (CQAs) including specific metabolic rate (SMR) and specific growth rate (SGR). This implied that process understanding, and control is essential to maintain key cell characteristics, reduce process variation and retain CQAs. Examination of cell dynamics and CPPs permitted the formation of a defined protocol for culturing H9 hESCs. The authors recommend that H9 seeding densities of 20,000 cells/cm2, four-day cultures or three-day cultures following a recovery passage from cryopreservation and 100% medium exchange after 48 hours are optimal. These parameters gave ~SGR of 0.018 hour-1 ± 1.5x10-3 over three days and cell viabilities ≥95%±0.4, while producing cells which highly expressed pluripotent and proliferation markers, Oct3/4 (>99% positive) and Ki-67 (>99% positive).

Raspberry Pi nest cameras: An affordable tool for remote behavioral and conservation monitoring of bird nests

Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioral ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.Here, we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof-especially to humidity and salt spray. The camera was paired with a passive infrared sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviors and interspecific interactions of two sympatric species of storm-petrel (Monteiro's storm-petrel Hydrobates monteiroi and Madeiran storm-petrel Hydrobates castro) during their respective breeding seasons.Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30-s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.Here, we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently, the camera-build methods and scripts could be applied easily to many different species that also utilize cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviors and interspecies interactions.

Temporal patterns of wildlife roadkill in the UK

Wildlife-vehicle collisions are one of the main causes of mortality for wild mammals and birds in the UK. Here, using a dataset of 54,000+ records collated by a citizen science roadkill recording scheme between 2014–2019, we analyse and present temporal patterns of wildlife roadkill of the 19 most commonly reported taxa in the UK (84% of all reported roadkill). Most taxa (13 out of 19) showed significant and consistent seasonal variations in road mortality and fitted one of two seasonal patterns; bimodal or unimodal: only three species (red fox Vulpes vulpes, European polecat Mustela putorius and Reeves’ muntjac deer Muntiacus reevesi) showed no significant seasonality. Species that increase movement in spring and autumn potentially have bimodal patterns in roadkill due to the increase in mate-searching and juvenile dispersal during these respective time periods (e.g. European badger Meles meles). Unimodal patterns likely represent increased mortality due to a single short pulse in activity associated with breeding (e.g. birds) or foraging (e.g. grey squirrels Sciurus carolinensis in autumn). Importantly, these patterns also indicate periods of increased risk for drivers, potentially posing a greater threat to human welfare. In addition to behaviour-driven annual patterns, abiotic factors (temperature and rainfall) explained some variance in roadkill. Notably, high rainfall was associated with decreased observations of two bird taxa (gulls and Eurasian magpies Pica pica) and European rabbit Oryctolagus cuniculus. By quantifying seasonal patterns in roadkill, we highlight a significant anthropogenic impact on wild species, which is important in relation to conservation, animal welfare, and human safety.

Deep Learning-Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness

PURPOSE

Obstructive sleep apnea (OSA) is associated with altered pairwise connections between brain regions, which might explain cognitive impairment and daytime sleepiness. By adopting a deep learning method, we investigated brain connectivity related to the severity of OSA and daytime sleepiness.

PATIENTS AND METHODS

A cross-sectional design applied a deep learning model on structural brain networks obtained from 553 subjects (age, 59.2 ± 7.4 years; men, 35.6%). The model performance was evaluated with the Pearson's correlation coefficient (R) and probability of absolute error less than standard deviation (PAE

RESULTS

We achieved a meaningful R (up to 0.74) and PAE

CONCLUSION

A deep learning method can assess the association of brain network characteristics with OSA severity and daytime sleepiness and specify the relevant brain connectivity.

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Key Words

• convolutional neural network
• daytime sleepiness
• diffusion tensor imaging
• obstructive sleep apnea
• structural brain network

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MESH Headings Collapse

Grants Collapse

Sleep structure and electroencephalographic spectral power of middle-aged or older adults: Normative values by age and sex in the Korean population

The fine structure of sleep electrocortical activity reflects health and disease. The current study provides normative data for sleep structure and electroencephalography (EEG) spectral power measures derived from overnight polysomnography (PSG) and examines the effect of age and sex among Korean middle-aged and older adults with or without obstructive sleep apnea (OSA). We analysed home PSG data from 1,153 adult participants of an ongoing population-based cohort study, the Korean Genome and Epidemiology Study. Sleep stages were visually scored and spectral power was measured on a single-channel EEG (C4-A1). We computed spectral power for five frequency ranges. The EEG power was reported in relative (%) and log-transformed absolute values (µV² ). With ageing, the proportion of N1 sleep increased, whereas N3 decreased, which is more noticeable in men than in women. The amount of N3 was relatively low in this cohort. With ageing, relative delta power decreased and alpha and sigma power increased for the whole sleep period, which was more pronounced during REM sleep in non-OSA. For men compared with women, relative theta power was lower during REM and sigma and beta were higher during N1 sleep. The differences of relative powers by age and sex in OSA were comparable to those in non-OSA. In a community-based Korean population, we present normative data of sleep structure and spectral power for middle-aged or older adults of a non-Caucasian ethnicity. The values varied with age and sex and were not influenced by sleep apnea.

A longitudinal observational population-based study of brain volume associated with changes in sleep timing from middle to late-life

STUDY OBJECTIVES

Sleep behaviors are related to brain structure and function, but the impact of long-term changes in sleep timing on brain health has not been clearly addressed. The purpose of this study was to examine the association of longitudinal changes in sleep timing from middle to late-life with gray matter volume (GMV), an important marker of brain aging.

METHODS

We enrolled 1798 adults (aged 49-82 years, men 54.6%) who underwent magnetic resonance imaging (MRI) between 2011 and 2014. Midsleep time (MST) on free days corrected for sleep debt on workdays was adopted as a marker of sleep timing. Data on MST were available at the time of MRI assessment and at examinations that were given 9 years earlier (2003-2004). Longitudinal changes in MST over the 9-year period were derived and categorized into quartiles. Subjects in quartile 1 were defined as "advancers" (MST advanced ≥ 1 h) while those in quartile 4 were defined as "delayers" (MST delayed ≥ 0.2 h). Quartiles 2-3 defined a reference group (MST change was considered modest). The relationship of GMV with MST changes over 9 years was investigated.

RESULTS

Nine-year change in MST were significantly associated with GMV. Compared to the reference group, advancers had smaller GMVs in the frontal and temporal regions. A delay in MST was also associated with smaller cerebellar GMV.

CONCLUSIONS

In middle-to-late adulthood, the direction of change in MST is associated with GMV. While advancers and delayers in MST tend to present lower GMV, associations appear to differ across brain regions.

Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure

Study Objectives

Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies “expressed/manifest” HLG via a cyclical self-similarity feature in effort-based respiration signals.

Methods

Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 10. Central apnea labels are obtained both from manual scoring by sleep technologists and from an automated algorithm developed for this study. The Massachusetts General Hospital sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings.

Results

Diagnostic CAI based on technologist labels predicted REC with an area under the curve (AUC) of 0.82 ± 0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ± 0.02. A subanalysis was performed on a population with technologist-labeled diagnostic CAI higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels.

Conclusions

The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

Sleep staging from electrocardiography and respiration with deep learning

STUDY OBJECTIVES

Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.

METHODS

Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals.

RESULTS

ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen's kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications.

CONCLUSIONS

Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.

HIV Increases Sleep-based Brain Age Despite Antiretroviral Therapy

STUDY OBJECTIVES

Age-related comorbidities and immune activation raise concern for advanced brain aging in people living with HIV (PLWH). The brain age index (BAI) is a machine learning model that quantifies deviations in brain activity during sleep relative to healthy individuals of the same age. High BAI was previously found to be associated with neurological, psychiatric, cardiometabolic diseases, and reduced life expectancy among people without HIV. Here, we estimated the effect of HIV infection on BAI by comparing PLWH and HIV-controls.

METHODS

Clinical data and sleep EEGs from 43 PLWH on antiretroviral therapy (HIV+) and 3,155 controls (HIV-) were collected from Massachusetts General Hospital. The effect of HIV infection on BAI, and on individual EEG features, was estimated using causal inference.

RESULTS

The average effect of HIV on BAI was estimated to be +3.35 years (p < 0.01, 95% CI = [0.67, 5.92]) using doubly robust estimation. Compared to HIV- controls, HIV+ participants exhibited a reduction in delta band power during deep sleep and rapid eye movement sleep.

CONCLUSION

We provide causal evidence that HIV contributes to advanced brain aging reflected in sleep EEG. A better understanding is greatly needed of potential therapeutic targets to mitigate the effect of HIV on brain health, potentially including sleep disorders and cardiovascular disease.