Explore the vast range of titles available.

The aim of this study was to characterize children with transient nystagmus. Eleven children(six males, five females) developed nystagmus in infancy and then experienced regression of the problem, usually within a few months. Mean age at onset was 2.7 months, and mean age at regression was 8.5 months. No etiology could be ascertained in any of the patients, although four children had other eye or vision abnormalities (regressed retinopathy of prematurity, n=1; asymmetric fundus colobomata, n=1; delayed visual maturation, n=2). Results of this study suggest that mechanisms which allow ocular motor stability undergo a period of postnatal maturation, during which nystagmus can occur, but also during which nystagmus may disappear. Not every case of transient nystagmus should be categorized as spasmus nutans. There is a subset of infants and young children who develop transient nystagmus with no other findings and in whom the nystagmus disappears.

The aim of this study was to characterize children with transient nystagmus. Eleven children(six males, five females) developed nystagmus in infancy and then experienced regression of the problem, usually within a few months. Mean age at onset was 2.7 months, and mean age at regression was 8.5 months. No etiology could be ascertained in any of the patients, although four children had other eye or vision abnormalities (regressed retinopathy of prematurity, n=1; asymmetric fundus colobomata, n=1; delayed visual maturation, n=2). Results of this study suggest that mechanisms which allow ocular motor stability undergo a period of postnatal maturation, during which nystagmus can occur, but also during which nystagmus may disappear. Not every case of transient nystagmus should be categorized as spasmus nutans. There is a subset of infants and young children who develop transient nystagmus with no other findings and in whom the nystagmus disappears.

This case report describes a unique case of Peter's anomaly in a child with Kabuki make-up syndrome. Kabuki make-up syndrome can be identified on the basis of the following eye findings: lateral lower lid ectropion; high arched eyebrows; and sparse hair in the lateral third of the eyebrow. These physical findings are highly characteristic, but subtle. We present a boy with signs of Kabuki make-up syndrome and Peter's anomaly. Clinicians should be aware of the features of Kabuki make-up syndrome and the possible association with Peter's anomaly. Kabuki make-up syndrome is commonly overlooked as a diagnosis, but its recognition may considerably aid families in planning social and developmental support. We discuss the relationship of Peter's anomaly to Kabuki make-up syndrome and other systemic, genetic diseases.

Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associated events during polysomnography (PSG). This position statement outlines the potential opportunities and limitations of integrating artificial intelligence (AI) into the practice of sleep medicine. Additionally, although the most apparent and immediate application of AI in our field is the assisted scoring of PSG, we propose potential clinical use cases that transcend the sleep laboratory and are expected to deepen our understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase our knowledge of the role of sleep in health at a population level. Citation: Goldstein CA, Berry RB, Kent DT, et al. Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement. J Clin Sleep Med . 2020;16(4):605–607.

Interactions between hosts and their resident microbial communities are a fundamental component of fitness for both agents. Though recent research has highlighted the importance of interactions between animals and their bacterial communities, comparative evidence for fungi is lacking, especially in natural populations. Using data from 49 species, we present novel evidence of strong covariation between fungal and bacterial communities across the host phylogeny, indicative of recruitment by hosts for specific suites of microbes. Using co-occurrence networks, we demonstrate marked variation across host taxonomy in patterns of covariation between bacterial and fungal abundances. Host phylogeny drives differences in the overall richness of bacterial and fungal communities, but the effect of diet on richness was only evident in the mammalian gut microbiome. Sample type, tissue storage and DNA extraction method also affected bacterial and fungal community composition, and future studies would benefit from standardized approaches to sample processing. Collectively these data indicate fungal microbiomes may play a key role in host fitness and suggest an urgent need to study multiple agents of the animal microbiome to accurately determine the strength and ecological significance of host–microbe interactions.

The effectiveness of regulatory actions designed to improve air quality is often assessed by predicting changes in public health resulting from their implementation. Risk of premature mortality from long-term exposure to ambient air pollution is the single most important contributor to such assessments and is estimated from observational studies generally assuming a log-linear, no-threshold association between ambient concentrations and death. There has been only limited assessment of this assumption in part because of a lack of methods to estimate the shape of the exposure-response function in very large study populations. In this paper, we propose a new class of variable coefficient risk functions capable of capturing a variety of potentially non-linear associations which are suitable for health impact assessment. We construct the class by defining transformations of concentration as the product of either a linear or log-linear function of concentration multiplied by a logistic weighting function. These risk functions can be estimated using hazard regression survival models with currently available computer software and can accommodate large population-based cohorts which are increasingly being used for this purpose. We illustrate our modeling approach with two large cohort studies of long-term concentrations of ambient air pollution and mortality: the American Cancer Society Cancer Prevention Study II (CPS II) cohort and the Canadian Census Health and Environment Cohort (CanCHEC). We then estimate the number of deaths attributable to changes in fine particulate matter concentrations over the 2000 to 2010 time period in both Canada and the USA using both linear and non-linear hazard function models.

Interactions between hosts and their resident microbial communities are a fundamental component of fitness for both agents. Though recent research has highlighted the importance of interactions between animals and their bacterial communities, comparative evidence for fungi is lacking, especially in natural populations. Using data from 49 species, we present novel evidence of strong covariation between fungal and bacterial communities across the host phylogeny, indicative of recruitment by hosts for specific suites of microbes. Using co-occurrence networks, we demonstrate marked variation across host taxonomy in patterns of covariation between bacterial and fungal abundances. Host phylogeny drives differences in the overall richness of bacterial and fungal communities, but the effect of diet on richness was only evident in the mammalian gut microbiome. Sample type, tissue storage and DNA extraction method also affected bacterial and fungal community composition, and future studies would benefit from standardized approaches to sample processing. Collectively these data indicate fungal microbiomes may play a key role in host fitness and suggest an urgent need to study multiple agents of the animal microbiome to accurately determine the strength and ecological significance of host–microbe interactions.

ABSTRACT Interactions between hosts and their resident microbial communities are a fundamental component of fitness for both agents. Though recent research has highlighted the importance of interactions between animals and their bacterial communities, comparative evidence for fungi is lacking, especially in natural populations. Using data from 49 species, we present novel evidence of strong covariation between fungal and bacterial communities across the host phylogeny, indicative of recruitment by hosts for specific suites of microbes. Using co-occurrence networks, we demonstrate that fungi form critical components of putative microbial interaction networks, where the strength and frequency of interactions varies with host taxonomy. Host phylogeny drives differences in overall richness of bacterial and fungal communities, but the effect of diet on richness was only evident in mammals and for the bacterial microbiome. Collectively these data indicate fungal microbiomes may play a key role in host fitness and suggest an urgent need to study multiple agents of the animal microbiome to accurately determine the strength and ecological significance of host-microbe interactions. SIGNIFICANCE STATEMENT Microbes perform vital metabolic functions that shape the physiology of their hosts. However, almost all research to date in wild animals has focused exclusively on the bacterial microbiota, to the exclusion of other microbial groups. Although likely to be critical components of the host microbiome, we have limited knowledge of the drivers of fungal composition across host species. Here we show that fungal community composition is determined by host species identity and phylogeny, and that fungi form extensive interaction networks with bacteria in the microbiome of a diverse range of animal species. This highlights the importance of microbial interactions as mediators of microbiome-health relationships in the wild. Competing Interest Statement The authors have declared no competing interest. Footnotes * Figure 1 and main text updated to include host dietary analysis; Figure 2 updated to include additional analysis on host-microbe and bacteria-fungi correlations; Figure 3 and 4 merged and updated to include randomisations of network structure; main text streamlined.