Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome is a rare disease of concurrent respiratory dysfunction and autonomic dysregulation ...with endocrine abnormalities. ROHHADNET includes ROHHAD plus coexisting neuroendocrine tumors (NETs). We describe an eight-year-old boy, who originally presented at four years of age with rapid weight gain and hyperhidrosis and who developed mild obstructive sleep apnea (OSA). His clinical course was eventually complicated by hypoxic respiratory failure requiring admission to the pediatric intensive care unit (PICU). Echocardiogram at that time demonstrated dilated cardiomyopathy left ventricular ejection fraction (LVEF) of 28% at time of admission. His respiratory failure persisted despite average volume-assured pressure support (AVAPS) around the clock leading to tracheostomy placement for cardiopulmonary support. He also demonstrated autonomic instability with multiple pituitary hormone deficiencies. Computed tomography (CT) imaging of the abdomen and pelvis demonstrated a presacral soft tissue mass consistent with a tumor of neural crest origin. Daytime somnolence and confusion progressed and a low cerebrospinal fluid hypocretin level revealed a diagnosis of narcolepsy type 1.
Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome is a rare disease of concurrent respiratory dysfunction and autonomic dysregulation ...with endocrine abnormalities. ROHHADNET includes ROHHAD plus coexisting neuroendocrine tumors (NETs). We describe an eight-year-old boy, who originally presented at four years of age with rapid weight gain and hyperhidrosis and who developed mild obstructive sleep apnea (OSA). His clinical course was eventually complicated by hypoxic respiratory failure requiring admission to the pediatric intensive care unit (PICU). Echocardiogram at that time demonstrated dilated cardiomyopathy left ventricular ejection fraction (LVEF) of 28% at time of admission. His respiratory failure persisted despite average volume-assured pressure support (AVAPS) around the clock leading to tracheostomy placement for cardiopulmonary support. He also demonstrated autonomic instability with multiple pituitary hormone deficiencies. Computed tomography (CT) imaging of the abdomen and pelvis demonstrated a presacral soft tissue mass consistent with a tumor of neural crest origin. Daytime somnolence and confusion progressed and a low cerebrospinal fluid hypocretin level revealed a diagnosis of narcolepsy type 1.
•Citizen-scientist (CS) datasets offer unique opportunities and challenges to the study of global conservation priorities.•Fortunately, issues of error and bias found in CS data are similar to those ...found in other large-scale databases.•As a consequence, statistical tools exist to handle many kinds of error and bias common to CS data.•We highlight some statistical approaches that are used in ecological contexts and are available in free software packages.
Networks of citizen scientists (CS) have the potential to observe biodiversity and species distributions at global scales. Yet the adoption of such datasets in conservation science may be hindered by a perception that the data are of low quality. This perception likely stems from the propensity of data generated by CS to contain greater levels of variability (e.g., measurement error) or bias (e.g., spatio-temporal clustering) in comparison to data collected by scientists or instruments. Modern analytical approaches can account for many types of error and bias typical of CS datasets. It is possible to (1) describe how pseudo-replication in sampling influences the overall variability in response data using mixed-effects modeling, (2) integrate data to explicitly model the sampling process and account for bias using a hierarchical modeling framework, and (3) examine the relative influence of many different or related explanatory factors using machine learning tools. Information from these modeling approaches can be used to predict species distributions and to estimate biodiversity. Even so, achieving the full potential from CS projects requires meta-data describing the sampling process, reference data to allow for standardization, and insightful modeling suitable to the question of interest.
Species' ranges are shifting globally in response to climate warming, with substantial variability among taxa, even within regions. Relationships between range dynamics and intrinsic species traits ...may be particularly apparent in the ocean, where temperature more directly shapes species' distributions. Here, we test for a role of species traits and climate velocity in driving range extensions in the ocean‐warming hotspot of southeast Australia. Climate velocity explained some variation in range shifts, however, including species traits more than doubled the variation explained. Swimming ability, omnivory and latitudinal range size all had positive relationships with range extension rate, supporting hypotheses that increased dispersal capacity and ecological generalism promote extensions. We find independent support for the hypothesis that species with narrow latitudinal ranges are limited by factors other than climate. Our findings suggest that small‐ranging species are in double jeopardy, with limited ability to escape warming and greater intrinsic vulnerability to stochastic disturbances.
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