Seasonal variability in body mass index change among children enrolled in the Pediatric Obesity Weight Evaluation Registry: A step in the right direction.

BACKGROUND: Research has shown children disproportionately gain excess weight over the summer months (vs. school months), with stronger effects for children with obesity. However, the question has not been investigated among children receiving care in paediatric weight management (PWM) programs. OBJECTIVE: To test for seasonal variability in weight change among youth with obesity in PWM care enrolled in the Pediatric Obesity Weight Evaluation Registry (POWER). METHOD: Longitudinal evaluation of a prospective cohort from 2014 to 2019 among youth in 31 PWM programs. Change in percentage of the 95th percentile for BMI (%BMIp95) was compared by quarter. RESULTS: Participants (N = 6816) were primarily ages 6-11 (48%), female (54%), 40% non-Hispanic White, 26% Hispanic and 17% Black, and 73% had severe obesity. Children were enrolled on average 424.9 ± 401.5 days. Participants reduced their %BMIp95 every season, but compared with Quarter 3 (July-September), reductions were significantly greater in Q1 (Jan-March, b = -0.27, 95%CI -0.46, -0.09), Q2 (April-June, b = -0.21, CI -0.40, -0.03), and Q4 (October-December, b = -0.44, CI -0.63, -0.26). CONCLUSION AND RELEVANCE: Across 31 clinics nationwide, children reduced their %BMIp95 every season, but reductions were significantly smaller during the summer quarter. While PWM successfully mitigated excess weight gain during every period, summer remains a high-priority time.

Fundamental Science and Engineering Questions in Planetary Cave Exploration.

Nearly half a century ago, two papers postulated the likelihood of lunar lava tube caves using mathematical models. Today, armed with an array of orbiting and fly-by satellites and survey instrumentation, we have now acquired cave data across our solar system-including the identification of potential cave entrances on the Moon, Mars, and at least nine other planetary bodies. These discoveries gave rise to the study of planetary caves. To help advance this field, we leveraged the expertise of an interdisciplinary group to identify a strategy to explore caves beyond Earth. Focusing primarily on astrobiology, the cave environment, geology, robotics, instrumentation, and human exploration, our goal was to produce a framework to guide this subdiscipline through at least the next decade. To do this, we first assembled a list of 198 science and engineering questions. Then, through a series of social surveys, 114 scientists and engineers winnowed down the list to the top 53 highest priority questions. This exercise resulted in identifying emerging and crucial research areas that require robust development to ultimately support a robotic mission to a planetary cave-principally the Moon and/or Mars. With the necessary financial investment and institutional support, the research and technological development required to achieve these necessary advancements over the next decade are attainable. Subsequently, we will be positioned to robotically examine lunar caves and search for evidence of life within Martian caves; in turn, this will set the stage for human exploration and potential habitation of both the lunar and Martian subsurface.

Seasonal Variability in Weight Gain Among American Indian, Black, White, and Hispanic Children: A 3.5-Year Study.

INTRODUCTION: Several studies have reported that children gain more weight during the summer season. Despite high obesity rates, little research has included American Indian/Alaskan Native children, and few studies have been longitudinal. This observational study examines seasonal weight variability over 3.5 years among ethnically diverse children, including 2,184 American Indian/Alaskan Native children. METHODS: Children's height and weight were measured before and after the summer from 2012-2015 and analyzed in 2019-2020, including children with ≥2 consecutive measurements (N=7,890, mean age=8.4 [SD=2.8] years). Mixed-effects models tested whether the percentage of the 95th BMI percentile and BMI differed by season (summer versus the rest of the year) and ethnicity. RESULTS: American Indian/Alaskan Native (23.7%), Hispanic (19.8%), and Black (17.8%) children had significantly higher baseline obesity rates than White children (7.1%). The percentage of the 95th BMI percentile significantly increased during the summer compared with the percentage during the rest of the year, with the strongest effects for children who were obese (b=2.69, 95% CI=1.35, 4.03, p<0.001) or overweight (b=1.47, 95% CI=0.56, 2.35, p<0.01). In BMI units, summer BMI increase was 0.50 kg/m2 higher (obese model) and 0.27 kg/m2 higher (overweight) than that of the rest of the year. Seasonal effects were significantly less pronounced for American Indian/Alaskan Native children than for White children. CONCLUSIONS: Children gained significantly more weight during the summer season, with the strongest effects for children who were obese. American Indian/Alaskan Native children had less seasonal variability than White children, but higher overall obesity rates. These data underscore summer as a critical time for obesity prevention among children who are overweight/obese but suggest that seasonal patterns may vary for American Indian/Alaskan Native children.

Geography and Location Are the Primary Drivers of Office Microbiome Composition.

In the United States, humans spend the majority of their time indoors, where they are exposed to the microbiome of the built environment (BE) they inhabit. Despite the ubiquity of microbes in BEs and their potential impacts on health and building materials, basic questions about the microbiology of these environments remain unanswered. We present a study on the impacts of geography, material type, human interaction, location in a room, seasonal variation, and indoor and microenvironmental parameters on bacterial communities in offices. Our data elucidate several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ on the basis of surface material (e.g., ceiling tile or carpet) but do differ on the basis of the location in a room (e.g., ceiling or floor), two features that are often conflated but that we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material was likely detecting differences based on different usage patterns. Next, we find that offices have city-specific bacterial communities, such that we can accurately predict which city an office microbiome sample is derived from, but office-specific bacterial communities are less apparent. This differs from previous work, which has suggested office-specific compositions of bacterial communities. We again suspect that the difference from prior work arises from different usage patterns. As has been previously shown, we observe that human skin contributes heavily to the composition of BE surfaces. IMPORTANCE Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities.

Massive dispersal of Coxiella burnetii among cattle across the United States.

Q-fever is an underreported disease caused by the bacterium Coxiella burnetii, which is highly infectious and has the ability to disperse great distances. It is a completely clonal pathogen with low genetic diversity and requires whole-genome analysis to identify discriminating features among closely related isolates. C. burnetii, and in particular one genotype (ST20), is commonly found in cow's milk across the entire dairy industry of the USA. This single genotype dominance is suggestive of host-specific adaptation, rapid dispersal and persistence within cattle. We used a comparative genomic approach to identify SNPs for high-resolution and high-throughput genotyping assays to better describe the dispersal of ST20 across the USA. We genotyped 507 ST20 cow milk samples and discovered three subgenotypes, all of which were present across the entire country and over the complete time period studied. Only one of these sub-genotypes was observed in a single dairy herd. The temporal and geographic distribution of these sub-genotypes is consistent with a model of large-scale, rapid, frequent and continuous dissemination on a continental scale. The distribution of subgenotypes is not consistent with wind-based dispersal alone, and it is likely that animal husbandry and transportation practices, including pooling of milk from multiple herds, have also shaped the patterns. On the scale of an entire country, there appear to be few barriers to rapid, frequent and large-scale dissemination of the ST20 subgenotypes.

A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes.

Viral lysis of microbial hosts releases organic matter that can then be assimilated by nontargeted microorganisms. Quantitative estimates of virus-mediated recycling of carbon in marine waters, first established in the late 1990s, were originally extrapolated from marine host and virus densities, host carbon content and inferred viral lysis rates. Yet, these estimates did not explicitly incorporate the cascade of complex feedbacks associated with virus-mediated lysis. To evaluate the role of viruses in shaping community structure and ecosystem functioning, we extend dynamic multitrophic ecosystem models to include a virus component, specifically parameterized for processes taking place in the ocean euphotic zone. Crucially, we are able to solve this model analytically, facilitating evaluation of model behavior under many alternative parameterizations. Analyses reveal that the addition of a virus component promotes the emergence of complex communities. In addition, biomass partitioning of the emergent multitrophic community is consistent with well-established empirical norms in the surface oceans. At steady state, ecosystem fluxes can be probed to characterize the effects that viruses have when compared with putative marine surface ecosystems without viruses. The model suggests that ecosystems with viruses will have (1) increased organic matter recycling, (2) reduced transfer to higher trophic levels and (3) increased net primary productivity. These model findings support hypotheses that viruses can have significant stimulatory effects across whole-ecosystem scales. We suggest that existing efforts to predict carbon and nutrient cycling without considering virus effects are likely to miss essential features of marine food webs that regulate global biogeochemical cycles.

Temporal dynamics of fine roots under long-term exposure to elevated CO2 in the Mojave Desert.

Deserts are considered 'below-ground dominated', yet little is known about the impact of rising CO(2) in combination with natural weather cycles on long-term dynamics of root biomass. This study quantifies the temporal dynamics of fine-root production, loss and standing crop in an intact desert ecosystem exposed to 10 yr of elevated CO(2). We used monthly minirhizotron observations from 4 yr (2003-2007) for two dominant shrub species and along community transects at the Nevada Desert free-air CO(2) enrichment Facility. Data were synthesized within a Bayesian framework that included effects of CO(2) concentration, cover type, phenological period, antecedent soil water and biological inertia (i.e. the influence of prior root production and loss). Elevated CO(2) treatment interacted with antecedent soil moisture and had significantly greater effects on fine-root dynamics during certain phenological periods. With respect to biological inertia, plants under elevated CO(2) tended to initiate fine-root growth sooner and sustain growth longer, with the net effect of increasing the magnitude of production and mortality cycles. Elevated CO(2) interacts with past environmental (e.g. antecedent soil water) and biological (e.g. biological inertia) factors to affect fine-root dynamics, and such interactions are expected to be important for predicting future soil carbon pools.

Effects of measurement error on the strength of concentration-response relationships in aquatic toxicology.

The effect that measurement error of predictor variables has on regression inference is well known in the statistical literature. However, the influence of measurement error on the ability to quantify relationships between chemical stressors and biological responses has received little attention in ecotoxicology. We present a common data-collection scenario and demonstrate that the relationship between explanatory and response variables is consistently underestimated when measurement error is ignored. A straightforward extension of the regression calibration method is to use a nonparametric method to smooth the predictor variable with respect to another covariate (e.g., time) and using the smoothed predictor to estimate the response variable. We conducted a simulation study to compare the effectiveness of the proposed method to the naive analysis that ignores measurement error. We conclude that the method satisfactorily addresses the problem when measurement error is moderate to large, and does not result in a noticeable loss of power in the case where measurement error is absent.