Pan-Enterovirus Characterization Reveals Cryptic Circulation of Clinically Relevant Subtypes in Arizona Wastewater.

Background: Most seasonally circulating enteroviruses result in asymptomatic or mildly symptomatic infections. In rare cases, however, infection with some subtypes can result in paralysis or death. Of the 300 subtypes known, only poliovirus is reportable, limiting our understanding of the distribution of other enteroviruses that can cause clinical disease. Objective: The overarching objectives of this study were to: 1) describe the distribution of enteroviruses in Arizona during the late summer and fall of 2022, the time of year when they are thought to be most abundant, and 2) demonstrate the utility of viral pan-assay approaches for semi-agnostic discovery that can be followed up by more targeted assays and phylogenomics. Methods: This study utilizes pooled nasal samples collected from school-aged children and long-term care facility residents, and wastewater from multiple locations in Arizona during July-October of 2022. We used PCR to amplify and sequence a region common to all enteroviruses, followed by species-level bioinformatic characterization using the QIIME 2 platform. For Enterovirus-D68 (EV-D68), detection was carried out using RT-qPCR, followed by confirmation using near-complete whole EV-D68 genome sequencing using a newly designed tiled amplicon approach. Results: In the late summer and early fall of 2022, multiple enterovirus species were identified in Arizona wastewater, with Coxsackievirus A6, EV-D68, and Coxsackievirus A19 composing 86% of the characterized reads sequenced. While EV-D68 was not identified in pooled human nasal samples, and the only reported acute flaccid myelitis case in Arizona did not test positive for the virus, an in-depth analysis of EV-D68 in wastewater revealed that the virus was circulating from August through mid-October. A phylogenetic analysis on this relatively limited dataset revealed just a few importations into the state, with a single clade indicating local circulation. Significance: This study further supports the utility of wastewater-based epidemiology to identify potential public health threats. Our further investigations into EV-D68 shows how these data might help inform healthcare diagnoses for children presenting with concerning neurological symptoms.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring.

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

Soil-atmosphere fluxes of CO2, CH4, and N2O across an experimentally-grown, successional gradient of biocrust community types.

Biological soil crusts (biocrusts) are critical components of dryland and other ecosystems worldwide, and are increasingly recognized as novel model ecosystems from which more general principles of ecology can be elucidated. Biocrusts are often diverse communities, comprised of both eukaryotic and prokaryotic organisms with a range of metabolic lifestyles that enable the fixation of atmospheric carbon and nitrogen. However, how the function of these biocrust communities varies with succession is incompletely characterized, especially in comparison to more familiar terrestrial ecosystem types such as forests. We conducted a greenhouse experiment to investigate how community composition and soil-atmosphere trace gas fluxes of CO2, CH4, and N2O varied from early-successional light cyanobacterial biocrusts to mid-successional dark cyanobacteria biocrusts and late-successional moss-lichen biocrusts and as biocrusts of each successional stage matured. Cover type richness increased as biocrusts developed, and richness was generally highest in the late-successional moss-lichen biocrusts. Microbial community composition varied in relation to successional stage, but microbial diversity did not differ significantly among stages. Net photosynthetic uptake of CO2 by each biocrust type also increased as biocrusts developed but tended to be moderately greater (by up to ≈25%) for the mid-successional dark cyanobacteria biocrusts than the light cyanobacterial biocrusts or the moss-lichen biocrusts. Rates of soil C accumulation were highest for the dark cyanobacteria biocrusts and light cyanobacteria biocrusts, and lowest for the moss-lichen biocrusts and bare soil controls. Biocrust CH4 and N2O fluxes were not consistently distinguishable from the same fluxes measured from bare soil controls; the measured rates were also substantially lower than have been reported in previous biocrust studies. Our experiment, which uniquely used greenhouse-grown biocrusts to manipulate community composition and accelerate biocrust development, shows how biocrust function varies along a dynamic gradient of biocrust successional stages.

Characterization of neonatal opioid withdrawal syndrome in Arizona from 2010-2017.

In this paper, we describe a population of mothers who are opioid dependent at the time of giving birth and neonates exposed to opioids in utero who experience withdrawal following birth. While there have been studies of national trends in this population, there remains a gap in studies of regional trends. Using data from the Arizona Department of Health Services Hospital Discharge Database, this study aimed to characterize the population of neonates with neonatal opioid withdrawal syndrome (NOWS) and mothers who were opioid dependent at the time of giving birth, in Arizona. We analyzed approximately 1.2 million electronic medical records from the Arizona Department of Health Services Hospital Discharge Database to identify patterns and disparities across socioeconomic, ethnic, racial, and/or geographic groupings. In addition, we identified comorbid conditions that are differentially associated with NOWS in neonates or opioid dependence in mothers. Our analysis was designed to assess whether indicators such as race/ethnicity, insurance payer, marital status, and comorbidities are related to the use of opioids while pregnant. Our findings suggest that women and neonates who are non-Hispanic White and economically disadvantaged, tend be part of our populations of interest more frequently than expected. Additionally, women who are opioid dependent at the time of giving birth are unmarried more often than expected, and we suggest that marital status could be a proxy for support. Finally, we identified comorbidities associated with neonates who have NOWS and mothers who are opioid dependent not previously reported.

Ensuring identifiability in hierarchical mixed effects Bayesian models.

Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Quantifying ecological memory in plant and ecosystem processes.

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research.

Elk migration patterns and human activity influence wolf habitat use in the Greater Yellowstone Ecosystem.

Identifying the ecological dynamics underlying human-wildlife conflicts is important for the management and conservation of wildlife populations. In landscapes still occupied by large carnivores, many ungulate prey species migrate seasonally, yet little empirical research has explored the relationship between carnivore distribution and ungulate migration strategy. In this study, we evaluate the influence of elk (Cervus elaphus) distribution and other landscape features on wolf (Canis lupus) habitat use in an area of chronic wolf-livestock conflict in the Greater Yellowstone Ecosystem, USA. Using three years of fine-scale wolf (n = 14) and elk (n = 81) movement data, we compared the seasonal habitat use of wolves in an area dominated by migratory elk with that of wolves in an adjacent area dominated by resident elk. Most migratory elk vacate the associated winter wolf territories each summer via a 40-60 km migration, whereas resident elk remain accessible to wolves year-round. We used a generalized linear model to compare the relative probability of wolf use as a function of GIS-based habitat covariates in the migratory and resident elk areas. Although wolves in both areas used elk-rich habitat all year, elk density in summer had a weaker influence on the habitat use of wolves in the migratory elk area than the resident elk area. Wolves employed a number of alternative strategies to cope with the departure of migratory elk. Wolves in the two areas also differed in their disposition toward roads. In winter, wolves in the migratory elk area used habitat close to roads, while wolves in the resident elk area avoided roads. In summer, wolves in the migratory elk area were indifferent to roads, while wolves in resident elk areas strongly avoided roads, presumably due to the location of dens and summering elk combined with different traffic levels. Study results can help wildlife managers to anticipate the movements and establishment of wolf packs as they expand into areas with migratory or resident prey populations, varying levels of human activity, and front-country rangelands with potential for conflicts with livestock.

Hierarchical statistical modeling of xylem vulnerability to cavitation.

Cavitation of xylem elements diminishes the water transport capacity of plants, and quantifying xylem vulnerability to cavitation is important to understanding plant function. Current approaches to analyzing hydraulic conductivity (K) data to infer vulnerability to cavitation suffer from problems such as the use of potentially unrealistic vulnerability curves, difficulty interpreting parameters in these curves, a statistical framework that ignores sampling design, and an overly simplistic view of uncertainty. This study illustrates how two common curves (exponential-sigmoid and Weibull) can be reparameterized in terms of meaningful parameters: maximum conductivity (k(sat)), water potential (-P) at which percentage loss of conductivity (PLC) =X% (P(X)), and the slope of the PLC curve at P(X) (S(X)), a 'sensitivity' index. We provide a hierarchical Bayesian method for fitting the reparameterized curves to K(H) data. We illustrate the method using data for roots and stems of two populations of Juniperus scopulorum and test for differences in k(sat), P(X), and S(X) between different groups. Two important results emerge from this study. First, the Weibull model is preferred because it produces biologically realistic estimates of PLC near P = 0 MPa. Second, stochastic embolisms contribute an important source of uncertainty that should be included in such analyses.