Bayesian analysis for inference of an emerging epidemic: citrus canker in urban landscapes.

Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated? How soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of spread of the disease. A rich and complex epidemic behaviour is revealed. The spatial scale of spread is approximately constant over time and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive). In contrast, the rate of infection is characterised by strong monthly fluctuations that we associate with extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatiotemporal description of the epidemic and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a fundamental step in future modelling of weather-driven epidemics.FMN gratefully acknowledges financial support from BBSRC, USDA-ARS, USDA-Aphis PPQ, Citrus Research and Development Foundation. CAG gratefully acknowledges the support of a BBSRC Professorial Fellowship, with additional support from USDA and Defra. ARC was supported by BBSRC, USDA, the National University of Singapore, and NMRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

On the feasibility of imaging carbonatite-hosted rare earth element deposits using remote sensing

© 2016 Gold Open Access: this paper is published under the terms of the CC-BY license. Rare earth elements (REEs) generate characteristic absorption features in visible to shortwave infrared (VNIRSWIR) reflectance spectra. Neodymium (Nd) has among the most prominent absorption features of the REEs and thus represents a key pathfinder element for the REEs as a whole. Given that the world's largest REE deposits are associated with carbonatites, we present spectral, petrographic, and geochemical data from a predominantly carbonatitic suite of rocks that we use to assess the feasibility of imaging REE deposits using remote sensing. Samples were selected to cover a wide range of extents and styles of REE mineralization, and encompass calcio-, ferro-and magnesio-carbonatites. REE ores from the Bayan Obo (China) and Mountain Pass (United States) mines, as well as REE-rich alkaline rocks from the Motzfeldt and Ilímaussaq intrusions in Greenland, were also included in the sample suite. The depth and area of Nd absorption features in spectra collected under laboratory conditions correlate positively with the Nd content of whole-rock samples. The wavelength of Nd absorption features is predominantly independent of sample lithology and mineralogy. Correlations are most reliable for the two absorption features centered at ∼744 and ∼802 nm that can be observed in samples containing as little as ∼1,000 ppm Nd. By convolving laboratory spectra to the spectral response functions of a variety of remote sensing instruments we demonstrate that hyperspectral instruments with capabilities equivalent to the operational Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and planned Environmental Mapping and Analysis Program (EnMAP) systems have the spectral resolutions necessary to detect Nd absorption features, especially in high-grade samples with economically relevant REE accumulations (Nd> 30,000 ppm). Adding synthetic noise to convolved spectra indicates that correlations between Nd absorption area and whole-rock Nd content only remain robust when spectra have signal-to-noise ratios in excess of ∼250:1. Although atmospheric interferences are modest across the wavelength intervals relevant for Nd detection, most REE-rich outcrops are too small to be detectable using satellite-based platforms with>30-m spatial resolutions. However, our results indicate that Nd absorption features should be identifiable in high-quality, airborne, hyperspectral datasets collected at meter-scale spatial resolutions. Future deployment of hyperspec-tral instruments on unmanned aerial vehicles could enable REE grade to be mapped at the centimeter scale across whole deposits

DNA methylation in interleukin-11 predicts clinical response to antidepressants in GENDEP

Transcriptional differences in interleukin-11 (IL11) after antidepressant treatment have been found to correspond to clinical response in major depressive disorder (MDD) patients. Expression differences were partly mediated by a single-nucleotide polymorphism (rs1126757), identified as a predictor of antidepressant response as part of a genome-wide association study. Here we attempt to identify whether DNA methylation, another baseline factor known to affect transcription factor binding, might also predict antidepressant response, using samples collected from the Genome-based Therapeutic Drugs for Depression project (GENDEP). DNA samples from 113 MDD individuals from the GENDEP project, who were treated with either escitalopram (n=80) or nortriptyline (n=33) for 12 weeks, were randomly selected. Percentage change in Montgomery-� sberg Depression Rating Scale scores between baseline and week 12 were utilized as our measure of antidepressant response. The Sequenom EpiTYPER platform was used to assess DNA methylation across the only CpG island located in the IL11 gene. Regression analyses were then used to explore the relationship between CpG unit methylation and antidepressant response. We identified a CpG unit predictor of general antidepressant response, a drug by CpG unit interaction predictor of response, and a CpG unit by rs1126757 interaction predictor of antidepressant response. The current study is the first to investigate the potential utility of pharmaco-epigenetic biomarkers for the prediction of antidepressant response. Our results suggest that DNA methylation in IL11 might be useful in identifying those patients likely to respond to antidepressants, and if so, the best drug suited to each individual

From Classical Genetics to Quantitative Genetics to Systems Biology: Modeling Epistasis

Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments

The interplay of intrinsic and extrinsic bounded noises in genetic networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises

Garden and landscape-scale correlates of moths of differing conservation status: significant effects of urbanization and habitat diversity

Moths are abundant and ubiquitous in vegetated terrestrial environments and are pollinators, important herbivores of wild plants, and food for birds, bats and rodents. In recent years, many once abundant and widespread species have shown sharp declines that have been cited by some as indicative of a widespread insect biodiversity crisis. Likely causes of these declines include agricultural intensification, light pollution, climate change, and urbanization; however, the real underlying cause(s) is still open to conjecture. We used data collected from the citizen science Garden Moth Scheme (GMS) to explore the spatial association between the abundance of 195 widespread British species of moth, and garden habitat and landscape features, to see if spatial habitat and landscape associations varied for species of differing conservation status. We found that associations with habitat and landscape composition were species-specific, but that there were consistent trends in species richness and total moth abundance. Gardens with more diverse and extensive microhabitats were associated with higher species richness and moth abundance; gardens near to the coast were associated with higher richness and moth abundance; and gardens in more urbanized locations were associated with lower species richness and moth abundance. The same trends were also found for species classified as increasing, declining and vulnerable under IUCN (World Conservation Union) criteria

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

Beyond aridification: multiple explanations for the elevated diversification of cacti in the New World Succulent Biome

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106989/1/nph12752.pd

Effects of Carbon Dioxide Aerosols on the Viability of Escherichia coli during Biofilm Dispersal

A periodic jet of carbon dioxide (CO2) aerosols is a very quick and effective mechanical technique to remove biofilms from various substrate surfaces. However, the impact of the aerosols on the viability of bacteria during treatment has never been evaluated. In this study, the effects of high-speed CO2 aerosols, a mixture of solid and gaseous CO2, on bacteria viability was studied. It was found that when CO2 aerosols were used to disperse biofilms of Escherichia coli, they led to a significant loss of viability, with approximately 50% of the dispersed bacteria killed in the process. By comparison, 75.6% of the biofilm-associated bacteria were viable when gently dispersed using Proteinase K and DNase I. Indirect proof that the aerosols are damaging the bacteria was found using a recombinant E. coli expressing the cyan fluorescent protein, as nearly half of the fluorescence was found in the supernatant after CO2 aerosol treatment, while the rest was associated with the bacterial pellet. In comparison, the supernatant fluorescence was only 9% when the enzymes were used to disperse the biofilm. As such, these CO2 aerosols not only remove biofilm-associated bacteria effectively but also significantly impact their viability by disrupting membrane integrity.open