Modelling the spread of Wolbachia in spatially heterogeneous environments

The endosymbiont Wolbachia infects a large number of insect species and is capable of rapid spread when introduced into a novel host population. The bacteria spread by manipulating their hosts' reproduction, and their dynamics are influenced by the demographic structure of the host population and patterns of contact between individuals. Reaction–diffusion models of the spatial spread of Wolbachia provide a simple analytical description of their spatial dynamics but do not account for significant details of host population dynamics. We develop a metapopulation model describing the spatial dynamics of Wolbachia in an age-structured host insect population regulated by juvenile density-dependent competition. The model produces similar dynamics to the reaction–diffusion model in the limiting case where the host's habitat quality is spatially homogeneous and Wolbachia has a small effect on host fitness. When habitat quality varies spatially, Wolbachia spread is usually much slower, and the conditions necessary for local invasion are strongly affected by immigration of insects from surrounding regions. Spread is most difficult when variation in habitat quality is spatially correlated. The results show that spatial variation in the density-dependent competition experienced by juvenile host insects can strongly affect the spread of Wolbachia infections, which is important to the use of Wolbachia to control insect vectors of human disease and other pests

Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity.

ObjectiveEmerging evidence suggests that individual appetitive traits may usefully explain patterns of weight loss in behavioral weight loss treatments for children. The objective of this study was to identify trajectories of child appetitive traits and the impact on child weight changes over time.MethodsSecondary data analyses of a randomized noninferiority trial conducted between 2011 and 2015 evaluated children's appetitive traits and weight loss. Children with overweight and obesity (mean age = 10.4; mean BMI z = 2.0; 67% girls; 32% Hispanic) and their parent (mean age = 42.9; mean BMI = 31.9; 87% women; 31% Hispanic) participated in weight loss programs and completed assessments at baseline, 3, 6,12, and 24 months. Repeated assessments of child appetitive traits, including satiety responsiveness, food responsiveness and emotional eating, were used to identify parsimonious grouping of change trajectories. Linear mixed-effects models were used to identify the impact of group trajectory on child BMIz change over time.ResultsOne hundred fifty children and their parent enrolled in the study. The three-group trajectory model was the most parsimonious and included a high satiety responsive group (HighSR; 47.4%), a high food responsive group (HighFR; 34.6%), and a high emotional eating group (HighEE; 18.0%). Children in all trajectories lost weight at approximately the same rate during treatment, however, only the HighSR group maintained their weight loss during follow-ups, while the HighFR and HighEE groups regained weight (adjusted p-value < 0.05).ConclusionsDistinct trajectories of child appetitive traits were associated with differential weight loss maintenance. Identified high-risk subgroups may suggest opportunities for targeted intervention and maintenance programs

Assessing the impact of future greenhouse gas emissions from natural gas production

Greenhouse gases (GHGs) produced by the extraction of natural gas are an important contributor to lifecycle emissions and account for a significant fraction of anthropogenic methane emissions in the USA. The timing as well as the magnitude of these emissions matters, as the short term climate warming impact of methane is up to 120 times that of CO 2 . This study uses estimates of CO 2 and methane emissions associated with different upstream operations to build a deterministic model of GHG emissions from conventional and unconventional gas fields as a function of time. By combining these emissions with a dynamic, techno-economic model of gas supply we assess their potential impact on the value of different types of project and identify stranded resources in various carbon price scenarios. We focus in particular on the effects of different emission metrics for methane, using the global warming potential (GWP) and the global temperature potential (GTP), with both fixed 20-year and 100-year CO 2 -equivalent values and in a time-dependent way based on a target year for climate stabilisation. We report a strong time dependence of emissions over the lifecycle of a typical field, and find that bringing forward the stabilisation year dramatically increases the importance of the methane contribution to these emissions. Using a commercial database of the remaining reserves of individual projects, we use our model to quantify future emissions resulting from the extraction of current US non-associated reserves. A carbon price of at least 400 USD/tonne CO 2 is effective in reducing cumulative GHGs by 30–60%, indicating that decarbonising the upstream component of the natural gas supply chain is achievable using carbon prices similar to those needed to decarbonise the energy system as a whole. Surprisingly, for large carbon prices, the choice of emission metric does not have a significant impact on cumulative emissions

Impact of drilling costs on the US gas industry: prospects for automation

Recent low gas prices have greatly increased pressure on drilling companies to reduce costs and increase efficiency. Field trials have shown that implementing automation can dramatically reduce drilling costs by reducing the time required to drill wells. This study uses the DYNamic upstreAm gAs MOdel (DYNAAMO), a new techno-economic, bottom-up model of natural gas supply, to quantitatively assess the economic impact of lower drilling costs on the US upstream gas industry. A sensitivity analysis of three key economic indicators is presented, with results quoted for the most common field types currently producing, including unconventional and offshore gas. While all operating environments show increased profitability from drilling automation, it is found that conventional onshore reserves can benefit to the greatest extent. For large gas fields, a 50% reduction in drilling costs is found to reduce initial project breakevens by up to 17 million USD per billion cubic metres (MUSD/BCM) and mid-plateau breakevens by up to 8 MUSD/BCM. In this same scenario, additional volumes of around 160 BCM of unconventional gas are shown to become commercial due to both the lower costs of additional production wells in mature fields and the viability of developing new resources held in smaller fields. The capital efficiency of onshore projects increases by 50%-100%, with initial project net present value (NPV) gains of up to 32%

Orbitally-driven Behavior: Mott Transition, Quantum Oscillations and Colossal Magnetoresistance in Bilayered Ca3Ru2O7

We report recent transport and thermodynamic experiments over a wide range of temperatures for the Mott-like system Ca3Ru2O7 at high magnetic fields, B, up to 30 T. This work reveals a rich and highly anisotropic phase diagram, where applying B along the a-, b-, and c-axis leads to vastly different behavior. A fully spin-polarized state via a first order metamagnetic transition is obtained for B||a, and colossal magnetoresistance is seen for B||b, and quantum oscillations in the resistivity are observed for B||c, respectively. The interplay of the lattice, orbital and spin degrees of freedom are believed to give rise to this strongly anisotropic behavior.Comment: 26 pages and 8 figure

The role of passive surveillance and citizen science in plant health

The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through “passive surveillance”. These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these “messy” unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers’ probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly

Mating Season, Egg-Laying Season, and Internal Gametic Association in the Sympatrically Occurring Fluffy Sculpin (Oligocottus snyderi) and Rosy Sculpin (O. rubellio)

Some marine sculpins (Psychrolutidae) exhibit an unusual reproductive mode called internal gametic association (IGA), in which sperm transfer between the sexes occurs during copulation, but fertilization is delayed until the eggs are released in seawater. IGA is suggested in many internally inseminating marine sculpins, but experimental evidence of IGA is limited to a few species. The Fluffy Sculpin (Oligocottus snyderi) and its sister species, the Rosy Sculpin (O. rubellio), occur in sympatry in intertidal zones along the central California coast. Although these species likely exhibit internal insemination, their reproductive strategy is not well understood. Here, we investigate reproductive mode, mating season, egg-laying season, and sperm morphology and activity in Fluffy and Rosy Sculpins near Pillar Point, California. Delayed embryonic development was observed for one clutch of eggs of the Rosy Sculpin after exposure to seawater, indicating IGA in this species. We were unable to demonstrate IGA by initiation of development in the Fluffy Sculpin because we were unable to collect females with ovulated oocytes. Nevertheless, we found that sperm morphology with elongated head and high motility in isotonic solution while immotile in seawater in both species represent characteristics associated with IGA. Seasonal changes in gonadosomatic index (GSI) of both sexes revealed asynchronous gonadal maturation between the sexes in the Fluffy Sculpin and suggest a similar pattern in the Rosy Sculpin; however, the latter was affected by small sample size. These patterns indicate that males copulate with females before egg maturation, and females store sperm for several months. Our study supports the generality of IGA across marine sculpins and provides an understanding of its relationship to asynchrony in GSI between the sexes. Further, while Fluffy and Rosy Sculpins are similar in body morphology, habitat, and reproductive mode, the slight difference in mating season (pre-mating isolation) and sperm head and flagellum length (post-mating isolation) may have contributed to divergence in sympatry with reduced probability of hybridization

Optimal discrete stopping times for reliability growth tests

Often, the duration of a reliability growth development test is specified in advance and the decision to terminate or continue testing is conducted at discrete time intervals. These features are normally not captured by reliability growth models. This paper adapts a standard reliability growth model to determine the optimal time for which to plan to terminate testing. The underlying stochastic process is developed from an Order Statistic argument with Bayesian inference used to estimate the number of faults within the design and classical inference procedures used to assess the rate of fault detection. Inference procedures within this framework are explored where it is shown the Maximum Likelihood Estimators possess a small bias and converges to the Minimum Variance Unbiased Estimator after few tests for designs with moderate number of faults. It is shown that the Likelihood function can be bimodal when there is conflict between the observed rate of fault detection and the prior distribution describing the number of faults in the design. An illustrative example is provided

Voter Model with Time dependent Flip-rates

We introduce time variation in the flip-rates of the Voter Model. This type of generalisation is relevant to models of ageing in language change, allowing the representation of changes in speakers' learning rates over their lifetime and may be applied to any other similar model in which interaction rates at the microscopic level change with time. The mean time taken to reach consensus varies in a nontrivial way with the rate of change of the flip-rates, varying between bounds given by the mean consensus times for static homogeneous (the original Voter Model) and static heterogeneous flip-rates. By considering the mean time between interactions for each agent, we derive excellent estimates of the mean consensus times and exit probabilities for any time scale of flip-rate variation. The scaling of consensus times with population size on complex networks is correctly predicted, and is as would be expected for the ordinary voter model. Heterogeneity in the initial distribution of opinions has a strong effect, considerably reducing the mean time to consensus, while increasing the probability of survival of the opinion which initially occupies the most slowly changing agents. The mean times to reach consensus for different states are very different. An opinion originally held by the fastest changing agents has a smaller chance to succeed, and takes much longer to do so than an evenly distributed opinion.Comment: 16 pages, 6 figure