Long-distance dispersal and its influence on adaptation to host resistance in a heterogeneous landscape

Small propagules like pollen or fungal spores may be dispersed by the wind over distances of hundreds or thousands of kilometres,even though the median dispersal may be only a few metres. Such long-distance dispersal is a stochastic event which may be exceptionally important in shaping a population. It has been found repeatedly in field studies that subpopulations of wind-dispersed fungal pathogens virulent on cultivars with newly introduced, effective resistance genes are dominated by one or very few genotypes. The role of propagule dispersal distributions with distinct behaviour at long distances in generating this characteristic population structure was studied by computer simulation of dispersal of clonal organisms in a heterogeneous environment with fields of unselective and selective hosts. Power-law distributions generated founder events in which new, virulent genotypes rapidly colonized fields of resistant crop varieties and subsequently dominated the pathogen population on both selective and unselective varieties, in agreement with data on rust and powdery mildew fungi. An exponential dispersal function, with extremely rare dispersal over long distances, resulted in slower colonization of resistant varieties by virulent pathogens or even no colonization if the distance between susceptible source and resistant target fields was sufficiently large. The founder events resulting from long-distance dispersal were highly stochastic and exact quantitative prediction of genotype frequencies will therefore always be difficult.

A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems

We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The “disaggregation” approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a “zero-order” model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set “truth.” Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality.

Heterogeneous surface expression of EspA translocon filaments by Escherichia coli O157:H7 is controlled at the posttranscriptional level

Type III secretion systems of enteric bacteria enable translocation of effector proteins into host cells. Secreted proteins of verotoxigenic Escherichia coli O157 strains include components of a translocation apparatus, EspA, -B, and -D, as well as "effectors" such as the translocated intimin receptor (Tir) and the mitochondrion-associated protein (Map). This research has investigated the regulation of LEE4 translocon proteins, in particular EspA. EspA filaments could not be detected on the bacterial cell surface when E. coli O157:H7 was cultured in M9 minimal medium but were expressed from only a proportion of the bacterial population when cultured in minimal essential medium modified with 25 mM HEPES. The highest proportions of EspA-filamented bacteria were detected in late exponential phase, after which filaments were lost rapidly from the bacterial cell surface. Our previous research had shown that human and bovine E. coli O157:H7 strains exhibit marked differences in EspD secretion levels. Here it is demonstrated that the proportion of the bacterial population expressing EspA filaments was associated with the level of EspD secretion. The ability of individual bacteria to express EspA filaments was not controlled at the level of LEE1-4 operon transcription, as demonstrated by using both beta-galactosidase and green fluorescent protein (GFP) promoter fusions. All bacteria, whether expressing EspA filaments or not, showed equivalent levels of GFP expression when LEEI-4 translational fusions were used. Despite this, the LEE4-espADB mRNA was more abundant from populations with a high proportion of nonsecreting bacteria (low secretors) than from populations with a high proportion of secreting and therefore filamented bacteria (high secretors). This research demonstrates that while specific environmental conditions are required to induce LEEI-4 expression, a further checkpoint exists before EspA filaments are produced on the bacterial surface and secretion of effector proteins occurs. This checkpoint in E. coli O157:H7 translocon expression is controlled by a posttranscriptional mechanism acting on LEE4-espADB mRNA. The heterogeneity in EspA filamentation could arise from phase-variable expression of regulators that control this posttranscriptional mechanism.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

The (de)merits of minimum-variance hedging: application to the crack spread

We study the empirical performance of the classical minimum-variance hedging strategy, comparing several econometric models for estimating hedge ratios of crude oil, gasoline and heating oil crack spreads. Given the great variability and large jumps in both spot and futures prices, considerable care is required when processing the relevant data and accounting for the costs of maintaining and re-balancing the hedge position. We find that the variance reduction produced by all models is statistically and economically indistinguishable from the one-for-one “naïve” hedge. However, minimum-variance hedging models, especially those based on GARCH, generate much greater margin and transaction costs than the naïve hedge. Therefore we encourage hedgers to use a naïve hedging strategy on the crack spread bundles now offered by the exchange; this strategy is the cheapest and easiest to implement. Our conclusion contradicts the majority of the existing literature, which favours the implementation of GARCH-based hedging strategies.

Quantitative genetic divergence and standing genetic (co)variance in thermal reaction norms along latitude

Although the potential to adapt to warmer climate is constrained by genetic trade-offs, our understanding of how selection and mutation shape genetic (co)variances in thermal reaction norms is poor. Using 71 isofemale lines of the fly Sepsis punctum, originating from northern, central, and southern European climates, we tested for divergence in juvenile development rate across latitude at five experimental temperatures. To investigate effects of evolutionary history in different climates on standing genetic variation in reaction norms, we further compared genetic (co)variances between regions. Flies were reared on either high or low food resources to explore the role of energy acquisition in determining genetic trade-offs between different temperatures. Although the latter had only weak effects on the strength and sign of genetic correlations, genetic architecture differed significantly between climatic regions, implying that evolution of reaction norms proceeds via different trajectories at high latitude versus low latitude in this system. Accordingly, regional genetic architecture was correlated to region-specific differentiation. Moreover, hot development temperatures were associated with low genetic variance and stronger genetic correlations compared to cooler temperatures. We discuss the evolutionary potential of thermal reaction norms in light of their underlying genetic architectures, evolutionary histories, and the materialization of trade-offs in natural environments.

Sodomy laws and gender variance in Tahiti and Hawai‘i

In both Hawaiian and Tahitian, the central meaning of mahu denotes gender-variant individuals, particularly male-bodied persons who have a significant investment in femininity. However, in Hawai‘i, unlike Tahiti, the word mahu is now more commonly used as an insult against gay or transgender people. The negative connotation of the term in Hawaiian indexes lower levels of social acceptability for mahu identity on O‘ahu (Hawai‘i’s most populous island) as compared to Tahiti. The article argues that these differences are partly due to a historical legacy of sexually repressive laws. The article traces the history of sodomy laws in these two Polynesian societies and argues that this history supports the hypothesis that sodomy laws (in conjunction with such social processes as urbanisation and Christianisation) are partially to blame for the diminished social status of mahu on O‘ahu. A different social and legal history in Tahiti accounts for the fact that the loss of social status experienced by Tahitian mahu has been lesser than that of their Hawaiian counterparts.

Polynomial-based noise variance estimation for MIMO-SCBT systems

In this paper, we present a polynomial-based noise variance estimator for multiple-input multiple-output single-carrier block transmission (MIMO-SCBT) systems. It is shown that the optimal pilots for noise variance estimation satisfy the same condition as that for channel estimation. Theoretical analysis indicates that the proposed estimator is statistically more efficient than the conventional sum of squared residuals (SSR) based estimator. Furthermore, we obtain an efficient implementation of the estimator by exploiting its special structure. Numerical results confirm our theoretical analysis.

Fashion magazines: design implications of focus and perception variance between Chinese and UK females

A growing segment of Chinese women are willing to spend a high percentage of their income on fashion related products, however there appears to be concern over the quality of Chinese fashion magazines. Concern can be focused in two major issues: i) fashion magazine design, and ii) pictorial and textual distribution of content. This paper investigates how human factors (i.e. social norms and individual differences) influence fashion magazine design/format preferences, and investigates the difference in readership patterns between British and Chinese Women. Our study identifies significant differences between UK and Chinese readership; which has an impact on magazine viewing patterns and content preference.

US inflation expectations and heterogeneous loss functions, 1968-2010

Recent literature has suggested that macroeconomic forecasters may have asymmetric loss functions, and that there may be heterogeneity across forecasters in the degree to which they weigh under- and over-predictions. Using an individual-level analysis that exploits the Survey of Professional Forecasters respondents’ histogram forecasts, we find little evidence of asymmetric loss for the inflation forecasters

Forecasting exchange rate volatility using conditional variance models selected by information criteria

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used GARCH(1, 1) model. An examination of the orders of models selected by the criteria reveals that (1, 1) models are typically selected less than 20% of the time.

Emergence of spatially heterogeneous burst suppression in a neural field model of electrocortical activity

Burst suppression in the electroencephalogram (EEG) is a well-described phenomenon that occurs during deep anesthesia, as well as in a variety of congenital and acquired brain insults. Classically it is thought of as spatially synchronous, quasi-periodic bursts of high amplitude EEG separated by low amplitude activity. However, its characterization as a “global brain state” has been challenged by recent results obtained with intracranial electrocortigraphy. Not only does it appear that burst suppression activity is highly asynchronous across cortex, but also that it may occur in isolated regions of circumscribed spatial extent. Here we outline a realistic neural field model for burst suppression by adding a slow process of synaptic resource depletion and recovery, which is able to reproduce qualitatively the empirically observed features during general anesthesia at the whole cortex level. Simulations reveal heterogeneous bursting over the model cortex and complex spatiotemporal dynamics during simulated anesthetic action, and provide forward predictions of neuroimaging signals for subsequent empirical comparisons and more detailed characterization. Because burst suppression corresponds to a dynamical end-point of brain activity, theoretically accounting for its spatiotemporal emergence will vitally contribute to efforts aimed at clarifying whether a common physiological trajectory is induced by the actions of general anesthetic agents. We have taken a first step in this direction by showing that a neural field model can qualitatively match recent experimental data that indicate spatial differentiation of burst suppression activity across cortex.

Gender, race, and heterogeneous effects of epidemic malaria on human capital and income

This article investigates the impact of exposure to a serious, unusual, and unforeseen malaria epidemic in northeast Brazil in 1938–40 on subsequent human capital attainment and income. Arguing the event was exogenous, the article exploits cohort and regional heterogeneity in exposure to identify effects. Results are consistent with differential mortality rates according to gender and socioeconomic status, such that heterogeneous selection and scarring effects are observed. Analyzing by gender alone, positive (selection) effects are found for men, and mixed (positive and negative) effects for women. Allowing for heterogeneity by race, selection effects persist for men. In contrast, positive (selection) effects are observed for nonwhite women, and negative (scarring) effects for white women. Results contribute to evidence suggesting that exposure to negative environmental shocks affects human capital attainment, while also suggesting it heterogeneously affects cohort composition.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.