Multifrequency electromagnetic geophysical tools for evaluating the hydrologic conditions and performance of evapotranspiration barriers

Surface barriers are designed to isolate subsurface contaminants for 1000 years or longer, functionally limiting water infiltration and removing the driving force for contaminant transport to groundwater. Cost-effective monitoring is challenging because of the long design life for surface barriers, spatial limitations and finite lifetime of in situ sensors, and performance metrics related to drainage. Hence, ground-penetrating radar (GPR) and electromagnetic induction (EMI) tools were evaluated for use in performance monitoring of surface barriers. GPR and EMI were used to non-invasively interrogate the Prototype Hanford Barrier (PHB), an evapotranspiration-capillary break barrier established in 1994 at the Hanford Site, in southeastern Washington State. Both geophysical methods were evaluated for providing indirect estimates of subsurface moisture content conditions that were compared to point scale measurements from borehole neutron logs. Surveys were performed during characteristically wet and dry periods to observe a range of hydrologic states of the barrier soil. Although EMI surveys were expected to show seasonal changes associated with changes in the bulk conductivity of the barrier soil layers, the effectiveness of the method was limited by the effects of metallic infrastructure embedded in the barrier. GPR estimates of volumetric water content were typically within 2-3% of the highest water contents from neutron probe measurements for both wet and dry periods, providing reasonable estimates of water content. Given that PHB monitoring data over the past 25 years has demonstrated its success in limiting deep drainage, GPR was found to be a cost-effective method for demonstrating continued barrier performance, with a greater capacity to quantify moisture content distributions over much larger areas relative to point measurements.

Advancing an interdisciplinary framework to study seed dispersal ecology

Although dispersal is generally viewed as a crucial determinant for the fitness of any organism, our understanding of its role in the persistence and spread of plant populations remains incomplete. Generalizing and predicting dispersal processes are challenging due to context dependence of seed dispersal, environmental heterogeneity and interdependent processes occurring over multiple spatial and temporal scales. Current population models often use simple phenomenological descriptions of dispersal processes, limiting their ability to examine the role of population persistence and spread, especially under global change. To move seed dispersal ecology forward, we need to evaluate the impact of any single seed dispersal event within the full spatial and temporal context of a plant's life history and environmental variability that ultimately influences a population's ability to persist and spread. In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity.

Consequences of intraspecific variation in seed dispersal for plant demography, communities, evolution and global change

As the single opportunity for plants to move, seed dispersal has an important impact on plant fitness, species distributions and patterns of biodiversity. However, models that predict dynamics such as risk of extinction, range shifts and biodiversity loss tend to rely on the mean value of parameters and rarely incorporate realistic dispersal mechanisms. By focusing on the mean population value, variation among individuals or variability caused by complex spatial and temporal dynamics is ignored. This calls for increased efforts to understand individual variation in dispersal and integrate it more explicitly into population and community models involving dispersal. However, the sources, magnitude and outcomes of intraspecific variation in dispersal are poorly characterized, limiting our understanding of the role of dispersal in mediating the dynamics of communities and their response to global change. In this manuscript, we synthesize recent research that examines the sources of individual variation in dispersal and emphasize its implications for plant fitness, populations and communities. We argue that this intraspecific variation in seed dispersal does not simply add noise to systems, but, in fact, alters dispersal processes and patterns with consequences for demography, communities, evolution and response to anthropogenic changes. We conclude with recommendations for moving this field of research forward.

Employing plant functional groups to advance seed dispersal ecology and conservation

Seed dispersal enables plants to reach hospitable germination sites and escape natural enemies. Understanding when and how much seed dispersal matters to plant fitness is critical for understanding plant population and community dynamics. At the same time, the complexity of factors that determine if a seed will be successfully dispersed and subsequently develop into a reproductive plant is daunting. Quantifying all factors that may influence seed dispersal effectiveness for any potential seed-vector relationship would require an unrealistically large amount of time, materials and financial resources. On the other hand, being able to make dispersal predictions is critical for predicting whether single species and entire ecosystems will be resilient to global change. Building on current frameworks, we here posit that seed dispersal ecology should adopt plant functional groups as analytical units to reduce this complexity to manageable levels. Functional groups can be used to distinguish, for their constituent species, whether it matters (i) if seeds are dispersed, (ii) into what context they are dispersed and (iii) what vectors disperse them. To avoid overgeneralization, we propose that the utility of these functional groups may be assessed by generating predictions based on the groups and then testing those predictions against species-specific data. We suggest that data collection and analysis can then be guided by robust functional group definitions. Generalizing across similar species in this way could help us to better understand the population and community dynamics of plants and tackle the complexity of seed dispersal as well as its disruption.

Inferring stratified parasitoid dispersal mechanisms and parameters from coarse data using mathematical and Bayesian methods

Biological invasions have movement at the core of their success. However, due to difficulties in collecting data, medium- and long-distance dispersal of small insects has long been poorly understood and likely to be underestimated. The agricultural release of parasitic hymenoptera, a group of wasps that are critical for biological pest control, represents a rare opportunity to study the spread of insects on multiple spatial scales. As these insects are typically less than 1 mm in size and are challenging to track individually, a first-time biocontrol release will provide a known spatial position and time of initial release for all individuals that are subsequently collected. In this paper, we develop and validate a new mathematical model for parasitoid wasp dispersal from point release, as in the case of biocontrol. The model is derived from underlying stochastic processes but is fully deterministic and admits an analytical solution. Using a Bayesian framework, we then fit the model to an Australian dataset describing the multi-scale wind-borne dispersal pattern of Eretmocerus hayati Zolnerowich & Rose (Hymenoptera: Aphelinidae). Our results confirm that both local movements and long-distance wind dispersal are significant to the movement of parasitoids. The model results also suggest that low velocity winds are the primary indicator of dispersal direction on the field scale shortly after release, and that average wind data may be insufficient to resolve long-distance movement given inherent nonlinearities and heterogeneities in atmospheric flows. The results highlight the importance of collecting wind data when developing models to predict the spread of parasitoids and other tiny organisms.

Towards a continuous population model for natural language vowel shift

The Great English Vowel Shift of 16th-19th centuries and the current Northern Cities Vowel Shift are two examples of collective language processes characterized by regular phonetic changes, that is, gradual changes in vowel pronunciation over time. Here we develop a structured population approach to modeling such regular changes in the vowel systems of natural languages, taking into account learning patterns and effects such as social trends. We treat vowel pronunciation as a continuous variable in vowel space and allow for a continuous dependence of vowel pronunciation in time and age of the speaker. The theory of mixtures with continuous diversity provides a framework for the model, which extends the McKendrick-von Foerster equation to populations with age and phonetic structures. We develop the general balance equations for such populations and propose explicit expressions for the factors that impact the evolution of the vowel pronunciation distribution. For illustration, we present two examples of numerical simulations. In the first one we study a stationary solution corresponding to a state of phonetic equilibrium, in which speakers of all ages share a similar phonetic profile. We characterize the variance of the phonetic distribution in terms of a parameter measuring a ratio of phonetic attraction to dispersion. In the second example we show how vowel shift occurs upon starting with an initial condition consisting of a majority pronunciation that is affected by an immigrant minority with a different vowel pronunciation distribution. The approach developed here for vowel systems may be applied also to other learning situations and other time-dependent processes of cognition in self-interacting populations, like opinions or perceptions.

Modeling the presence probability of invasive plant species with nonlocal dispersal

Mathematical models for the spread of invading plant organisms typically utilize population growth and dispersal dynamics to predict the time-evolution of a population distribution. In this paper, we revisit a particular class of deterministic contact models obtained from a stochastic birth process for invasive organisms. These models were introduced by Mollison (J R Stat Soc 39(3):283, 1977). We derive the deterministic integro-differential equation of a more general contact model and show that the quantity of interest may be interpreted not as population size, but rather as the probability of species occurrence. We proceed to show how landscape heterogeneity can be included in the model by utilizing the concept of statistical habitat suitability models which condense diverse ecological data into a single statistic. As ecologists often deal with species presence data rather than population size, we argue that a model for probability of occurrence allows for a realistic determination of initial conditions from data. Finally, we present numerical results of our deterministic model and compare them to simulations of the underlying stochastic process.

Automated purification and quantification of oligonucleotides

We have developed automated methods for the trityl-on purification and quantification of synthetic oligonucleotides. Oligonucleotide purification is by solid-phase extraction cartridges using Amberchrom CG-50 resin on an XYZ-axis robotic system. Quantification is by OD260nm using an online UV-visible spectrophotometer with sipper. The purification of 20 oligonucleotides requires 5 min of user set-up time, plus 20 min per sample of robot time. For a 15-25-mer at the 40 nmol scale of synthesis, the method gives a yield of 2.8 ODs from a load of 10.1 OD, i.e., a 28% average yield. Oligonucleotides purified by this method have proven to be successful for primers for automated DNA sequencing.

Charge is the major discriminating factor for glutathione reductase versus trypanothione reductase inhibitors

Benson et al. (Biochem. J. 1992, 286, 9) reported three novel competitive inhibitors of trypanothione reductase (TR), which were selected to complement a hydrophobic region identified on the TR structure which was not present on human glutathione reductase (hGR). Benson et al. also noted that chlorpromazine, a tricyclic antidepressant known to have trypanocidal activity, was an inhibitor of TR. Here we show that chlorpromazine is a competitive inhibitor of TRs from Crithidia fasciculata (Ki = 14 microM) and Trypanosoma cruzi (Ki = 10 microM), but the drug binds > 50-fold more weakly (Ki = 762 microM) to hGR. Analogues of chlorpromazine differing in the length of the side chain carrying the positively charged R-group are also selective TR inhibitors whereas, a tricyclic structure carrying a negatively charged side chain is a competitive inhibitor with selectivity for hGR (K(hGR)i = 165 microM vs. K(TR)i = 1400 microM). This finding suggests that simple charge characteristics, rather than differences in hydrophobicity, may account for a significant portion of the selectivity of this series of inhibitors for these two enzymes. Electrostatic analysis of the structures of TR and hGR thus provides a rationale for these results, and offers a new principle for inhibitor design. The principle gains further support from the observation that all known tricyclic competitive inhibitors of TR are positively charged. In order to investigate the in vivo relevance of our findings we have examined the effect of chlorpromazine and its negatively charged analogue on the growth of C. fasciculata parasites. Consistent with our kinetics, chlorpromazine (50 microM) inhibited the growth of parasites by 50%, while no measurable decrease in parasite growth rate was noted in the presence of the negatively charged inhibitor (400 microM). Furthermore, the highly similar inhibitory profiles of C. fasciculata TR and T. cruzi TR suggest that drug-design studies using the structurally better-studied C. fasciculata TR are also relevant to the human pathogen T. cruzi.

Kukoamine A and other hydrophobic acylpolyamines: potent and selective inhibitors of Crithidia fasciculata trypanothione reductase

The enzyme trypanothione reductase (TR), together with its substrate, the glutathione-spermidine conjugate trypanothione, plays an essential role in protecting parasitic trypanosomatids against oxidative stress and is a target for drug design. Here we show that a naturally occurring spermine derivative, the antihypertensive agent kukoamine A [N1N12-bis(dihydrocaffeoyl)-spermine] inhibits TR as a mixed inhibitor (Ki = 1.8 microM, Kii = 13 microM). Kukoamine shows no significant inhibition of human glutathione reductase (Ki > 10 mM) and thus provides a novel selective drug lead. The corresponding N1N8-bis(dihydrocaffeoyl)spermidine derivative was synthesized and acted as a purely competitive inhibitor with Ki = 7.5 microM. A series of mono- and di-acylated spermines and spermidines were synthesized to gain an insight into the effect of polyamine chain length, the nature and position of the acyl substituent and the importance of conformational mobility. These compounds inhibited TR with Ki values ranging from 11 to 607 microM.

Self-efficacy related to smoking cessation in general practice patients

Many investigators have assessed smoking cessation rates following physician advice to quit. The reasons why some patients comply and others do not need to be examined. The present study assessed the relationship among three dimensions of self-perception and smoking cessation in a group of 199 patients of family practitioners. The cohort was followed for 6 months, and statistical models were developed based on measurements made immediately after advice and smoking status 6 months later. Analyses revealed that 70.0% of the group could be correctly classified as to their smoking status at follow-up and that some of the constructs of self-perception could be used to explain their behavior. Implications for treatment of smoking in family practice settings are discussed.