Brodifacoum is effective against Norway Rats (Rattus norvegicus) in a tyrosine139cysteine focus of anticoagulant resistance, Westphalia, Germany

BACKGROUND: The single nucleotide polymorphism (SNP), and consequent amino acid exchange from tyrosine to cysteine at location 139 of the vkorc1 gene (i.e. tyrosine139cysteine or Y139C), is the most widespread anticoagulant resistance mutation in Norway rats (Rattus norvegicus Berk.) in Europe. Field trials were conducted to determine incidence of the Y139C SNP at two rat infested farms in Westphalia, Germany, and to estimate the practical efficacy against them of applications, using a pulsed baiting treatment regime, of a proprietary bait (KleratTM) containing 50 ppm brodifacoum. RESULTS: DNA analysis for the Y139C mutation showed that resistant rats were prevalent at the two farms, with an incidence of 80.0% and 78.6% respectively. Applications of brodifacoum bait achieved results of 99.2% and 100.0% control at the two farms, when measured by census baiting, although the treatment was somewhat prolonged at one site due to the abundance of attractive alternative food. CONCLUSION: The study showed that 50 ppm brodifacoum bait is fully effective against the Y139C SNP at the Münsterland focus and is likely to be so elsewhere in Europe where this mutation is found. The pulsed baiting regime reduced to relatively low levels the quantity of bait required to control these two substantial resistant Norway rat infestations. Previous studies had shown much larger quantities of bromadiolone and difenacoum baits used in ineffective treatments against Y139C resistant rats in the Münsterland. These results should be considered when making decisions about the use of anticoagulant against resistant Norway rats and their potential environmental impacts.

Towards effective water reuse: drivers, challenges and strategies shaping the organisational management of reclaimed water in Jordan

Treated wastewater or reclaimed water is gaining recognition as a valuable water resource around the world. To assess why, where and how water reuse takes place in Jordan, semi-structured interviews were conducted with representatives of 29 key organisations in 2008. The analysis reveals that water scarcity is a key driver for water reuse. However, despite such recognition, reuse was described positively by only a small proportion of the interviewees (n = 6). Negative and neutral perceptions regarding reuse dominated and the research found that this was related to two underlying challenges: (i) the requirement for more intensive management when using reclaimed water compared with freshwater and (ii) concern over societal acceptance of water reuse. These factors were found to be associated with the risks posed to humans and their environments, combined with negative emotional and cultural responses to human waste and its applications. Numerous strategies are identified that are employed by organisations to overcome these challenges. Wastewater treatment, regulation, monitoring, the mixing of treated effluent with freshwater and limited public discussion of water reuse are all employed to achieve maximum use of reclaimed water. Each strategy presents benefits of sort, but some may paradoxically also inhibit optimal use of reclaimed water. Careful modifications to the existing strategies of Jordanian agencies, such as more open discussion of reuse, could lead to greater social, economic and environmental gains.

Simple and effective technical procedures for the packing and application of nanospray PicoFrit HPLC columns for proteomic analyses

A simple procedure was developed for packing PicoFrit HPLC columns with chromatographic stationary phase using a reservoir fabricated from standard laboratory HPLC fittings. Packed columns were mounted onto a stainless steel ultra-low volume precolumn filter assembly containing a 0.5-mu m pore size steel frit. This format provided a conduit for the application of the nanospray voltage and protected the column from obstruction by sample material. The system was characterised and operational performance assessed by analysis of a range of peptide standards (n = 9).

Describing seasonal variability in the distribution of daily effective temperatures for 1985-2009 compared to 1904-1984 for De Bilt, Holland

This paper proposes a method for describing the distribution of observed temperatures on any day of the year such that the distribution and summary statistics of interest derived from the distribution vary smoothly through the year. The method removes the noise inherent in calculating summary statistics directly from the data thus easing comparisons of distributions and summary statistics between different periods. The method is demonstrated using daily effective temperatures (DET) derived from observations of temperature and wind speed at De Bilt, Holland. Distributions and summary statistics are obtained from 1985 to 2009 and compared to the period 1904–1984. A two-stage process first obtains parameters of a theoretical probability distribution, in this case the generalized extreme value (GEV) distribution, which describes the distribution of DET on any day of the year. Second, linear models describe seasonal variation in the parameters. Model predictions provide parameters of the GEV distribution, and therefore summary statistics, that vary smoothly through the year. There is evidence of an increasing mean temperature, a decrease in the variability in temperatures mainly in the winter and more positive skew, more warm days, in the summer. In the winter, the 2% point, the value below which 2% of observations are expected to fall, has risen by 1.2 °C, in the summer the 98% point has risen by 0.8 °C. Medians have risen by 1.1 and 0.9 °C in winter and summer, respectively. The method can be used to describe distributions of future climate projections and other climate variables. Further extensions to the methodology are suggested.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.

An effective approach for selection of terrain modelling method

This letter presents an effective approach for selection of appropriate terrain modeling methods in forming a digital elevation model (DEM). This approach achieves a balance between modeling accuracy and modeling speed. A terrain complexity index is defined to represent a terrain's complexity. A support vector machine (SVM) classifies terrain surfaces into either complex or moderate based on this index associated with the terrain elevation range. The classification result recommends a terrain modeling method for a given data set in accordance with its required modeling accuracy. Sample terrain data from the lunar surface are used in constructing an experimental data set. The results have shown that the terrain complexity index properly reflects the terrain complexity, and the SVM classifier derived from both the terrain complexity index and the terrain elevation range is more effective and generic than that designed from either the terrain complexity index or the terrain elevation range only. The statistical results have shown that the average classification accuracy of SVMs is about 84.3% ± 0.9% for terrain types (complex or moderate). For various ratios of complex and moderate terrain types in a selected data set, the DEM modeling speed increases up to 19.5% with given DEM accuracy.

The relationship between community-based organisations and the effective management of coastal and marine resources in the Western Indian Ocean region

This study examines the relationship between community based organisations and marine and coastal resource management in the Western Indian Ocean Region.

The interactive responses of water quality and hydrology to changes in multiple stressors, and implications for the long-term effective management of phosphorus

Soluble reactive phosphorus (SRP) plays a key role in eutrophication, a global problem decreasing habitat quality and in-stream biodiversity. Mitigation strategies are required to prevent SRP fluxes from exceeding critical levels, and must be robust in the face of potential changes in climate, land use and a myriad of other influences. To establish the longevity of these strategies it is therefore crucial to consider the sensitivity of catchments to multiple future stressors. This study evaluates how the water quality and hydrology of a major river system in the UK (the River Thames) respond to alterations in climate, land use and water resource allocations, and investigates how these changes impact the relative performance of management strategies over an 80-year period. In the River Thames, the relative contributions of SRP from diffuse and point sources vary seasonally. Diffuse sources of SRP from agriculture dominate during periods of high runoff, and point sources during low flow periods. SRP concentrations rose under any future scenario which either increased a) surface runoff or b) the area of cultivated land. Under these conditions, SRP was sourced from agriculture, and the most effective single mitigation measures were those which addressed diffuse SRP sources. Conversely, where future scenarios reduced flow e.g. during winters of reservoir construction, the significance of point source inputs increased, and mitigation measures addressing these issues became more effective. In catchments with multiple point and diffuse sources of SRP, an all-encompassing effective mitigation approach is difficult to achieve with a single strategy. In order to attain maximum efficiency, multiple strategies might therefore be employed at different times and locations, to target the variable nature of dominant SRP sources and pathways.

Planning autonomous vehicles in the absence of speed lanes using an elastic strip

Planning of autonomous vehicles in the absence of speed lanes is a less-researched problem. However, it is an important step toward extending the possibility of autonomous vehicles to countries where speed lanes are not followed. The advantages of having nonlane-oriented traffic include larger traffic bandwidth and more overtaking, which are features that are highlighted when vehicles vary in terms of speed and size. In the most general case, the road would be filled with a complex grid of static obstacles and vehicles of varying speeds. The optimal travel plan consists of a set of maneuvers that enables a vehicle to avoid obstacles and to overtake vehicles in an optimal manner and, in turn, enable other vehicles to overtake. The desired characteristics of this planning scenario include near completeness and near optimality in real time with an unstructured environment, with vehicles essentially displaying a high degree of cooperation and enabling every possible(safe) overtaking procedure to be completed as soon as possible. Challenges addressed in this paper include a (fast) method for initial path generation using an elastic strip, (re-)defining the notion of completeness specific to the problem, and inducing the notion of cooperation in the elastic strip. Using this approach, vehicular behaviors of overtaking, cooperation, vehicle following,obstacle avoidance, etc., are demonstrated.

A time-invariant visco-elastic windkessel model relating blood flow and blood volume

The difference between the rate of change of cerebral blood volume (CBV) and cerebral blood flow (CBF) following stimulation is thought to be due to circumferential stress relaxation in veins (Mandeville, J.B., Marota, J.J.A., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679–689). In this paper we explore the visco-elastic properties of blood vessels, and present a dynamic model relating changes in CBF to changes in CBV. We refer to this model as the visco-elastic windkessel (VW) model. A novel feature of this model is that the parameter characterising the pressure–volume relationship of blood vessels is treated as a state variable dependent on the rate of change of CBV, producing hysteresis in the pressure–volume space during vessel dilation and contraction. The VW model is nonlinear time-invariant, and is able to predict the observed differences between the time series of CBV and that of CBF measurements following changes in neural activity. Like the windkessel model derived by Mandeville, J.B., Marota, J.J.A., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679–689, the VW model is primarily a model of haemodynamic changes in the venous compartment. The VW model is demonstrated to have the following characteristics typical of visco-elastic materials: (1) hysteresis, (2) creep, and (3) stress relaxation, hence it provides a unified model of the visco-elastic properties of the vasculature. The model will not only contribute to the interpretation of the Blood Oxygen Level Dependent (BOLD) signals from functional Magnetic Resonance Imaging (fMRI) experiments, but also find applications in the study and modelling of the brain vasculature and the haemodynamics of circulatory and cardiovascular systems.

Developing a reliable strategy to infer the effective soil hydraulic properties from field evaporation experiments for agro-hydrological models

The Richards equation has been widely used for simulating soil water movement. However, the take-up of agro-hydrological models using the basic theory of soil water flow for optimizing irrigation, fertilizer and pesticide practices is still low. This is partly due to the difficulties in obtaining accurate values for soil hydraulic properties at a field scale. Here, we use an inverse technique to deduce the effective soil hydraulic properties, based on measuring the changes in the distribution of soil water with depth in a fallow field over a long period, subject to natural rainfall and evaporation using a robust micro Genetic Algorithm. A new optimized function was constructed from the soil water contents at different depths, and the soil water at field capacity. The deduced soil water retention curve was approximately parallel but higher than that derived from published pedo-tranfer functions for a given soil pressure head. The water contents calculated from the deduced soil hydraulic properties were in good agreement with the measured values. The reliability of the deduced soil hydraulic properties was tested in reproducing data measured from an independent experiment on the same soil cropped with leek. The calculation of root water uptake took account for both soil water potential and root density distribution. Results show that the predictions of soil water contents at various depths agree fairly well with the measurements, indicating that the inverse analysis is an effective and reliable approach to estimate soil hydraulic properties, and thus permits the simulation of soil water dynamics in both cropped and fallow soils in the field accurately. (C) 2009 Elsevier B.V. All rights reserved.

Habitat manipulation in lowland rice-coconut cropping systems of the Philippines: an effective rodent pest management strategy?

BACKGROUND: Reduction of vegetation height is recommended as a management strategy for controlling rodent pests of rice in South-east Asia, but there are limited field data to assess its effectiveness. The breeding biology of the main pest species of rodent in the Philippines, Rattus tanezumi, suggests that habitat manipulation in irrigated rice–coconut cropping systems may be an effective strategy to limit the quality and availability of their nesting habitat. The authors imposed a replicated manipulation of vegetation cover in adjacent coconut groves during a single rice-cropping season, and added artificial nest sites to facilitate capture and culling of young. RESULTS: Three trapping sessions in four rice fields (two treatments, two controls) adjacent to coconut groves led to the capture of 176 R. tanezumi, 12Rattus exulans and seven Chrotomysmindorensis individuals. There was no significant difference in overall abundance between crop stages or between treatments, and there was no treatment effect on damage to tillers or rice yield. Only two R. tanezumi were caught at the artificial nest sites. CONCLUSION: Habitat manipulation to reduce the quality of R. tanezumi nesting habitat adjacent to rice fields is not effective as a lone rodent management tool in rice–coconut cropping systems.

Privatisation, empowerment and accountability: What are the policy implications for establishing effective farmer organisations?

Weak institutional development and information flows have constrained the extent to which the small-holder farming sector in developing countries can significantly drive growth and poverty reduction. Thisis despite widely implemented economic liberalisation policies focussing on market efficiency. Farmerorganisations are viewed as a potential means of addressing public and private institutional failure but thishas frequently been limited by inequalities in access to power and information. This article investigatestwo issues that have received little research attention to date: what role downward accountability plays inenabling farmer organisations to improve services and markets, and what influences the extent to whichdownward accountability is achieved. Kenya Tea Development Agency (KTDA), one of the largest farmerorganisations in the world (>400,000 farmers) is examined alongside wider literature. Mixed methodswere used including key informant interviews, and eight months of participant observation followedby a questionnaire survey. The article concludes that without effective downward accountability farmerorganisations can become characterised by institutions and mechanisms that favour elites, restrictedweak coordination and regulation, and manipulated information flows. This in turn reduces individuals’incentives to invest. If farmer organisations are to realise their potential as a means of enabling the small-holder sector to significantly contribute to economic growth and poverty reduction, policy and researchneeds to address key factors which influence accountability including: how to ensure initial processes information of farmer organisations establish appropriate structures and rules; strong state regulation toenhance corporate accountability; transparent information provision regarding actions of farmer organi-sation leaders; and the role independent non-government organisations can play. Consequently attentionneeds to focus on developing means of legitimising rights, building poor people’s capacity to challengeexclusion, and moving from rights to obligations regarding information provision.