Biology subjects

van den Bosch, F.

Publications and source records attributed to van den Bosch, F..

10 recordsLinked to original sources

bioRxiv2024

Assessing the potential antifungal resistance risk from dual use of a mode of action in agriculture and medical treatment of human pathogens.

A mechanistic basis is described for assessment of resistance risk to medical anti-fungal treatments from agricultural use of fungicides of the same mode of action. The following need to occur in landscape environments for a risk to be posed by dual use: (i) emergence, whereby a resistant strain emerges by mutation and invasion, (ii) selection, whereby a mutation conferring a fitness advantage is selected for in the presence of fungicide, and (iii) exposure of humans to resistant strains from the landscape, potentially resulting in invasive fungal infection (IFI). We identify 20 human fungal pathogens for which there is evidence that all three processes above could, in principle, occur. A model is derived for quantitative analysis to explore what determines resistance emergence and selection in human pathogens in landscape environments. Emergence and selection were particularly affected by fitness cost associated with the resistance mutation(s) and fungicide concentration. Emergence was also determined by the amount of pathogen reproduction (related to pathogen population size). The findings were related to an example case of observational data from the Netherlands for Aspergillus fumigatus. The analysis supports previous work that compost, including bulb waste, is towards the high-risk end of the spectrum for this species. Agricultural soils, non-agricultural land and grassland were lower risk. More generally, across species, the model output suggests that if fungicide resistance is associated with even a small fitness cost, then environments with low fungicide concentrations, such as field soils and semi-natural environments (e.g. woodland), may not be conducive to resistance emergence or selection.

pathology↗

bioRxiv2022

The value of volunteer surveillance for the early detection of biological invaders.

Early detection of invaders requires finding small numbers of individuals across large landscapes. It has been argued that the only feasible way to achieve the sampling effort needed for early detection of an invader is to involve volunteer groups (citizen scientists, passive surveyors, etc.). A key concern is that volunteers may have a considerable false-positive and false-negative rate. The question then becomes whether verification of a report from a volunteer is worth the effort. This question is the topic of this paper. We show that the maximum plausible incidence when the expert samples on its own, [Formula], and the maximum plausible incidence when the expert only verifies cases reported by the volunteer surveyor to be infected, [Formula], are related as O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD Where {theta}fp and {theta}fn are the false positive and false negative rate of the volunteer surveyor, respectively. We also show that the optimal monitoring programme consists of verifying only the cases reported by the volunteer surveyor if O_FD O_INLINEFIG[Formula 2]C_INLINEFIGM_FD(2)C_FD Where TN is the cost of a sample taken by the expert and TX is the cost of an expert verifying a case reported by a volunteer surveyor.

ecology↗

bioRxiv2022

Out of sight: Surveillance strategies for emerging vectored plant pathogens.

Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.

plant biology↗

bioRxiv2020

The Effect of Introgression of an R2R3 MYB Transcription Factor on Sulphur Metabolism in Brassica oleracea

BackgroundA diet rich in cruciferous vegetables is reported to have beneficial health effects, partially mediated by 4-methylsulfinylbutyl glucosinolate, or glucoraphanin, which is predominantly found within broccoli (Brassica oleracea var italica). We describe the downstream effects on transcription and metabolism in broccoli following the introgression of a genetic variant of MYB28 into broccoli from a wild Brassica relative which has previously been associated with enhancement of glucoraphanin. ResultsWhole genome sequencing, RNA expression and metabolite analyses were used to characterise the consequences of the introgression of either one or two copies of a genetic variant of the MYB28 transcription factor into a commercial broccoli genetic background. The introgression of the variant of MYB28 resulted in enhanced expression of genes involved in primary sulphate assimilation, sulphur metabolism and aliphatic glucosinolate biosynthesis, and enhanced accumulation of 4-methylsulphinyl butyl glucosinolate in florets. Other changes in transcription that may be related to non-targeted introgression events are reported. There were no consistent effects upon sulphur metabolites pools, apart from methionine-derived glucosinolates. ConclusionThis study illustrates the downstream effects on transcription and metabolism of the introgression of a genetic variant of MYB28 from a wild species into a commercial broccoli genotype.

plant biology↗

bioRxiv2020

Deep learning for monitoring network optimisation--a practical example using a national insect survey

Monitoring networks are improved by additional sensors. Optimal configurations of sensors give better representations of the process of interest, maximising its exploration while minimising the need for costly infrastructure. By modelling the monitored process, we can identify gaps in its representation, i.e. uncertain predictions, where additional sensors should be located. Here, with data collected from the Rothamsted Insect Survey network, we train an artificial neural network to predict the seasonal aphid arrival from environmental variables. We focus on estimating prediction uncertainty across the UK to guide the addition of a sensor to the network. We first illustrate how to estimate uncertainty in neural networks, hence making them relevant for model-based monitoring network optimisation. Then we highlight critical areas of agricultural importance where additional traps would improve decision support and crop protection in the UK. Possible applications include most ecological monitoring and surveillance activities, but also the weather or pollution monitoring.

ecology↗

bioRxiv2019

Optimising risk-based surveillance for early detection of invasive plant pathogens.

Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest risk sites, and that the optimal strategy differs depending on, not only patterns of pathogen entry and spread, but also the choice of detection method. We use the example of the economically important arboreal disease huanglongbing to demonstrate how our approach outperforms conventional methods of targeted surveillance.

ecology↗

bioRxiv2019

Cassava planting material movement and grower behaviour in Zambia: implications for disease management

Cassava is an important food crop for most small-holder growers across sub-Saharan Africa, where production is largely limited by the presence of two viral diseases: cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), both propagated by a vector whitefly and via human-mediated movement of infected cassava stems. Despite its importance, there is limited knowledge of growers behaviour related to planting material movement, as well as growers perception and knowledge of cassava diseases, which have major implications for disease spread and control. This study was conducted to address this knowledge gap by surveying small-holder growers in Zambia. A total of 96 subsistence cassava growers across five provinces were surveyed between 2015 and 2017. Most growers interviewed used planting materials from their own fields (94%) or those of nearby (<10 km) family and friends, although some large transactions with markets, middlemen, and NGOs occurred over longer distances. At the same time, information related to cassava diseases and uninfected planting material only reached 48% of growers, where those with access to information were more concerned about the disease. These data provide a basis for future planning of cassava clean seed systems to control virus diseases, emphasising the critical role of grower knowledge, and consequently education, in success of these systems. In particular, we highlight the importance of extension workers in this education process, as well as farmers groups and the media.

epidemiology↗

bioRxiv2018

Translating surveillance data into incidence estimates

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

epidemiology↗

bioRxiv2018

Sampling for disease absence-deriving informed monitoring from epidemic traits

Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould.

epidemiology↗

bioRxiv2017

Using epidemiological principles to explain fungicide resistance management strategies: why do mixtures outperform alternations?

O_LIWhether fungicide resistance management is optimised by spraying chemicals with different modes of action as a mixture (i.e. simultaneously) or in alternation (i.e. sequentially) has been studied by experimenters and modellers for decades, largely inconclusively.\nC_LIO_LIWe use previously-parameterised and validated mathematical models of wheat septoria leaf blotch and grapevine powdery mildew to test which strategy provides better resistance management, using the total yield before fungicide-resistance causes disease control to become economically-ineffective (\"lifetime yield\") to measure effectiveness.\nC_LIO_LILifetime yield is optimised by spraying as much low-risk fungicide as is permitted, combined with slightly more high-risk fungicide than needed for acceptable initial disease control, applying these fungicides as a mixture. This is invariant to model parameterisation and structure, as well as the pathosystem in question. However if comparison focuses on other metrics, for example lifetime yield at full label dose, either mixtures or alternation can be optimal.\nC_LIO_LIOur work shows how epidemiological principles can explain the evolution of fungicide resistance, and highlights a theoretical framework to address the question of whether mixtures or alternation provide better resistance management. Our work also demonstrates that precisely how spray strategies are compared must be given extremely careful consideration.\nC_LI

epidemiology↗