Whitened kernel surface: A fast and reliable method for assessing Fusarium severity on cereal grains by digital picture analysis

Fusarium head blight (FHB) is a cereal disease of major importance responsible for yield losses and mycotoxin contaminations in grains. Here, we introduce a new measurement approach to quantify FHB severity on grains based on the evaluation of the whitened kernel surface (WKS) using digital image analysis. The applicability of WKS was assessed on two bread wheat and one triticale grain sample sets (265 samples). Pearson correlation coefficients between Fusarium‐damaged kernels (FDK) and WKS range from r = 0.77 to r = 0.81 and from r = 0.61 to r = 0.86 for the correlation between deoxynivalenol (DON) content and WKS. This new scoring method facilitates fast and reliable assessment of the resistance to kernel infection and shows significant correlation with mycotoxin content. WKS can be automated and does not suffer from the “human factor” inherent to visual scorings. As a low‐cost and fast approach, this method appears particularly attractive for breeding and genetic analysis of FHB resistance where typically large numbers of experimental lines need to be evaluated, and for which WKS is suggested as an alternative to visual FDK scorings.     

Prevalence of mycoplasmas in the respiratory tracts of pneumonic calves.

The prevalence of mycoplasmas in the respiratory tracts of 148 pneumonic calves originating from 25 herds in the Netherlands is reported. Four types of culture media were used to isolate mycoplasmas: solid modified Edward medium, 2 types of Friis media, and A7B differential agar medium. Mycoplasmas were isolated both from nasal swab specimens and lung lavage fluids collected from live calves and from nasal mucosa and lung tissue specimens collected post mortem. All of the mycoplasma strains isolated could be identified as either Ureaplasma diversum (isolated from 80% of 25 herds), Mycoplasma dispar (92%), M. bovirhinis (88%), M. bovis (20%), M. bovigenitalium (4%), M. arginini (16%), or M. canis (12%). Isolation rates of M. dispar and U. diversum were considerably higher from lung lavage fluids than from nasal swab specimens. M. bovis was detected only in fattening herds and not in dairy herds. The respiratory tracts of 75% of the calves examined contained at least 2 mycoplasma species. In total, 25 different combinations of mycoplasma species were detected in specimens collected from noses and lungs. The pathogenic species U. diversum and M. dispar had not been isolated before in the Netherlands.     

Mystery swine disease in The Netherlands: the isolation of Lelystad virus.

In early 1991, the Dutch pig-industry was struck by the so-called mystery swine disease. Large-scale laboratory investigations were undertaken to search for the etiological agent. We focused on isolating viruses and mycoplasmas, and we tested paired sera of affected sows for antibodies against ten known pig viruses. The mycoplasmas M. hyosynoviae, M. hyopneumoniae, and Acholeplasma laidlawii, and the viruses encephalomyocarditis virus and porcine enterovirus types 2 and 7 were isolated from individual pigs. An unknown agent, however, was isolated from 16 of 20 piglets and from 41 of 63 sows. This agent was characterised as a virus and designated Lelystad virus. No relationship between this virus and other viruses has yet been established. Of 165 sows reportedly afflicted by the disease, 123 (75 per cent) seroconverted to Lelystad virus, whereas less than 10 per cent seroconverted to any of the other virus isolates or to the known viral pathogens. Antibodies directed against Lelystad virus were also found in pigs with mystery swine disease in England, Germany, and in the United States. We conclude that infection with Lelystad virus is the likely cause of mystery swine disease.     

Segmentation of 3D images of plant tissues at multiple scales using the level set method

Background

Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue.

Results

We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue.

Conclusion

The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.

     

Growth pattern of Juncus effusus and Juncus conglomeratus in response to cutting frequency

Increasing abundance of Juncus effusus (soft rush) and Juncus conglomeratus (compact rush) in pastures and meadows in western Norway has caused reductions in forage yield and quality in recent decades. Understanding plant development and regrowth following cutting is essential in devising cost‐effective means to control rushes. In a field experiment in western Norway, we investigated development of above‐ and below‐ground fractions of rush from seedlings to three‐year‐old plants, including the impact on vigour of disturbing growth by different cutting frequencies during the period 2009–2012. Each year, the plants were exposed to one or two annual cuts or left untreated and five destructive samplings were performed from March to early December. Juncus effusus showed significantly more vigorous growth than Juncus conglomeratus in the last two years of the study period. The above‐ground:below‐ground biomass ratio of both species increased mainly in spring and early summer and was reduced in late summer and autumn. Removal of aerial shoots also reduced the below‐ground fraction of both species. One annual cut in July effectively reduced biomass production in both species by 30–82%, which was only a slightly smaller reduction than with two annual cuts, in June and August. Mechanical control measures such as cutting can thus effectively reduce rush vigour when performed late in the growing season.     

Conservation planning with spatially explicit models: a case for horseshoe bats in complex mountain landscapes

Context

Context Bats are considered as an ecological indicator of habitat quality due to their sensitivity to human-induced ecosystem changes. Hence, we will focus the study on two indicator species of bats as a proxy to evaluate structure and composition of the landscape to analyze anthropic pressures driving changes in patterns.

Objectives

This study develops a spatially-explicit model to highlight key habitat nodes and corridors which are integral for maintaining functional landscape connectivity for bat movement. We focus on a complex mountain landscape and two bat species: greater (Rhinolophus ferrumequinum) and lesser (Rhinolophus hipposideros) horseshoe bats which are known to be sensitive to landscape composition and configuration.

Methods

Species distribution models are used to delineate high-quality foraging habitat for each species using opportunistic ultrasonic bat data. We then performed connectivity analysis combining (modelled) suitable foraging habitat and (known) roost sites. We use graph-theory and the deviation in the probability of connectivity to quantify resilience of the landscape connectivity to perturbations.

Results

Both species were confined to lowlands (<1000 m elevation) and avoided areas with high road densities. Greater horseshoe bats were more generalist than lesser horseshoe bats which tended to be associated with broadleaved and mixed forests.

Conclusions

The spatially-explicit models obtained were proven crucial for prioritizing foraging habitats, roost sites and key corridors for conservation. Hence, our results are being used by key stakeholders to help integrate conservation measures into forest management and conservation planning at the regional level. The approach used can be integrated into conservation initiatives elsewhere.