The effect of short-term changes in air pollution on respiratory and cardiovascular morbidity in Nicosia, Cyprus.

Presented at the 6th International Conference on Urban Air Quality, Limassol, March, 2007. Short-paper was submitted for peer-review and appears in proceedings of the conference.This study investigates the effect of daily changes in levels of PM10 on the daily volume of respiratory and cardiovascular admissions in Nicosia, Cyprus during 1995-2004. After controlling for long- (year and month) and short-term (day of the week) patterns as well as the effect of weather in Generalized Additive Poisson models, some positive associations were observed with all-cause and cause-specific admissions. Risk of hospitalization increased stepwise across quartiles of days with increasing levels of PM10 by 1.3% (-0.3, 2.8), 4.9% (3.3, 6.6), 5.6% (3.9, 7.3) as compared to days with the lowest concentrations. For every 10μg/m3 increase in daily average PM10 concentration, there was a 1.2% (-0.1%, 2.4%) increase in cardiovascular admissions. With respects to respiratory admissions, an effect was observed only in the warm season with a 1.8% (-0.22, 3.85) increase in admissions per 10μg/m3 increase in PM10. The effect on respiratory admissions seemed to be much stronger in women and, surprisingly, restricted to people of adult age

Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

Spatiotemporal dynamics of stress factors in wheat analysed by multisensoral remote sensing and geostatistics

Plant stresses, in particular fungal diseases, basically show a high variability in space and time with respect to their impact on the host. Recent ‘Precision Agriculture’ techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stress detection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is a profound knowledge about the controlled phenomena as well as their accurate sensor-based detection. Therefore, the present study focused on spatiotemporal dynamics of stress factors in wheat, Europe’s main crop. Primarily, the spatiotemporal characteristics of the fungal diseases, powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita), were analysed by remote sensing techniques and geo-statistics on leaf and field scale. Basically, there are two different approaches to sensor-based detection of crop stresses: near-range sensors and airborne-/satellite-borne sensors. In order to assess the potential of both approaches, various experiments in field and laboratory were carried out with the use of multiple sensors operated at different scales. Besides the spatial dimension of crop stresses, all studies focussed on the temporal dimension of these phenomena, since this is the key question for an operational use of these techniques. In addition, a comparison between multispectral and hyperspectral data gave an indication of their suitability for this purpose. The results exhibit very high spatiotemporal dynamics for both fungal diseases. However, powdery mildew and leaf rust showed different characteristics, with leaf rust showing a more systematic temporal progress. The physiological behaviours of the phenomena, which are strongly influenced by various environmental factors, define the optimal disease detection date as well as the temporal resolution required for sensor-based disease detection. Due to the high spatiotemporal dynamics of the investigated diseases, a general recommendation of optimal detection periods can not be given, but critical periods are highlighted for each pathogen. The results indicate that multispectral remote sensing data with high spatial resolution shows a high potential for quantifying crop vigour by using spectral mixture analyses. Simulated endmembers for the identification of stressed wheat areas were utilized, whereby promising results could be achieved. However, due to the low spectral resolution of these data, a discrimination of stress factors or early disease detection is not possible. Hyperspectral data was therefore used to point out the potential of early detection of crop diseases, which is a crucial and restrictive factor for Precision Agriculture applications. In a laboratory experiment, leaf rust infections could be detected by hyperspectral data five days after inoculation. In a field experiment with respect to early stress detection, it could be demonstrated that hyperspectral data outperformed multispectral data. High accuracy for the detection of powdery mildew infections in the field was thereby achieved. Due to the fact that typical spatiotemporal characteristics for each pathogen were found, there is a high potential for decision support systems, considering all variables that affect the disease progress. Besides the further analysis of hyperspectral data for disease detection, the development of a decision support system is the subject of the upcoming last period of the Research Training Group 722

Fully integrated digital microfluidics platform for automated immunoassay; a versatile tool for rapid, specific detection of a wide range of pathogens

© 2018 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.With the tangible threat posed by the release of chemical and biological warfare (CBW) agents, detection of airborne pathogens is a critical military and security concern. Recent air sampling techniques developed for biocollection take advantage of Electrowetting on Dielectric (EWOD) to recover material, producing highly concentrated droplet samples. Bespoke EWOD-based digital microfluidics platforms are very well suited to take full advantage of the microlitre concentrated droplet resulting from this recovery process. In this paper we present a free-standing, fully automated DMF platform for immunoassay. Using this system, we demonstrate the automated detection of four classes of CBW agent simulant biomolecules and organisms each representing credible threat agents. Taking advantage of the full magnetic separation process with antibody-bound microbeads, rapid and complete separation of specific target antigen can be achieved with minimal washing steps allowing for very rapid detection. Here, we report clear detection of four categories of antigens achieved with assay completion times of between six and ten minutes. Detection of HSA, Bacillus atrophaeus (BG spores), MS2 bacteriophage and Escherichia coli are demonstrated with estimated limit of detection of respectively 30 ng ml -1, 4 × 10 4 cfu ml -1, 10 6 pfu ml -1 and 2 × 10 7 cfu ml -1. The fully-integrated portable platform described in this paper is highly compatible with the next generation of electrowetting-coupled air samplers and thus shows strong potential toward future in-field deployable biodetection systems and could have key implication in life-changing sectors such as healthcare, environment or food security.Peer reviewe

Proceedings of the Second Airborne Imaging Spectrometer Data Analysis Workshop

Topics addressed include: calibration, the atmosphere, data problems and techniques, geological research, and botanical and geobotanical research

Modelling soil water conent in a tomato field: proximal gamma ray spectroscopy and soil-crop system models

Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space-time trade off methods at field scale. This study proved the feasibility and reliability of soil water content monitoring through a seven-month continuous acquisition of terrestrial gamma radiation in a tomato test field. By employing a 1 L sodium iodide detector placed at a height of 2.25 m, we investigated the gamma signal coming from an area having a ~25 m radius and from a depth of approximately 30 cm. Experimental values, inferred after a calibration measurement and corrected for the presence of biomass, were corroborated with gravimetric data acquired under different soil moisture conditions, giving an average absolute discrepancy of about 2%. A quantitative comparison was carried out with data simulated by AquaCrop, CRITeRIA, and IRRINET soil-crop system models. The different goodness of fit obtained in bare soil condition and during the vegetated period highlighted that CRITeRIA showed the best agreement with the experimental data over the entire data-taking period while, in presence of the tomato crop, IRRINET provided the best results.Comment: 18 pages, 9 Figures, 3 Table

Modelling mechanisms of change in crop populations

Computer -based simulation models of changes occurring within crop populations when subjected to agents of phenotypic change, have been developed for use on commonly available personal computer equipment. As an underlying developmental principle, the models have been designed as general -case, mechanistic, stochastic models, in contrast to the predominantly empirically- derived, system -specific, deterministic (predictive) models currently available. A modelling methodology has evolved, to develop portable simulation models, written in high - level, general purpose code, allowing for use, modification and continued development by biologists with little requirement for computer programming expertise.The initial subject of these modelling activities was the simulation of the effects of selection and other agents of genetic change in crop populations, resulting in the computer model, PSELECT. Output from PSELECT, specifically phenotypic and genotypic response to phenotypic truncation selection, conformed to expectation, as defined by results from established analogue modelling work. Validation of the model by comparison of output with the results from an experimental -scale plant breeding exercise was less conclusive, and, owing to the fact that the genetic basis of the phenotypic characters used in the selection programme was insufficiently defined, the validation exercise provided only broad qualitative agreement with the model output. By virtue of the predominantly subjective nature of plant breeding programmes, the development of PSELECT resulted in a model of theoretical interest, but with little current practical application.Modelling techniques from the development of the PSELECT model were applied to the simulation of plant disease epidemics, where the modelled system is well characterised, and simulation modelling is an area of active research. The model SATSUMA, simulating the spatial and temporal development of diseases within crop populations, was developed. The model generates output which conforms to current epidemiological theory, and is compatible with contemporary methods of temporal and spatial analysis of crop disease epidemics. Temporal disease progress in the simulations was accurately described by variations of a generalised logistic model. Analysis of the spatial pattern of simulated epidemics by frequency distribution fitting or distance class methods was found to give good qualitative agreement with observed biological systems.The mechanistic nature of SATSUMA and its deliberate design as a general case model make it especially suitable for the investigation of component processes in a generalised plant disease epidemic, and valuable as an educational tool. Subject to validation against observational data, such models can be utilised as predictive tools by the incorporation of information (concerning crop species, pathogen etc.) specifically relevant to the modelled system. In addition to its educational use, SATSUMA has been used as research tool for the examination of the effect of spatial pattern of disease and disease incidence on the efficiency of sampling protocols and in parameterising a general theoretical model for describing the spatio -temporal development of plant diseases

A generic model of interactions between FSPM, foliar pathogens and microclimate

International audienceA framework was defined to model the interactions between FSPM, foliar fungal pathogens and microclimate, with the concern of interoperability of the components and extensibility. The framework was applied on two existing models of pathosystems (powdery mildew on grapevine and septoria on wheat) to make them more modular and extensible. It will facilitate the design of new disease models on FSPMs