Development of a open-vessel single-stage respirometer.

This paper describes the development and accuracy analysis of a single-stage respirometer which can be used both in the laboratory for wastewater characterization and in the plant as a process instrument. It is based on an accurate model of parasitic aeration, making the two-stage assumption unnecessary. Its operation is supervised by a real-time software, written in Lab View, managing the various measurement procedures and estimating the wastewater characteristics. Its accuracy is assessed through sensitivity and error propagation analysis, proving superior to the conventional model. A laboratory implementation of the instrument was tested with readily degradable substrate, yielding consistent and accurate respirograms

Development of a versatile tool for the simultaneous differential detection of Pseudomonas savastanoi pathovars by End Point and Real-Time PCR

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas savastanoi </it>pv. <it>savastanoi </it>is the causal agent of olive knot disease. The strains isolated from oleander and ash belong to the pathovars <it>nerii </it>and <it>fraxini</it>, respectively. When artificially inoculated, pv. <it>savastanoi </it>causes disease also on ash, and pv. <it>nerii </it>attacks also olive and ash. Surprisingly nothing is known yet about their distribution in nature on these hosts and if spontaneous cross-infections occur. On the other hand sanitary certification programs for olive plants, also including <it>P. savastanoi</it>, were launched in many countries. The aim of this work was to develop several PCR-based tools for the rapid, simultaneous, differential and quantitative detection of these <it>P. savastanoi </it>pathovars, in multiplex and <it>in planta</it>.</p> <p>Results</p> <p>Specific PCR primers and probes for the pathovars <it>savastanoi</it>, <it>nerii </it>and <it>fraxini </it>of <it>P. savastanoi </it>were designed to be used in End Point and Real-Time PCR, both with SYBR^®Green or TaqMan^®chemistries. The specificity of all these assays was 100%, as assessed by testing forty-four <it>P. savastanoi </it>strains, belonging to the three pathovars and having different geographical origins. For comparison strains from the pathovars <it>phaseolicola </it>and <it>glycinea </it>of <it>P. savastanoi </it>and bacterial epiphytes from <it>P. savastanoi </it>host plants were also assayed, and all of them tested always negative. The analytical detection limits were about 5 - 0.5 pg of pure genomic DNA and about 10²genome equivalents per reaction. Similar analytical thresholds were achieved in Multiplex Real-Time PCR experiments, even on artificially inoculated olive plants.</p> <p>Conclusions</p> <p>Here for the first time a complex of PCR-based assays were developed for the simultaneous discrimination and detection of <it>P. savastanoi </it>pv. <it>savastanoi</it>, pv. <it>nerii </it>and pv. <it>fraxini</it>. These tests were shown to be highly reliable, pathovar-specific, sensitive, rapid and able to quantify these pathogens, both in multiplex reactions and <it>in vivo</it>. Compared with the other methods already available for <it>P. savastanoi</it>, the identification procedures here reported provide a versatile tool both for epidemiological and ecological studies on these pathovars, and for diagnostic procedures monitoring the asymptomatic presence of <it>P. savastanoi </it>on olive and oleander propagation materials.</p

Practical identifiability analysis of environmental models

Identifiability of a system model can be considered as the extent to which one can capture its parameter values from observational data and other prior knowledge of the system. Identifiability must be considered in context so that the objectives of the modelling must also be taken into account in its interpretation. A model may be identifiable for certain objective functions but not others; its identifiability may depend not just on the model structure but also on the level and type of noise, and may even not be identifiable when there is no noise on the observational data. Context also means that non-identifiability might not matter in some contexts, such as when representing pluralistic values among stakeholders, and may be very important in others, such as where it leads to intolerable uncertainties in model predictions. Uncertainty quantification of environmental systems is receiving increasing attention especially through the development of sophisticated methods, often statistically-based. This is partly driven by the desire of society and its decision makers to make more informed judgments as to how systems are better managed and associated resources efficiently allocated. Less attention seems to be given by modellers to understand the imperfections in their models and their implications. Practical methods of identifiability analysis can assist greatly here to assess if there is an identifiability problem so that one can proceed to decide if it matters, and if so how to go about modifying the model (transforming parameters, selecting specific data periods, changing model structure, using a more sophisticated objective function). A suite of relevant methods is available and the major useful ones are discussed here including sensitivity analysis, response surface methods, model emulation and the quantification of uncertainty. The paper also addresses various perspectives and concepts that warrant further development and use