In situ physico-chemical disinfection of high risk faecal waste is both effective and widely used as a sanitation management strategy for infection prevention and control. Systematic tests where the performance of alternative physico-chemical disinfection methods is systematically compared and optimized must be based on reliable protocols. These protocol are currently not adequately addressing the neutralization related issues: the neutralization of the tested disinfectant after specified conditions of concentration and contact time (CT) is necessary to prevent continued disinfection after the intended contact time; moreover such neutralization is often necessary in practice and on a large scale to prevent adverse health and ecological impacts from remaining disinfectant after the target CT is achieved. Few studies adequately assess the extent of neutralization of the chemical disinfectant and are intended to optimize on-site disinfection practices for waste matrices posing high microbial risks. Hence, there is a need for effective and reproducible neutralization protocols in chemical disinfection trials and practice. Furthermore, for most of chemical disinfectants used in healthcare settings there is no practical methodology to reliably and conveniently measure the residual disinfectant concentration after its neutralization and also determine the optimum concentration of the neutralizer. Because some neutralizing compounds can themselves be toxic to the test microorganisms, it is necessary to optimize neutralization procedures in disinfection experiments for the development of infection control practices using accepted positive control microbes. In the presented work, a stepwise bioassay-based protocol using representative faecal indicator microbes is described for optimizing chemical disinfection and subsequent disinfectant neutralization of any infectious faecal waste matrix. The example described is for the quaternary ammonium compound benzalkonium chloride and its recommended chemical neutralizer in a high strength human faecal waste matrix.

BACKGROUND: Advances in forward and reverse genetic techniques have enabled the discovery and identification of several plant defence genes based on quantifiable disease phenotypes in mutant populations. Existing models for testing the effect of gene inactivation or genes causing these phenotypes do not take into account eventual uncertainty of these datasets and potential noise inherent in the biological experiment used, which may mask downstream analysis and limit the use of these datasets. Moreover, elucidating biological mechanisms driving the induced disease resistance and influencing these observable disease phenotypes has never been systematically tackled, eliciting the need for an efficient model to characterize completely the gene target under consideration.
RESULTS: We developed a post-gene silencing bioinformatics (post-GSB) protocol which accounts for potential biases related to the disease phenotype datasets in assessing the contribution of the gene target to the plant defence response. The post-GSB protocol uses Gene Ontology semantic similarity and pathway dataset to generate enriched process regulatory network based on the functional degeneracy of the plant proteome to help understand the induced plant defence response. We applied this protocol to investigate the effect of the NPR1 gene silencing to changes in Arabidopsis thaliana plants following Pseudomonas syringae pathovar tomato strain DC3000 infection. Results indicated that the presence of a functionally active NPR1 reduced the plant\'s susceptibility to the infection, with about 99% of variability in Pseudomonas spore growth between npr1 mutant and wild-type samples. Moreover, the post-GSB protocol has revealed the coordinate action of target-associated genes and pathways through an enriched process regulatory network, summarizing the potential target-based induced disease resistance mechanism.
CONCLUSIONS: This protocol can improve the characterization of the gene target and, potentially, elucidate induced defence response by more effectively utilizing available phenotype information and plant proteome functional knowledge.

Incredible progress has been made over the last 20 years in understanding the components and mechanisms governing plant innate immunity. The most important discoveries concern pathogen recognition mechanisms, which divide perception of conserved elicitors at the cell periphery, and recognition of variable elicitors within the host cytoplasm. The underlying mechanisms of immunity post elicitation are complex and poorly defined. This review highlights emergent themes in plant-microbe interactions with a particular focus on the plant immune responses against infection by the bacterium Pseudomonas syringae.

The AraC/XylS family of transcription factors, which include proteins that are involved in the regulation of diverse biological processes, has been of considerable interest recently and has been constantly expanding by means of in silico predictions and experimental analysis. In this work, using a HMM based on the DNA binding domain of 58 experimentally characterized proteins from the AraC/XylS (A/X), 1974 A/X proteins were found in 149 out of 212 bacterial genomes. This domain was used as a template to generate a phylogenetic tree and as a tool to predict the putative regulatory role of the new members of this family based on their proximity to a particular functional cluster in the tree. Based on this approach we assigned a functional regulatory role for 75% of the TFs dataset. Of these, 33.7% regulate genes involved in carbon-source catabolism, 9.6% global metabolism, 8.3% nitrogen metabolism, 2.9% adaptation responses, 8.9% stress responses, and 11.7% virulence. The abundance of TFs involved in the regulation of metabolic processes indicates that bacteria have optimized their regulatory systems to control energy uptake. In contrast, the lower percentage of TFs required for stress, adaptation and virulence regulation reflects the specialization acquired by each subset of TFs associated with those processes. This approach would be useful in assigning regulatory roles to uncharacterized members of other transcriptional factor families and it might facilitate their experimental analysis.

Localized infection of a plant can be mapped by a sequence of images capturing chlorophyll fluorescence transients in actinic light. Choice of the actinic light protocol co-determines fluorescence contrast between infected leaf segment and surrounding healthy tissue. Frequently, biology cannot predict with which irradiance protocol, in which fluorescence image of the sequence, and in which segment of the image there will be the highest contrast between the healthy and infected tissue. Here, we introduce a new technique that can be applied to identify the combination of chlorophyll fluorescence images yielding the highest contrast. The sets of the most contrasting images vary throughout the progress of the infection. Such specific image sets, stress-revealing fluorescence signatures, can be found for the initial and late phases of the infection. Using these signatures, images can be divided into segments that show tissue in different infection phases. We demonstrate the capacity of the algorithm in an investigation of infection of the model plant Arabidopsis thaliana by the bacterium Pseudomonas syringae. We show that the highest contrast is found with transients elicited by fluctuating, harmonically modulated irradiance with long periods.