Pathogenic bacteria interact not only with the host organism but most probably also with the resident microbial flora. In the knot disease of the olive tree (Olea europaea), the causative agent is the bacterium Pseudomonas savastanoi pv. savastanoi (Psv). Two bacterial species, namely Pantoea agglomerans and Erwinia toletana, which are not pathogenic and are olive plant epiphytes and endophytes, have been found very often to be associated with the olive knot. We identified the chemical signals that are produced by strains of the three species isolated from olive knot and found that they belong to the N-acyl-homoserine lactone family of QS signals. The luxI/R family genes responsible for the production and response to these signals in all three bacterial species have been identified and characterized. Genomic knockout mutagenesis and in planta experiments showed that virulence of Psv critically depends on QS; however, the lack of signal production can be complemented by wild-type E. toletana or P. agglomerans. It is also apparent that the disease caused by Psv is aggravated by the presence of the two other bacterial species. In this paper we discuss the potential role of QS in establishing a stable consortia leading to a poly-bacterial disease.

OnofriA, CarbonellEA, PiephoH‐P, MortimerAM & CousensRD (2010). Current statistical issues inWeed Research.Weed Research50, 5–24.Summary: The correct design of experimental studies, the selection of the appropriate statistical analysis of data and the efficient presentation of results are key to the good conduct and communication of science. The last Guidance for the use and presentation of statistics inWeed Researchwas published in 1988. Since then, there have been developments in both the scope of research covered by the journal and in the statistical techniques available. This paper addresses the changes in statistics and provides a reference work that will aid researchers in the design and analysis of their work. It will also provide guidance for editors and reviewers. The paper is organised into sections, which will aid the selection of relevant paragraphs, as we recognise that particular approaches require particular statistical analysis. It also uses examples, questions and checklists, so that non‐specialists can work towards the correct approach. Statistics can be complex, so knowing when to seek specialist advice is important. The structure and layout of this contribution should help weed scientists, but it cannot provide a comprehensive guide to every technique. Therefore, we provide references to further reading. We would like to reinforce the idea that statistical methods are not a set of recipes whose mindless application is required by convention; each experiment or study may involve subtleties that these guidelines cannot cover. Nevertheless, we anticipate that this paper will help weed scientists in their initial designs for research, in the analysis of data and in the presentation of results for publication.

Germination and emergence assays represent the most notable examples of time-to-event data in agriculture and related disciplines. In spite of the peculiar characteristics of this type of data, there has been little effort to establish a specific and comprehensive framework for their analyses. Indeed, a brief survey of the literature shows that germination and emergence data, along with other phenological measurements such as flowering time, have been analyzed through myriad approaches, giving rise to confusion and uncertainty among scientists and practitioners as to what may represent the best statistical practice. This lack of coherence in statistical approach may reduce the efficiency of research, while making the communication of results and the cross-study comparisons extremely challenging. Here, we attempt to provide a coherent framework and protocol for the analyses of germination/emergence and other time-to-event data in weed science and related disciplines, together with a software implementation in the form of a new R package. We propose a similar approach to biological assays in ecotoxicology, based on: (1) fitting a time-to-event model to describe the whole time course of events; (2) comparing time-to-event curves across experimental treatments, and (3) deriving further information from the fitted model to better focus on some traits of interest. The most appropriate methods to accomplish this procedure were carefully selected from the framework of survival analysis and related sources and were modified to comply with the specific needs of weed, seed, and plant sciences. Finally, they were implemented in the new R package drcte. In this article, we describe the procedure and its limitations by way of providing examples of several types of germination/emergence assays. We highlight that our proposed procedure can also serve as the first step of data analyses, with its output subsequently submitted to traditional or meta-analytic approaches.

The current study investigated the effects of Lactobacillus acidophilus and Bacillus subtilis, used as probiotics, on the microflora, morphology, and morphometry of the gut in organic laying hens. The birds (180 Hy-Line laying hens) were divided into 3 homogenous groups and received a pre-deposition diet from 16 to 20 wk of age and a deposition diet for the remaining 7 months of the experiment. The control group (CTR) was fed a corn-soybean cake-based diet, the second group (L) received the same diet supplemented with 0.1% of L. acidophilus while in the third group (B) the basal diet was supplemented with 0.05% of B. subtilis. At 18 wk of age (T1) and at 5 (T2) and 7 months (T3) from the beginning of deposition, 9 subjects per group were humanely killed for microbiological, morphological and morphometric analyses of the intestinal tract. The 2 probiotic-supplemented diets increased Lactobacillus spp. and Bifidobacterium spp. counts compared with the CTR diet. The lowest viable counts of E. coli, coliforms and staphylococci were observed in the L group (P < 0.001). Clostridium spp. decreased (P < 0.001) in both L and B subjects. The probiotic supplementation appeared to affect the intestinal microbial population, promoting the presence of beneficial bacteria such as Lactobacillus spp. and Bifidobacterium spp. and reducing potential harmful bacteria such as E. coli, clostridia and staphylococci. Morphological and morphometric analyses did not reveal substantial differences among groups. At T3, the plasma cell infiltrate in the villi of the CTR hens was more severe than that observed in the L and B groups (P = 0.009).