The phenazine pyocyanin is a terminal signalling factor in the quorum sensing network of Pseudomonas aeruginosa

Certain members of the fluorescent pseudomonads produce and secrete phenazines. These heterocyclic, redox-active compounds are toxic to competing organisms, and the cause of these antibiotic effects has been the focus of intense research efforts. It is largely unknown, however, how pseudomonads themselves respond to – and survive in the presence of – these compounds. Using Pseudomonas aeruginosa DNA microarrays and quantitative RT-PCR, we demonstrate that the phenazine pyocyanin elicits the upregulation of genes/operons that function in transport [such as the resistance-nodulation-cell division (RND) efflux pump MexGHI-OpmD] and possibly in redox control (such as PA2274, a putative flavin-dependant monooxygenase), and downregulates genes involved in ferric iron acquisition. Strikingly, mexGHI-opmD and PA2274 were previously shown to be regulated by the PA14 quorum sensing network that controls the production of virulence factors (including phenazines). Through mutational analysis, we show that pyocyanin is the physiological signal for the upregulation of these quorum sensing-controlled genes during stationary phase and that the response is mediated by the transcription factor SoxR. Our results implicate phenazines as signalling molecules in both P. aeruginosa PA14 and PAO1

Satellite detection of vegetative damage and alteration caused by pollutants emitted by a zinc smelter

The author has identified the following significant results. Field observations and data collected by low flying aircraft were used to verify the accuracy of maps produced from the satellite data. Although areas of vegetation as small as six acres can accurately be detected, a white pine stand that was severely damaged by sulfur dioxide could not be differentiated from a healthy white pine stand because spectral differences were not large enough. When winter data were used to eliminate interference from herbaceous and deciduous vegetation, the damage was still undetectable. The analysis was able to produce a character map that accurately delineated areas of vegetative alteration due to high zinc levels accumulating in the soil. The map depicted a distinct gradient of less damage and alteration as the distance from the smelter increased. Although the satellite data will probably not be useful for detecting small acreages of damaged vegetation, it is concluded that the data may be very useful as an inventory tool to detect and delineate large vegetative areas possessing differing spectral signatures

Cellular automata and Lyapunov exponents

In this article we give a new definition of some analog of Lyapunov exponents for cellular automata . Then for a shift ergodic and cellular automaton invariant probability measure we establish an inequality between the entropy of the automaton, the entropy of the shift and the Lyapunov exponent

Some triviality results for quasi-Einstein manifolds and Einstein warped products

In this paper we prove a number of triviality results for Einstein warped products and quasi-Einstein manifolds using different techniques and under assumptions of various nature. In particular we obtain and exploit gradient estimates for solutions of weighted Poisson-type equations and adaptations to the weighted setting of some Liouville-type theorems.Comment: 15 pages, fixed minor mistakes in Section

Differences in views of experts about their role in particulate matter policy advice: Empirical evidence from an international expert consultation

There is ample scientific evidence of adverse health effects of air pollution at exposure levels that are common among the general population. Some points of uncertainty remain, however. Several theories exist regarding the various roles that experts may play when they offer policy advice on uncertain issues such as particulate matter (PM). Roles may vary according to e.g. the views of the expert on the science-policy interface or the extent to which she/he involves stakeholders. Empirical underpinning of these theories, however, does not exist. We therefore conducted a consultation with experts on the following research question: What are PM experts’ views on their roles when providing policy advice? Q methodology was used to empirically test theoretical notions concerning the existence of differences in views on expert roles. Experts were selected based on a structured nominee process. In total, 31 international PM experts participated. Responses were examined via Principal Component Analysis, and for the open-ended questions, we used Atlas.ti software. Four different expert roles were identified among the participating experts. Main differences were found with respect to views on the need for precautionary measures and on the experts positioning within the science-policy interface. There was consensus on certain issues such as the need for transparency, general disagreement with current policies and general agreement on key scientific issues. This empirical study shows that while most PM experts consider their views on the risks of PM to be in line with those of their colleagues, four distinct expert roles were observed. This provides support for thus far largely theoretical debates on the existence of different roles of experts when they provide policy advice

European expectations of disaster information provided by critical infrastructure operators

Previous research into social media crisis communication has tended to focus on use by emergency managers rather than other key stakeholder, critical infrastructure (CI) operators. This article adds to the field by empirically investigating public expectations of information provided by CI operators during crisis situations and if CI operators currently meet such expectations. It draws on key themes that emerged from a review of the literature on public expectations of disaster related information shared via social media. Then, it presents the results of an online questionnaire and interview-based study of disaster-vulnerable communities in France, Norway, Portugal and Sweden. Results indicate that members of the public expect CI operators to provide disaster related information via traditional and social media, but not necessarily respond to their queries on social media. Operators appear to meet public expectations of traditional media use, but should expand their current practices to include digital media. Recommendations for CI operators on how to do use social media follow

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net

Single-machine scheduling with stepwise tardiness costs and release times

We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems