Insights into the multitrophic interactions between the biocontrol agent Bacillus subtilis MBI 600, the pathogen Botrytis cinerea and their plant host

Botrytis cinerea is a plant pathogen causing the gray mold disease in a plethora of host plants. The control of the disease is based mostly on chemical pesticides, which are responsible for environmental pollution, while they also pose risks for human health. Furthermore, B. cinerea resistant isolates have been identified against many fungicide groups, making the control of this disease challenging. The application of biocontrol agents can be a possible solution, but requires deep understanding of the molecular mechanisms in order to be effective. In this study, we investigated the multitrophic interactions between the biocontrol agent Bacillus subtilis MBI 600, a new commercialized biopesticide, the pathogen B. cinerea and their plant host. Our analysis showed that this biocontrol agent reduced B. cinerea mycelial growth in vitro, and was able to suppress the disease incidence on cucumber plants. Moreover, treatment with B. subtilis led to induction of genes involved in plant immunity. RNAseq analysis of B. cinerea transcriptome upon exposure to bacterial secretome, showed that genes coding for MFS and ABC transporters were highly induced. Deletion of the Bcmfs1 MFS transporter gene, using a CRISP/Cas9 editing method, affected its virulence and the tolerance of B. cinerea to bacterial secondary metabolites. These findings suggest that specific detoxification transporters are involved in these interactions, with crucial role in different aspects of B. cinerea physiology

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study

© 2018, The Author(s). Abstract: To determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients. Methods: Prospective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy. Results: 2493 patients, with median age 78–80 years (DK–Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7–8.6 vs. 5.8 95% CI 5.6–5.9) and Danish mean 7.1 (95% CI 6.6–7.5 vs. 5.5 95% CI 5.4–5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL’s accuracy was only modest for in-hospital death prediction in either setting. Conclusions: The modified CriSTAL tool (with CFS instead of Fried’s frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician’s confidence in initiating earlier end-of-life discussions

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

P2P, ad hoc and sensor networks – All the different or all the same?

Currently, data management technologies are in the process of finding their way into evolving networks, i.e. P2P, ad hoc and wireless sensor networks. We examine the properties, differences and commonalities of the different types of evolving networks, in order to enable the development of adequate technologies suiting their characteristics. We start with presenting definitions for the different network types, before arranging them in a network hierarchy, to gain a clear view of the area. Then, we analyze and compare the example applications for each of the types using different design dimensions. Based on this work, we finally present a comparison of P2P, ad hoc and wireless sensor networks

Fusarium equiseti as an Emerging Foliar Pathogen of Lettuce in Greece: Identification and Development of a Real-Time PCR for Quantification of Inoculum in Soil Samples

Lettuce is the most commonly cultivated leafy vegetable in Greece, available in the market throughout the year. In this study, an emerging foliar disease observed in commercial farms has been associated to the pathogen Fusarium equiseti, a member of the Fusarium incarnatum-equiseti species complex (FIESC). Thirty F. equiseti isolates obtained from symptomatic lettuce plants were identified on the basis of morphology and evaluated for their pathogenicity. The isolates were further characterized using amplification and sequence analysis of the internal transcribed region (ITS-rDNA), and of the translation elongation factor 1-alpha (TEF1-a), calmodulin (CAM), beta-tubulin (Bt), and small subunit (SSU) genes. Moreover, a novel RT-qPCR assay was developed, designing a primer pair and a probe based on the TEF1-a sequences. This assay showed high specificity, amplifying F. equiseti DNA samples, while no amplification product was observed from samples of other common soilborne fungi. The generated RT-qPCR assay could be a useful tool for the detection and quantification of F. equiseti in soil samples deriving from fields cultivated with lettuce and other leafy vegetables, hosts of this specific pathogen

Laminarin Induces Defense Responses and Efficiently Controls Olive Leaf Spot Disease in Olive

Olive leaf spot (OLS) caused by Fusicladiumoleagineum is mainly controlled using copper fungicides. However, the replacement of copper-based products with eco-friendly alternatives is a priority. The use of plant resistance-inducers (PRIs) or biological control agents (BCAs) could contribute in this direction. In this study we investigated the potential use of three PRIs (laminarin, acibenzolar-S-methyl, harpin) and a BCA (Bacillus amyloliquefaciens FZB24) for the management of OLS. The tested products provided control efficacy higher than 68%. In most cases, dual applications provided higher (p < 0.05) control efficacies compared to that achieved by single applications. The highest control efficacy of 100% was achieved by laminarin. Expression analysis of the selected genes by RT-qPCR revealed different kinetics of induction. In laminarin-treated plants, for most of the tested genes a higher induction rate (p < 0.05) was observed at 3 days post application. Pal, Lox, Cuao and Mpol were the genes with the higher inductions in laminarin-treated and artificially inoculated plants. The results of this study are expected to contribute towards a better understanding of PRIs in olive culture and the optimization of OLS control, while they provide evidence for potential contributions in the reduction of copper accumulation in the environment

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments