Phytochemicals as Biopesticides against the Pinewood Nematode Bursaphelenchus xylophilus: A Review on Essential Oils and Their Volatiles

The impacts of a rapidly changing environment together with the growth in global trade activities has promoted new plant pest pandemic events in forest ecosystems. The pinewood nematode (PWN), Bursaphelenchus xylophilus, causes strong worldwide economic and ecological impacts. Direct control is performed through trunk injection of powerful nematicides, however many of these (hemi)synthetic compounds have raised ecological and human health concerns for affecting non-target species and accumulating in food products. As sustainable alternatives, essential oils (EOs) have shown very promising results. In this work, available literature on the direct activity of EOs against PWN is reviewed, as a contribution to advance the search for safer and greener biopesticides to be used in sustainable PWD pest management strategies. For the first time, important parameters concerning the bioassays performed, the PWNs bioassayed, and the EOs used are summarized and comparatively analyzed. Ultimately, an overview of the chemical composition of the most active EOs allowed to uncover preliminary guidelines for anti-PWN EO efficiency. The analysis of important information on the volatile phytochemicals composing nematicidal EOs provides a solid basis to engineer sustainable biopesticides capable of controlling the PWN under an integrated pest management framework and contributes to improved forest health

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

Cutaneous streptococcal abscess treated by photodynamic therapy

Background: Photodynamic therapy has been investigated in different areas of health through experimental conditions. Its action can alter fundamental structures for the survival of microorganisms without any development of microbial resistance.Materials and Methods: Young sheep presenting with abscess in the left forelimb caused by Streptococcus spp. was previously treated with antibiotics. There was no clinical improvement with the treatments, and the bacteria presented sensitivity in vitro. Therefore, Photodynamic therapy associating methylene blue and red laser (660 nm) was used to treat the abscess.Results: After a day of treatment, complete healing was witnessed with no recurrence was observed during the 3-month follow-up period.Conclusion: The scientific results of the antimicrobial effect of PDT proved to be a therapeutic option with great potential for clinical application.Keywords: Photoinactivation, Laser, Sheep, Streptococcus spp

Unravelling migratory connectivity in marine turtles using multiple methods

Comprehensive knowledge of the fundamental spatial ecology of marine species is critical to allow the identification of key habitats and the likely sources of anthropogenic threats, thus informing effective conservation strategies. 2. Research on migratory marine vertebrates has lagged behind many similar terrestrial animal groups, but studies using electronic tagging systems and molecular techniques offer great insights. 3. Marine turtles have complex life history patterns, spanning wide spatio-temporal scales. As a result of this multidimensional complexity, and despite extensive effort, there are no populations for which a truly holistic understanding of the spatial aspects of the life history has been attained. There is a particular lack of information regarding the distribution and habitats utilized during the first few years of life. 4. We used satellite tracking technology to track individual turtles following nesting at the green turtle Chelonia mydas nesting colony at Poilão Island, Guinea Bissau; the largest breeding aggregation in the eastern Atlantic. 5. We further contextualize these data with pan-Atlantic molecular data and oceanographic current modelling to gain insights into likely dispersal patterns of hatchlings and small pelagic juveniles. 6. All adult turtles remained in the waters of West Africa, with strong connectivity demonstrated with Banc D’Arguin, Mauritania. 7. Despite shortcomings in current molecular markers, we demonstrate evidence for profound sub-structuring of marine turtle stocks across the Atlantic; with a high likelihood based on oceanographic modelling that most turtles from Guinea-Bissau are found in the eastern Atlantic. 8. Synthesis and applications. There is an increased need for a better understanding of spatial distribution of marine vertebrates demonstrating life histories with spatio-temporal complexity. We propose the synergistic use of the technologies and modelling used here as a working framework for the future rapid elucidation of the range and likely key habitats used by the different life stages from such species

Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach

This is the final version. Available from Cambridge University Press via the DOI in this record. Objective: To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion centrality as a new predictive measure of CDI. Design: Retrospective cohort study. Methods: A mobility network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Results: Closeness centrality was a statistically significant measure associated with unit susceptibility (P< .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion centrality measure was statistically significant (P< .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems Conclusions: Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.University of Rochester Clinical and Translational Science InstituteNational Institutes of HealthBurroughs Wellcome Fund Institutional Program Unifying Population and Laboratory Based Science