Bioengineering bacterial outer membrane vesicles as delivery system for RNA therapeutics targeted to lung epithelial cytosols

Intact epithelia lining the airways and alveoli in the lung are essential to maintain lung function. Structural or functional damage of epithelial cells leads in severe diseases, including COPD/emphysema, ibrosis or ALI/ARDS. This central role of epithelia in pulmonary diseases identifies these cells as primary candidates for targeted therapy. With the exception of surface-expressed molecules, however, targeting intracellular components is severely restricted due to poor delivery. We aim to overcome this obstacle using topically administered, bioengineered, biocompatible bacterial outer membrane vesicles (OMVs) as recombinant drug delivery systems for novel biopharmaceuticals. Engineering recombinant surface expression of eukaryotic receptor ligands in ClearColi®, a commercial E.coli BL21 (DE3) strain deficient in lipopolysaccharide production, we have used red fluorescent protein reporters to track OMV loading, transgene expression, and eukaryotic cell trafficking. We demonstrate statistically significant differences in the levels of over 700 proteins between differentially engineered and purified OMV preps with additional differences in transcriptome and lipidome consistency. We also characterised visual and particle size differences observed by transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). Here we report early bioadhesion and culture of re-differentiated lung epithelia. This project aims to bridge the biotechnological gap in the intracellular biopharmaceutics drug delivery challenge for respiratory epithelia through highly controlled, and scalable bio-nanotechnology process. If successful, our work will unlock intracellular imaging and therapeutics research for respiratory diseases with a significant epithelial component, paving the way for other targeting ligands and potentially non-respiratory indications. cellular uptake results in A549 culture as well as air-liquid interface

Improving proactive decision making with object trend displays

Operators of dynamic systems often use time-series data to support their diagnostic and proactive decision-making. Those data have traditionally been displayed in the form of separate trend charts, for example, line graphs of pressure and temperature over time. Configural object displays are a widely advocated approach to the visual integration of information yet have been applied only rarely to time-series data. One example was the 'time tunnel' format but its benefits were equivocal, seemingly compromised by its graphical complexity. There is then the need to investigate other graphical forms for object displays of time series data. This research will require a microworld representing a knowledge-rich task domain accessible to multiple participants (the nuclear power plant simulation used with the time tunnel display studies required participants to have 20 hours of experience with the system). We report a design for such a microworld that adopts the domain of financial control of a business where decisions need to be made about the pricing of products to optimize returns in a changing and sometimes volatile market. Alternative visual displays of the essential time series data for this domain are possible and whilst decision making is knowledge rich, involving reasoning about high level relationships, pilot tests showed that it is accessible to participants with only moderate training

“Innocent” Hashtags? A Cautionary Tale:#IStandWithGreece as a Network of Intolerance on Twitter During a Land Border Crisis

This study explores the hashtag #IStandWithGreece as part of the meaning-making processes on Twitter around a border crisis at Europe’s periphery, the Greek–Turkish land border. Adopting a network perspective, we located the most influential Twitter accounts using #IStandWithGreece, the communities they formed in the microblog, and other hashtags used to communicate their views in relation to the unfolding events. This allowed the scanning of the broader ideological character of these influencers and their respective communities and, by extent, that of the debate around the hashtag. The study exposes the strategic use of a seemingly innocent hashtag by certain influential actors to disseminate antimigrant stances that cut across national contexts of the Global North

Treatment of aortic stenosis with a self-expanding transcatheter valve: the International Multi-centre ADVANCE Study

Aim Transcatheter aortic valve implantation has become an alternative to surgery in higher risk patients with symptomatic aortic stenosis. The aim of the ADVANCE study was to evaluate outcomes following implantation of a self-expanding transcatheter aortic valve system in a fully monitored, multi-centre ‘real-world' patient population in highly experienced centres. Methods and results Patients with severe aortic stenosis at a higher surgical risk in whom implantation of the CoreValve System was decided by the Heart Team were included. Endpoints were a composite of major adverse cardiovascular and cerebrovascular events (MACCE; all-cause mortality, myocardial infarction, stroke, or reintervention) and mortality at 30 days and 1 year. Endpoint-related events were independently adjudicated based on Valve Academic Research Consortium definitions. A total of 1015 patients [mean logistic EuroSCORE 19.4 ± 12.3% [median (Q1,Q3), 16.0% (10.3, 25.3%)], age 81 ± 6 years] were enrolled. Implantation of the CoreValve System led to a significant improvement in haemodynamics and an increase in the effective aortic valve orifice area. At 30 days, the MACCE rate was 8.0% (95% CI: 6.3-9.7%), all-cause mortality was 4.5% (3.2-5.8%), cardiovascular mortality was 3.4% (2.3-4.6%), and the rate of stroke was 3.0% (2.0-4.1%). The life-threatening or disabling bleeding rate was 4.0% (2.8-6.3%). The 12-month rates of MACCE, all-cause mortality, cardiovascular mortality, and stroke were 21.2% (18.4-24.1%), 17.9% (15.2-20.5%), 11.7% (9.4-14.1%), and 4.5% (2.9-6.1%), respectively. The 12-month rates of all-cause mortality were 11.1, 16.5, and 23.6% among patients with a logistic EuroSCORE ≤10%, EuroSCORE 10-20%, and EuroSCORE >20% (P< 0.05), respectively. Conclusion The ADVANCE study demonstrates the safety and effectiveness of the CoreValve System with low mortality and stroke rates in higher risk real-world patients with severe aortic stenosi

Improving Performance of P2P Networks Through Semantic Topological Adaption.

In today's world a tremendous amount of information is produced daily. The plethora of data represents a challenge in terms of how to manage, represent and share it, in a meaningful and effective way. This increasing size of on-line information and its weak structuring, suggests the need for principles to make information accessible through well-defined operations on well-defined data. The mixture of P2P and semantic technologies could harvest the advantages of both mechanisms and address the challenges of on-line information management and sharing. P2P networks combined with semantic technologies promise to achieve distributed management and exchange of information in an efficient manner. On the one hand, P2P computing offers a more effective alternative to existing client-server applications by providing a radical way of decentralised information management and sharing. On the other hand, semantic-oriented technologies present new approaches in solving the problems of information complexity by adding meaning and structure to data, in order to facilitate better information management, indexing and retrieval. This thesis proposes a system for distributed information management and sharing in unstructured P2P networks through the utilisation of semantic information, extracted from the network resources. Its aim is to facilitate an efficient way of information exchange while keeping low messaging cost and normal load balancing among the peers of the network. In particular, the proposed architecture follows a two-layer approach where the upper layer forms the semantic knowledge of the network through super-peers, and the lower layer of peers represents the network resources. The network knowledge is formally represented by a domain specific ontology using collective intelligence techniques to extract knowledge from the available resources. This semantic-driven approach along with dynamic topological adaptations is facilitated by two key components: the Semantic-Driven Architecture (SDA) and Dynamic Adaptive Topology (DAT). SDA aims to improve query efficiency and achieves this by mapping the network ontology to the overlay topology creating in this way a semantic-driven architecture. During the resource discovery process the query is intelligently routed in the semantic layer via ontology supported decisions and attaining in this way better query success and reduced network traffic. DAT introduces dynamic topological optimisation on top of the SDA component having as objectives scalability and fault tolerance. Since DAT component is based on SDA model, topological adaptations follow a semantic-driven approach for retaining the successful architecture of SDA. Scalability is achieved through load balancing mechanisms, where overloaded or under-loaded super-peers are optimised accordingly, and the overall semantic image of the network is retained up to date, through reconceptualisation procedures. A number of experiments were carried out and showed that the proposed model demonstrates high success rate and low traffic, and therefore outperforms two existing popular P2P paradigms used as benchmarks, Social P2P and Traditional super-peer architecture. In particular, SDA and DAT demonstrate a success rate in the range of 80-90% as opposed to Traditional and Social super-peer based P2P which achieve 65% and 55% success rate correspondingly. The experimental results have confirmed both the soundness of the design of the SDA model, since the ontology-topology mapping with combination to query-ontology mapping, consequently lead to the successful query-topology mapping; and have also verified that a dynamic adaptive model can achieve load balancing and scalability through semantic topological optimisations

Improving Performance of P2P Networks Through Semantic Topological Adaption.

In today's world a tremendous amount of information is produced daily. The plethora of data represents a challenge in terms of how to manage, represent and share it, in a meaningful and effective way. This increasing size of on-line information and its weak structuring, suggests the need for principles to make information accessible through well-defined operations on well-defined data. The mixture of P2P and semantic technologies could harvest the advantages of both mechanisms and address the challenges of on-line information management and sharing. P2P networks combined with semantic technologies promise to achieve distributed management and exchange of information in an efficient manner. On the one hand, P2P computing offers a more effective alternative to existing client-server applications by providing a radical way of decentralised information management and sharing. On the other hand, semantic-oriented technologies present new approaches in solving the problems of information complexity by adding meaning and structure to data, in order to facilitate better information management, indexing and retrieval. This thesis proposes a system for distributed information management and sharing in unstructured P2P networks through the utilisation of semantic information, extracted from the network resources. Its aim is to facilitate an efficient way of information exchange while keeping low messaging cost and normal load balancing among the peers of the network. In particular, the proposed architecture follows a two-layer approach where the upper layer forms the semantic knowledge of the network through super-peers, and the lower layer of peers represents the network resources. The network knowledge is formally represented by a domain specific ontology using collective intelligence techniques to extract knowledge from the available resources. This semantic-driven approach along with dynamic topological adaptations is facilitated by two key components: the Semantic-Driven Architecture (SDA) and Dynamic Adaptive Topology (DAT). SDA aims to improve query efficiency and achieves this by mapping the network ontology to the overlay topology creating in this way a semantic-driven architecture. During the resource discovery process the query is intelligently routed in the semantic layer via ontology supported decisions and attaining in this way better query success and reduced network traffic. DAT introduces dynamic topological optimisation on top of the SDA component having as objectives scalability and fault tolerance. Since DAT component is based on SDA model, topological adaptations follow a semantic-driven approach for retaining the successful architecture of SDA. Scalability is achieved through load balancing mechanisms, where overloaded or under-loaded super-peers are optimised accordingly, and the overall semantic image of the network is retained up to date, through reconceptualisation procedures. A number of experiments were carried out and showed that the proposed model demonstrates high success rate and low traffic, and therefore outperforms two existing popular P2P paradigms used as benchmarks, Social P2P and Traditional super-peer architecture. In particular, SDA and DAT demonstrate a success rate in the range of 80-90% as opposed to Traditional and Social super-peer based P2P which achieve 65% and 55% success rate correspondingly. The experimental results have confirmed both the soundness of the design of the SDA model, since the ontology-topology mapping with combination to query-ontology mapping, consequently lead to the successful query-topology mapping; and have also verified that a dynamic adaptive model can achieve load balancing and scalability through semantic topological optimisations

For Girls Made of Fire

Poem by Eleni Eftychiou as part of Call for Conversations: Education in the Era of Trump