Event-based awareness services for P2P groupware systems

P2P systems enable decentralised applications for supporting collaborating groups and communities, where the collaboration may involve both sharing of data and sharing of group processes among group members. In such applications, monitoring and awareness are critical functionalities required for an effective collaboration. However, to date there has been little research into providing generic, application-independent awareness in P2P groupware systems. We present a distributed event-based awareness approach for such systems that provides different forms of awareness through a set of interoperating, low-level awareness services. The user and technical requirements for the approach are motivated with reference to Project-Based Learning in a P2P environment. We describe the implementation of a superpeer P2P network on a Cloud platform and the provision of reliable awareness services (AaaS - Awareness as a Service) from the Cloud. We report on the outcomes of an empirical evaluation of the performance and scalability of the approach

An enzymatic Finkelstein reaction : fluorinase catalyses direct halogen exchange

We thank the Engineering and Physical Sciences Research Council, UK, for a research grant.The fluorinase enzyme from Streptomyces cattleya is shown to catalyse a direct displacement of bromide and iodide by fluoride ion from 5′-bromodeoxyadenosine (5′-BrDA) and 5′-iododeoxyadenosine (5′-IDA) respectively to form 5′-fluorodeoxyadenosine (5′-FDA) in the absence of L-methionine (L-Met) or S-adenosyl-L-methionine (SAM). 5′-BrDA is the most efficient substrate for this enzyme catalysed Finkelstein reaction.PostprintPeer reviewe

Efficient biotransformations in Cunninghamella elegans and Streptomyces sp. JCM9888 of selectively fluorinated benzoic acids to the corresponding benzamides and benzyl alcohols

Support was received from the Commonwealth Scholarship Council for a Split Site Studentship (OO) and also from the Royal Society of Chemistry for a travel Grant (C.I).An efficient conversion of ortho, meta and para fluoro- and trifluoromethyl-substituted benzoic acids to the corresponding benzamides in fermentations of the soil bacterium Streptomyces sp. JCM9888 is described. We also report the efficient reduction of the same class of substrates to the corresponding benzyl alcohols with the fungi Cunninghamella elegans. These biotransformations were surprisingly efficient and may have value as disruptive technologies in process chemistry.Publisher PDFPeer reviewe

Evaluation of elicitation methods to quantify Bayes linear models

The Bayes linear methodology allows decision makers to express their subjective beliefs and adjust these beliefs as observations are made. It is similar in spirit to probabilistic Bayesian approaches, but differs as it uses expectation as its primitive. While substantial work has been carried out in Bayes linear analysis, both in terms of theory development and application, there is little published material on the elicitation of structured expert judgement to quantify models. This paper investigates different methods that could be used by analysts when creating an elicitation process. The theoretical underpinnings of the elicitation methods developed are explored and an evaluation of their use is presented. This work was motivated by, and is a precursor to, an industrial application of Bayes linear modelling of the reliability of defence systems. An illustrative example demonstrates how the methods can be used in practice

Enzymatic radiosynthesis of a 18F-Glu-Ureido-Lys ligand for the prostate-specific membrane antigen (PSMA)

We thank the Engineering and Physical Sciences Research Council, UK, for a research grant (EP/M01262X/1).Peer reviewedPostprin

Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs. 1

Lipid-based nanoparticles for delivery of vaccine adjuvants and antigens : toward multicomponent vaccines

Despite the many advances that have occurred in the field of vaccine adjuvants, there are still unmet needs that may enable the development of vaccines suitable for more challenging pathogens (e.g., HIV and tuberculosis) and for cancer vaccines. Liposomes have already been shown to be highly effective as adjuvant/delivery systems due to their versatility and likely will find further uses in this space. The broad potential of lipid-based delivery systems is highlighted by the recent approval of COVID-19 vaccines comprising lipid nanoparticles with encapsulated mRNA. This review provides an overview of the different approaches that can be evaluated for the design of lipid-based vaccine adjuvant/delivery systems for protein, carbohydrate, and nucleic acid-based antigens and how these strategies might be combined to develop multicomponent vaccines

Investigating the impact of delivery system design on the efficacy of self-amplifying RNA vaccines

Messenger RNA (mRNA)-based vaccines combine the positive attributes of both live-attenuated and subunit vaccines. In order for these to be applied for clinical use, they require to be formulated with delivery systems. However, there are limited in vivo studies which compare different delivery platforms. Therefore, we have compared four different cationic platforms: (1) liposomes, (2) solid lipid nanoparticles (SLNs), (3) polymeric nanoparticles (NPs) and (4) emulsions, to deliver a self-amplifying mRNA (SAM) vaccine. All formulations contained either the non-ionizable cationic lipid 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) or dimethyldioctadecylammonium bromide (DDA) and they were characterized in terms of physico-chemical attributes, in vitro transfection efficiency and in vivo vaccine potency. Our results showed that SAM encapsulating DOTAP polymeric nanoparticles, DOTAP liposomes and DDA liposomes induced the highest antigen expression in vitro and, from these, DOTAP polymeric nanoparticles were the most potent in triggering humoral and cellular immunity among candidates in vivo