Building the Core Architecture of a Multiagent System Product Line: With an example from a future NASA Mission

The field of Software Product Lines (SPL) emphasizes building a core architecture for a family of software products from which concrete products can be derived rapidly. This helps to reduce time-to-market, costs, etc., and can result in improved software quality and safety. Current AOSE methodologies are concerned with developing a single Multiagent System. We propose an initial approach to developing the core architecture of a Multiagent Systems Product Line (MAS-PL), exemplifying our approach with reference to a concept NASA mission based on multiagent technology

A Model-Driven Architecture Approach for Modeling, Specifying and Deploying Policies in Autonomous and Autonomic Systems

Autonomic Computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems that hide complexity and conquer IT management costs. Effectively, AC may be viewed as Policy-Based Self-Management. The Model Driven Architecture (MDA) approach focuses on building models that can be transformed into code in an automatic manner. In this paper, we look at ways to implement Policy-Based Self-Management by means of models that can be converted to code using transformations that follow the MDA philosophy. We propose a set of UML-based models to specify autonomic and autonomous features along with the necessary procedures, based on modification and composition of models, to deploy a policy as an executing system

Ponatinib in patients with refractory acute myeloid leukaemia: findings from a phase 1 study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99099/1/bjh12382.pd

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Drug-resistant genotypes and multi-clonality in Plasmodium falciparum analysed by direct genome sequencing from peripheral blood of malaria patients.

Naturally acquired blood-stage infections of the malaria parasite Plasmodium falciparum typically harbour multiple haploid clones. The apparent number of clones observed in any single infection depends on the diversity of the polymorphic markers used for the analysis, and the relative abundance of rare clones, which frequently fail to be detected among PCR products derived from numerically dominant clones. However, minority clones are of clinical interest as they may harbour genes conferring drug resistance, leading to enhanced survival after treatment and the possibility of subsequent therapeutic failure. We deployed new generation sequencing to derive genome data for five non-propagated parasite isolates taken directly from 4 different patients treated for clinical malaria in a UK hospital. Analysis of depth of coverage and length of sequence intervals between paired reads identified both previously described and novel gene deletions and amplifications. Full-length sequence data was extracted for 6 loci considered to be under selection by antimalarial drugs, and both known and previously unknown amino acid substitutions were identified. Full mitochondrial genomes were extracted from the sequencing data for each isolate, and these are compared against a panel of polymorphic sites derived from published or unpublished but publicly available data. Finally, genome-wide analysis of clone multiplicity was performed, and the number of infecting parasite clones estimated for each isolate. Each patient harboured at least 3 clones of P. falciparum by this analysis, consistent with results obtained with conventional PCR analysis of polymorphic merozoite antigen loci. We conclude that genome sequencing of peripheral blood P. falciparum taken directly from malaria patients provides high quality data useful for drug resistance studies, genomic structural analyses and population genetics, and also robustly represents clonal multiplicity

Endothelial protein kinase MAP4K4 promotes vascular inflammation and atherosclerosis

Signalling pathways that control endothelial cell (EC) permeability, leukocyte adhesion and inflammation are pivotal for atherosclerosis initiation and progression. Here we demonstrate that the Sterile-20-like mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), which has been implicated in inflammation, is abundantly expressed in ECs and in atherosclerotic plaques from mice and humans. On the basis of endothelial-specific MAP4K4 gene silencing and gene ablation experiments in Apoe(-/-) mice, we show that MAP4K4 in ECs markedly promotes Western diet-induced aortic macrophage accumulation and atherosclerotic plaque development. Treatment of Apoe(-/-) and Ldlr(-/-) mice with a selective small-molecule MAP4K4 inhibitor also markedly reduces atherosclerotic lesion area. MAP4K4 silencing in cultured ECs attenuates cell surface adhesion molecule expression while reducing nuclear localization and activity of NFkappaB, which is critical for promoting EC activation and atherosclerosis. Taken together, these results reveal that MAP4K4 is a key signalling node that promotes immune cell recruitment in atherosclerosis

Octahedral molybdenum cluster as a photoactive antimicrobial additive to a fluoroplastic

Finding methods that fight bacterial infection or contamination, while minimising our reliance on antibiotics is one of the most pressing needs of this century. Although the utilisation of UV-C light and strong oxidising agents, such as bleach, are still efficacious methods for eliminating bacterial surface contamination, both methods present severe health and/or environmental hazards. Materials with intrinsic photodynamic activity (i.e. a material's ability upon photoexcitation to convert molecular oxygen into reactive oxygen species such as singlet oxygen), which work with light within the visible photomagnetic spectrum could offer a significantly safer alternative. Here we present a new, bespoke molybdenum cluster (Bu4N)2[Mo6I8(n-C7F15COO)6], which is both efficient in the generation of singlet oxygen upon photoirradiation and compatible with the fluoropolymer (F23-L) known for its good oxygen permeability. Thus, (Bu4N)2[Mo6I8(n-C7F15COO)6]/F23-L mixtures have been solution-processed to give homogenous films of smooth and fibrous morphologies and which displayed high photoinduced antibacterial activity against four common pathogens under visible light irradiation. These materials thus have potential in applications ranging from antibacterial coatings to filtration membranes and air conditioners to prevent spread of bacterial infections

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample