Vascular immune-inflammatory responses play a crucial role in the progression and outcome of atherosclerosis. The ability to assess localized inflammation through detection of specific vascular inflammatory biomarkers would significantly improve cardiovascular risk assessment and management; however, no multi-parameter molecular imaging technologies have been established to date. Here, we report the targeted in vivo imaging of multiple vascular biomarkers using antibody-functionalized nanoparticles and surface-enhanced Raman scattering (SERS).Methods: A series of antibody-functionalized gold nanoprobes (BFNP) were designed containing unique Raman signals in order to detect intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1) and P-selectin using SERS.Results: SERS and BFNP were utilized to detect, discriminate and quantify ICAM-1, VCAM-1 and P-selectin in vitro on human endothelial cells and ex vivo in human coronary arteries. Ultimately, non-invasive multiplex imaging of adhesion molecules in a humanized mouse model was demonstrated in vivo following intravenous injection of the nanoprobes.Conclusion: This study demonstrates that multiplexed SERS-based molecular imaging can indicate the status of vascular inflammation in vivo and gives promise for SERS as a clinical imaging technique for cardiovascular disease in the future.

A prevailing viewpoint in palaeoclimate science is that a single palaeoclimate record contains insufficient information to discriminate between most competing explanatory models. Results we present here suggest the contrary. Using SMC 2 combined with novel Brownian bridge type proposals for the state trajectories, we show that even with relatively short time series it is possible to estimate Bayes factors to sufficient accuracy to be able to select between competing models. The results show that Monte Carlo methodology and computer power have now advanced to the point where a full Bayesian analysis for a wide class of conceptual climate models is now possible. The results also highlight a problem with estimating the chronology of the climate record prior to further statistical analysis, a practice which is common in palaeoclimate science. Using two datasets based on the same record but with different estimated chronologies results in conflicting conclusions about the importance of the orbital forcing on the glacial cycle, and about the internal dynamics generating the glacial cycle, even though the difference between the two estimated chronologies is consistent with dating uncertainty. This highlights a need for chronology estimation and other inferential questions to be addressed in a joint statistical procedure. * Email: Jake.Carson@warwick.ac.uk 1

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In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.