Imaging intracellular and systemic in vivo gold nanoparticles to enhance radiotherapy

Nanoparticles offer alternative options in cancer therapy both as drug delivery carriers and as direct therapeutic agents for cancer cell inactivation. More recently, gold nanoparticles (AuNPs) have emerged as promising radiosensitizers achieving significantly elevated radiation dose enhancement factors when irradiated with both kilo-electron-volt and mega-electronvolt X-rays. Use of AuNPs in radiobiology is now being intensely driven by the desire to achieve precise energy deposition in tumours. As a consequence, there is a growing demand for efficient and simple techniques for detection, imaging and characterization of AuNPs in both biological and tumour samples. Spatially accurate imaging on the nanoscale poses a serious challenge requiring high- or super-resolution imaging techniques. In this mini review, we discuss the challenges in using AuNPs as radiosensitizers as well as various current and novel imaging techniques designed to validate the uptake, distribution and localization in mammalian cells. In our own work, we have used multiphoton excited plasmon resonance imaging to map the AuNP intracellular distribution. The benefits and limitations of this approach will also be discussed in some detail. In some cases, the same "excitation" mechanism as is used in an imaging modality can be harnessed tomake it also a part of therapymodality (e.g. phototherapy)-such examples are discussed in passing as extensions to the imaging modality concerned.

In situ, high-resolution imaging of labile phosphorus in sediments of a large eutrophic lake

Understanding the labile status of phosphorus (P) in sediments is crucial for managing a eutrophic lake, but it is hindered by lacking in situ data particularly on a catchment scale. In this study, we for the first time characterized in situ labile P in sediments with the Zr-oxide diffusive gradients in thin films (Zr-oxide DGT) technique at a two-dimensional (2D), submillimeter resolution in a large eutrophic lake (Lake Taihu, China, with an area of 2338km²). The concentration of DGT-labile P in the sediment profiles showed strong variation mostly ranging from 0.01 to 0.35mgL^-1 with a considerable number of hotspots. The horizontal heterogeneity index of labile P varied from 0.04 to 4.5. High values appeared at the depths of 0-30mm, likely reflecting an active layer of labile P under the sediment-water interface (SWI). Concentration gradients of labile P were observed from the high-resolution 1D DGT profiles in both the sediment and overlying water layers close to the SWI. The apparent diffusion flux of P across the SWI was calculated between -21 and 65ngcm^-2d^-1, which showed that the sediments tended to be a source and sink of overlying water P in the algal- and macrophyte-dominated regions, respectively. The DGT-labile P in the 0-30mm active layer showed a better correlation with overlying water P than the labile P measured by ex situ chemical extraction methods. It implies that in situ, high-resolution profiling of labile P with DGT is a more reliable approach and will significantly extend our ability in in situ monitoring of the labile status of P in sediments in the field.

ROSA: A High-cadence, Synchronized Multi-camera Solar Imaging System

The Rapid Oscillations in the Solar Atmosphere (ROSA) instrument is a synchronized, six-camera high-cadence solar imaging instrument developed by Queen's University Belfast and recently commissioned at the Dunn Solar Telescope at the National Solar Observatory in Sunspot, New Mexico, USA, as a common-user instrument. Consisting of six 1k x 1k Peltier-cooled frame-transfer CCD cameras with very low noise (0.02 - 15 e/pixel/s), each ROSA camera is capable of full-chip readout speeds in excess of 30 Hz, and up to 200 Hz when the CCD is windowed. ROSA will allow for multi-wavelength studies of the solar atmosphere at a high temporal resolution. We will present the current instrument set-up and parameters, observing modes, and future plans, including a new high QE camera allowing 15 Hz for Halpha. Interested parties should see https://habu.pst.qub.ac.uk/groups/arcresearch/wiki/de502/ROSA.html

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Markievicz Commission Report: Gender equality document

Report on implementation of the candidate gender quota in the Fianna Fail Party.

Modulating mythology in a post-traumatic era: Murals and re-imaging in Northern Ireland.

Arguably the most ancient of the social media, wall paintings have been a persistent vehicle of cultural meaning management. The dynamics of myth markets are reflected in the sectarian murals of Northern Ireland. In this paper, we draw from consumer research literature on mythology and street art to explore the continuous revision of these wallscapes that seeks to address the enduring contradictions of civic ideology in contested political space. In particular, we focus on the use of classical, historical and pop-cultural mythologies to transform private space into public place. We examine the decommissioning of murals occurring in the wake of the Peace Accords, and speculate on the implications of the creation of a shared mythology for the future of mural painting and the state.

Resonantly loaded apertures for high-resolution near field surface imaging

A novel type of microwave probes based on the loaded aperture geometry has been proposed and experimentally evaluated for dielectrics characterisation and high-resolution near-field imaging. Experimental results demonstrate the possibility of very accurate microwave spectroscopic characterisation of thin lossy dielectric samples and biological materials containing water. High-resolution images of the subwavelength lossy dielectric strips and wet and dry leaves have been obtained with amplitude contrast around 10-20 dB and spatial resolution better than one-tenth of a wavelength in the near-field zone. A microwave imaging scenario for the early-stage skin cancer identification based on the artificial dielectric model has also been explored. This model study shows that the typical resolution of an artificial malignant tumour with a characteristic size of one-tenth of a wavelength can be discriminated with at least 6 dB amplitude and 50° phase contrast from the artificial healthy skin and with more than 3 dB contrast from a benign lesion of the same size. It has also been demonstrated that the proposed device can efficiently deliver microwave energy to very small, subwavelength, focal areas which is highly sought in the microwave hyperthermia applications.

Nanohole arrays enable multiple-point-source imaging

A new type of focal-palne array made of a nanoscale metal screen mimics the function of a lens, focuses light (and plasmons) into subwavelength hot spots, and achieves high-resolution imaging of complex sources.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.