Thermodynamically Stable One-Component Metallic Quasicrystals

Classical density-functional theory is employed to study finite-temperature trends in the relative stabilities of one-component quasicrystals interacting via effective metallic pair potentials derived from pseudopotential theory. Comparing the free energies of several periodic crystals and rational approximant models of quasicrystals over a range of pseudopotential parameters, thermodynamically stable quasicrystals are predicted for parameters approaching the limits of mechanical stability of the crystalline structures. The results support and significantly extend conclusions of previous ground-state lattice-sum studies.Comment: REVTeX, 13 pages + 2 figures, to appear, Europhys. Let

A five year outbreak of methicillin-susceptible Staphylococcus aureus phage type 53,85 in a regional neonatal unit

We identified a 5-year outbreak of a methicillin-susceptible Staphylococcus aureus (MSSA) strain, affecting 202 babies on a neonatal unit, by routine weekly phage typing all S. aureus isolates. Multiple staged control measures including strict emphasis on hand hygiene, environmental and staff surveillance sampling, and application of topical hexachlorophane powder failed to end the outbreak. S. aureus PT 53,85 (SA5385) was found on opened packs of Stomahesive®, used as a neonatal skin protectant. Only following the implementation of aseptic handling of Stomahesive®, and the use of topical mupirocin for staff nasal carriers of SA5385, and for babies colonized or infected with S. aureus, did the isolation rate of SA5385 decline. DNA fingerprinting indicated that [gt-or-equal, slanted]95% of SA5385 isolates were clonal. In vitro death rates of SA5385 on Stomahesive® with human serum were significantly lower than on Stomahesive® alone (P = 0·04), and on cotton sheet with serum (P = 0·04), highlighting the potential of this material as a survival niche. Phage typing remains a valuable, inexpensive and simple method for monitoring nosocomial MSSA infection

Charge Renormalization, Effective Interactions, and Thermodynamics of Deionized Colloidal Suspensions

Thermodynamic properties of charge-stabilised colloidal suspensions depend sensitively on the effective charge of the macroions, which can be substantially lower than the bare charge in the case of strong counterion-macroion association. A theory of charge renormalization is proposed, combining an effective one-component model of charged colloids with a thermal criterion for distinguishing between free and associated counterions. The theory predicts, with minimal computational effort, osmotic pressures of deionized suspensions of highly charged colloids in close agreement with large-scale simulations of the primitive model.Comment: 15 pages, 7 figure

Stability of Colloidal Quasicrystals

Freezing of charge-stabilized colloidal suspensions and relative stabilities of crystals and quasicrystals are studied using thermodynamic perturbation theory. Macroion interactions are modelled by effective pair potentials combining electrostatic repulsion with polymer-depletion or van der Waals attraction. Comparing free energies -- counterion terms included -- for elementary crystals and rational approximants to icosahedral quasicrystals, parameters are identified for which one-component quasicrystals are stabilized by a compromise between packing entropy and cohesive energy.Comment: 6 pages, 4 figure

Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses

Diseases are often umbrella terms for many subcategories of disease. The identification of these subcategories is vital if we are to develop personalised treatments that are better focussed on individual patients. In this short paper, we explore the use of a combination of unsupervised learning to identify potential subclasses, and supervised learning to build models for better predicting a number of different health outcomes for patients that suffer from systemic sclerosis, a rare chronic connective tissue disorder - but one that shares many characteristics with other diseases. We explore a number of different algorithms for constructing models that simultaneously predict health outcomes and identify subcategories

MyWallMate: An Investigation into the Use of Mobile Technology in Enhancing Student Engagement

Student engagement is a multifaceted and widely debated topic throughout higher education. In studying this subject, it is important to acknowledge the enhancements in students’ learning that are associated with regular attendance and active participation within taught sessions. Engaging with large numbers of students in lectures remains problematic, although the use of technology has been shown to mitigate against this issue. This includes the use of mobile applications (“apps”) and bring your own device (BYOD) type approaches. The supporting technologies are now ubiquitous and offer lecturers innovative ways to engage with learners. The overall purpose of this research is to examine how mobile devices can be used to engage with the modern student. The paper reports on the results from an investigation where mobile technologies were used during large lecture-type sessions in tandem with the Textwall™ software. This online program is able to receive and present messages from students’ devices that the lecturer may then share. It can also be used to collect student votes on multiple-choice questions, facilitating dynamic formative assessments during class. A study has been undertaken around the MyWallMate mobile application. This program has been developed by Liverpool John Moores University to expedite the process of sending messages and votes to Textwall™. Results of this study indicated that students had a positive reaction to both Textwall™ and the MyWallMate application. They reported being more comfortable in expressing their opinions via the MyWallMate mobile application and felt that using mobile technology within their lectures enhanced their learning. It has been concluded that the use of mobile technologies and BYOD-type approaches are avenues worth further exploration in global higher education

Crowding of Polymer Coils and Demixing in Nanoparticle-Polymer Mixtures

The Asakura-Oosawa-Vrij (AOV) model of colloid-polymer mixtures idealizes nonadsorbing polymers as effective spheres that are fixed in size and impenetrable to hard particles. Real polymer coils, however, are intrinsically polydisperse in size (radius of gyration) and may be penetrated by smaller particles. Crowding by nanoparticles can affect the size distribution of polymer coils, thereby modifying effective depletion interactions and thermodynamic stability. To analyse the influence of crowding on polymer conformations and demixing phase behaviour, we adapt the AOV model to mixtures of nanoparticles and ideal, penetrable polymer coils that can vary in size. We perform Gibbs ensemble Monte Carlo simulations, including trial nanoparticle-polymer overlaps and variations in radius of gyration. Results are compared with predictions of free-volume theory. Simulation and theory consistently predict that ideal polymers are compressed by nanoparticles and that compressibility and penetrability stabilise nanoparticle-polymer mixtures.Comment: 18 pages, 4 figure

Fluorescence from a few electrons

Systems containing few Fermions (e.g., electrons) are of great current interest. Fluorescence occurs when electrons drop from one level to another without changing spin. Only electron gases in a state of equilibrium are considered. When the system may exchange electrons with a large reservoir, the electron-gas fluorescence is easily obtained from the well-known Fermi-Dirac distribution. But this is not so when the number of electrons in the system is prevented from varying, as is the case for isolated systems and for systems that are in thermal contact with electrical insulators such as diamond. Our accurate expressions rest on the assumption that single-electron energy levels are evenly spaced, and that energy coupling and spin coupling between electrons are small. These assumptions are shown to be realistic for many systems. Fluorescence from short, nearly isolated, quantum wires is predicted to drop abruptly in the visible, a result not predicted by the Fermi-Dirac distribution. Our exact formulas are based on restricted and unrestricted partitions of integers. The method is considerably simpler than the ones proposed earlier, which are based on second quantization and contour integration.Comment: 10 pages, 3 figures, RevTe

Random-Matrix Theory of Quantum Size Effects on Nuclear Magnetic Resonance in Metal Particles

The distribution function of the local density of states is computed exactly for the Wigner-Dyson ensemble of random Hamiltonians. In the absence of time-reversal symmetry, precise agreement is obtained with the "supersymmetry" theory by Efetov and Prigodin of the NMR lineshape in disordered metal particles. Upon breaking time-reversal symmetry, the variance of the Knight shift in the smallest particles is reduced by a universal factor of 2/3. ***To be published in Physical Review B.****Comment: 4 pages, REVTeX-3.0, 1 postscript figure, INLO-PUB-940819; [2017: figure included in text

Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease

Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The new algorithm combines K-means with consensus clustering in order build cohort-specific decision trees that improve classification as well as aid the understanding of the underlying differences of the discovered groups. The methods are tested on a real-world freely available breast cancer dataset and data from a London hospital on systemic sclerosis, a rare potentially fatal condition. Results show that “nearest consensus clustering classification” improves the accuracy and the prediction significantly when this algorithm has been compared with competitive similar methods