Design Principles for Plasmonic Nanoparticle Devices

For all applications of plasmonics to technology it is required to tailor the resonance to the optical system in question. This chapter gives an understanding of the design considerations for nanoparticles needed to tune the resonance. First the basic concepts of plasmonics are reviewed with a focus on the physics of nanoparticles. An introduction to the finite element method is given with emphasis on the suitability of the method to nanoplasmonic device simulation. The effects of nanoparticle shape on the spectral position and lineshape of the plasmonic resonance are discussed including retardation and surface curvature effects. The most technologically important plasmonic materials are assessed for device applicability and the importance of substrates in light scattering is explained. Finally the application of plasmonic nanoparticles to photovoltaic devices is discussed.Comment: 29 pages, 15 figures, part of an edited book: "Linear and Non-Linear Nanoplasmonics

Growth dynamics and the evolution of cooperation in microbial populations

Microbes providing public goods are widespread in nature despite running the risk of being exploited by free-riders. However, the precise ecological factors supporting cooperation are still puzzling. Following recent experiments, we consider the role of population growth and the repetitive fragmentation of populations into new colonies mimicking simple microbial life-cycles. Individual-based modeling reveals that demographic fluctuations, which lead to a large variance in the composition of colonies, promote cooperation. Biased by population dynamics these fluctuations result in two qualitatively distinct regimes of robust cooperation under repetitive fragmentation into groups. First, if the level of cooperation exceeds a threshold, cooperators will take over the whole population. Second, cooperators can also emerge from a single mutant leading to a robust coexistence between cooperators and free-riders. We find frequency and size of population bottlenecks, and growth dynamics to be the major ecological factors determining the regimes and thereby the evolutionary pathway towards cooperation.Comment: 26 pages, 6 figure

Population Dynamics Constrain the Cooperative Evolution of Cross-Feeding

Cross-feeding is the exchange of nutrients among species of microbes. It has two potential evolutionary origins, one as an exchange of metabolic wastes or byproducts among species, the other as a form of cooperation known as reciprocal altruism. This paper explores the conditions favoring the origin of cooperative cross-feeding between two species. There is an extensive literature on the evolution of cooperation, and some of the requirements for the evolution of cooperative cross-feeding follow from this prior work–specifically the requirement that interactions be limited to small groups of individuals, such as colonies in a spatially structured environment. Evolution of cooperative cross-feeding by a species also requires that cross-feeding from the partner species already exists, so that the cooperating mutant will automatically be reciprocated for its actions. Beyond these considerations, some unintuitive dynamical constraints apply. In particular, the benefit of cooperative cross-feeding applies only in the range of intermediate cell densities. At low density, resource concentrations are too low to offset the cost of cooperation. At high density, resources shared by both species become limiting, and the two species become competitors. These considerations suggest that the evolution of cooperative cross-feeding in nature may be more challenging than for other types of cooperation. However, the principles identified here may enable the experimental evolution of cross-feeding, as born out by a recent study

Dynamic modeling of mean-reverting spreads for statistical arbitrage

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.Comment: 34 pages, 6 figures. Submitte

What traits are carried on mobile genetic elements, and why?

Although similar to any other organism, prokaryotes can transfer genes vertically from mother cell to daughter cell, they can also exchange certain genes horizontally. Genes can move within and between genomes at fast rates because of mobile genetic elements (MGEs). Although mobile elements are fundamentally self-interested entities, and thus replicate for their own gain, they frequently carry genes beneficial for their hosts and/or the neighbours of their hosts. Many genes that are carried by mobile elements code for traits that are expressed outside of the cell. Such traits are involved in bacterial sociality, such as the production of public goods, which benefit a cell's neighbours, or the production of bacteriocins, which harm a cell's neighbours. In this study we review the patterns that are emerging in the types of genes carried by mobile elements, and discuss the evolutionary and ecological conditions under which mobile elements evolve to carry their peculiar mix of parasitic, beneficial and cooperative genes

A global perspective on marine photosynthetic picoeukaryote community structure

A central goal in ecology is to understand the factors affecting the temporal dynamics and spatial distribution of microorganisms and the underlying processes causing differences in community structure and composition. However, little is known in this respect for photosynthetic picoeukaryotes (PPEs), algae that are now recognised as major players in marine CO2 fixation. Here, we analysed dot blot hybridisation and cloning–sequencing data, using the plastid-encoded 16S rRNA gene, from seven research cruises that encompassed all four ocean biomes. We provide insights into global abundance, α- and β-diversity distribution and the environmental factors shaping PPE community structure and composition. At the class level, the most commonly encountered PPEs were Prymnesiophyceae and Chrysophyceae. These taxa displayed complementary distribution patterns, with peak abundances of Prymnesiophyceae and Chrysophyceae in waters of high (25:1) or low (12:1) nitrogen:phosphorus (N:P) ratio, respectively. Significant differences in phylogenetic composition of PPEs were demonstrated for higher taxonomic levels between ocean basins, using Unifrac analyses of clone library sequence data. Differences in composition were generally greater between basins (interbasins) than within a basin (intrabasin). These differences were primarily linked to taxonomic variation in the composition of Prymnesiophyceae and Prasinophyceae whereas Chrysophyceae were phylogenetically similar in all libraries. These data provide better knowledge of PPE community structure across the world ocean and are crucial in assessing their evolution and contribution to CO2 fixation, especially in the context of global climate change

Freeze-In Production of FIMP Dark Matter

We propose an alternate, calculable mechanism of dark matter genesis, "thermal freeze-in," involving a Feebly Interacting Massive Particle (FIMP) interacting so feebly with the thermal bath that it never attains thermal equilibrium. As with the conventional "thermal freeze-out" production mechanism, the relic abundance reflects a combination of initial thermal distributions together with particle masses and couplings that can be measured in the laboratory or astrophysically. The freeze-in yield is IR dominated by low temperatures near the FIMP mass and is independent of unknown UV physics, such as the reheat temperature after inflation. Moduli and modulinos of string theory compactifications that receive mass from weak-scale supersymmetry breaking provide implementations of the freeze-in mechanism, as do models that employ Dirac neutrino masses or GUT-scale-suppressed interactions. Experimental signals of freeze-in and FIMPs can be spectacular, including the production of new metastable coloured or charged particles at the LHC as well as the alteration of big bang nucleosynthesis.Comment: 30 pages, 7 figures, PDFLaTex. References adde

Can Disease Management Target Patients Most Likely to Generate High Costs? The Impact of Comorbidity

CONTEXT: Disease management programs are increasingly used to manage costs of patients with chronic disease. OBJECTIVE: We sought to examine the clinical characteristics and measure the health care expenditures of patients most likely to be targeted by disease management programs. DESIGN: Retrospective analysis of prospectively obtained data. SETTING: A general medicine practice with both faculty and residents at an urban academic medical center. PARTICIPANTS: Five thousand eight hundred sixty-one patients enrolled in the practice for at least 1 year. MAIN OUTCOMES: Annual cost of diseases targeted by disease management. MEASUREMENTS: Patients’ clinical and demographic information were collected from a computer system used to manage patients. Data included diagnostic information, medications, and resource usage over 1 year. We looked at 10 common diseases targeted by disease management programs. RESULTS: Unadjusted annual median costs for chronic diseases ranged between $1,100 and$1,500. Congestive heart failure ($1,500), stroke ($1,500), diabetes ($1,500), and cancer ($1,400) were the most expensive. As comorbidity increased, annual adjusted costs increased exponentially. Those with comorbidity scores of 2 or more accounted for 26% of the population but 50% of the overall costs. CONCLUSIONS: Costs for individual chronic conditions vary within a relatively narrow range. However, the costs for patients with multiple coexisting medical conditions increase rapidly. Reducing health care costs will require focusing on patients with multiple comorbid diseases, not just single diseases. The overwhelming impact of comorbidity on costs raises significant concerns about the potential ability of disease management programs to limit the costs of care