Mode imaging and selection in strongly coupled nanoantennas

The number of eigenmodes in plasmonic nanostructures increases with complexity due to mode hybridization, raising the need for efficient mode characterization and selection. Here we experimentally demonstrate direct imaging and selective excitation of the bonding and antibonding plasmon mode in symmetric dipole nanoantennas using confocal two-photon photoluminescence mapping. Excitation of a high-quality-factor antibonding resonance manifests itself as a two-lobed pattern instead of the single spot observed for the broad bonding resonance, in accordance with numerical simulations. The two-lobed pattern is observed due to the fact that excitation of the antibonding mode is forbidden for symmetric excitation at the feedgap, while concomitantly the mode energy splitting is large enough to suppress excitation of the bonding mode. The controlled excitation of modes in strongly coupled plasmonic nanostructures is mandatory for efficient sensors, in coherent control as well as for implementing well-defined functionalities in complex plasmonic devices.Comment: 11 pages, 5 figures, 1 supplementary informatio

The context and potential of epigenetics in oncology

Cancer has long been known to be a disease caused by alterations in the genetic blueprint of cells. In the past decade it has become evident that epigenetic processes have a function, at least equally important, in neoplasia. Epigenetics describes the mechanisms that result in heritable alterations in gene expression profiles without an accompanying change in DNA sequence. Genetics and epigenetics intricately interact in the pathogenesis of cancer (Esteller, 2007). In this review, we paint a broad picture of current understanding of epigenetic changes in cancer cells and reflect on the immense clinical potential of emerging knowledge of epigenetics in the diagnosis, prognostic assessment, treatment, and screening of cancer

Using near-term forecasts and uncertainty partitioning to improve predictions of low-frequency cyanobacterial events

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water and air quality. Importantly, ecological forecasts can identify where uncertainty enters the forecasting system, which is necessary to refine and improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance (uncertainty) introduced by different sources, including specification of the model structure, errors in driver data, and estimation of initial state conditions. Uncertainty partitioning could be particularly useful in improving forecasts of high-density cyanobacterial events, which are difficult to predict and present a persistent challenge for lake managers. Cyanobacteria can produce toxic or unsightly surface scums and advance warning of these events could help managers mitigate water quality issues. Here, we calibrate fourteen Bayesian state-space models to evaluate different hypotheses about cyanobacterial growth using data from eight summers of weekly cyanobacteria density samples in an oligotrophic (low nutrient) lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We identify dominant sources of uncertainty for near-term (one-week to four-week) forecasts of G. echinulata densities over two years. Water temperature was an important predictor in calibration and at the four-week forecast horizon. However, no environmental covariates improved over a simple autoregressive (AR) model at the one-week horizon. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and often did not capture rare peak density occurrences, indicating that significant explanatory variables in calibration are not always effective for near-term forecasting of low-frequency events. Uncertainty partitioning revealed that model process specification and initial conditions uncertainty dominated forecasts at both time horizons. These findings suggest that observed densities result from both growth and movement of G. echinulata, and that imperfect observations as well as spatial misalignment of environmental data and cyanobacteria observations affect forecast skill. Future research efforts should prioritize long-term studies to refine process understanding and increased sampling frequency and replication to better define initial conditions. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.Accepted manuscrip

Anisotropic nanomaterials: structure, growth, assembly, and functions

Comprehensive knowledge over the shape of nanomaterials is a critical factor in designing devices with desired functions. Due to this reason, systematic efforts have been made to synthesize materials of diverse shape in the nanoscale regime. Anisotropic nanomaterials are a class of materials in which their properties are direction-dependent and more than one structural parameter is needed to describe them. Their unique and fine-tuned physical and chemical properties make them ideal candidates for devising new applications. In addition, the assembly of ordered one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) arrays of anisotropic nanoparticles brings novel properties into the resulting system, which would be entirely different from the properties of individual nanoparticles. This review presents an overview of current research in the area of anisotropic nanomaterials in general and noble metal nanoparticles in particular. We begin with an introduction to the advancements in this area followed by general aspects of the growth of anisotropic nanoparticles. Then we describe several important synthetic protocols for making anisotropic nanomaterials, followed by a summary of their assemblies, and conclude with major applications

Phylogenomic analysis of a 55.1 kb 19-gene dataset resolves a monophyletic Fusarium that includes the Fusarium solani Species Complex

Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user¿s needs and established successful practice. In 2013, the Fusarium community voiced near unanimous support for a concept of Fusarium that represented a clade comprising all agriculturally and clinically important Fusarium species, including the F. solani species complex (FSSC). Subsequently, this concept was challenged in 2015 by one research group who proposed dividing the genus Fusarium into seven genera, including the FSSC described as members of the genus Neocosmospora, with subsequent justification in 2018 based on claims that the 2013 concept of Fusarium is polyphyletic. Here, we test this claim and provide a phylogeny based on exonic nucleotide sequences of 19 orthologous protein-coding genes that strongly support the monophyly of Fusarium including the FSSC. We reassert the practical and scientific argument in support of a genus Fusarium that includes the FSSC and several other basal lineages, consistent with the longstanding use of this name among plant pathologists, medical mycologists, quarantine officials, regulatory agencies, students, and researchers with a stake in its taxonomy. In recognition of this monophyly, 40 species described as genus Neocosmospora were recombined in genus Fusarium, and nine others were renamed Fusarium. Here the global Fusarium community voices strong support for the inclusion of the FSSC in Fusarium, as it remains the best scientific, nomenclatural, and practical taxonomic option availabl