Chemical constituent analysis of the crown-of-thorns starfish Acanthaster planci and potential utilization value of the starfish as feed ingredient for animals

The crown-of-thorns starfish Acanthaster planci is a major management issue on coral reefs and the exploring of effective control methods to the starfish is an interesting goal. In this study, the chemical constituent of the starfish were analyzed and the toxicity of the starfish was tested when it was used as mice diet. The results showed that protein content of the starfish was 19.8 to 22.0% of dry weight and the amino acid composition was similar to that of fish meal. Though the starfish had little fatty acids (<1%), the fatty acids contained rich variety and unsaturated fatty acids on average accounted for more than 60% of total fatty acids. In addition, per gram (dry weight) of the starfish contained 65.4 to 97.4 μg astaxanthin, which was higher than that of shrimps. The starfish used as the feed for mice did not have negative influence on the growth and the health of the mice. Based on these results, we consider that the crown-of-thorns starfish A. planci has the potential to be an ingredient for animal feeds, thus reducing the usage of fish meal, fish oil and carotenoids. Hence, a method for resource utilization and control of A. planci was suggested.Key words: Chemical constituents, Acanthaster planci, astaxanthin, resource utilization, feed ingredient

Analyze the risks of biological invasion : an agent based simulation model for introducing non-native oysters in Chesapeake Bay, USA

2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Spatiotemporal Dynamics and Control of Strong Coupling in Plasmonic Nanocavities

© 2017 American Chemical Society. In the light-matter strong coupling regime, the excited state of quantum emitters is inextricably linked to a photonic mode, leading to hybrid states that are part light and part matter. Recently, there has been a huge effort to realize strong coupling with nanoplasmonics, since it provides a versatile environment to study and control molecules in ambient conditions. Among the most promising designs are plasmonic nanocavities that confine light to unprecedentedly small volumes. Such nanocavities, though, support multiple types of modes, with different field profiles and radiative decay rates (bright and dark modes). Here, we show theoretically that the different nature of these modes leads to mode beating within the nanocavity and the Rabi oscillations, which alters the spatiotemporal dynamics of the hybrid system. By specifically designing the illumination setup, we decompose and control the dark and bright plasmon mode excitation and therefore their coupling with quantum emitters. Hence, this work opens new routes for dynam ically dressing emitters, to tailor their hybrid states with external radiation

How Does Experience Modulate Auditory Spatial Processing in Individuals with Blindness?

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Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than two orders of magnitude over the first round, where multiple analysis methods are allowed. This article highlights many pitfalls in conducting 'routine' degradation analysis, and it addresses some of the factors that must be considered when comparing degradation results reported by different analysts or methods

Boundaries of Disk-like Self-affine Tiles

Let $T:= T(A, {\mathcal D})$ be a disk-like self-affine tile generated by an integral expanding matrix $A$ and a consecutive collinear digit set ${\mathcal D}$, and let $f(x)=x^{2}+px+q$ be the characteristic polynomial of $A$. In the paper, we identify the boundary $\partial T$ with a sofic system by constructing a neighbor graph and derive equivalent conditions for the pair $(A,{\mathcal D})$ to be a number system. Moreover, by using the graph-directed construction and a device of pseudo-norm $\omega$, we find the generalized Hausdorff dimension $\dim_H^{\omega} (\partial T)=2\log \rho(M)/\log |q|$ where $\rho(M)$ is the spectral radius of certain contact matrix $M$. Especially, when $A$ is a similarity, we obtain the standard Hausdorff dimension $\dim_H (\partial T)=2\log \rho/\log |q|$ where $\rho$ is the largest positive zero of the cubic polynomial $x^{3}-(|p|-1)x^{2}-(|q|-|p|)x-|q|$, which is simpler than the known result.Comment: 26 pages, 11 figure

A perspective on using experiment and theory to identify design principles in dye-sensitized solar cells

Dye-sensitized solar cells (DSCs) have been the subject of wide-ranging studies for many years because of their potential for large-scale manufacturing using roll-to-roll processing allied to their use of earth abundant raw materials. Two main challenges exist for DSC devices to achieve this goal; uplifting device efficiency from the 12 to 14% currently achieved for laboratory-scale ‘hero’ cells and replacement of the widely-used liquid electrolytes which can limit device lifetimes. To increase device efficiency requires optimized dye injection and regeneration, most likely from multiple dyes while replacement of liquid electrolytes requires solid charge transporters (most likely hole transport materials – HTMs). While theoretical and experimental work have both been widely applied to different aspects of DSC research, these approaches are most effective when working in tandem. In this context, this perspective paper considers the key parameters which influence electron transfer processes in DSC devices using one or more dye molecules and how modelling and experimental approaches can work together to optimize electron injection and dye regeneration. This paper provides a perspective that theory and experiment are best used in tandem to study DSC device

Application advances of artificial intelligence algorithms in dynamics simulation of railway vehicle

The application examples and domestic and foreign literatures using artificial intelligence algorithm for railway vehicle system dynamics simulation were reviewed. The machine learning and deep learning algorithms commonly used in railway vehicle dynamics simulation were summarized, and the application classifications of the 2 algorithms in railway vehicle system dynamics modelling and simulation were concluded and interpreted. According to railway vehicle system dynamics modelling, dynamics performance prediction and dynamics performance optimization, the advantages and limitations of applying artificial intelligence algorithms in force-elements modelling and simulation, track irregularity prediction, running stability prediction, noise prediction, crosswind safety prediction, running safety prediction, suspension optimization, wheel-rail matching optimization, structure optimization, and active and semi-active control were discussed in detail. The problems of applications of artificial intelligence algorithms in railway dynamics simulation were lack of training samples, generalization ability and interpretability. The development directions and key research contents of the interdisciplinary research between artificial intelligence and vehicle system dynamics were given. Research result shows that the hybrid modelling theory combining classical mechanics and artificial intelligence algorithms can be as a key research direction in the future. There is great potential to use the artificial intelligence algorithms to solve the random uncertainty in stochastic dynamics and improve the performance of stochastic dynamics. The artificial intelligence algorithms combinated with optimization algorithms can exploit their advantages in the dynamics performance optimization.

Sign-reversal of the in-plane resistivity anisotropy in hole-doped iron pnictides

The in-plane anisotropy of the electrical resistivity across the coupled orthorhombic and magnetic transitions of the iron pnictides has been extensively studied in the parent and electron-doped compounds. All these studies universally show that the resistivity $\rho_{a}$ across the long orthorhombic axis $a_{O}$ - along which the spins couple antiferromagnetically below the magnetic transition temperature - is smaller than the resistivity $\rho_{b}$ of the short orthorhombic axis $b_{O}$, i. e. $\rho_{a}<\rho_{b}$. Here we report that in the hole-doped compounds Ba$_{1-x}$K$_{x}$Fe$_{2}$As$_{2}$, as the doping level increases, the resistivity anisotropy initially becomes vanishingly small, and eventually changes sign for sufficiently large doping, i. e. $\rho_{b}<\rho_{a}$. This observation is in agreement with a recent theoretical prediction that considers the anisotropic scattering of electrons by spin-fluctuations in the orthorhombic/nematic state.Comment: This paper has been replaced by the new version offering new explanation of the experimental results first reported her

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.&#xd;&#xa;&#xd;&#xa