Reliability of rank order in sampled networks

In complex scale-free networks, ranking the individual nodes based upon their importance has useful applications, such as the identification of hubs for epidemic control, or bottlenecks for controlling traffic congestion. However, in most real situations, only limited sub-structures of entire networks are available, and therefore the reliability of the order relationships in sampled networks requires investigation. With a set of randomly sampled nodes from the underlying original networks, we rank individual nodes by three centrality measures: degree, betweenness, and closeness. The higher-ranking nodes from the sampled networks provide a relatively better characterisation of their ranks in the original networks than the lower-ranking nodes. A closeness-based order relationship is more reliable than any other quantity, due to the global nature of the closeness measure. In addition, we show that if access to hubs is limited during the sampling process, an increase in the sampling fraction can in fact decrease the sampling accuracy. Finally, an estimation method for assessing sampling accuracy is suggested

Macroscopic Kinetic Effect of Cell-to-Cell Variation in Biochemical Reactions

Genetically identical cells under the same environmental conditions can show strong variations in protein copy numbers due to inherently stochastic events in individual cells. We here develop a theoretical framework to address how variations in enzyme abundance affect the collective kinetics of metabolic reactions observed within a population of cells. Kinetic parameters measured at the cell population level are shown to be systematically deviated from those of single cells, even within populations of homogeneous parameters. Because of these considerations, Michaelis-Menten kinetics can even be inappropriate to apply at the population level. Our findings elucidate a novel origin of discrepancy between in vivo and in vitro kinetics, and offer potential utility for analysis of single-cell metabolomic data

Anatomy of Scientific Evolution

The quest for historically impactful science and technology provides invaluable insight into the innovation dynamics of human society, yet many studies are limited to qualitative and small-scale approaches. Here, we investigate scientific evolution through systematic analysis of a massive corpus of digitized English texts between 1800 and 2008. Our analysis reveals great predictability for long-prevailing scientific concepts based on the levels of their prior usage. Interestingly, once a threshold of early adoption rates is passed even slightly, scientific concepts can exhibit sudden leaps in their eventual lifetimes. We developed a mechanistic model to account for such results, indicating that slowly-but-commonly adopted science and technology surprisingly tend to have higher innate strength than fast-and-commonly adopted ones. The model prediction for disciplines other than science was also well verified. Our approach sheds light on unbiased and quantitative analysis of scientific evolution in society, and may provide a useful basis for policy-making.Comment: Supplementary material attache

Energized soft tissue dissection in surgery simulation

With the development of virtual reality technology, surgery simulation has become an effective way to train the operation skills for surgeons. Soft tissue dissection, as one of the most frequently performed operations in surgery, is indispensable to an immersive and high-fidelity surgery simulator. Energized dissection tools are much more commonly used than the traditional sharp scalpels for patient safety. Unfortunately, the interaction of such tools with the soft tissues has been largely ignored in the research of surgical simulators. In this paper, we have proposed an energized soft tissue dissection model. We categorize the soft tissues into three types (fascia, membrane, and fat) and simulate their physical property accordingly. The dissection algorithm we propose employs an edge-based structure, which offers an effective mechanism for the generation of incisions dissected with energized tools. The mesh topology will not be changed when it is dissected by an energized tool, rather it is controlled by the heat transfer model. Our dissection method is highly compatible and efficient to the physically based simulation resolved by a pre-factorized linear system. We have proposed an energized soft tissue dissection model. We categorize the soft tissues into three types (fascia, membrane, and fat) and simulate their physical property accordingly. The dissection algorithm employs an edge-based structure, which offers an effective mechanism for the generation of incisions dissected with energized tools. Our dissection method is highly compatible and efficient to the physically based simulation resolved by a pre-factorized linear system

Pattern Formation in a Two-Dimensional Array of Oscillators with Phase-Shifted Coupling

We investigate the dynamics of a two-dimensional array of oscillators with phase-shifted coupling. Each oscillator is allowed to interact with its neighbors within a finite radius. The system exhibits various patterns including squarelike pinwheels, (anti)spirals with phase-randomized cores, and antiferro patterns embedded in (anti)spirals. We consider the symmetry properties of the system to explain the observed behaviors, and estimate the wavelengths of the patterns by linear analysis. Finally, we point out the implications of our work for biological neural networks

Robust nodal superconductivity induced by isovalent doping in Ba(Fe1−x_{1-x}Rux_x)2_2As2_2 and BaFe2_2(As1−x_{1-x}Px_x)2_2

We present the ultra-low-temperature heat transport study of iron-based superconductors Ba(Fe$_{1-x}$Ru$_x$)$_2$As$_2$ and BaFe$_2$(As$_{1-x}$P$_x$)$_2$. For optimally doped Ba(Fe$_{0.64}$Ru$_{0.36}$)$_2$As$_2$, a large residual linear term $\kappa_0/T$ at zero field and a $\sqrt{H}$ dependence of $\kappa_0(H)/T$ are observed, which provide strong evidences for nodes in the superconducting gap. This result demonstrates that the isovalent Ru doping can also induce nodal superconductivity, as P does in BaFe$_2$(As$_{0.67}$P$_{0.33}$)$_2$. Furthermore, in underdoped Ba(Fe$_{0.77}$Ru$_{0.23}$)$_2$As$_2$ and heavily underdoped BaFe$_2$(As$_{0.82}$P$_{0.18}$)$_2$, $\kappa_0/T$ manifests similar nodal behavior, which shows the robustness of nodal superconductivity in the underdoped regime and puts constraint on theoretical models.Comment: 5 pages, 4 figures - with two underdoped samples added, this paper supersedes arXiv:1106.541

Cost-effective circadian mechanism: rhythmic degradation of circadian proteins spontaneously emerges without rhythmic post-translational regulation

Circadian protein oscillations are maintained by the lifelong repetition of protein production and degradation in daily balance. It comes at the cost of ever-replayed, futile protein synthesis each day. This biosynthetic cost with a given oscillatory protein profile is relievable by a rhythmic, not constant, degradation rate that selectively peaks at the right time of day but remains low elsewhere, saving much of the gross protein loss and of the replenishing protein synthesis. Here, our mathematical modeling reveals that the rhythmic degradation rate of proteins with circadian production spontaneously emerges under steady and limited activity of proteolytic mediators and does not necessarily require rhythmic post-translational regulation of previous focus. Additional (yet steady) post-translational modifications in a proteolytic pathway can further facilitate the degradation's rhythmicity in favor of the biosynthetic cost saving. Our work is supported by animal and plant circadian data, offering a generic mechanism for potentially widespread, time-dependent protein turnover