We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis, colorectal cancer prescreening and therapeutic choices in melanoma. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic and cardiovascular diseases. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks.

This paper is concerned with the controller design of networked control systems (NCSs). A new model of the NCSs is provided under consideration of both the network-induced delay and the data packet dropout in the transmission. In terms of the given model, a controller design method is proposed based on a delay-dependent approach. The feedback gain of a memoryless controller and the maximum allowable value of the network-induced delay can be derived by solving a set of linear matrix inequalities. Two examples are given to show the effectiveness of our method.Index Terms-Lyapunov functional, networked control system (NCS), network-induced delay, stability.

Metal nanoparticles are used as catalysts in a variety of important chemical reactions, and can have a range of different shapes, with facets and sites that differ in catalytic reactivity. To develop better catalysts it is necessary to determine where catalysis occurs on such nanoparticles and what structures are more reactive. Surface science experiments or theory can be used to predict the reactivity of surfaces with a known structure, and the reactivity of nanocatalysts can often be rationalized from a knowledge of their well-defined surface facets. Here, we show that a knowledge of the surface facets of a gold nanorod catalyst is insufficient to predict its reactivity, and we must also consider defects on the surface of the nanorod. We use super-resolution fluorescence microscopy to quantify the catalysis of the nanorods at a temporal resolution of a single catalytic reaction and a spatial resolution of ∼40 nm. We find that within the same surface facets on the sides of a single nanorod, the reactivity is not constant and exhibits a gradient from the centre of the nanorod towards its two ends. Furthermore, the ratio of the reactivity at the ends of the nanorod to the reactivity at the sides varies significantly from nanorod to nanorod, even though they all have the same surface facets.