De Novo Genome Sequence of "Candidatus Liberibacter solanacearum" from a Single Potato Psyllid in California.

The draft genome sequence of "Candidatus Liberibacter solanacearum" strain RSTM from a potato psyllid (Bactericera cockerelli) in California is reported here. The RSTM strain has a genome size of 1,286,787 bp, a G+C content of 35.1%, 1,211 predicted open reading frames (ORFs), and 43 RNA genes

Reply to Kok and Dwyer

Letter (Correspondence)postprin

Reducing the impact of the next influenza pandemic using household-based public health interventions.

Household-based public health interventions can effectively mitigate the impact of influenza pandemic, and the resources and compliance requirement are realistic and feasible.published_or_final_versio

Formal Availability Analysis using Theorem Proving

Availability analysis is used to assess the possible failures and their restoration process for a given system. This analysis involves the calculation of instantaneous and steady-state availabilities of the individual system components and the usage of this information along with the commonly used availability modeling techniques, such as Availability Block Diagrams (ABD) and Fault Trees (FTs) to determine the system-level availability. Traditionally, availability analyses are conducted using paper-and-pencil methods and simulation tools but they cannot ascertain absolute correctness due to their inaccuracy limitations. As a complementary approach, we propose to use the higher-order-logic theorem prover HOL4 to conduct the availability analysis of safety-critical systems. For this purpose, we present a higher-order-logic formalization of instantaneous and steady-state availability, ABD configurations and generic unavailability FT gates. For illustration purposes, these formalizations are utilized to conduct formal availability analysis of a satellite solar array, which is used as the main source of power for the Dong Fang Hong-3 (DFH-3) satellite.Comment: 16 pages. arXiv admin note: text overlap with arXiv:1505.0264

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%

Universality in Systems with Power-Law Memory and Fractional Dynamics

There are a few different ways to extend regular nonlinear dynamical systems by introducing power-law memory or considering fractional differential/difference equations instead of integer ones. This extension allows the introduction of families of nonlinear dynamical systems converging to regular systems in the case of an integer power-law memory or an integer order of derivatives/differences. The examples considered in this review include the logistic family of maps (converging in the case of the first order difference to the regular logistic map), the universal family of maps, and the standard family of maps (the latter two converging, in the case of the second difference, to the regular universal and standard maps). Correspondingly, the phenomenon of transition to chaos through a period doubling cascade of bifurcations in regular nonlinear systems, known as "universality", can be extended to fractional maps, which are maps with power-/asymptotically power-law memory. The new features of universality, including cascades of bifurcations on single trajectories, which appear in fractional (with memory) nonlinear dynamical systems are the main subject of this review.Comment: 23 pages 7 Figures, to appear Oct 28 201

Facile Synthesis of Three-Dimensional ZnO Nanostructure: Realization of a Multifunctional Stable Superhydrophobic Surface

BACKGROUND: After comprehensive study of various superhydrophobic phenomena in nature, it is no longer a puzzle for researchers to realize such fetching surfaces. However, the different types of artificial surfaces may get wetted and lose its water repellence if there exist defects or the liquid is under pressure. With respect to the industry applications, in which the resistance of wetting transition is critical important, new nanostructure satisfied a certain geometric criterion should be designed to hold a stable gas film at the base area to avoid the wet transition. METHODOLOGY: A thermal deposition method was utilized to produce a thin ZnO seeds membrane on the aluminum foil. And then a chemical self-assemble technology was developed in present work to fabricate three-dimensional (3D) hierarchical dune-like ZnO architecture based on the prepared seeds membrane. RESULTS: Hierarchical ZnO with micro scale dune-like structure and core-sharing nanosheets was generated. The characterization results showed that there exist plenty of gaps and interfaces among the micro-dune and nanosheets, and thus the surface area was enlarged by such a unique morphology. Benefited from this unique 3D ZnO hierarchical nanostructure, the obtained surface exhibited stable water repellency after modification with Teflon, and furthermore, based on solid theory analysis, such 3D ZnO nanostructure would exhibit excellent sensing performance

Computational models for inferring biochemical networks

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917

Complex sequencing rules of birdsong can be explained by simple hidden Markov processes

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical propertiesof the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable sequences, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. This property is shared with other complex sequential behaviors. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model (GMM)), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex sequences with higher-order dependencies