Differential Domain Distribution of gnomAD- and Disease-Linked Connexin Missense Variants

Twenty-one human genes encode connexins, a family of homologous proteins making gap junction (GJ) channels, which mediate direct intercellular communication to synchronize tissue/organ activities. Genetic variants in more than half of the connexin genes are associated with dozens of different Mendelian inherited diseases. With rapid advances in DNA sequencing technology, more variants are being identified not only in families and individuals with diseases but also in people in the general population without any apparent linkage to Mendelian inherited diseases. Nevertheless, it remains challenging to classify the pathogenicity of a newly identified connexin variant. Here, we analyzed the disease- and Genome Aggregation Database (gnomAD, as a proxy of the general population)-linked variants in the coding region of the four disease-linked α connexin genes. We found that the most abundant and position-sensitive missense variants showed distinct domain distribution preference between disease- and gnomAD-linked variants. Plotting missense variants on topological and structural models revealed that disease-linked missense variants are highly enriched on the structurally stable/resolved domains, especially the pore-lining domains, while the gnomAD-linked missense variants are highly enriched in the structurally unstable/unresolved domains, especially the carboxyl terminus. In addition, disease-linked variants tend to be on highly conserved residues and those positions show evolutionary co-variation, while the gnomAD-linked missense variants are likely on less conserved residue positions and on positions without co-variation. Collectively, the revealed distribution patterns of disease- and gnomAD-linked missense variants further our understanding of the GJ structure–biological function relationship, which is valuable for classifying the pathogenicity of newly identified connexin variants.     

Boron removal from water and its recovery using iron (Fe) oxide/hydroxide-based nanoparticles (NanoFe) and NanoFe-impregnated granular activated carbon as adsorbent

This study presents information obtained by the synthesis of Fe(3) oxide/hydroxide nanoparticles sol (NanoFe) and NanoFe-impregnated granular activated carbon as adsorbents for boron removal from solutions. The research describes an adsorption method for cleaning a solution containing boron contaminants followed by recovery of the adsorbent and the adsorbed material for safe removal or further reuse. The technology provides an efficient method of boron removal from water. A marked effect of NanoFe and NanoFe-impregnated GAC adsorbents concentration and pH level on boron removal efficiency was demonstrated. At least 95–98% boron recovery efficiency is possible using NanoFe sol and Fe-impregnated GAC that in fact also recover the adsorbent for reuse. Boron adsorption onto the NanoFe-impregnated GAC adsorbent may be described by pseudo-second-order reaction kinetics and the Langmuir isotherm model. The boron adsorption capacity on iron (3) oxide nanoparticles and Fe-impregnated GAC at an equilibrium concentration of 0.3 mg/dm³ as B in the solution is much higher than these values for similar adsorbents reported in the literature.     

Uncertainty modeling and robust minimax LQR control of multivariable nonlinear systems with application to hypersonic flight

For a class of multi‐input and multi‐output nonlinear uncertainty systems, a novel approach to design a nonlinear controller using minimax linear quadratic regulator (LQR) control is proposed. The proposed method combines a feedback linearization method with the robust minimax LQR approach in the presence of time‐varying uncertain parameters. The uncertainties, which are assumed to satisfy a certain integral quadratic constraint condition, do not necessarily satisfy a generalized matching condition. The procedure consists of feedback linearization of the nominal model and linearization of the remaining nonlinear uncertain terms with respect to each individual uncertainty at a local operating point. This two‐stage linearization process, followed by a robust minimax LQR control design, provides a robustly stable closed loop system. To demonstrate the effectiveness of the proposed approach, an application study is provided for a flight control problem of an air‐breathing hypersonic flight vehicle (AHFV), where the outputs to be controlled are the longitudinal velocity and altitude, and the control variables are the throttle setting and elevator deflection. The proposed method is used to derive a linearized uncertainty model for the longitudinal motion dynamics of the AHFV first, and then a robust minimax LQR controller is designed, which is based on this uncertainty model. The controller is synthesized considering seven uncertain aerodynamic and inertial parameters. The stability and performance of the synthesized controller is evaluated numerically via single scenario simulations for particular cruise conditions as well as a Monte‐Carlo type simulation based on numerous cases. It is observed that the control scheme proposed in this paper performs better, especially from the aspect of robustness to large ranges of uncertainties, than some controller design schemes previously published in the literature. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Design and evaluation of a mobile smart home interactive system with elderly users in Brazil

Personal and Ubiquitous Computing - This article presents a study concerning the evaluation of a smart home control system for elderly people with a sample of 10 users in a city in the interior of...     

Linear nearest neighbor optimization in quantum circuits: a multiobjective perspective

Several current implementations of quantum circuits rely on the linear nearest neighbor restriction, which only allows interaction between adjacent qubits. Most methods that address the process of converting a generic circuit to an equivalent circuit which satisfies this restriction, minimize the number of additional SWAP gates required by this process. Moreover, most methods which address this problem are designed for 1D circuits. Considering the new and promising proposals for 2D quantum circuits, what we propose is a new perspective on this problem, namely that it can be seen as a multiobjective optimization problem. To test our hypothesis, we developed a multiobjective evolutionary algorithm that solves this problem by considering two objectives: minimizing the size of the 2D grid where the circuit is placed, and minimizing the number of additional SWAP gates. Of the methods designed for 2D circuits, only one considers different grid sizes which are much larger than strictly necessary. Consequently, our algorithm makes considerations which other methods do not make, since it naturally finds the grid which requires fewer SWAP gates for the circuit conversion, whether it is one-dimensional or two-dimensional. Our experimental results indicate that allowing a larger grid size results in fewer additional SWAP gates in about 73% of the tested circuits. Additionally, the average improvement we found when using larger grid sizes is about 30%, while the best improvement over using the smallest possible grid is 63.8%.     

Analytical benchmark example for risk allocation in structural optimization

Recently, Rozvany and Maute presented a simple problem of topology optimization under uncertainty with a single load condition that can be solved without Monte Carlo simulation. This note extends this idea to a two-bar truss under two load conditions for the purpose of illustrating differences between deterministic designs and reliability–based ones in terms of risk allocation. For this example, for the same probability of failure, a deterministic design based on the same safety factor for both loads can be substantially heavier than the reliability–based design. The difference in weight is due to very different risk allocation between the two failure modes.     

Cryptanalysis of simple three-party key exchange protocol (S-3PAKE)

Password-authenticated key exchange (PAKE) protocols allow parties to share secret keys in an authentic manner based on an easily memorizable password. Recently, Lu and Cao proposed a three-party password-authenticated key exchange protocol, so called S-3PAKE, based on ideas of the Abdalla and Pointcheval two-party SPAKE extended to three parties. S-3PAKE can be seen to have a structure alternative to that of another three-party PAKE protocol (3PAKE) by Abdalla and Pointcheval. Furthermore, a simple improvement to S-3PAKE was proposed very recently by Chung and Ku to resist the kind of attacks that applied to earlier versions of 3PAKE. In this paper, we show that S-3PAKE falls to unknown key-share attacks by any other client, and undetectable online dictionary attacks by any adversary. The latter attack equally applies to the recently improved S-3PAKE. Indeed, the provable security approach should be taken when designing PAKEs; and furthermore our results highlight that extra cautions still be exercised when defining models and constructing proofs in this direction.     

Distributed control parallelism in multidisciplinary aircraft design

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g. the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.). This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance. Copyright © 1999 John Wiley & Sons, Ltd.