Solvent viscosity dependence of the folding rate of a small protein: distributed computing study

By using distributed computing techniques and a supercluster of more than 20,000 processors we simulated folding of a 20-residue Trp Cage miniprotein in atomistic detail with implicit GB/SA solvent at a variety of solvent viscosities (gamma). This allowed us to analyze the dependence of folding rates on viscosity. In particular, we focused on the low-viscosity regime (values below the viscosity of water). In accordance with Kramers' theory, we observe approximately linear dependence of the folding rate on 1/gamma for values from 1-10(-1)x that of water viscosity. However, for the regime between 10(-4)-10(-1)x that of water viscosity we observe power-law dependence of the form k approximately gamma(-1/5). These results suggest that estimating folding rates from molecular simulations run at low viscosity under the assumption of linear dependence of rate on inverse viscosity may lead to erroneous results.     

Dynamic Entanglement and Separability Criteria for Quantum Computing Bit States

A theoretical framework is demonstrated to evaluate the degree of entanglement of bit states in quantum computing. Separability of general superposition of Hilbert space unit vectors is discussed, and criteria in amplitude as well as in phase are derived. By these criteria the possibility of different quantum gates such as controlled not (CN), controlled controlled not (CCN), controlled rotation (CR), and controlled phase shift (CPS), to create the entanglement is examined. Furthermore, the selection of measurement mode external to the quantum system is incorporated in the formula using Kronecker delta (δ _kx ), introducing the concept of dynamic entanglement. With this the process of wavefunction collapse upon measurement is understood as the result of the activation of the dynamic entanglement. A firefly in a box model is used to show a pure state of ontological uncertainty, which is in a dynamically entangled state in Hilbert space.     

基于扫描隧道显微镜的原子电子云结构

本文简要介绍了扫描隧道显微镜的基本工作原理，展示了一些扫描样品（石墨和铜）的电子云图，在简要介绍量子力学关于原子电子云结构基本理论的基础上，通过理论上的电子云结构与扫描结果的比较，在一定程度上验证了量子力学关于原子电子云结构理论的正确性．     

HPLC method development and validation of cytotoxic agent phenyl‐heptatriyne in Bidens pilosa with ultrasonic‐assisted cloud point extraction and preconcentration

Extraction and pre‐concentration of a bioactive marker compound, phenyl‐1,3,5‐heptatriyne from Bidens pilosa, prior to HPLC has been demonstrated using both organic and ecofriendly solvents. Non‐ionic surfactants, viz. Triton X‐100, Triton X‐114 and Genapol X‐80, were used for extraction. No back‐extraction or liquid chromatographic steps were required to remove the target phytochemical from the surfactant‐rich extractant phase. The optimized cloud point extraction procedure has been shown to be a potentially useful methodology for the preconcentration of the target analyte, with a preconcentration factor of 4–99. Moreover, the method is simple, sensitive, rapid and consumes lesser solvent than traditional methods. An isocratic chromatographic separation and quantitation was accomplished on a C₁₈ column with acetonitrile–acidified aqueous as mobile phase at a flow rate of 1.0 mL/min, UV detection at 254 nm and specificity with photo diode‐array detector (PDA) and MS. Under the optimum experimental conditions recovery was satisfactory (99.18–100.33%) without interference from the surfactant. The method seems to be reliable with intraday precision and interday precision below 2.0%. Good linearity was obtained in the working range from 7.5 to 30 µg/mL with correlation coefficient >0.99. The limits of detection and quantitation were 1.84 and 6.13 µg/mL, respectively. The method was validated following international guidelines and successfully applied for quantitative assays of cytotoxic compound phenyl‐1,3,5‐heptatriyne in Bidens pilosa. Copyright © 2010 John Wiley & Sons, Ltd.     

Algorithmic inference: From information granules to subtending functions

To get a true hybrid framework for taking operational decisions from data, we extend the Algorithmic Inference approach to the Granular Computing paradigm. The key idea is that whether or not we need to make decisions instead of mere computations depends on the fact that collected data are not sufficiently definite; rather, they are representative of whole sets of data that could be virtually observed, and we need to manage this indeterminacy. The distinguishing feature is that we face indeterminacy exactly where it affects the quality of the decision. This gives rise to a family of inference algorithms which can be tailored to many specific decisional problems that are generally solved only in approximate ways. In the paper we discuss the bases of the paradigm and provide some examples of its implementation.     

Nanoscale Science and Technology at Los Alamos National Laboratory

Research in the emerging field of nanoscale science and technology has grown steadily at Los Alamos National Laboratory since 1990. This article summarizes some of this work, examining research highlights within the seven key categories of nanoscience in which Los Alamos has ongoing projects, capabilities, and facilities: (1) Materials and chemistry, (2) Theory and modeling, (3) Bioscience, (4) Investigative tools and facilities, (5) Sensors and devices, (6) Synthesis and fabrication, and (7) Education and outreach. Future research horizons are indicated throughout while institutional strategies for advancing nanoscale science are summarized at the end.     

Semiconductor Research Needs in the Nanoscale Physical Sciences: A Semiconductor Research Corporation Working Paper

The purpose of this paper is to identify areas in the basic physical sciences where additional research is needed to sustain the extraordinary progress in electronics that has now extended for several decades. Also, it is argued that basic research will provide the foundation for the discovery of new generations of nanoelectronic devices that will continue the experimental rate of reduction in cost per function. Some of the fundamental areas requiring further research are the chemistry and physics of material interfaces, conductivity at small dimensions, deterministic doping effects, and nanomagnetics. Discovery research also is needed in the functional synergy of nanoelectronic materials and non-traditional fabrication methods.     

A new domain decomposition method with overlapping patches for ultrascale simulations: Application to biological flows

We address the failure in scalability of large-scale parallel simulations that are based on (semi-)implicit time-stepping and hence on the solution of linear systems on thousands of processors. We develop a general algorithmic framework based on domain decomposition that removes the scalability limitations and leads to optimal allocation of available computational resources. It is a non-intrusive approach as it does not require modification of existing codes. Specifically, we present here a two-stage domain decomposition method for the Navier–Stokes equations that combines features of discontinuous and continuous Galerkin formulations. At the first stage the domain is subdivided into overlapping patches and within each patch a C⁰ spectral element discretization (second stage) is employed. Solution within each patch is obtained separately by applying an efficient parallel solver. Proper inter-patch boundary conditions are developed to provide solution continuity, while a Multilevel Communicating Interface (MCI) is developed to provide efficient communication between the non-overlapping groups of processors of each patch. The overall strong scaling of the method depends on the number of patches and on the scalability of the standard solver within each patch. This dual path to scalability provides great flexibility in balancing accuracy with parallel efficiency. The accuracy of the method has been evaluated in solutions of steady and unsteady 3D flow problems including blood flow in the human intracranial arterial tree. Benchmarks on BlueGene/P, CRAY XT5 and Sun Constellation Linux Cluster have demonstrated good performance on up to 96,000 cores, solving up to 8.21B degrees of freedom in unsteady flow problem. The proposed method is general and can be potentially used with other discretization methods or in other applications.     

A fast algorithm for finding small solutions of <Emphasis Type=”Italic”>F(X,Y)=G(X,Y)</Emphasis> over number fields

Summary In the definition of semi-open sets and other kinds of generalized open sets, the role of the starting topology is given to a generalized topology. Some properties of the classes of sets so obtained is examined.