The Uniaxial Tensile Deformation of Ni Nanowire: Atomic-Scale Computer Simulations

The mechanical deformations of nickel nanowire subjected to uniaxial tensile strain at 300 K are simulated by using molecular dynamics with the quantum corrected Sutten-Chen many-body force field. We have used common neighbor analysis method to investigate the structural evolution of Ni nanowire during the elongation process. For the strain rate of 0.1%/ps, the elastic limit is up to about 11% strain with the yield stress of 8.6 GPa. At the elastic stage, the deformation is carried mainly through the uniform elongation of the distances between the layers (perpendicular to the Z-axis) while the atomic structure remains basically unchanged. With further strain, the slips in the {111} planes start to take place in order to accommodate the applied strain to carry the deformation partially, and subsequently the neck forms. The atomic rearrangements in the neck region result in a zigzag change in the stress-strain curve; the atomic structures beyond the region, however, have no significant changes. With the strain close to the point of the breaking, we observe the formation of a one-atom thick necklace in Ni nanowire. The strain rates have no significant effect on the deformation mechanism, but have some influence on the yield stress, the elastic limit, and the fracture strain of the nanowire.

Transfer of dynamic learning across postures.

When learning a difficult motor task, we often decompose the task so that the control of individual body segments is practiced in isolation. But on re-composition, the combined movements can result in novel and possibly complex internal forces between the body segments that were not experienced (or did not need to be compensated for) during isolated practice. Here we investigate whether dynamics learned in isolation by one part of the body can be used by other parts of the body to immediately predict and compensate for novel forces between body segments. Subjects reached to targets while holding the handle of a robotic, force-generating manipulandum. One group of subjects was initially exposed to the novel robot dynamics while seated and was then tested in a standing position. A second group was tested in the reverse order: standing then sitting. Both groups adapted their arm dynamics to the novel environment, and this movement learning transferred between seated and standing postures and vice versa. Both groups also generated anticipatory postural adjustments when standing and exposed to the force field for several trials. In the group that had learned the dynamics while seated, the appropriate postural adjustments were observed on the very first reach on standing. These results suggest that the CNS can immediately anticipate the effect of learned movement dynamics on a novel whole-body posture. The results support the existence of separate mappings for posture and movement, which encode similar dynamics but can be adapted independently.

Forced Dissociation of Selectin-ligand Complexes Using Steered Molecular Dynamics Simulation

Selectin-ligand interactions are crucial to such biological processes as inflammatory cascade or tumor metastasis. How transient formation and dissociation of selectin-ligand bonds in blood flow are coupled to molecular conformation at atomic level, however, has not been well understood. In this study, steered molecular dynamics (SMD) simulations were used to elucidate the intramolecular and intermolecular conformational evolutions involved in forced dissociation of three selectin-ligand systems: the construct consisting of P-selectin lectin (Lec) and epidermal growth factor (EGF)-like domains (P-LE) interacting with synthesized sulfoglycopeptide or SGP-3, P-LE with sialyl Lewis X (sLeX), and E-LE with sLeX. SMD simulations were based on newly built-up force field parameters including carbohydrate units and sulfated tyrosine(s) using an analogy approach. The simulations demonstrated that the complex dissociation was coupled to the molecular extension. While the intramolecular unraveling in P-LESGP-3 system mainly resulted from the destroy of the two anti-parallel sheets of EGF domain and the breakage of hydrogen-bond cluster at the Lec-EGF interface, the intermolecular dissociation was mainly determined by separation of fucose (FUC) from Ca2+ ion in all three systems. Conformational changes during forced dissociations depended on pulling velocities and forces, as well as on how the force was applied. This work provides an insight into better understanding of conformational changes and adhesive functionality of selectin-ligand interactions under external forces.

Size effects on the melting of nickel nanowires: a molecular dynamics study

The melting process of nickel nanowires are simulated by using molecular dynamics with the quantum Sutten-Chen many-body force field. The wires studied were approximately cylindrical in cross-section and periodic boundary conditions were applied along their length; the atoms were arranged initially in a face-centred cubic structure with the [0 0 1] direction parallel to the long axis of the wire. The size effects of the nanowires on the melting temperatures are investigated. We find that for the nanoscale regime, the melting temperatures of Ni nanowires are much lower than that of the bulk and are linear with the reciprocal of the diameter of the nanowire. When a nanowire is heated up above the melting temperature, the neck of the nanowire begins to arise and the diameter of neck decreases rapidly with the equilibrated running time. Finally, the breaking of nanowire arises, which leads to the formation of the spherical clusters. (C) 2004 Elsevier B.V. All rights reserved.

Protein structure refinement algorithms

Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics.

Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMCGRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.

Abnormal phenomenon of ac Stark splitting in magneto-optical traps

We experimentally study the ac Stark splitting in D2 line of cold Rb-87 atoms. The frequency span between the Autler-Townes doublets is obviously larger than that derived from theoretical calculation. Two physical effects, which increase the effective Rabi frequency, contribute to the splitting broadening. First, atoms tend to distribute in strong lield places of a inhomogeneous red-detuned light field. Second, atoms reabsorb scattered light when they are huge in number and high in density.

Structure Prediction of G-Protein Coupled Receptors

G-protein coupled receptors (GPCRs) form a large family of proteins and are very important drug targets. They are membrane proteins, which makes computational prediction of their structure challenging. Homology modeling is further complicated by low sequence similarly of the GPCR superfamily.

In this dissertation, we analyze the conserved inter-helical contacts of recently solved crystal structures, and we develop a unified sequence-structural alignment of the GPCR superfamily. We use this method to align 817 human GPCRs, 399 of which are nonolfactory. This alignment can be used to generate high quality homology models for the 817 GPCRs.

To refine the provided GPCR homology models we developed the Trihelix sampling method. We use a multi-scale approach to simplify the problem by treating the transmembrane helices as rigid bodies. In contrast to Monte Carlo structure prediction methods, the Trihelix method does a complete local sampling using discretized coordinates for the transmembrane helices. We validate the method on existing structures and apply it to predict the structure of the lactate receptor, HCAR1. For this receptor, we also build extracellular loops by taking into account constraints from three disulfide bonds. Docking of lactate and 3,5-dihydroxybenzoic acid shows likely involvement of three Arg residues on different transmembrane helices in binding a single ligand molecule.

Protein structure prediction relies on accurate force fields. We next present an effort to improve the quality of charge assignment for large atomic models. In particular, we introduce the formalism of the polarizable charge equilibration scheme (PQEQ) and we describe its implementation in the molecular simulation package Lammps. PQEQ allows fast on the fly charge assignment even for reactive force fields.

A vontade das coisas

A vontade das coisas em tempos de biopolítica pesquisa modos de resistência ao poder biopolítico através de uma filosofia orientada para as coisas na dança contemporânea em articulação com o estudo de caso Modo Operativo_and de João Fiadeiro e Fernanda Eugénio. Entender de que forma o campo das artes do corpo, nomeadamente o coreográfico em relação com a filosofia orientada para as coisas se pode revelar mecanismo de reflexão estético-afetivo e se tornam, na sua interdependência, um campo de força potencial no pensamento de resistência às formas de governabilidade neoliberalismo. Assim através de uma reflexão histórica acerca do corpo no campo da biopolítica chega-se à emergência do corpo vibrátil de Suely Rolnik enquanto forma outra de ativação ético-social de todos os coletivos que constituem, compõem mundo. Aborda-se a virada afetiva em relação com a teoria não humana para chegar a uma idéia de ontologia plana entre humanos e não humanos assumindo a importância das redes criadas por ambos os atantes na construção dum plano social baseado no afeto, a idéia de afetar e ser afetado enquanto manifestação de ativismo sensível contemporâneo. Finalmente concluo com a importância do fazer artístico, suas implicações éticas e estéticas, enquanto ferramenta de resistência ao neoliberalismo em articulação com o caso de estudo modo operativo and do coreógrafo João Fiadeiro e da antropóloga Fernanda Eugénio, método este que atua num modo conetivo ao redor de idéias colaborativas e estéticas relacionais, essencial no perspectivar modos de se fazer e se pensar sociedade

Separate representations of dynamics in rhythmic and discrete movements: evidence from motor learning.

Rhythmic and discrete arm movements occur ubiquitously in everyday life, and there is a debate as to whether these two classes of movements arise from the same or different underlying neural mechanisms. Here we examine interference in a motor-learning paradigm to test whether rhythmic and discrete movements employ at least partially separate neural representations. Subjects were required to make circular movements of their right hand while they were exposed to a velocity-dependent force field that perturbed the circularity of the movement path. The direction of the force-field perturbation reversed at the end of each block of 20 revolutions. When subjects made only rhythmic or only discrete circular movements, interference was observed when switching between the two opposing force fields. However, when subjects alternated between blocks of rhythmic and discrete movements, such that each was uniquely associated with one of the perturbation directions, interference was significantly reduced. Only in this case did subjects learn to corepresent the two opposing perturbations, suggesting that different neural resources were employed for the two movement types. Our results provide further evidence that rhythmic and discrete movements employ at least partially separate control mechanisms in the motor system.

Reynolds number influence on turbulent blocking effects of a rigid plane boundary (2nd report, direct numerical simulation using townsend's eddy forcing)

The Reynolds number influence on turbulent blocking effects by a rigid plane boundary is studied using direct numerical simulation (DNS). A new forcing method using 'simple model eddies' (Townsend 1976) for DNS of stationary homogeneous isotropic turbulence is proposed. A force field is obtained in real space by sprinkling many space-filling 'simple model eddies' whose centers are randomly but uniformly distributed in space and whose axes of rotation are random. The method is applied to a shear-free turbulent boundary layer over a rigid plane boundary and the blocking effects are investigated. The results show that stationary homogeneous isotropic turbulence is generated in real space using the present method. By using different model eddies with different sizes and rotation speeds, we could change the turbulence properties such as the integral and micro scales, the turbulent Reynolds number and the isotropy of turbulence. Turbulence intensities near the wall showed good agreements with the previous measurement and the linear analysis based on a rapid distortion theory (RDT). The splat effect (i.e., turbulence intensities of the components parallel to the boundary are amplified) occurs near the boundary and the viscous effect prohibits the splat effect at the quasi steady state at low Reynolds number.

Reynolds number influence on turbulent blocking effects of a rigid plane boundary (3rd report, direct numerical simulation on wall blocking effects for anisotropic turbulence)

The Reynolds number influence on turbulent blocking effects by a rigid plane boundary is studied using direct numerical simulation (DNS). A new forcing method proposed in the second report using Townsend's "simple model eddies" for DNS was extended to generate axisymmetric anisotropic turbulence. A force field is obtained in real space by sprinkling many space-filling "simple model eddies" whose centers are randomly but uniformly distributed in space. The axes of rotation are controlled in this study to generate axisymmetric anisotropic turbulence. The method is applied to a shear-free turbulent boundary layer over a rigid plane boundary and the blocking effects for anisotropic turbulence are investigated. The results show that stationary axisymmetric anisotropic turbulence is generated using the present method. Turbulence intensities near the wall showed good agreements with the rapid distortion theory (RDT) for small t (t ≪ TL), where TL. is the eddy turnover time. The splat effect (i. e. turbulence intensities of the components parallel to the surface are amplified) occurs near the boundary and the viscous effect attenuates the splat effect at the quasi steady state at low Reynolds number as for Isotropic turbulence. Prandtl's secondary flow of the second kind does not occur for low Reynolds number flows, which qualitatively agrees with previous observetion in a mixing-box.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics.

This study compared the mechanisms of adaptation to stable and unstable dynamics from the perspective of changes in joint mechanics. Subjects were instructed to make point to point movements in force fields generated by a robotic manipulandum which interacted with the arm in either a stable or an unstable manner. After subjects adjusted to the initial disturbing effects of the force fields they were able to produce normal straight movements to the target. In the case of the stable interaction, subjects modified the joint torques in order to appropriately compensate for the force field. No change in joint torque or endpoint force was required or observed in the case of the unstable interaction. After adaptation, the endpoint stiffness of the arm was measured by applying displacements to the hand in eight different directions midway through the movements. This was compared to the stiffness measured similarly during movements in a null force field. After adaptation, the endpoint stiffness under both the stable and unstable dynamics was modified relative to the null field. Adaptation to unstable dynamics was achieved by selective modification of endpoint stiffness in the direction of the instability. To investigate whether the change in endpoint stiffness could be accounted for by change in joint torque or endpoint force, we estimated the change in stiffness on each trial based on the change in joint torque relative to the null field. For stable dynamics the change in endpoint stiffness was accurately predicted. However, for unstable dynamics the change in endpoint stiffness could not be reproduced. In fact, the predicted endpoint stiffness was similar to that in the null force field. Thus, the change in endpoint stiffness seen after adaptation to stable dynamics was directly related to changes in net joint torque necessary to compensate for the dynamics in contrast to adaptation to unstable dynamics, where a selective change in endpoint stiffness occurred without any modification of net joint torque.

Different mechanisms involved in adaptation to stable and unstable dynamics.

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a "velocity-dependent force field" (VF), which interacted with the arm in a stable manner, and learning in a "divergent force field" (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.