Complex temporal patterns in molecular dynamics:a direct measure of the phase-space exploration by the trajectory at macroscopic time scales

Computer simulated trajectories of bulk water molecules form complex spatiotemporal structures at the picosecond time scale. This intrinsic complexity, which underlies the formation of molecular structures at longer time scales, has been quantified using a measure of statistical complexity. The method estimates the information contained in the molecular trajectory by detecting and quantifying temporal patterns present in the simulated data (velocity time series). Two types of temporal patterns are found. The first, defined by the short-time correlations corresponding to the velocity autocorrelation decay times (â‰0.1â€ps), remains asymptotically stable for time intervals longer than several tens of nanoseconds. The second is caused by previously unknown longer-time correlations (found at longer than the nanoseconds time scales) leading to a value of statistical complexity that slowly increases with time. A direct measure based on the notion of statistical complexity that describes how the trajectory explores the phase space and independent from the particular molecular signal used as the observed time series is introduced. © 2008 The American Physical Society.

Complexity and chirality indices for molecular informatics::differential geometry approach

Novel molecular complexity measures are designed based on the quantum molecular kinematics. The Hamiltonian matrix constructed in a quasi-topological approximation describes the temporal evolution of the modelled electronic system and determined the time derivatives for the dynamic quantities. This allows to define the average quantum kinematic characteristics closely related to the curvatures of the electron paths, particularly, the torsion reflecting the chirality of the dynamic system. A special attention has been given to the computational scheme for this chirality measure. The calculations on realistic molecular systems demonstrate reasonable behaviour of the proposed molecular complexity indices.

Molecular phase space transport in water:non-stationary random walk model

Molecular transport in phase space is crucial for chemical reactions because it defines how pre-reactive molecular configurations are found during the time evolution of the system. Using Molecular Dynamics (MD) simulated atomistic trajectories we test the assumption of the normal diffusion in the phase space for bulk water at ambient conditions by checking the equivalence of the transport to the random walk model. Contrary to common expectations we have found that some statistical features of the transport in the phase space differ from those of the normal diffusion models. This implies a non-random character of the path search process by the reacting complexes in water solutions. Our further numerical experiments show that a significant long period of non-stationarity in the transition probabilities of the segments of molecular trajectories can account for the observed non-uniform filling of the phase space. Surprisingly, the characteristic periods in the model non-stationarity constitute hundreds of nanoseconds, that is much longer time scales compared to typical lifetime of known liquid water molecular structures (several picoseconds).

Elements of molecular neurobiology

This edition of the popular text incorporates recent advances in neurobiology enabled by modern molecular biology techniques. Understanding how the brain works from a molecular level allows research to better understand behaviours, cognition, and neuropathologies. Since the appearance six years ago of the second edition, much more has been learned about the molecular biology of development and its relations with early evolution. This "evodevo" (as it has come to be known) framework also has a great deal of bearing on our understanding of neuropathologies as dysfunction of early onset genes can cause neurodegeneration in later life. Advances in our understanding of the genomes and proteomes of a number of organisms also greatly influence our understanding of neurobiology. This book will be of particular interest to biomedical undergraduates undertaking a neuroscience unit, neuroscience postgraduates, physiologists, pharmacologists. It is also a useful basic reference for university libraries.

T-cell epitope prediction and immune complex simulation using molecular dynamics:state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Temporal lobe pathology in neurodegenerative disease:a comparative study of eight disorders with special reference to the hippocampus

The temporal lobe is a major site of pathology in a number of neurodegenerative diseases. In this chapter, the densities of the characteristic pathological lesions in various regions of the temporal lobe were compared in eight neurodegenerative disorders, viz., Alzheimer’s disease (AD), Down’s syndrome (DS), dementia with Lewy bodies (DLB), Pick’s disease (PiD), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), sporadic Creutzfeldt-Jakob disease (sCJD), and neuronal intermediate filament inclusion disease (NIFID). Temporal lobe pathology was observed in all of these disorders most notably in AD, DS, PiD, sCJD, and NIFID. The regions of the temporal lobe affected by the pathology, however, varied between disorders. In AD and DS, the greatest densities of ?-amyloid (A?) deposits were recorded in cortical regions adjacent to the hippocampus (HC), DS exhibiting greater densities of A? deposits than AD. Similarly, in sCJD, greatest densities of prion protein (PrPsc) deposits were recorded in cortical areas of the temporal lobe. In AD and PiD, significant densities of neurofibrillary tangles (NFT) and Pick bodies (PB) respectively were present in sector CA1 of the HC while in CBD, the greatest densities of tau-immunoreactive neuronal cytoplasmic inclusions (NCI) were present in the parahippocampal gyrus (PHG). Particularly high densities of PB were present in the DG in PiD, whereas NFT in AD and Lewy bodies (LB) in DLB were usually absent in this region. These data confirm that the temporal lobe is an important site of pathology in the disorders studied regardless of their molecular ‘signature’. However, disorders differ in the extent to which the pathology spreads to affect the HC which may account for some of the observed differences in clinical dementia.

The evolution of learning systems:to Bayes or not to be

Bayesian algorithms pose a limit to the performance learning algorithms can achieve. Natural selection should guide the evolution of information processing systems towards those limits. What can we learn from this evolution and what properties do the intermediate stages have? While this question is too general to permit any answer, progress can be made by restricting the class of information processing systems under study. We present analytical and numerical results for the evolution of on-line algorithms for learning from examples for neural network classifiers, which might include or not a hidden layer. The analytical results are obtained by solving a variational problem to determine the learning algorithm that leads to maximum generalization ability. Simulations using evolutionary programming, for programs that implement learning algorithms, confirm and expand the results. The principal result is not just that the evolution is towards a Bayesian limit. Indeed it is essentially reached. In addition we find that evolution is driven by the discovery of useful structures or combinations of variables and operators. In different runs the temporal order of the discovery of such combinations is unique. The main result is that combinations that signal the surprise brought by an example arise always before combinations that serve to gauge the performance of the learning algorithm. This latter structures can be used to implement annealing schedules. The temporal ordering can be understood analytically as well by doing the functional optimization in restricted functional spaces. We also show that there is data suggesting that the appearance of these traits also follows the same temporal ordering in biological systems. © 2006 American Institute of Physics.

Spatio-temporal intensity dynamics of passively mode-locked fiber laser

We present recent results on measurements of intensity spatio-temporal dynamics in passively mode-locked fibre laser. We experimentally uncover distinct, dynamic and stable spatio-temporal generation regimes of various stochasticity and periodicity properties in though-to-be unstable laser. We present a method to distinguish various types of generated coherent structures, including rogue and shock waves, within the radiation by means of introducing of intensity ACF evolution map. We also discuss how the spectral dynamics could be measured in fiber lasers generating irregular train of pulses of quasi-CW generation via combination of heterodyning and intensity spatio-temporal measurement concept.