Structural analysis of peptides of PSII light-harvesting complexes in siphonous green algae, Codium fragile

peptide composition and arrangement of 4 major light-harvesting complexes LHCP1-3 and LHCP3, isolated from siphonous green algae (Codium fragile (Sur.) Hariot.) were investigated. LHCP1 showed five main peptides, 34.4, 31.5, 29.5, 28.2 and 26.5 kD in SDS-PAGE, the 34.4 and 31.5 kD peptides were never found in higher plants. LHCP3 contained the other four kinds of LHCP1 peptides except 34.4 kD, while LHCP3, consisted of only 28.2 and 26.5 kD peptides. We found that 34.4, 28.2 and 26.5 kD peptides were easy to decompose from LHCP1 when subjected to SDS-PACE without pretreatment. They might be located at the exterior of LHCP1, while the 31.5 and 29.5 kD peptides were at the central part. The 28.2 and 26.5 kD peptides often occurred in CPa, the center complex of PS II. They are possibly the LHC II peptides tightly associated with CC II. According to the results described above, a peptide map of LHCP1 was sketched.

Structural analysis of SNARE motifs from sea perch, Lateolabrax japonicus by computerized approaches

Three cDNA sequences encoding four SNARE (N-ethylmaleimide-sensitive fusion protein attachment protein receptors) motifs were cloned from sea perch, and the deduced peptide sequences were analyzed for structural prediction by using 14 different web servers and softwares. The "ionic layer" structure, the three dimensional extension and conformational characters of the SNARE 7S core complex by using bioinformatics approaches were compared respectively with those from mammalian X-ray crystallographic investigations. The result suggested that the formation and stabilization of fish SNARE core complex might be driven by hydrophobic association, hydrogen bond among R group of core amino acids and electrostatic attraction at molecular level. This revealed that the SNARE proteins interaction of the fish may share the same molecular mechanism with that of mammal, indicating the universality and solidity of SNARE core complex theory. This work is also an attempt to get the protein 3D structural information which appears to be similar to that obtained through X-ray crystallography, only by using computerized approaches. (C) 2007 Elsevier Ltd. All rights reserved.

Models of Noise and Robust Estimates

Given n noisy observations g; of the same quantity f, it is common use to give an estimate of f by minimizing the function Eni=1(gi-f)2. From a statistical point of view this corresponds to computing the Maximum likelihood estimate, under the assumption of Gaussian noise. However, it is well known that this choice leads to results that are very sensitive to the presence of outliers in the data. For this reason it has been proposed to minimize the functions of the form Eni=1V(gi-f), where V is a function that increases less rapidly than the square. Several choices for V have been proposed and successfully used to obtain "robust" estimates. In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V.

View-Based Models of 3D Object Recognition and Class-Specific Invariances

This paper describes the main features of a view-based model of object recognition. The model tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network in which each of the hidden units is broadly tuned to a specific view of the object to be recognized.

Synthesis and structural characterization of (pyrazolyl)alkenyl Fischer carbene complexes of chromium and tungsten

Michael addition of substituted pyrazoles 2 to 1-alkynyl Fischer carbene complexes (CO)(5)M=C(OEt)(CdropCPh) (1) (a, M = Cr and b M = W) afforded (pyrazolyl)alkenyl Fischer carbene complexes (CO)(5)M=C(OEt)(CH=C(R(1)R(2)R(3)pz)Ph) (R(1)R(2)R(3)pz = pyrazolyl) 3 (M = Cr) and 4 (M = W), respectively, with an exclusive (E)-configuration in mild to excellent yields. The reaction of la and 3,5-dimethylpyrazole (2b) was monitored to demonstrate the formation and decomposition of complex 3b by H-1 NMR measurements in CDCl3 at 23degreesC. Complexes 3 and 4 were characterized with H-1, C-13{H-1} NMR, IR spectroscopies and elemental analysis. When the substituted pyrazoles were 3-methylpyrazole (2a) and 3,5-di-tert-butylpyrazole (2d), molecular structures of the corresponding (pyrazolyl)alkenyl Fischer carbene complexes 3a and 4d were characterized by X-ray crystallographic study. (C) 2004 Elsevier Ltd. All rights reserved.

Models of Backwardness versus Transformation in Eastern Europe. Review Article

This paper is critical analysis of book by Anna Sosnowska, "Zrozumieć zacofanie. Spory historyków o Europę Wschodnią (1947-1994)" [To Understand Backwardness: Historians' Deabates about Eastern Europe (1947-1994)]. Warszawa 2004.

Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.

Adaptive Neural Models of Queuing and Timing in Fluent Action

Temporal structure in skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefrontal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables, such as time-to-contact. At a fine scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over-shoot the amounts needed for the precise acts. Each context of action may require a much different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive parallel patterns of analog signals. From some parts of the cerebellum, such signals controls muscles. But a recent model shows how the lateral cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (in frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine system design to serve the lowest and the highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between levels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing.

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.

Neural Models of Temporally Organized Behaviors: Handwriting Production and Working Memory

Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309)

Differential and numerical models of hysteretic systems with stochastic and deterministic inputs

Many deterministic models with hysteresis have been developed in the areas of economics, finance, terrestrial hydrology and biology. These models lack any stochastic element which can often have a strong effect in these areas. In this work stochastically driven closed loop systems with hysteresis type memory are studied. This type of system is presented as a possible stochastic counterpart to deterministic models in the areas of economics, finance, terrestrial hydrology and biology. Some price dynamics models are presented as a motivation for the development of this type of model. Numerical schemes for solving this class of stochastic differential equation are developed in order to examine the prototype models presented. As a means of further testing the developed numerical schemes, numerical examination is made of the behaviour near equilibrium of coupled ordinary differential equations where the time derivative of the Preisach operator is included in one of the equations. A model of two phenotype bacteria is also presented. This model is examined to explore memory effects and related hysteresis effects in the area of biology. The memory effects found in this model are similar to that found in the non-ideal relay. This non-ideal relay type behaviour is used to model a colony of bacteria with multiple switching thresholds. This model contains a Preisach type memory with a variable Preisach weight function. Shown numerically for this multi-threshold model is a pattern formation for the distribution of the phenotypes among the available thresholds.