Inference on microsatellite mutation processes in the invasive mite, Varroa destructor, using reversible jump Markov chain Monte Carlo

Varroa destructor is a parasitic mite of the Eastern honeybee Apis cerana. Fifty years ago, two distinct evolutionary lineages (Korean and Japanese) invaded the Western honeybee Apis mellifera. This haplo-diploid parasite species reproduces mainly through brother sister matings, a system which largely favors the fixation of new mutations. In a worldwide sample of 225 individuals from 21 locations collected on Western honeybees and analyzed at 19 microsatellite loci, a series of de novo mutations was observed. Using historical data concerning the invasion, this original biological system has been exploited to compare three mutation models with allele size constraints for microsatellite markers: stepwise (SMM) and generalized (GSM) mutation models, and a model with mutation rate increasing exponentially with microsatellite length (ESM). Posterior probabilities of the three models have been estimated for each locus individually using reversible jump Markov Chain Monte Carlo. The relative support of each model varies widely among loci, but the GSM is the only model that always receives at least 9% support, whatever the locus. The analysis also provides robust estimates of mutation parameters for each locus and of the divergence time of the two invasive lineages (67,000 generations with a 90% credibility interval of 35,000-174,000). With an average of 10 generations per year, this divergence time fits with the last post-glacial Korea Japan land separation. (c) 2005 Elsevier Inc. All rights reserved.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

Nutrimetabonomics: applications for nutritional sciences, with specific reference to gut microbial interactions

Understanding the role of the diet in determining human health and disease is one major objective of modern nutrition. Mammalian biocomplexity necessitates the incorporation of systems biology technologies into contemporary nutritional research. Metabonomics is a powerful approach that simultaneously measures the low-molecular-weight compounds in a biological sample, enabling the metabolic status of a biological system to be characterized. Such biochemical profiles contain latent information relating to inherent parameters, such as the genotype, and environmental factors, including the diet and gut microbiota. Nutritional metabonomics, or nutrimetabonomics, is being increasingly applied to study molecular interactions between the diet and the global metabolic system. This review discusses three primary areas in which nutrimetabonomics has enjoyed successful application in nutritional research: the illumination of molecular relationships between nutrition and biochemical processes; elucidation of biomarker signatures of food components for use in dietary surveillance; and the study of complex trans-genomic interactions between the mammalian host and its resident gut microbiome. Finally, this review illustrates the potential for nutrimetabonomics in nutritional science as an indispensable tool to achieve personalized nutrition.

On the effect of I/Q imbalance on MIMO transmit-receive diversity systems

In practice, all I/Q signal processing receivers face the problem of I/Q imbalance. In this paper, we investigate the effect of I/Q imbalance on the performance of MIMO maximal ratio combining (MRC) systems that perform the combining at the radio frequency (RF) level, thereby requiring only one RF chain. Based on a system modeling that takes the I/Q imbalance into account, we evaluate the performance in terms of average symbol error probability (SEP), outage probability and system capacity, which are derived considering transmission over uncorrelated Rayleigh fading channels. Numerical results are provided to illustrate the effects of system parameters, such as the image- leakage ratio, numbers of transmit and receive antennas, and modulation order of quadrature amplitude modulation (QAM), on the system performance.

Methods for kinematic modeling of biological and robotic systems

The main objective is to develop methods that automatically generate kinematic models for the movements of biological and robotic systems. Two methods for the identification of the kinematics are presented. The first method requires the elimination of the displacement variables that cannot be measured while the second method attempts to estimate the changes in these variables. The methods were tested using a planar two-revolute-joint linkage. Results show that the model parameters obtained agree with the actual parameters to within 5%. Moreover, the methods were applied to model head and neck movements in the sagittal plane. The results indicate that these movements are well modeled by a two-revolute-joint system. A spatial three-revolute-joint model was also discussed and tested.

Molecule modeling of human pentameric alpha(7) neuronal nicotinic acetylcholine receptor and its interaction with its agonist and competitive antagonist

The nicotinic Acetylcholine Receptor (nAChR) is the major class of neurotransmitter receptors that is involved in many neurodegenerative conditions such as schizophrenia, Alzheimer's and Parkinson's diseases. The N-terminal region or Ligand Binding Domain (LBD) of nAChR is located at pre- and post-synaptic nervous system, which mediates synaptic transmission. nAChR acts as the drug target for agonist and competitive antagonist molecules that modulate signal transmission at the nerve terminals. Based on Acetylcholine Binding Protein (AChBP) from Lymnea stagnalis as the structural template, the homology modeling approach was carried out to build three dimensional model of the N-terminal region of human alpha(7)nAChR. This theoretical model is an assembly of five alpha(7) subunits with 5 fold axis symmetry, constituting a channel, with the binding picket present at the interface region of the subunits. alpha-netlrotoxin is a potent nAChR competitive antagonist that readily blocks the channel resulting in paralysis. The molecular interaction of alpha-Bungarotoxin, a long chain alpha-neurotoxin from (Bungarus multicinctus) and human alpha(7)nAChR seas studied. Agonists such as acetylcholine, nicotine, which are used in it diverse array of biological activities, such as enhancements of cognitive performances, were also docked with the theoretical model of human alpha(7)nAChR. These docked complexes were analyzed further for identifying the crucial residues involved in interaction. These results provide the details of interaction of agonists and competitive antagonists with three dimensional model of the N-terminal region of human alpha(7)nAChR and thereby point to the design of novel lead compounds.

Expression-system-dependent modulation of HIV-1 envelope glycoprotein antigenicity and immunogenicity.

Recombinant expression systems differ in the type of glycosylation they impart on expressed antigens such as the human immunodeficiency virus type 1 (HIV-1) envelope glycoproteins, potentially affecting their biological properties. We performed head-to-head antigenic, immunogenic and molecular profiling of two distantly related Env surface (gp120) antigens produced in different systems: (a) mammalian (293 FreeStyle cells; 293F) cells in the presence of kifunensine, which impart only high-mannose glycans; (b) insect cells (Spodoptera frugiperda, Sf9), which confer mainly paucimannosidic glycans; (c) Sf9 cells recombinant for mammalian glycosylation enzymes (Sf9 Mimic), which impart high-mannose, hybrid and complex glycans without sialic acid; and (d) 293F cells, which impart high-mannose, hybrid and complex glycans with sialic acid. Molecular models revealed a significant difference in gp120 glycan coverage between the Sf9-derived and wild-type mammalian-cell-derived material that is predicted to affect ligand binding sites proximal to glycans. Modeling of solvent-exposed surface electrostatic potentials showed that sialic acid imparts a significant negative surface charge that may influence gp120 antigenicity and immunogenicity. Gp120 expressed in systems that do not incorporate sialic acid displayed increased ligand binding to the CD4 binding and CD4-induced sites compared to those expressed in the system that do, and imparted other more subtle differences in antigenicity in a gp120 subtype-specific manner. Non-sialic-acid-containing gp120 was significantly more immunogenic than the sialylated version when administered in two different adjuvants, and induced higher titers of antibodies competing for CD4 binding site ligand-gp120 interaction. These findings suggest that non-sialic-acid-imparting systems yield gp120 immunogens with modified antigenic and immunogenic properties, considerations that should be considered when selecting expression systems for glycosylated antigens to be used for structure-function studies and for vaccine use.

A flow system for the on-line quantitative measurement of the retention of dosage forms on biological surfaces using spectroscopy and image analysis

Measuring the retention, or residence time, of dosage forms to biological tissue is commonly a qualitative measurement, where no real values to describe the retention can be recorded. The result of this is an assessment that is dependent upon a user's interpretation of visual observation. This research paper outlines the development of a methodology to quantitatively measure, both by image analysis and by spectrophotometric techniques, the retention of material to biological tissues, using the retention of polymer solutions to ocular tissue as an example. Both methods have been shown to be repeatable, with the spectrophotometric measurement generating data reliably and quickly for further analysis.

Modeling requirement driven architecture of adaptive healthcare system based on semiotics

In order to improve the quality of healthcare services, the integrated large-scale medical information system is needed to adapt to the changing medical environment. In this paper, we propose a requirement driven architecture of healthcare information system with hierarchical architecture. The system operates through the mapping mechanism between these layers and thus can organize functions dynamically adapting to user’s requirement. Furthermore, we introduce the organizational semiotics methods to capture and analyze user’s requirement through ontology chart and norms. Based on these results, the structure of user’s requirement pattern (URP) is established as the driven factor of our system. Our research makes a contribution to design architecture of healthcare system which can adapt to the changing medical environment.

Multi-agent based modeling and simulation of complex system in hospital

This study puts forward a method to model and simulate the complex system of hospital on the basis of multi-agent technology. The formation of the agents of hospitals with intelligent and coordinative characteristics was designed, the message object was defined, and the model operating mechanism of autonomous activities and coordination mechanism was also designed. In addition, the Ontology library and Norm library etc. were introduced using semiotic method and theory, to enlarge the method of system modelling. Swarm was used to develop the multi-agent based simulation system, which is favorable for making guidelines for hospital's improving it's organization and management, optimizing the working procedure, improving the quality of medical care as well as reducing medical charge costs.