Separation and detection of compounds in Honeysuckle by integration of ion-exchange chromatography fractionation with reversed-phase liquid chromatography-atmospheric pressure chemical ionization mass spectrometer and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis

A hyphenated method for the isolation and identification of components in a traditional Chinese medicine of Honeysuckle was developed. Ion-exchange chromatography (IEC) was chosen for the fractionation of Honeysuckle extract, and then followed by concentration of all the fractions with rotary vacuum evaporator. Each of the enriched fractions was then further analyzed by reversed-phase liquid chromatography-atmospheric pressure chemical ionization mass spectrometer (RPLC-APCI/MS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) with matrix of oxidized carbon nanotubes, respectively. It can be noted totally more than 117 components were detected by UV detector, APCI/MS and MALDI-TOF/MS in Honeysuckle extract except the, 145 components identified by MALDI-TOF/MS alone with this integrated approach, and 7 of them were preliminary identified according to their UV spectra and mass spectra performed by APCI/MS and MALDI-TOF/MS, respectively. The obtained analytical results not only indicated the approach of integration IEC fractionation with RPLC-APCI/MS and MALDI-TOF/MS is capable of analyzing complex samples, but also exhibited the potential power of the mass spectrometer in detection of low-mass compounds, such as traditional Chinese medicines (TCMs) and complex biological samples. (c) 2005 Elsevier B.V. All rights reserved.

Integration of genomic and phenotypic data

Clare, A. (2005) Integration of genomic and phenotypic data. In Data Analysis and Visualization in Genomics and Proteomics, Eds. Francisco Azuaje and Joaquin Dopazo, Wiley, London. ISBN: 0-470-09439-7

Reader development and ICT: an overview of projects in Welsh public libraries

Bird, A.,& Tedd, L. (2004). Reader development and ICT: an overview of projects in Welsh public libraries. Journal of Librarianship and Information Science, 36(4), 159-174.

Qualitative evaluation of KA24 (Knowledge Access 24)

Durbin, J. & Urquhart, C. (2003). Qualitative evaluation of KA24 (Knowledge Access 24). Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: Knowledge Access 24 (NHS)

Training for professional librarians in Slovakia by distance-learning methods: an overview of the PROLIB and EDULIB projects

Tedd, L.A., Dahl, K., Francis, S.,Tet?evov?, M.& ?ihlavn?kov?, E.(2002).Training for professional librarians in Slovakia by distance-learning methods: an overview of the PROLIB and EDULIB projects. Library Hi Tech, 20(3), 340-351. Sponsorship: European Union and the Open Society Institute

Rings around the World: Notes on the Challenges, Problems & Possibilities of International Audience Projects

Egan, K., Barker, M. (2006). Rings around the World: Notes on the Challenges, Problems & Possibilities of International Audience Projects. Participations: Journal of Audience & Reception Studies, 3 (2). Sponsorship: This research was made possible by a grant from the Economic and Social Research Council (ESRC Grant No. 000-22-0323)

The Limitations of Law in promoting synergy between environment and development policies in developing countries

Journal of Energy and Natural Resources Law, 24(4) pp.574-606 RAE2008

Rola Unii Europejskiej w budowie nowego ładu międzynarodowego - stan obecny i perspekytywy

W nauce o stosunkach międzynarodowych porządek, czy też ład międzynarodowy, to zbiór podmiotów państwowych i niepaństwowych złączonych wzajemnymi relacjami o różnorodnym charakterze i działających według wspólnie wypracowanych lub narzuconych z zewnątrz reguł. Po zakończeniu zimnej wojny i utracie na znaczeniu ładu jałtańskiego rozgorzała dyskusja nad kształtem jaki ma przybrać ład postzimnowojenny. Z poczynionych rozważań wynika, że polityczny i gospodarczy ład międzynarodowy w najbliższych dekadach będzie wielobiegunowy. Szczególne znaczenie odgrywać w nim będą USA, Chiny , a także Unia Europejska, Japonia, Indie i Brazylia. Obecnie UE odgrywa jedną z najważniejszych ról w budowie ładu międzynarodowego. Widać to zwłaszcza pod względem gospodarczym jako że Unia UE jest obecnie największym światowym eksporterem i drugim (po USA) importerem. Wydaje się jednak, że tradycyjne opisywanie Unii Europejskiej jako przykładu obszaru sukcesu gospodarczego może w najbliższym czasie ulec zmianie. Europa w długiej perspektywie ulegnie marginalizacji jeśli nie wróci do projektów ściślejszej integracji, nie wykorzysta lepiej swojego potencjału intelektualnego i gospodarczego oraz nie przyspieszy rozwoju poprzez stawianie na nowe technologie. Dla podtrzymania swojej pozycji powinna ona aktywniej współpracować z USA, utrzymywać poprawne stosunki z krajami azjatyckimi a także zaangażować się we współpracę z organizacjami między- i pozarządowymi, które w najbliższych latach będą zyskiwać na znaczeniu

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

A Quantitative Evaluation of the AVITEWRITE Model of Handwriting Learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.

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.

Grid Cell Hexagonal Patterns Formed by Fast Self-Organized Learning within Entorhinal Cortex

Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. How these hexagonal patterns arise has excited intense interest. It has previously been shown how a selforganizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? A neural model is proposed that converts path integration signals into hexagonal grid cell patterns of multiple scales. This GRID model creates only grid cell patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support a unified computational framework for explaining how entorhinal-hippocampal interactions support spatial navigation.

Variable Rate Working Memories for Phonetic Categorization and Invariant Speech Perception

Speech can be understood at widely varying production rates. A working memory is described for short-term storage of temporal lists of input items. The working memory is a cooperative-competitive neural network that automatically adjusts its integration rate, or gain, to generate a short-term memory code for a list that is independent of item presentation rate. Such an invariant working memory model is used to simulate data of Repp (1980) concerning the changes of phonetic category boundaries as a function of their presentation rate. Thus the variability of categorical boundaries can be traced to the temporal in variance of the working memory code.

The VITEWRITE Model of Handwriting Production

This article describes the VITEWRITE model for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in the outflow command to a given synergy occurs. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. Each synergy exhibits a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

A Neural Theory of Visual Search: Recursive Attention to Segmentations and Surfaces

A neural theory is proposed in which visual search is accomplished by perceptual grouping and segregation, which occurs simultaneous across the visual field, and object recognition, which is restricted to a selected region of the field. The theory offers an alternative hypothesis to recently developed variations on Feature Integration Theory (Treisman, and Sato, 1991) and Guided Search Model (Wolfe, Cave, and Franzel, 1989). A neural architecture and search algorithm is specified that quantitatively explains a wide range of psychophysical search data (Wolfe, Cave, and Franzel, 1989; Cohen, and lvry, 1991; Mordkoff, Yantis, and Egeth, 1990; Treisman, and Sato, 1991).