bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comparison of Descriptive Models of a Single Spike Train by Information-Geometric Measure
Authors: Nakahara Hiroyuki , Amari Shun-ichi , Richmond Barry J.
Published: May 2006

In examining spike trains, different models are used to describe their structure. The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes in a common coordinate system. Within such a framework, it becomes straightforward to visualize higher-order correlations of different models and thereby assess the differences between models. We apply the information-geometric measure to compare two similar but not identical models of neuronal spike trains: the inhomogeneous Markov and the mixture of Poisson models. It is shown that they differ in the secondand higher-order interaction terms. In the mixture of Poisson model, the second- and higher-order interactions are of comparable magnitude within each order, whereas in the inhomogeneous Markov model, they have alternating signs over different orders. This provides guidance about what measurements would effectively separate the two models. As newer models are proposed, they also can be compared to these models using information geometry.
Effects of Fast Presynaptic Noise in Attractor Neural Networks
Authors: Cortes J. M. , Torres J. J. , Marro J. , Garrido P. L. , Kappen H. J.
Published: May 2006

We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological findings that show that synaptic strength may either increase or decrease on a short timescale depending on presynaptic activity. We thus describe a mechanism by which fast presynaptic noise enhances the neural network sensitivity to an external stimulus. The reason is that, in general, presynaptic noise induces nonequilibrium behavior and, consequently, the space of fixed points is qualitatively modified in such a way that the system can easily escape from the attractor. As a result, the model shows, in addition to pattern recognition, class identification and categorization, which may be relevant to the understanding of some of the brain complex tasks.
The Costs of Ignoring High-Order Correlations in Populations of Model Neurons
Authors: Michel Melchi M. , Jacobs Robert A.
Published: May 2006

Investigators debate the extent to which neural populations use pairwise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.
Connection and Coordination: The Interplay Between Architecture and Dynamics in Evolved Model Pattern Generators
Authors: Psujek Sean , Ames Jeffrey , Beer Randall D.
Published: May 2006

We undertake a systematic study of the role of neural architecture in shaping the dynamics of evolved model pattern generators for a walking task. First, we consider the minimum number of connections necessary to achieve high performance on this task. Next, we identify architectural motifs associated with high fitness. We then examine how high-fitness architectures differ in their ability to evolve. Finally, we demonstrate the existence of distinct parameter subgroups in some architectures and show that these subgroups are characterized by differences in neuron excitabilities and connection signs.
Computation of the Phase Response Curve: A Direct Numerical Approach
Authors: Govaerts W. , Sautois B.
Published: May 2006

Neurons are often modeled by dynamical systems—parameterized systems of differential equations. A typical behavioral pattern of neurons is periodic spiking; this corresponds to the presence of stable limit cycles in the dynamical systems model. The phase resetting and phase response curves (PRCs) describe the reaction of the spiking neuron to an input pulse at each point of the cycle. We develop a new method for computing these curves as a by-product of the solution of the boundary value problem for the stable limit cycle. The method is mathematically equivalent to the adjoint method, but our implementation is computationally much faster and more robust than any existing method. In fact, it can compute PRCs even where the limit cycle can hardly be found by time integration, for example, because it is close to another stable limit cycle. In addition, we obtain the discretized phase response curve in a form that is ideally suited for most applications. We present several examples and provide the implementation in a freely available Matlab code.
Smooth Gradient Representations as a Unifying Account of Chevreul's Illusion, Mach Bands, and a Variant of the Ehrenstein Disk
Authors: Keil Matthias S.
Published: May 2006

Recent evidence suggests that the primate visual system generates representations for object surfaces (where we consider representations for the surface attribute brightness). Object recognition can be expected to perform robustly if those representations are invariant despite environmental changes (e.g., in illumination). In real-world scenes, it happens, however, that surfaces are often overlaid by luminance gradients, which we define as smooth variations in intensity. Luminance gradients encode highly variable information, which may represent surface properties (curvature), nonsurface properties (e.g., specular highlights, cast shadows, illumination inhomogeneities), or information about depth relationships (cast shadows, blur). We argue, on grounds of the unpredictable nature of luminance gradients, that the visual system should establish corresponding representations, in addition to surface representations. We accordingly present a neuronal architecture, the so-called gradient system, which clarifies how spatially accurate gradient representations can be obtained by relying on only high-resolution retinal responses. Although the gradient system was designed and optimized for segregating, and generating, representations of luminance gradients with real-world luminance images, it is capable of quantitatively predicting psychophysical data on both Mach bands and Chevreul's illusion. It furthermore accounts qualitatively for a modified Ehrenstein disk.
Kernel Fisher Discriminants for Outlier Detection
Authors: Roth Volker
Published: May 2006

The problem of detecting atypical objects or outliers is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVM classifiers. The method presented in this letter bridges the gap between kernelized one-class classification and gaussian density estimation in the induced feature space. Having established the exact relation between the two concepts, it is now possible to identify atypical objects by quantifying their deviations from the gaussian model. This model-based formalization of outliers overcomes the main conceptual shortcoming of most one-class approaches, which, in a strict sense, are unable to detect outliers, since the expected fraction of outliers has to be specified in advance. In order to overcome the inherent model selection problem of unsupervised kernel methods, a cross-validated likelihood criterion for selecting all free model parameters is applied. Experiments for detecting atypical objects in image databases effectively demonstrate the applicability of the proposed method in real-world scenarios.
Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation
Authors: Bo Liefeng , Wang Ling , Jiao Licheng
Published: May 2006

Kernel fisher discriminant analysis (KFD) is a successful approach to classification. It is well known that the key challenge in KFD lies in the selection of free parameters such as kernel parameters and regularization parameters. Here we focus on the feature-scaling kernel where each feature individually associates with a scaling factor. A novel algorithm, named FS-KFD, is developed to tune the scaling factors and regularization parameters for the feature-scaling kernel. The proposed algorithm is based on optimizing the smooth leave-one-out error via a gradient-descent method and has been demonstrated to be computationally feasible. FS-KFD is motivated by the following two fundamental facts: the leave-one-out error of KFD can be expressed in closed form and the step function can be approximated by a sigmoid function. Empirical comparisons on artificial and benchmark data sets suggest that FS-KFD improves KFD in terms of classification accuracy.
A Reflexive Neural Network for Dynamic Biped Walking Control
Authors: Geng Tao , Porr Bernd , Wörgötter Florentin
Published: May 2006

Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, & Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincaré map analysis applied on a model of the robot in order to assess its stability.
A Set Probability Technique for Detecting Relative Time Order Across Multiple Neurons
Authors: Smith Anne C. , Smith Peter
Published: May 2006

With the development of multielectrode recording techniques, it is possible to measure the cell firing patterns of multiple neurons simultaneously, generating a large quantity of data. Identification of the firing patterns within these large groups of cells is an important and a challenging problem in data analysis. Here, we consider the problem of measuring the significance of a repeat in the cell firing sequence across arbitrary numbers of cells. In particular, we consider the question, given a ranked order of cells numbered 1 to N, what is the probability that another sequence of length n contains j consecutive increasing elements? Assuming each element of the sequence is drawn with replacement from the numbers 1 through N, we derive a recursive formula for the probability of the sequence of length j or more. For n < 2j, a closed-form solution is derived. For n ≥ 2j, we obtain upper and lower bounds for these probabilities for various combinations of parameter values. These can be computed very quickly. For a typical case with small N (<10) and large n (<3000), sequences of 7 and 8 are statistically very unlikely. A potential application of this technique is in the detection of repeats in hippocampal place cell order during sleep. Unlike most previous articles on increasing runs in random lists, we use a probability approach based on sets of overlapping sequences.
Payoff-Monotonic Game Dynamics and the Maximum Clique Problem
Authors: Pelillo Marcello , Torsello Andrea
Published: May 2006

Evolutionary game-theoretic models and, in particular, the so-called replicator equations have recently proven to be remarkably effective at approximately solving the maximum clique and related problems. The approach is centered around a classic result from graph theory that formulates the maximum clique problem as a standard (continuous) quadratic program and exploits the dynamical properties of these models, which, under a certain symmetry assumption, possess a Lyapunov function. In this letter, we generalize previous work along these lines in several respects. We introduce a wide family of game-dynamic equations known as payoff-monotonic dynamics, of which replicator dynamics are a special instance, and show that they enjoy precisely the same dynamical properties as standard replicator equations. These properties make any member of this family a potential heuristic for solving standard quadratic programs and, in particular, the maximum clique problem. Extensive simulations, performed on random as well as DIMACS benchmark graphs, show that this class contains dynamics that are considerably faster than and at least as accurate as replicator equations. One problem associated with these models, however, relates to their inability to escape from poor local solutions. To overcome this drawback, we focus on a particular subclass of payoff-monotonic dynamics used to model the evolution of behavior via imitation processes and study the stability of their equilibria when a regularization parameter is allowed to take on negative values. A detailed analysis of these properties suggests a whole class of annealed imitation heuristics for the maximum clique problem, which are based on the idea of varying the parameter during the imitation optimization process in a principled way, so as to avoid unwanted inefficient solutions. Experiments show that the proposed annealing procedure does help to avoid poor local optima by initially driving the dynamics toward promising regions in state space. Furthermore, the models outperform state-of-the-art neural network algorithms for maximum clique, such as mean field annealing, and compare well with powerful continuous-based heuristics.
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
Authors: Pfister Jean-Pascal , Toyoizumi Taro , Barber David , Gerstner Wulfram
Published: May 2006

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. However, our approach gives no unique reason for synaptic depression under reversed spike timing. In fact, the presence and amplitude of depression of synaptic efficacies for reversed spike timing depend on how constraints are implemented in the optimization problem. Two different constraints, control of postsynaptic rates and control of temporal locality, are studied. The relation of our results to spike-timing-dependent plasticity and reinforcement learning is discussed.
An Oscillatory Neural Model of Multiple Object Tracking
Authors: Kazanovich Yakov , Borisyuk Roman
Published: May 2006

An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.
Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images
Authors: Eckes Christian , Triesch Jochen , von der Malsburg Christoph
Published: May 2006

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.
Support Vector Machines for Dyadic Data
Authors: Hochreiter Sepp , Obermayer Klaus
Published: May 2006

We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the column objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks; the second scheme leads to the selection of a small, informative set of row support objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real-world data sets. The results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets. In addition, a theoretical justification is provided for the new approach.
A Fast Learning Algorithm for Deep Belief Nets
Authors: Hinton Geoffrey E. , Osindero Simon , Teh Yee-Whye
Published: May 2006

We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.
How Inhibitory Oscillations Can Train Neural Networks and Punish Competitors
Authors: Norman Kenneth A. , Newman Ehren , Detre Greg , Polyn Sean
Published: May 2006

We present a new learning algorithm that leverages oscillations in the strength of neural inhibition to train neural networks. Raising inhibition can be used to identify weak parts of target memories, which are then strengthened. Conversely, lowering inhibition can be used to identify competitors, which are then weakened. To update weights, we apply the Contrastive Hebbian Learning equation to successive time steps of the network. The sign of the weight change equation varies as a function of the phase of the inhibitory oscillation. We show that the learning algorithm can memorize large numbers of correlated input patterns without collapsing and that it shows good generalization to test patterns that do not exactly match studied patterns.
The Artwork Caught by the Tail
Authors: Baker George
Published: May 2006

If it were married to logic, art would be living in incest, engulfing, swallowing its own tail.… —Tristan Tzara, Manifeste Dada 1918

The only word that is not ephemeral is the word death…. To death, to death, to death. The only thing that doesn't die is money, it just leaves on trips. —Francis Picabia, Manifeste Cannibale Dada, 1920
Two Unusual Projection Spaces
Authors: Naimark Michael
Published: May 2006

Two immersive projection environments, both unconventional, both exploring different methods of 3D and panoramic imaging, and both produced as art installations, are described. Displacements (1980–1984) recreated an interior living space using a panoramic motion picture method and relief projection. Be Now Here (1995–1997) recreated outdoor public plazas using a panoramic motion picture method, stereopsis, and four channel sound. Both installations were unusual in that no intentions existed for anything more general or useful than the installations themselves as individual artworks.
The Effects of Text Drawing Styles, Background Textures, and Natural Lighting on Text Legibility in Outdoor Augmented Reality
Authors: Gabbard Joseph L. , Swan J. Edward , Hix Deborah
Published: May 2006

A challenge in presenting augmenting information in outdoor augmented reality (AR) settings lies in the broad range of uncontrollable environmental conditions that may be present, specifically large-scale fluctuations in natural lighting and wide variations in likely backgrounds or objects in the scene. In this paper, we motivate the need for research on the effects of text drawing styles, outdoor background textures, and natural lighting on user performance in outdoor AR. We present a pilot study and a follow-on user-based study that examined the effects on user performance of outdoor background textures, changing outdoor illuminance values, and text drawing styles in a text identification task using an optical, see-through AR system. We report significant effects for all these variables, and discuss user interface design guidelines and ideas for future work.
Evolving an Immersive Medical Communication Skills Trainer
Authors: Johnsen Kyle , Dickerson Robert , Raij Andrew , Harrison Cyrus , Lok Benjamin , Stevens Amy , Lind D. Scott
Published: May 2006

This paper presents our experiences in evolving the Virtual Objective Structured Clinical Exam (VOSCE) system. This system allows medical students to experience the interaction between a patient and a medical doctor using natural methods of interaction with a high level of immersion. These features enable the system to provide training on medical communication skills. We discuss the experiences of a group of medical and physician assistant students that pilot tested the system. Further, we examine the impact of evolving the system based on their feedback. The VOSCE system's performance in subsequent studies has indicated that end-user feedback improvements have significantly impacted overall performance and efficacy.
Occlusion in Mirror-Based Co-Located Augmented Reality Systems
Authors: Mulder Jurriaan D.
Published: May 2006

This paper describes the incorporation of realistic occlusion effects into mirror-based, stereoscopic co-location augmented reality display systems. By adding a light-blocking device in the form of an LCD panel underneath the semitransparent mirror, the view of the physical world can be selectively blocked out such that virtual objects can fully occlude physical objects. Furthermore, by discarding pixels of the virtual objects rendered on the reflected display, physical objects seen through the semitransparent mirror and the transmissive LCD panel appear to occlude these virtual objects. The governing principles of the approach are described, and two algorithmic approaches (model based and vision based) for scene reconstruction and the generation of the occlusion masks are presented. Finally, a prototype implementation of the system is presented.
Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv
Authors: Silverman Barry G. , Johns Michael , Cornwell Jason , O'Brien Kevin
Published: May 2006

This paper focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of personality/cultural values and affect as well as biology/stress upon individual coping and group decision making. The first section offers an assessment of the state of the practice and of the need to integrate valid human performance moderator functions (PMFs) from traditionally separated subfields of the behavioral literature. The second section pursues this goal by postulating a unifying architecture and principles for integrating existing PMF theories and models. It also illustrates a PMF testbed called PMFserv created for implementating and studying how PMFs may contribute to such an architecture. To date it interconnects versions of PMFs on physiology and stress; personality, cultural and emotive processes (Cognitive Appraisal-OCC, value systems); perception (Gibsonian affordance); social processes (relations, identity, trust, nested intentionality); and cognition (affect- and stress-augmented decision theory, bounded rationality). The third section summarizes several usage case studies (asymmetric warfare, civil unrest, and political leaders) and concludes with lessons learned. Implementing and interoperating this broad collection of PMFs helps to open the agenda for research on syntheses that can help the field reach a greater level of maturity. The companion paper, Part II, presents a case study in using PMFserv for rapid scenario composability and realistic agent behavior.
Body-Based Haptic Interaction Model for Touch-Enabled Virtual Environments
Authors: Chen Hui , Sun Hanqiu
Published: May 2006

In this paper, we propose a novel body-based haptic interaction model that simulates the intrinsic physical properties of the tool and virtual objects during the haptic interaction. When tracing the haptic tool interacting with objects, the body-based force evaluation model based on Hertz's contact theory including both frictional and frictionless contacts is developed in our system. Physical properties of different object materials expressed by Poisson's ratio and Young's modulus are involved to simulate the realistic touch perception between the haptic tool and objects. The neighborhood of transmitted force is dynamically determined in relation to the contact load, and a discrete solution method is applied to accelerate the computation rate of realistic haptic interaction. Our experimental results have shown satisfactory performance of the body-based haptic model we have developed while interacting in touch-enabled virtual environments.
Place, Sense of Place, and Presence
Authors: Turner Phil , Turner Susan
Published: May 2006

Recreating real places—as distinct from virtual spaces or environments—using virtual reality technology raises a series of significant challenges. Fortunately there is a large body of existing research into the experience of place which might reasonably contribute to our understanding of the task. This paper reviews key aspects of the place literature, relates them to the concept of presence, and then illustrates their application in the context of virtual reality. We conclude that some modification of existing theories of sense of place is necessary for this context and outline proposals for further work.
New National Healthcare Information System
Authors: Bergamaschi Walter , Rapone Laura , Sorda Elianna
Published: May 2006

Currently the increased healthcare needs of the population call for strategic planning that provides tools to balance the costs of the system with the quality of services offered. The first requirement for this end is reliable, timely and comparable data. Therefore, a New Health Information System (NSIS) is needed that will contribute to achieving this goal.
Errors in clinical laboratories or errors in laboratory medicine?
Authors: Plebani Mario
Published: May 2006

Laboratory testing is a highly complex process and, although laboratory services are relatively safe, they are not as safe as they could or should be. Clinical laboratories have long focused their attention on quality control methods and quality assessment programs dealing with analytical aspects of testing. However, a growing body of evidence accumulated in recent decades demonstrates that quality in clinical laboratories cannot be assured by merely focusing on purely analytical aspects. The more recent surveys on errors in laboratory medicine conclude that in the delivery of laboratory testing, mistakes occur more frequently before (pre-analytical) and after (post-analytical) the test has been performed. Most errors are due to pre-analytical factors (46–68.2% of total errors), while a high error rate (18.5–47% of total errors) has also been found in the post-analytical phase. Errors due to analytical problems have been significantly reduced over time, but there is evidence that, particularly for immunoassays, interference may have a serious impact on patients. A description of the most frequent and risky pre-, intra- and post-analytical errors and advice on practical steps for measuring and reducing the risk of errors is therefore given in the present paper. Many mistakes in the Total Testing Process are called “laboratory errors”, although these may be due to poor communication, action taken by others involved in the testing process (e.g., physicians, nurses and phlebotomists), or poorly designed processes, all of which are beyond the laboratory's control. Likewise, there is evidence that laboratory information is only partially utilized. A recent document from the International Organization for Standardization (ISO) recommends a new, broader definition of the term “laboratory error” and a classification of errors according to different criteria. In a modern approach to total quality, centered on patients' needs and satisfaction, the risk of errors and mistakes in pre- and post-examination steps must be minimized to guarantee the total quality of laboratory services.
Point-of-care-testing and Clinical Governance
Authors: Pearson Joan
Published: May 2006

Clinical Governance is a quality framework encompassing risk management, clinical and cost effectiveness, and patient outcomes. These principles apply to point-of-care testing (POCT), which is becoming widely used within hospitals and the community. Formal regulation of POCT is variable throughout the world, but it is vital that the principles of clinical governance are followed in the implementation and management of POCT and laboratory professionals have a responsibility to advise users of POCT accordingly.
External Quality Assessment: an effective tool for Clinical Governance in Laboratory Medicine
Authors: Sciacovelli Laura , Secchiero Sandra , Zardo Lorena , Zaninotto Martina , Plebani Mario
Published: May 2006

The implementation of Clinical Governance will require a redefinition of duties and accountability as a prerequisite to develop and achieve an overall improvement in clinical care through a culture of assessment and monitoring of quality. External Quality Assessment Schemes (EQAS) are the main tool enabling laboratories to measure the quality of their results; they must carefully assess and monitor all elements contributing to the formulation of laboratory information (results, reference ranges/decisional levels, interpretative comments and diagnostic algorithms). There are different ways to design and manage a Scheme and EQAS coordinators are mainly responsible for its effectiveness. The present paper reports, as an example, some experiences of the Centre of Biomedical Research (CRB), which manages EQAS according to high quality specifications and laboratories' needs, that can reflect the Clinical Governance philosophy. Our findings show that EQAS are able to control all the above aspects and, if organisers are committed to fulfilling the responsibility and accountability principles, they will be of great value in quality assessment and in developing an External Quality Assurance Program (EQAP). This is an inter-laboratory comparison designed and conducted to assure the following: evaluation of participants' performance (by evaluating not only analytical performance, but also test interpretation, and advice for clinicians on laboratory requests and diagnosis); evaluation of method performance; and continuous education, training and help. The main aim of the activities of an EQAP in Laboratory Medicine is to sustain improvements in the quality of services provided by participating laboratories for the benefit of patients.
Teaching and Continuing Professional Development: an Italian experience
Authors: Putoto Giovanni
Published: May 2006

Continuing Professional Development (CPD) is a key factor for effective implementation of Clinical Governance. The development of a quality system is, in fact, strictly related to the development of competencies of healthcare professionals. Lifelong learning has emerged as a new paradigm that underlines the need to maintain and continuously improve knowledge, competencies and attitudes in the changing scenario of healthcare systems. CPD accreditation is another fundamental issue and a variety of voluntary and compulsory approaches can be recognised at an international level. The experience of the University Hospital of Padova may be useful to demonstrate that CPD can be addressed to implement a Clinical Governance project.
Technology to improve quality and accountability
Authors: Kay Jonathan
Published: May 2006

A body of evidence has been accumulated to demonstrate that current practice is not sufficiently safe for several stages of central laboratory testing. In particular, while analytical and perianalytical steps that take place within the laboratory are subjected to quality control procedures, this is not the case for several pre- and post-analytical steps. The ubiquitous application of auto-identification technology seems to represent a valuable tool for reducing error rates. A series of projects in Oxford has attempted to improve processes which support several areas of laboratory medicine, including point-of-care testing, blood transfusion, delivery and interpretation of reports, and support of decision-making by clinicians. The key tools are auto-identification, Internet communication technology, process re-engineering, and knowledge management.
Multi-Level Modeling in Systems Biology by Discrete Event Approaches (Mehrebenen-Modellierung in der Systembiologie innerhalb diskret-ereignisbasierter Ansätze)
Authors: Uhrmacher Adelinde , Kuttler Céline
Published: May 2006

The creation of models for heterogeneous and complex cellular networks is a central goal of Systems Biology. When modeling a biological network, one may wish to account for certain aspects in detail, while a bird's eye perspective would seem more appropriate for other parts. Multi-level models combine such overview and detail representations. We illustrate multi-level modeling with gene regulation of the Tryptophan operon in E. coli. We review three discrete event modeling formalisms and discuss model design therein: DEVS, STATECHARTS, and stochastic π-CALCULUS. This introductory presentation already reveals some of their respective virtues and shortcomings.
Chemical Organization Theory Applied to Virus Dynamics (Theorie chemischer Organisationen angewendet auf Infektionsmodelle)
Authors: Matsumaru Naoki , Centler Florian , Fenizio Pietro Speroni di , Dittrich Peter
Published: May 2006

Chemical organization theory has been proposed to provide a new perspective to study complex dynamical reaction networks. It decomposes a reaction network into overlapping sub-networks called organizations. An organization is an algebraically closed and self-maintaining set of molecular species. The set of organizations form a hierarchical “organizational structure”, which is here a lattice. In order to evaluate the usefulness of this approach we apply the theory to five models of immune response to HIV infection. We found four different lattices of organizations, which can be used as a first classification of the models. Furthermore, each organization found can be assigned to a functional state of the system. And finally, the lattice of organizations can be used to explain a treatment strategy on a more abstract level, i. e., as a movement from one organization into another.
Invariants of Gene-Assembly in Stichotrichous Ciliates (Invarianten der Gen-Assemblierung in Ciliaten der Unterklasse Stichotrichia)
Authors: Petre Ion
Published: May 2006

Ciliates have developed a puzzling genomic organization. Genes are broken into many pieces in the micronucleus, separated by non-coding sequences, and sometimes also shuffled and inverted. During sexual reproduction, all pieces are precisely assembled in the orthodox order to yield the transcriptionally active macronuclear genes. We give in this paper a brief survey of the genome structure in stichotrichous ciliates and present two molecular models proposed for gene assembly. We then establish a set of invariants for gene assembly. We prove that in any model based on the paradigm of pointer-directed assembly, the set of molecules excised during the assembly of a given gene, the linearity of the assembled gene, and its IES context are all uniquely defined by the micronuclear structure of the gene and thus, are invariants of the gene assembly process.
Artificial Immune Systems for IT-Security (Künstliche Immunsysteme für IT-Sicherheit)
Authors: Stibor Thomas , Eckert Claudia , Timmis Jonathan
Published: May 2006

The immune system is an impressive information processing system with many appealing properties for solving problems. Artificial immune systems are a paradigm inspired by the immune system and are used for solving computational and information processing problems. In this paper, we outline two different immune-inspired approaches typically used for IT-security problems. Specifically, we present one of the first proposed immune inspired approaches for network intrusion detection, this is then complimented with an overview of recent investigations on the latest immunological theories and how they may be exploited in IT-security. We then present an artificial immune system concept for database security which encompasses issues such as confidentiality of database information and prevention of privacy-preserving data mining.
Angiotensin-converting enzyme signalling in human preadipocytes and adipocytes
Authors: Böttcher Andreas , Böttcher Alfred , Schmitz Gerd , Schling Petra
Published: May 2006

Angiotensin-converting enzyme (ACE, kininase II) is a plasma membrane zinc metallopeptidase that acts as a key enzyme for the extracellular conversion of vasoactive peptides. Recently, ACE outside-in signalling in endothelial cells has been described. The present study tested the hypothesis that ACE signalling is not restricted to endothelial cells and may act as an additional peptide receptor on human preadipocytes and adipocytes. ACE protein levels were not changed during adipose conversion of human primary preadipocytes. The enzyme was primarily localized to the non-detergent-resistant fraction of the membrane and phosphorylated in non-dividing cells. Antibody arrays of whole cell lysate detected putative ACE-interacting proteins, which all share important roles in cell cycle control and/or apoptosis. These findings suggest that ACE is a versatile molecule, involved both in the regulation of extracellular peptide concentrations and direct intracellular signalling. In human adipose cells ACE may potentially influence exit from the cell cycle, differentiation, and programmed cell death signalling.
Hypothalamic response to leptin changes during a hormonally induced estrous cycle in rats
Authors: Sjöholm Kajsa , Carlsson Björn , Carlsson Lena
Published: May 2006

The leptin system regulates body fat mass through a feedback loop between adipose tissue and the hypothalamus. To test if leptin responsiveness may be regulated we assayed hypothalamic response to leptin during the estrous cycle; when changes in food intake are known to occur. Immature rats were treated with pregnant mare’s serum gonadotropin (PMSG) to induce synchronized follicular development, ovulation and corpus luteum formation. Leptin response was estimated by measuring the in vitro induction of tis11, a primary response gene activated by STAT3-dependent cytokines in hypothalamic explants after leptin stimulation. In addition, mRNA levels of the suppressor cytokine signaling-3 (SOCS-3), a possible mediator of leptin resistance, were analyzed. Serum leptin levels did not change between days 2 and day 3 (corresponding to proestrus and estrus, respectively) but the response to leptin was higher on day 2 than on day 3 (p=0.05). Food intake displayed a tendency towards downregulation between day 1 and day 2 (p=0.057), and a tendency towards upregulation between day 2 and day 3 (p=0.072), although the body weight increased on day of the study (p<0.0001). There was no significant difference in hypothalamic expression of SOCS-3 between day 2 and day 3. In conclusion, we have shown that leptin responsiveness changes during a hormonally induced estrous cycle in rats. Our data suggest that a change in the hypothalamic response to leptin may cause the cyclic feeding behavior seen in rats.
Long‐term safety and efficacy of etanercept in children with polyarticular‐course juvenile rheumatoid arthritis
Authors: Lovell Daniel J. , Reiff Andreas , Jones Olcay Y. , Schneider Rayfel , Nocton James , Stein Leonard D. , Gedalia Abraham , Ilowite Norman T. , Wallace Carol A. , Whitmore James B. , White Barbara , Giannini Edward H. ,   Pediatric Rheumatology Collaborative Study Group
Published: May 2006

Objective

Previous studies showed that etanercept treatment in patients with polyarticular‐course juvenile rheumatoid arthritis (JRA) provided rapid clinical improvement that was sustained for up to 2 years. The goal of our study was to provide data on safety and efficacy after 4 years of etanercept treatment in patients with JRA.

Methods

Patients with active polyarticular‐course JRA who participated in an efficacy study continued etanercept treatment in an open‐label extension. Safety was assessed by measuring rates of serious adverse events (SAEs) and serious infections. Efficacy was assessed using the American College of Rheumatology (ACR) Pediatric 30 criteria for improvement and standard measures of disease activity. (The ACR Pediatric 30 criteria are defined as improvement of ≥30% in at least 3 of 6 core response variables used to assess disease activity, with no more than 1 variable worsening by ≥30%.)

Results

Of the 69 patients who enrolled in the original efficacy study, 58 patients (84%) enrolled in the extension, 34 patients received etanercept treatment for ≥4 years, and 32 of these received complete efficacy assessments. The rate of SAEs was 0.13 per patient‐year, and the rate of serious infections was 0.04 per patient‐year, in a total etanercept exposure of 225 patient‐years. Eighty‐two percent of patients who received corticosteroids at any time during the extension were able to decrease their dosage to ≤5 mg/day prednisone equivalent. Of the 32 patients with complete efficacy data who received etanercept for ≥4 years, 94% achieved an ACR Pediatric 30 response and 78% achieved an ACR Pediatric 70 response at the last study visit.

Conclusion

Etanercept offers an acceptable safety profile in children with polyarticular‐course JRA and provides significant improvement in disease manifestations that are sustained for ≥4 years.