bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Learning of Mapping and its Jacobian in Multilayer Neural Networks
Authors: Lee Jeong-Woo , Oh Jun-Ho
Published: May 2006

There are some learning problems for which a priori information, such as the Jacobian of mapping, is available in addition to input-output examples. This kind of information can be beneficial in neural network learning if it can be embedded into the network. This article is concerned with the method for learning the mapping and available Jacobian simultaneously. The basic idea is to minimize the cost function, which is composed of the mapping error and the Jacobian error. Prior to developing the Jacobian learning rule, we develop an explicit and general method for computing the Jacobian of the neural network using the parameters computed in error backpropagation. Then the Jacobian learning rule is derived. The method is simpler and more general than tangent propagation (Simard, Victorri, Le Cun, & Denker, 1992). Through hybridization of the error backpropagation and the Jacobian learning, the hybrid learning algorithm is presented. The method shows good performance in accelerating the learning speed and improving generalization. And through computer experiments, it is shown that using the Jacobian synthesized from noise-corrupted data can accelerate learning speed.
On Bias Plus Variance
Authors: Wolpert David H.
Published: May 2006

This article presents several additive corrections to the conventional quadratic loss bias-plus-variance formula. One of these corrections is appropriate when both the target is not fixed (as in Bayesian analysis) and training sets are averaged over (as in the conventional bias plus variance formula). Another additive correction casts conventional fixed-trainingset Bayesian analysis directly in terms of bias plus variance. Another correction is appropriate for measuring full generalization error over a test set rather than (as with conventional bias plus variance) error at a single point. Yet another correction can help explain the recent counterintuitive bias-variance decomposition of Friedman for zero-one loss. After presenting these corrections, this article discusses some other loss function-specific aspects of supervised learning. In particular, there is a discussion of the fact that if the loss function is a metric (e.g., zero-one loss), then there is bound on the change in generalization error accompanying changing the algorithm's guess from h1 to h2, a bound that depends only on h1 and h2 and not on the target. This article ends by presenting versions of the bias-plus-variance formula appropriate for logarithmic and quadratic scoring, and then all the additive corrections appropriate to those formulas. All the correction terms presented are a covariance, between the learning algorithm and the posterior distribution over targets. Accordingly, in the (very common) contexts in which those terms apply, there is not a “bias-variance trade-off” or a “bias-variance dilemma,” as one often hears. Rather there is a bias-variance-covariance trade-off.
Note on Free Lunches and Cross-Validation
Authors: Goutte Cyril
Published: May 2006

The “no-free-lunch” theorems (Wolpert & Macready, 1995) have sparked heated debate in the computational learning community. A recent communication (Zhu & Rohwer, 1996) attempts to demonstrate the inefficiency of cross-validation on a simple problem. We elaborate on this result by considering a broader class of cross-validation. When used more strictly, cross-validation can yield the expected results on simple examples.
Multiunit Normalized Cross Correlation Differs from the Average Single-Unit Normalized Correlation
Authors: Bedenbaugh Purvis , Gerstein George L.
Published: May 2006

As the technology for simultaneously recording from many brain locations becomes more available, more and more laboratories are measuring the cross-correlation between single-neuron spike trains, and between composite spike trains derived from several undiscriminated cells recorded on a single electrode (multiunit clusters). The relationship between single-unit correlations and multiunit cluster correlations has not yet been fully explored.

We calculated the normalized cross-correlation (NCC) between single unit spike trains and between small clusters of units recorded in the rat somatosensory cortex. The NCC between small clusters of units was larger than the NCC between single units. To understand this result, we investigated the scaling of the NCC with the number of units in a cluster.

Multiunit cross-correlation can be a more sensitive detector of neuronal relationship than single-unit cross-correlation. However, changes in multiunit cross-correlation are difficult to interpret uniquely because they depend on the number of cells recorded on each electrode and because they can arise from changes in the correlation between cells recorded on a single electrode or from changes in the correlation between cells recorded on two electrodes.
A Simple Common Contexts Explanation for the Development of Abstract Letter Identities
Authors: Polk Thad A. , Farah Martha J.
Published: May 2006

Abstract letter identities (ALIs) are an early representation in visual word recognition that are specific to written language. They do not reflect visual or phonological features, but rather encode the identities of letters independent of case, font, sound, and so forth. How could the visual system come to develop such a representation? We propose that because many letters look similar regardless of case, font, and other characteristics, these provide common contexts for visually dissimilar uppercase and lowercase forms of other letters (e.g., e between k and y in key and E in the visually similar context K-Y). Assuming that the distribution of words' relative frequencies is comparable in upper and lowercase (that just as key is more frequent than pew, KEY is more frequent than PEW), these common contexts will also be similarly distributed in the two cases. We show how this statistical regularity could lead Hebbian learning to produce ALIs in a competitive architecture. We present a self-organizing artificial neural network that illustrates this idea and produces ALIs when presented with the most frequent words from a beginning reading corpus, as well as with artificial input.
A Unifying Objective Function for Topographic Mappings
Authors: Goodhill Geoffrey J. , Sejnowski Terrence J.
Published: May 2006

Many different algorithms and objective functions for topographic mappings have been proposed. We show that several of these approaches can be seen as particular cases of a more general objective function. Consideration of a very simple mapping problem reveals large differences in the form of the map that each particular case favors. These differences have important consequences for the practical application of topographic mapping methods.
Faithful Representation of Separable Distributions
Authors: Lin Juan K. , Grier David G. , Cowan Jack D.
Published: May 2006

A geometric approach to data representation incorporating information theoretic ideas is presented. The task of finding a faithful representation, where the input distribution is evenly partitioned into regions of equal mass, is addressed. For input consisting of mixtures of statistically independent sources, we treat independent component analysis (ICA) as a computational geometry problem. First, we consider the separation of sources with sharply peaked distribution functions, where the ICA problem becomes that of finding high-density directions in the input distribution. Second, we consider the more general problem for arbitrary input distributions, where ICA is transformed into the task of finding an aligned equipartition. By modifying the Kohonen self-organized feature maps, we arrive at neural networks with local interactions that optimize coding while simultaneously performing source separation. The local nature of our approach results in networks with nonlinear ICA capabilities.
A Constructive, Incremental-Learning Network for Mixture Modeling and Classification
Authors: Williamson James R.
Published: May 2006

Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a gaussian mixture model of the input space, with learned mappings from the mixture components to output classes. We show a close relationship between GAM and the well-known expectation-maximization (EM) approach to mixture modeling. GAM outper forms an EM classification algorithm on three classification benchmarks, thereby demonstrating the advantage of the ART match criterion for regulating learning and the ARTMAP match tracking operation for incorporating environmental feedback in supervised learning situations.
Minimax Entropy Principle and Its Application to Texture Modeling
Authors: Zhu Song Chun , Wu Ying Nian , Mumford David
Published: May 2006

This article proposes a general theory and methodology, called the minimax entropy principle, for building statistical models for images (or signals) in a variety of applications. This principle consists of two parts. The first is the maximum entropy principle for feature binding (or fusion): for a given set of observed feature statistics, a distribution can be built to bind these feature statistics together by maximizing the entropy over all distributions that reproduce them. The second part is the minimum entropy principle for feature selection: among all plausible sets of feature statistics, we choose the set whose maximum entropy distribution has the minimum entropy. Computational and inferential issues in both parts are addressed; in particular, a feature pursuit procedure is proposed for approximately selecting the optimal set of features. The minimax entropy principle is then corrected by considering the sample variation in the observed feature statistics, and an information criterion for feature pursuit is derived. The minimax entropy principle is applied to texture modeling, where a novel Markov random field (MRF) model, called FRAME (filter, random field, and minimax entropy), is derived, and encouraging results are obtained in experiments on a variety of texture images. The relationship between our theory and the mechanisms of neural computation is also discussed.
Solitary Waves of Integrate-and-Fire Neural Fields
Authors: Horn David , Opher Irit
Published: May 2006

Arrays of interacting identical neurons can develop coherent firing patterns, such as moving stripes that have been suggested as possible explanations of hallucinatory phenomena. Other known formations include rotating spirals and expanding concentric rings. We obtain all of them using a novel two-variable description of integrate-and-fire neurons that allows for a continuum formulation of neural fields. One of these variables distinguishes between the two different states of refractoriness and depolarization and acquires topological meaning when it is turned into a field. Hence, it leads to a topologic characterization of the ensuing solitary waves, or excitons. They are limited to pointlike excitations on a line and linear excitations, including all the examples noted above, on a two dimensional surface. A moving patch of firing activity is not an allowed solitary wave on our neural surface. Only the presence of strong inhomogeneity that destroys the neural field continuity allows for the appearance of patchy incoherent firing patterns driven by excitatory interactions.
Long Short-Term Memory
Authors: Hochreiter Sepp , Schmidhuber Jürgen
Published: May 2006

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration
Authors: Lin Tzu-Chao , Yu Pao-Ta
Published: May 2006

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.
Dynamical Behaviors of Delayed Neural Network Systems with Discontinuous Activation Functions
Authors: Lu Wenlian , Chen Tianping
Published: May 2006

In this letter, without assuming the boundedness of the activation functions, we discuss the dynamics of a class of delayed neural networks with discontinuous activation functions. A relaxed set of sufficient conditions is derived, guaranteeing the existence, uniqueness, and global stability of the equilibrium point. Convergence behaviors for both state and output are discussed. The constraints imposed on the feedback matrix are independent of the delay parameter and can be validated by the linear matrix inequality technique. We also prove that the solution of delayed neural networks with discontinuous activation functions can be regarded as a limit of the solutions of delayed neural networks with high-slope continuous activation functions.
A Generalized Contrast Function and Stability Analysis for Overdetermined Blind Separation of Instantaneous Mixtures
Authors: Zhu Xiao-Long , Zhang Xian-Da , Ye Ji-Min
Published: May 2006

In this letter, the problem of blind separation of n independent sources from their m linear instantaneous mixtures is considered. First, a generalized contrast function is defined as a valuable extension of the existing classical and nonsymmetrical contrast functions. It is applicable to the overdetermined blind separation (m > n) with an unknown number of sources, because not only independent components but also redundant ones are allowed in the outputs of a separation system. Second, a natural gradient learning algorithm developed primarily for the complete case (m = n) is shown to work as well with an n × m or m × m separating matrix, for each optimizes a certain mutual information contrast function. Finally, we present stability analysis for a newly proposed generalized orthogonal natural gradient algorithm (which can perform the overdetermined blind separation when n is unknown), obtaining an expectable result that its local stability conditions are slightly stricter than those of the conventional natural gradient algorithm using an invertible mixing matrix (m = n).
An Invariance Property of Predictors in Kernel-Induced Hypothesis Spaces
Authors: Ancona Nicola , Stramaglia Sebastiano
Published: May 2006

We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer remains unchanged if we constrain the risk minimization to hypothesis spaces induced by suitable kernel functions. We show that not all kernel-induced hypothesis spaces enjoy this property. We present sufficient conditions ensuring that the risk minimizer does not change and show that they hold for inhomogeneous polynomial and gaussian radial basis function kernels. We also provide examples of kernel-induced hypothesis spaces whose risk minimizer changes if independent variables are added as input.
Modeling Sensorimotor Learning with Linear Dynamical Systems
Authors: Cheng Sen , Sabes Philip N.
Published: May 2006

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation— the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a sub-stantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.
Changing Roles for Temporal Representation of Odorant During the Oscillatory Response of the Olfactory Bulb
Authors: Kim Soyoun , Singer Benjamin H. , Zochowski Michal
Published: May 2006

It has been hypothesized that the brain uses combinatorial as well as temporal coding strategies to represent stimulus properties. The mechanisms and properties of the temporal coding remain undetermined, although it has been postulated that oscillations can mediate formation of this type of code. Here we use a generic model of the vertebrate olfactory bulb to explore the possible role of oscillatory behavior in temporal coding. We show that three mechanisms—synaptic inhibition, slow self-inhibition and input properties—mediate formation of a temporal sequence of simultaneous activations of glomerular modules associated with specific odorants within the oscillatory response. The sequence formed depends on the relative properties of odorant features and thus may mediate discrimination of odorants activating overlapping sets of glomeruli. We suggest that period-doubling transitions may be driven through excitatory feedback from a portion of the olfactory network acting as a coincidence modulator. Furthermore, we hypothesize that the period-doubling transition transforms the temporal code from a roster of odorant components to a signal of odorant identity and facilitates discrimination of individual odorants within mixtures.
Memory Capacity for Sequences in a Recurrent Network with Biological Constraints
Authors: Leibold Christian , Kempter Richard
Published: May 2006

The CA3 region of the hippocampus is a recurrent neural network that is essential for the storage and replay of sequences of patterns that represent behavioral events. Here we present a theoretical framework to calculate a sparsely connected network's capacity to store such sequences. As in CA3, only a limited subset of neurons in the network is active at any one time, pattern retrieval is subject to error, and the resources for plasticity are limited. Our analysis combines an analytical mean field approach, stochastic dynamics, and cellular simulations of a time-discrete McCulloch-Pitts network with binary synapses. To maximize the number of sequences that can be stored in the network, we concurrently optimize the number of active neurons, that is, pattern size, and the firing threshold. We find that for one-step associations (i.e., minimal sequences), the optimal pattern size is inversely proportional to the mean connectivity c, whereas the optimal firing threshold is independent of the connectivity. If the number of synapses per neuron is fixed, the maximum number P of stored sequences in a sufficiently large, nonmodular network is independent of its number N of cells. On the other hand, if the number of synapses scales as the network size to the power of 3/2, the number of sequences P is proportional to N. In other words, sequential memory is scalable. Further-more, we find that there is an optimal ratio r between silent and nonsilent synapses at which the storage capacity α = P/[c (1 +r)N] assumes a maximum. For long sequences, the capacity of sequential memory is about one order of magnitude below the capacity for minimal sequences, but otherwise behaves similar to the case of minimal sequences. In a biologically inspired scenario, the information content per synapse is far below theoretical optimality, suggesting that the brain trades off error tolerance against information content in encoding sequential memories.
Strongly Improved Stability and Faster Convergence of Temporal Sequence Learning by Using Input Correlations Only
Authors: Porr Bernd , Wörgötter Florentin
Published: May 2006

Currently all important, low-level, unsupervised network learning algorithms follow the paradigm of Hebb, where input and output activity are correlated to change the connection strength of a synapse. However, as a consequence, classical Hebbian learning always carries a potentially destabilizing autocorrelation term, which is due to the fact that every input is in a weighted form reflected in the neuron's output. This self-correlation can lead to positive feedback, where increasing weights will increase the output, and vice versa, which may result in divergence. This can be avoided by different strategies like weight normalization or weight saturation, which, however, can cause different problems. Consequently, in most cases, high learning rates cannot be used for Hebbian learning, leading to relatively slow convergence. Here we introduce a novel correlation-based learning rule that is related to our isotropic sequence order (ISO) learning rule (Porr & Wörgötter, 2003a), but replaces the derivative of the output in the learning rule with the derivative of the reflex input. Hence, the new rule uses input correlations only, effectively implementing strict heterosynaptic learning. This looks like a minor modification but leads to dramatically improved properties. Elimination of the output from the learning rule removes the unwanted, destabilizing autocorrelation term, allowing us to use high learning rates. As a consequence, we can mathematically show that the theoretical optimum of one-shot learning can be reached under ideal conditions with the new rule. This result is then tested against four different experimental setups, and we will show that in all of them, very few (and sometimes only one) learning experiences are needed to achieve the learning goal. As a consequence, the new learning rule is up to 100 times faster and in general more stable than ISO learning.
Optimal Tuning Widths in Population Coding of Periodic Variables
Authors: Montemurro Marcelo A. , Panzeri Stefano
Published: May 2006

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.
Bridget Riley's Eye/Body Problem
Authors: Lee Pamela M.
Published: May 2006

Let the hand be numb, but let the eye be agile, perspicacious, cunning. —Félix Fénéon, as paraphrased by Bridget Riley
Efficient Collision Culling among Deformable Objects using Graphics Processors
Authors: Govindaraju Naga K. , Lin Ming C. , Manocha Dinesh
Published: May 2006

We present a fast collision culling algorithm for performing inter- and intra-object collision detection among complex models using graphics hardware. Our algorithm utilizes visibility queries on the GPUs to eliminate a subset of geometric primitives that are not in close proximity and computes a potentially colliding set (PCS) of primitives. We perform no precomputation and the algorithm proceeds in multiple stages: object-level PCS computation, subobject level PCS computation, followed by exact collision detection. We extend our PCS computation algorithm to perform intra-object or self-collisions between complex models. Furthermore, we describe a novel visibility-based classification scheme to reduce the size of potentially-colliding sets of objects and primitives, and the number of visibility queries for further improving the performance and culling efficiency. We have implemented our algorithm on a PC with an NVIDIA GeForce FX 6800 Ultra graphics card and applied it to three complex simulations, each consisting of objects with tens of thousands of triangles. In practice, we are able to compute all the self-collisions for cloth simulation up to image-space precision at interactive rates.
Shifting balances in a ‘craft community’
Authors: Venkatesan Soumhya
Published: May 2006

This article seeks to critically examine conceptualisations of community through the study of a ‘traditional craft industry’ in the Tamil town of Pattamadai. Arguing through detailed ethnographic analysis that both the substantialist and constructivist models of community ignore individual agency as well as tension within a group characterised as a ‘craft community’, this article attempts to present a nuanced portrait of a group of people bound together by locality, kinship and shared occupation, yet pursuing their own individual goals, especially in response to development interventions. The article focuses on three interconnected issues: (i) the way in which community is conceptualised in the context of ‘traditional craft groups’; (ii) the lived experience of one particular group of craft producers; and (iii) the role of individuals in the interface between ideology and practice.
The Methamphetamine-Sensitive Circadian Oscillator (MASCO) in Mice
Authors: Tataroglu Özgür , Davidson Alec J. , Benvenuto Luke J. , Menaker Michael
Published: May 2006

The suprachiasmatic nucleus (SCN) orchestrates synchrony among many peripheral oscillators and is required for circadian rhythms of locomotor activity and many physiological processes. However, the unique effects of methamphetamine (MAP) on circadian behavior suggest the presence of an SCN-independent, methamphetamine-sensitive circadian oscillator (MASCO). Substantial data collected using rat models show that chronic methamphetamine dramatically lengthens circadian period of locomotor activity rhythms and induces rhythms in animals lacking an SCN. However, the anatomical substrate and the molecular components of the MASCO are unknown. The response to MAP is less well studied in mice, a model that would provide the genetic tools to probe the molecular components of this extra-SCN oscillator. The authors tested the effects of chronic MAP on 2 strains of intact and SCN-lesioned mice in constant dark and constant light. Furthermore, they applied various MAP availability schedules to SCN-lesioned mice to confirm the circadian nature of the underlying oscillator. The results indicate that this oscillator has circadian properties. In intact mice, the MASCO interacts with the SCN in a manner that is strain, sex, and dose dependent. In SCN-lesioned mice, it induces robust free-running locomotor rhythmicity, which persists for up to 14 cycles after methamphetamine is withdrawn. In the future, localization of the MASCO and characterization of its underlying molecular mechanism, as well as its interactions with other oscillators in the body, will be essential to a complete understanding of the organization of the mammalian circadian system.
Phase Angle Difference Alters Coupling Relations of Functionally Distinct Circadian Oscillators Revealed by Rhythm Splitting
Authors: Gorman Michael R. , Steele Nicholas A.
Published: May 2006

The interactions (i.e., coupling) between multiple oscillators of a circadian system determine basic properties of the integrated pacemaker. Unfortunately, there are few experimental models to investigate the putative interactions of functionally defined oscillators comprising the mammalian circadian pacemaker. Here the authors induce in hamsters a novel circadian entrainment pattern that is characterized by the daily expression of robust wheel-running activity in each scotophase of a 24-h light:dark:light:dark cycle. The daily activity bouts are mediated by 2 circadian oscillators, here designated “daytime” and “nighttime,” that have been temporally dissociated under this light regime. To assess the phase dependence of interactions between oscillatory components, the phase relationship of the 2 daily scotophases was manipulated over a 4-h range, and the timing of activity of the daytime and nighttime components under entrained and probe conditions was examined. The average phase angle of entrainment and the day-to-day variability of activity onset of each activity component depended on the phase relationship of the respective scotophases and not on whether the component occurred in the daytime or the nighttime. Short-term denial of wheel access subsequently influenced amount and duration of wheel running but not timing of its onset, suggesting that only the former measures depend on a homeostatic mechanism sensitive to the time elapsed since prior intense running. Replacement of individual photophases with darkness revealed phase attraction between oscillators that was not dependent on the phase relationship of component oscillators but differed for daytime versus nighttime activity components. Entrainment patterns shown here cannot be accounted for by only nonparametric actions of light. Instead, the phase-dependent interactions of oscillators strongly influence entrainment properties, whereas intrinsic functional differences in dissociated oscillators apparently influence their attraction in darkness. This model system may be ideal for identifying genomic and physiological factors that mediate these interactions and thus contribute importantly to system properties of the mammalian circadian clock.
Workplace Interactions that Facilitate or Impede Reflective Practice
Authors: Heel David , Sparrow John , Ashford Robert
Published: May 2006

This article highlights a study undertaken in the workplace that observed the features of a National Health Service (NHS) general manager's job that determined ‘reflective space’ at work. The study was initiated following the suggestion that the workplace might conspire to limit reflective practice amongst NHS general managers who would ordinarily espouse the value of such an approach. Assessing this inference would, arguably, illuminate the relative productivity of meetings and workplace events and assist in future application amongst NHS employees. The study did not attempt to question whether NHS general managers were armed with the competencies of reflection but whether they were able to apply it having developed the skills and what might constrain them. In doing so there would then be opportunities to challenge convention through the application of a reflective tool.

The study involved the classification of work events where reflection was notably high or low. An analysis of data indicated five major characteristics of the reflective practitioner's workplace that can inhibit or facilitate reflective practice. This has enabled the development of a reflective tool to assist NHS general managers. The tool might therefore be added to the personal management development ‘toolkit’ that each general manager might wish to access.
Management of Hospital Pharmacy in Private Sector
Authors: Kaur Jasleen , Bapna J.S. , Bhoi Nilakantha , Singh O.P.
Published: May 2006

The functioning of a pharmacy at a private sector multi-specialty hospital was studied with the objective of improving its services. It was found that average number of drugs per prescription was 3.78. This was higher than the normal 1.8 to 2. Though the dispensing time was 6.51 mins, only 0.57 mins was utilised for providing the drug information. Al-though, the pharmacy used inventory management tools such as ABC and FSN analysis, other tools such as VEN analysis, EOQ and safety stock were not used. There is a need for improving the services through utilisation of modern management tools. The pharmacy was located outside the hospital, which was inconvenient to the patients. The pharmacists had to substitute the drug in 54 per cent of the new cases and in 46 per cent of the old cases. In case the prescribed brand was not available the pharmacist invariably consulted the doctor before the substitution with another drug. However, the patients were very unhappy about it. Patients also resented being dispensed the whole strip, which they saw as a waste of money. The study indicates that the quality of services can be improved consider-ably through good managerial practices.
The molecular classification of multiple myeloma
Authors: Zhan Fenghuang , Huang Yongsheng , Colla Simona , Stewart James P. , Hanamura Ichiro , Gupta Sushil , Epstein Joshua , Yaccoby Shmuel , Sawyer Jeffrey , Burington Bart , Anaissie Elias , Hollmig Klaus , Pineda-Roman Mauricio , Tricot Guido , van Rhee Frits , Walker Ronald , Zangari Maurizio , Crowley John , Barlogie Bart , Shaughnessy John D.
Published: May 2006

To better define the molecular basis of multiple myeloma (MM), we performed unsupervised hierarchic clustering of mRNA expression profiles in CD138-enriched plasma cells from 414 newly diagnosed patients who went on to receive high-dose therapy and tandem stem cell transplants. Seven disease subtypes were validated that were strongly influenced by known genetic lesions, such as c-MAF– and MAFB-, CCND1- and CCND3-, and MMSET-activating translocations and hyperdiploidy. Indicative of the deregulation of common pathways by gene orthologs, common gene signatures were observed in cases with c-MAF and MAFB activation and CCND1 and CCND3 activation, the latter consisting of 2 subgroups, one characterized by expression of the early B-cell markers CD20 and PAX5. A low incidence of focal bone disease distinguished one and increased expression of proliferation-associated genes of another novel subgroup. Comprising varying fractions of each of the other 6 subgroups, the proliferation subgroup dominated at relapse, suggesting that this signature is linked to disease progression. Proliferation and MMSET-spike groups were characterized by significant overexpression of genes mapping to chromosome 1q, and both exhibited a poor prognosis relative to the other groups. A subset of cases with a predominating myeloid gene expression signature, excluded from the profiling analyses, had more favorable baseline characteristics and superior prognosis to those lacking this signature.
An integrated approach to the prediction of domain-domain interactions
Authors: Lee Hyunju , Deng Minghua , Sun Fengzhu , Chen Ting
Published: May 2006

Background

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.

Results

We combine protein interaction data sets from multiple species, molecular sequences, and gene ontology to construct a set of high-confidence domain-domain interactions. First, we propose a new measure, the expected number of interactions for each pair of domains, to score domain interactions based on protein interaction data in one species and show that it has similar performance as the E-value defined by Riley et al. [1]. Our new measure is applied to the protein interaction data sets from yeast, worm, fruitfly and humans. Second, information on pairs of domains that coexist in known proteins and on pairs of domains with the same gene ontology function annotations are incorporated to construct a high-confidence set of domain-domain interactions using a Bayesian approach. Finally, we evaluate the set of domain-domain interactions by comparing predicted domain interactions with those defined in iPfam database [2, 3] that were derived based on protein structures. The accuracy of predicted domain interactions are also confirmed by comparing with experimentally obtained domain interactions from H. pylori [4]. As a result, a total of 2,391 high-confidence domain interactions are obtained and these domain interactions are used to unravel detailed protein and domain interactions in several protein complexes.

Conclusion

Our study shows that integration of multiple biological data sets based on the Bayesian approach provides a reliable framework to predict domain interactions. By integrating multiple data sources, the coverage and accuracy of predicted domain interactions can be significantly increased.
Cytogenetic characterization and H-ras associated transformation of immortalized human mammary epithelial cells
Authors: Rao Krishna , Alper Özge , Opheim Kent E , Bonnet George , Wolfe Kristine , Bryant Eileen , Larivee Siobhan O'Hara , Porter Peggy , McDougall James K
Published: May 2006

Introduction

Immortalization is a key step in malignant transformation, but immortalization alone is insufficient for transformation. Human mammary epithelial cell (HMEC) transformation is a complex process that requires additional genetic changes beyond immortalization and can be accomplished in vitro by accumulation of genetic changes and expression of H-ras.

Methods

HMEC were immortalized by serial passaging and transduction with the catalytic subunit of the human telomerase gene (hTERT). The immortalized cells were passaged in vitro and studied by a combination of G- banding and Spectral Karyotyping (SKY). H-ras transduced, hTERT immortalized cells were cloned in soft agar and injected into nude mice. Extensive analysis was performed on the tumors that developed in nude mice, including immunohistochemistry and western blotting.

Results

Immortal HMEC alone were not tumorigenic in γ-irradiated nude mice and could not grow in soft agar. Late passage hTERT immortalized HMEC from a donor transduced with a retroviral vector containing the mutant, autoactive, human H-ras 61L gene acquired anchorage independent growth properties and the capacity for tumorigenic growth in vivo. The tumors that developed in the nude mice were poorly differentiated epithelial carcinomas that continued to overexpress ras. These cells were resistant to doxorubicin mediated G1/S phase arrest but were sensitive to treatment with a farnesyltransferase inhibitor.

Conclusion

Some of the cytogenetic changes are similar to what is observed in premalignant and malignant breast lesions. Despite these changes, late passage immortal HMEC are not tumorigenic and could only be transformed with overexpression of a mutant H-ras oncogene.
Expression and processing of the Hepatitis E virus ORF1 nonstructural polyprotein
Authors: Sehgal Deepak , Thomas Saijo , Chakraborty Mahua , Jameel Shahid
Published: May 2006

Background

The ORF1 of hepatitis E virus (HEV) encodes a nonstructural polyprotein of ~186 kDa that has putative domains for four enzymes: a methyltransferase, a papain-like cysteine protease, a RNA helicase and a RNA dependent RNA polymerase. In the absence of a culture system for HEV, the ORF1 expressed using bacterial and mammalian expression systems has shown an ~186 kDa protein, but no processing of the polyprotein has been observed. Based on these observations, it was proposed that the ORF1 polyprotein does not undergo processing into functional units. We have studied ORF1 polyprotein expression and processing through a baculovirus expression vector system because of the high level expression and post-translational modification abilities of this system.

Results

The baculovirus expressed ORF1 polyprotein was processed into smaller fragments that could be detected using antibodies directed against tags engineered at both ends. Processing of this ~192 kDa tagged ORF1 polyprotein and accumulation of lower molecular weight species took place in a time-dependent manner. This processing was inhibited by E-64d, a cell-permeable cysteine protease inhibitor. MALDI-TOF analysis of a 35 kDa processed fragment revealed 9 peptide sequences that matched the HEV methyltransferase (MeT), the first putative domain of the ORF1 polyprotein. Antibodies to the MeT region also revealed an ORF1 processing pattern identical to that observed for the N-terminal tag.

Conclusion

When expressed through baculovirus, the ORF1 polyprotein of HEV was processed into smaller proteins that correlated with their proposed functional domains. Though the involvement of non-cysteine protease(s) could not be be ruled out, this processing mainly depended upon a cysteine protease.
Persistent and high levels of Hes1 expression regulate boundary formation in the developing central nervous system
Authors: Baek Joung Hee , Hatakeyama Jun , Sakamoto Susumu , Ohtsuka Toshiyuki , Kageyama Ryoichiro
Published: May 2006

The developing central nervous system is partitioned into compartments by boundary cells, which have different properties than compartment cells, such as forming neuron-free zones, proliferating more slowly and acting as organizing centers. We now report that in mice the bHLH factor Hes1 is persistently expressed at high levels by boundary cells but at variable levels by non-boundary cells. Expression levels of Hes1 display an inverse correlation to those of the proneural bHLH factor Mash1, suggesting that downregulation of Hes1 leads to upregulation of Mash1 in non-boundary regions,whereas persistent and high Hes1 expression constitutively represses Mash1 in boundary regions. In agreement with this notion, in the absence of Hes1 and its related genes Hes3 and Hes5, proneural bHLH genes are ectopically expressed in boundaries, resulting in ectopic neurogenesis and disruption of the organizing centers. Conversely, persistent Hes1 expression in neural progenitors prepared from compartment regions blocks neurogenesis and reduces cell proliferation rates. These results indicate that the mode of Hes1 expression is different between boundary and non-boundary cells, and that persistent and high levels of Hes1 expression constitutively repress proneural bHLH gene expression and reduce cell proliferation rates,thereby forming boundaries that act as the organizing centers.
BDNF increases synapse density in dendrites of developing tectal neurons in vivo
Authors: Sanchez Analiza L. , Matthews Benjamin J. , Meynard Margarita M. , Hu Bing , Javed Sana , Cohen-Cory Susana
Published: May 2006

Neuronal connections are established through a series of developmental events that involve close communication between pre- and postsynaptic neurons. In the visual system, BDNF modulates the development of neuronal connectivity by influencing presynaptic retinal ganglion cell (RGC) axons. Increasing BDNF levels in the optic tectum of Xenopus tadpoles significantly increases both axon arborization and synapse density per axon terminal within a few hours of treatment. Here, we have further explored the mechanisms by which BDNF shapes synaptic connectivity by imaging tectal neurons, the postsynaptic partners of RGCs. Individual neurons were co-labeled with DsRed2 and a GFP-tagged postsynaptic density protein (PSD95-GFP) to visualize dendritic morphology and postsynaptic specializations simultaneously in vivo. Immunoelectron microscopy confirmed that PSD95-GFP predominantly localized to ultrastructurally identified synapses. Time-lapse confocal microscopy of individual, double-labeled neurons revealed a coincident, activity-dependent mechanism of synaptogenesis and axon and dendritic arbor growth, which is differentially modulated by BDNF. Microinjection of BDNF into the optic tectum significantly increased synapse number in tectal neuron dendritic arbors within 24 hours, without significantly influencing arbor morphology. BDNF function-blocking antibodies had opposite effects. The BDNF-elicited increase in synapse number complements the previously observed increase in presynaptic sites on RGC axons. These results, together with the timescale of the response by tectal neurons, suggest that the effects of BDNF on dendritic synaptic connectivity are secondary to its effects on presynaptic RGCs. Thus, BDNF influences synaptic connectivity in multiple ways: it enhances axon arbor complexity expanding the synaptic territory of the axon, while simultaneously coordinating synapse formation and stabilization with individual postsynaptic cells.
TBX5 is required for embryonic cardiac cell cycle progression
Authors: Goetz Sarah C. , Brown Daniel D. , Conlon Frank L.
Published: May 2006

Despite the critical importance of TBX5 in normal development and disease,relatively little is known about the mechanisms by which TBX5 functions in the embryonic heart. Our present studies demonstrate that TBX5 is necessary to control the length of the embryonic cardiac cell cycle, with depletion of TBX5 leading to cardiac cell cycle arrest in late G1- or early S-phase. Blocking cell cycle progression by TBX5 depletion leads to a decrease in cardiac cell number, an alteration in the timing of the cardiac differentiation program, defects in cardiac sarcomere formation, and ultimately, to cardiac programmed cell death. In these studies we have also established that terminally differentiated cardiomyocytes retain the capacity to undergo cell division. We further show that TBX5 is sufficient to determine the length of the embryonic cardiac cell cycle and the timing of the cardiac differentiation program. Thus, these studies establish a role for TBX5 in regulating the progression of the cardiac cell cycle.