AtTRB1, a telomeric DNA-binding protein from Arabidopsis, is concentrated in the nucleolus and shows highly dynamic association with chromatin

AtTRB1, 2 and 3 are members of the SMH (single Myb histone) protein family, which comprises double-stranded DNA-binding proteins that are specific to higher plants. They are structurally conserved, containing a Myb domain at the N-terminus, a central H1/H5-like domain and a C-terminally located coiled-coil domain. AtTRB1, 2 and 3 interact through their Myb domain specifically with telomeric double-stranded DNA in vitro, while the central H1/H5-like domain interacts non-specifically with DNA sequences and mediates protein–protein interactions. Here we show that AtTRB1, 2 and 3 preferentially localize to the nucleus and nucleolus during interphase. Both the central H1/H5-like domain and the Myb domain from AtTRB1 can direct a GFP fusion protein to the nucleus and nucleolus. AtTRB1–GFP localization is cell cycle-regulated, as the level of nuclear-associated GFP diminishes during mitotic entry and GFP progressively re-associates with chromatin during anaphase/telophase. Using fluorescence recovery after photobleaching and fluorescence loss in photobleaching, we determined the dynamics of AtTRB1 interactions in vivo. The results reveal that AtTRB1 interaction with chromatin is regulated at two levels at least, one of which is coupled with cell-cycle progression, with the other involving rapid exchange.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Object displays and control of dynamic systems

Recent work has suggested that for some tasks, graphical displays which visually integrate information from more than one source offer an advantage over more traditional displays which present the same information in a separated format. Three experiments are described which investigate this claim using a task which requires subjects to control a dynamic system. In the first experiment, the integrated display is compared to two separated displays, one an animated mimic diagram, the other an alphanumeric display. The integrated display is shown to support better performance in a control task, but experiment 2 shows that part of this advantage may be due to its analogue nature. Experiment 3 considers performance on a fault detection task, and shows no difference between the integrated and separated displays. The paper concludes that previous claims made for integrated displays may not generalize from monitoring to control tasks.

Dynamic scaling of bred vectors in spatially extended chaotic systems

We unfold a profound relationship between the dynamics of finite-size perturbations in spatially extended chaotic systems and the universality class of Kardar-Parisi-Zhang (KPZ). We show how this relationship can be exploited to obtain a complete theoretical description of the bred vectors dynamics. The existence of characteristic length/time scales, the spatial extent of spatial correlations and how to time it, and the role of the breeding amplitude are all analyzed in the light of our theory. Implications to weather forecasting based on ensembles of initial conditions are also discussed.

Variations in the human cannabinoid receptor (CNR1) gene modulate striatal responses to happy faces.

Happy facial expressions are innate social rewards and evoke a response in the striatum, a region known for its role in reward processing in rats, primates and humans. The cannabinoid receptor 1 (CNR1) is the best-characterized molecule of the endocannabinoid system, involved in processing rewards. We hypothesized that genetic variation in human CNR1 gene would predict differences in the striatal response to happy faces. In a 3T functional magnetic resonance imaging (fMRI) scanning study on 19 Caucasian volunteers, we report that four single nucleotide polymorphisms (SNPs) in the CNR1 locus modulate differential striatal response to happy but not to disgust faces. This suggests a role for the variations of the CNR1 gene in underlying social reward responsivity. Future studies should aim to replicate this finding with a balanced design in a larger sample, but these preliminary results suggest neural responsivity to emotional and socially rewarding stimuli varies as a function of CNR1 genotype. This has implications for medical conditions involving hypo-responsivity to emotional and social stimuli, such as autism.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Stages of organizational transformation in transition economies: A dynamic capabilities approach

How do organizations previously dominated by the state develop dynamic capabilities that would support their growth in a competitive market economy? We develop a theoretical framework of organizational transformation that explains the processes by which organizations learn and develop dynamic capabilities in transition economies. Specifically, the framework theorizes about the importance of, and inter-relationships between, leadership, organizational learning, dynamic capabilities, and performance over three stages of transformation. Propositions derived from this framework explain the pre-conditions enabling organizational learning, the linkages between types of learning and functions of dynamic capabilities, and the feedback from dynamic capabilities to organizational learning that allows firms in transition economies to regain their footing and build long-term competitive advantage. We focus on transition contexts, where these processes have been magnified and thus offer new insights into strategizing in radically altered environments.