Improper organization of the actin cytoskeleton affects protein synthesis at initiation

Although the actin cytoskeleton and the translation machinery are considered to be separate cellular complexes, growing evidence supports overlapping regulation of the two systems. Because of its interaction with actin, the eukaryotic translation elongation factor 1A (eEF1A) is proposed to be a regulator or link between these processes. Using a genetic approach with the yeast Saccharomyces cerevisiae, specific regions of eEF1A responsible for actin interactions and bundling were identified. Five new mutations were identified along one face of eEF1A. Dramatic changes in cell growth, cell morphology, and actin cable and patch formation as well as a unique effect on total translation in strains expressing the F308L or S405P eEF1A mutant form were observed. The translation effects do not correlate with reduced translation elongation but instead include an initiation defect. Biochemical analysis of the eEF1A mutant forms demonstrated reduced actin-bundling activity in vitro. Reduced total translation and/or the accumulation of 80S ribosomes in strains with either a mutation or a null allele of genes encoding actin itself or actin-regulating proteins Tpm1p, Mdm20p, and Bnirp/Bni1p was observed. Our data demonstrate that eEF1A, other actin binding proteins, and actin mutants affect translation initiation through the actin cytoskeleton.

'Taking a sickie':Job satisfaction and job involvement as interactive predictors of absenteeism in a public organization

Correlations between absenteeism and work attitudes such as job satisfaction have often been found to be disappointingly weak. As prior work reveals, this might be due to ignoring interactive effects of attitudes with different attitude targets (e.g. job involvement and organizational commitment). Drawing on basic principles in personality research and insights about the situational variability of job satisfaction judgments, we proposed that similar interactions should be present also for attitudes with the same target. More specifically, it was predicted that job involvement affects absenteeism more if job satisfaction is low as this indicates a situation with weak constraints. Both attitudes were assessed in a sample of 436 employees working in a large civil service organization, and two indexes of absence data (frequency and time lost) were drawn from personnel records covering a 12-month period following the survey. Whereas simple correlations were not significant, a moderated regression documented that the hypothesized interaction was significant for both indicators of absence behaviour. As a range of controls (e.g. age, gender, job level) were accounted for, these findings lend strong support to the importance of this new, specific form of attitude interaction. Thus, we encourage researchers not only to consider interactions of attitudes with a different focus (e.g. job vs. organization) but also interactions between job involvement and job satisfaction as this will yield new insights into the complex function of attitudes in influencing absenteeism. © 2007 The British Psychological Society.

Dynamics and topographic organization of recursive self-organizing maps

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographicmaps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizingmap (SOM) for processing sequential data, recursive SOM (RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data. © 2006 Massachusetts Institute of Technology.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Relation discovery from web data for competency management

In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.

Mining web data for competency management

We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.

Research strategies for organizational history:a dialogue between historical theory and organization theory

If history matters for organization theory, then we need greater reflexivity regarding the epistemological problem of representing the past; otherwise, history might be seen as merely a repository of ready-made data. To facilitate this reflexivity, we set out three epistemological dualisms derived from historical theory to explain the relationship between history and organization theory: (1) in the dualism of explanation, historians are preoccupied with narrative construction, whereas organization theorists subordinate narrative to analysis; (2) in the dualism of evidence, historians use verifiable documentary sources, whereas organization theorists prefer constructed data; and (3) in the dualism of temporality, historians construct their own periodization, whereas organization theorists treat time as constant for chronology. These three dualisms underpin our explication of four alternative research strategies for organizational history: corporate history, consisting of a holistic, objectivist narrative of a corporate entity; analytically structured history, narrating theoretically conceptualized structures and events; serial history, using replicable techniques to analyze repeatable facts; and ethnographic history, reading documentary sources "against the grain." Ultimately, we argue that our epistemological dualisms will enable organization theorists to justify their theoretical stance in relation to a range of strategies in organizational history, including narratives constructed from documentary sources found in organizational archives. Copyright of the Academy of Management, all rights reserved.

Self-organization in social tagging systems

Individuals often imitate each other to fall into the typical group, leading to a self-organized state of typical behaviors in a community. In this paper, we model self-organization in social tagging systems and illustrate the underlying interaction and dynamics. Specifically, we introduce a model in which individuals adjust their own tagging tendency to imitate the average tagging tendency. We found that when users are of low confidence, they tend to imitate others and lead to a self-organized state with active tagging. On the other hand, when users are of high confidence and are stubborn to change, tagging becomes inactive. We observe a phase transition at a critical level of user confidence when the system changes from one regime to the other. The distributions of post length obtained from the model are compared to real data, which show good agreement. © 2011 American Physical Society.