RIM1alpha is required for presynaptic long-term potentiation.

Two main forms of long-term potentiation (LTP)-a prominent model for the cellular mechanism of learning and memory-have been distinguished in the mammalian brain. One requires activation of postsynaptic NMDA (N-methyl d-aspartate) receptors, whereas the other, called mossy fibre LTP, has a principal presynaptic component. Mossy fibre LTP is expressed in hippocampal mossy fibre synapses, cerebellar parallel fibre synapses and corticothalamic synapses, where it apparently operates by a mechanism that requires activation of protein kinase A. Thus, presynaptic substrates of protein kinase A are probably essential in mediating this form of long-term synaptic plasticity. Studies of knockout mice have shown that the synaptic vesicle protein Rab3A is required for mossy fibre LTP, but the protein kinase A substrates rabphilin, synapsin I and synapsin II are dispensable. Here we report that mossy fibre LTP in the hippocampus and the cerebellum is abolished in mice lacking RIM1alpha, an active zone protein that binds to Rab3A and that is also a protein kinase A substrate. Our results indicate that the long-term increase in neurotransmitter release during mossy fibre LTP may be mediated by a unitary mechanism that involves the GTP-dependent interaction of Rab3A with RIM1alpha at the interface of synaptic vesicles and the active zone.     

System identification and validation for output prediction of a dynamic hydrologic process

The paper presents an identification procedure for a dynamic model of am hydrologic process. The process involves solute transport in streams subject to aquifer interaction and unsteady flows and the intended use of the model is prediction. Detailed assumptions and results are provided to illustrate the level of comprehensive analysis required to assess model adequacy. The assessment procedure easily generalizes to any dynamic model which is linear-in-the-parameters. As a fundamental tool, instrumental variable algorithms can be adopted which have a number of attractive features. These algorithms make both model-order identification and specification among alternatives a straightforward task. They are known to be consistent estimators in the presence of a wide class of errors. It is seen that they can be made stable and robust in the presence of data outliers. Instrumental variable algorithms can also be used which are asymptotically efficient and provide a covariance matrix of parameter estimates. The paper shows how they aid the quantification of predictive uncertainty and investigates the validity of the underlying assumptions. Further, it illustrates that, when instrumental variable algorithms are used in recursive mode, they can be used not only as an additional tool to access model inadequacy but also as an aid to model improvements.     

Histone H3 lysine 9 methylation is an epigenetic imprint of facultative heterochromatin.

Post-translational modifications of histone amino termini are an important regulatory mechanism that induce transitions in chromatin structure, thereby contributing to epigenetic gene control and the assembly of specialized chromosomal subdomains. Methylation of histone H3 at lysine 9 (H3-Lys9) by site-specific histone methyltransferases (Suv39h HMTases) marks constitutive heterochromatin. Here, we show that H3-Lys9 methylation also occurs in facultative heterochromatin of the inactive X chromosome (Xi) in female mammals. H3-Lys9 methylation is retained through mitosis, indicating that it might provide an epigenetic imprint for the maintenance of the inactive state. Disruption of the two mouse Suv39h HMTases abolishes H3-Lys9 methylation of constitutive heterochromatin but not that of the Xi. In addition, HP1 proteins, which normally associate with heterochromatin, do not accumulate with the Xi. These observations suggest the existence of an Suv39h-HP1-independent pathway regulating H3-Lys9 methylation of facultative heterochromatin.     

A comparison of pyridoxal 5′-phosphate dependent decarboxylase and transaminase enzymes at a molecular level

Pyridoxal 5′-phosphate is a coenzyme for a number of enzymes which catalyse reactions at C^α of amino acid substrates including transaminases, decarboxylases and serine hydroxymethyltransferase. Using the X-ray coordinates for a transaminase, aspartate aminotransferase, and the results of stereochemical and mechanistic studies for decarboxylases and serine hydroxymethyltransferase, an active-site structure for the decarboxylase group is constructed. The structure of the active-site is further refined through active-site pyridoxyllysine peptide sequence comparison and a 3-D catalytic mechanism for the L-α-amino acid decarboxylases is proposed. The chemistry of serine hydroxymethyltransferase is re-examined in the light of the proposed decarboxylase mechanism.     

A comparison of pyridoxal 5'-phosphate dependent decarboxylase and transaminase enzymes at a molecular level.

Pyridoxal 5'-phosphate is a coenzyme for a number of enzymes which catalyse reactions at C alpha of amino acid substrates including transaminases, decarboxylases and serine hydroxymethyltransferase. Using the X-ray coordinates for a transaminase, aspartate aminotransferase, and the results of stereochemical and mechanistic studies for decarboxylases and serine hydroxymethyltransferase, an active-site structure for the decarboxylase group is constructed. The structure of the active-site is further refined through active-site pyridoxyllysine peptide sequence comparison and a 3-D catalytic mechanism for the L-alpha-amino acid decarboxylases is proposed. The chemistry of serine hydroxymethyltransferase is re-examined in the light of the proposed decarboxylase mechanism.     

AUTOMATIC DECISION-MAKING： NEURAL FIRING AND RESPONSE

1. Introduction This is the fourth exposition on theAnalytic Hierarchy Process. It is true thatmany decisions we make subconsciously ariseout of our physical and biological needs andderive from the pressure to survive physically.We also make subconsciou…     

Rough sets bankruptcy prediction models versus auditor signalling rates

Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause–effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non‐current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period. Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991–1997. This sample was then matched in terms of size and industry to 145 non‐bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included. In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy/non‐bankruptcy frequencies to develop the variable classifications, and (5) a wide range of industries and company sizes. Copyright © 2003 John Wiley & Sons, Ltd.     

Workplace Logics, Kinds of Knowledge and Action Research

Workplace logics signify attitudes that employees express in close interaction, formal decision-making and collaborative interventions. The term covers a metaphorical mental context rather than a mathematically coherent understanding of social control mechanisms that influence interlocutors in cooperative activity. Workplace logics relate to future-oriented choices that employees make so as to obtain targeted production goals. But logics also hold an explanatory potential. They enable for the researcher to account for social systems that need to be designed before they are acted on. The objective for analysing workplace logics is to turn attention away from language and focus on activity systems. Successful action research facilitates for individual and organizational learning, the latter of which is rarely fulfilled in contemporary research.     

Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network

The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data. Thomas L. Saaty holds the Chair of University Professor, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, and obtained his Ph.D. in mathematics from Yale University. Before that he was a professor at the Wharton School of the University of Pennsylvania for ten years. Prior to that he spent seven years at the Arms Control and Disarmament Agency in the State Department in Washington, DC, that carried out the arms reduction negotiations with the Soviets in Geneva. His current research interests include decision-making, planning, conflict resolution and synthesis in the brain. As a result of his search for an effective means to deal with weapons tradeoffs at the Disarmament Agency and, more generally, with decision-making and resource allocation, Professor Saaty developed The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP). He is co-developer of the software Expert Choice and of the software Super Decisions for decisions with dependence and feedback. He has authored or co-authored twelve books on the AHP/ANP. Professor Saaty has also written a number of other books that embrace a variety of topics, including Modern Nonlinear Equations, Nonlinear Mathematics, Graph Theory, The Four Color Problem, Behavioral Mathematics, Queuing Theory, Optimization in Integers, Embracing the Future and The Brain: Unraveling the Mystery of How It Works. His most recent book is Creative Thinking, Problem Solving & Decision Making. The book is a rich collection of ideas, incorporating research by a growing body of researchers and practitioners, profiles of creative people, projects and products, theory, philosophy, physics and metaphysics...all explained with a liberal dose of humor. He has published more than 300 refereed articles in a wide variety of professional journals. He has been on the editorial boards of Mathematical Reviews, Operations Research, Naval Research Logistics Quarterly, Mathematical and Computer Modeling, Socio-Economic Planning Sciences, Applied Mathematics Letters, and several others. He also served as consultant to many corporations and governments.