Isolation of the avian homologue of the homeobox gene Mox2 and analysis of its expression pattern in developing somites and limbs

We have isolated the cDNA of avian Mox2 and analyzed its expression pattern during somitogenesis and limb bud formation. Mox2 plays an important role in limb muscle differentiation in the mouse. Mox2 is expressed in the somites of developing chick embryos and in presumptive migrating myoblasts from the dermomyotome to the limb buds. It is also expressed in the ventral and dorsal part of limb buds and is associated with non-proliferating myoblasts. Significant differences were observed in chick and mouse expression patterns, namely in the chick dermomyotome and limb

Three-way catalytic converter modeling as a modern engineering design tool

The competition to deliver ultra low emitting vehicles at a reasonable cost is driving the automotive industry to invest significant manpower and test lab resources in the design optimization of increasingly complex exhaust aftertreatment systems. Optimization can no longer be based on traditional approaches, which are intensive in hardware use and lab testing. This paper discusses the extents and limitations of applicability of state-of-the-art mathematical models of catalytic converter performance. In-house software from the authors' lab, already in use during the last decade in design optimization studies, updated with recent, important model improvements, is employed as a reference in this discussion. Emphasis is on the engineering methodology of the computational tools and their application, which covers quality assurance of input data, advanced parameter estimation procedures, and a suggested performance measure that drives the parameter estimation code to optimum results and also allows a less subjective assessment of model prediction accuracy. Extensive comparisons between measured and computed instantaneous emissions over full cycles are presented, aiming to give a good picture of the capabilities of state of the art engineering models of automotive catalytic converter systems

Efficient algorithms for distortion and blocking techniques in association rule hiding

Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed in this context in order to extract this information in the most efficient way. However, efficiency is not our only concern in this study. The security and privacy issues over the extracted knowledge must be seriously considered as well. By taking this into consideration, we study the procedure of hiding sensitive association rules in binary data sets by blocking some data values and we present an algorithm for solving this problem. We also provide a fuzzification of the support and the confidence of an association rule in order to accommodate for the existence of blocked/unknown values. In addition, we quantitatively compare the proposed algorithm with other already published algorithms by running experiments on binary data sets, and we also qualitatively compare the efficiency of the proposed algorithm in hiding association rules. We utilize the notion of border rules, by putting weights in each rule, and we use effective data structures for the representation of the rules so as (a) to minimize the side effects created by the hiding process and (b) to speed up the selection of the victim transactions. Finally, we study the overall security of the modified database, using the C4.5 decision tree algorithm of the WEKA data mining tool, and we discuss the advantages and the limitations of blocking

A quantitative and qualitative analysis of blocking in association rule hiding

Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed in this context in order to extract this information in the most efficient way. However, efficiency is not our only concern in this study. The security and privacy issues over the extracted knowledge must be seriously considered as well. By taking this into consideration, we study the procedure of hiding sensitive association rules in binary data sets by blocking some data values and we present an algorithm for solving this problem. We also provide a fuzzification of the support and the confidence of an association rule in order to accommodate for the existence of blocked/unknown values. In addition, we quantitatively compare the proposed algorithm with other already published algorithms by running experiments on binary data sets, and we also qualitatively compare the efficiency of the proposed algorithm in hiding association rules. We utilize the notion of border rules, by putting weights in each rule, and we use effective data structures for the representation of the rules so as (a) to minimize the side effects created by the hiding process and (b) to speed up the selection of the victim transactions. Finally, we discuss the advantages and the limitations of blocking. Copyright 2004 ACM