PolyA_DB: a database for mammalian mRNA polyadenylation

Messenger RNA polyadenylation is one of the key post-transcriptional events in eukaryotic cells. A large number of genes in mammalian species can undergo alternative polyadenylation, which leads to mRNAs with variable 3′ ends. As the 3′ end of mRNAs often contains cis elements important for mRNA stability, mRNA localization and translation, the implications of the regulation of polyadenylation can be multifold. Alternative polyadenylation is controlled by cis elements and trans factors, and is believed to occur in a tissue- or disease-specific manner. Given the availability of many databases devoted to other aspects of mRNA metabolism, such as transcriptional initiation and splicing, systematic information on polyadenylation, including alternative polyadenylation and its regulation, is noticeably lacking. Here, we present a database named polyA_DB, through which we strive to provide several types of information regarding polyadenylation in mammalian species: (i) polyadenylation sites and their locations with respect to the genomic structure of genes; (ii) cis elements surrounding polyadenylation sites; (iii) comparison of polyadenylation configuration between orthologous genes; and (iv) tissue/organ information for alternative polyadenylation sites. Currently, polyA_DB contains 45 565 polyadenylation sites for 25 097 human and mouse genes, representing the most comprehensive polyadenylation database till date. The database is accessible via the website (http://polya.umdnj.edu/polyadb)

Decision Support for Perceived Threat in the Context of Intrustion Detection Systems

The objective of this research is to propose a novel approach for using a behavioral biometric known as keystroke analysis, to facilitate decision making in the context of an intrusion detection system (IDS). Regardless of the situation, individuals have a specific baseline or disposition to decision making based on two psychological factors: (1) indecisiveness, and (2) intolerance of uncertainty. The IDS provides a probability of intrusion and a set of objective situational characteristics. We propose a decision support system that allows the decision maker to state a level of perceived threat and to vary the security thresholds that determines the false acceptance rates of the IDS. Our hypothesis is that perceived threat depends not only on the keystroke technology but also on the social context and disposition toward decision making of the user. This research tests this hypothesis and provides guidance in the design of better security systems

Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer

BACKGROUND: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups. RESULTS: We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation. CONCLUSIONS: This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups

Performance Analysis of Progressive Recall in Partially Connected Recurrent Networks

An analysis of the storage capacity of a sparsely connected associative memory is presented. In these networks, recall performance is constrained by the mechanism for setting global threshold values. Recall performance improves if stored patterns are gradually reconstructed using a sequence of thresholds (progressive recall). We present a method for predicting the performance of progressive recall, which is used to test all possible threshold sequences, and to find the optimal sequence. We show that previous progressive recall strategies overestimated expected performance, and do not result in optimal storage capacity. Computer simulations studies are presented to support the theory. keywords: associative memory, progressive recall, hippocampus scientific conference Proceedings of International Conference on Artificial Neural Networks (ICANN'95), Vol.2, pp 509-514 Performance analysis of progressive recall in partially connected recurrent networks Hajime Hirase and Michael Recce ..

The representation of space in the rat hippocampus as revealed using new computer-based methods

While there is agreement that the hippocampus plays an important role in brain function, the details are hotly debated. Extracellular recordings from freely moving rats have provided significant but not conclusive evidence that the rodent hippocampus is specific to map based spatial navigation. The spatial hypothesis is supported by a well developed theory, and can be readily tested. These experiments require methods for accurately and simultaneously measuring the activity patterns of multiple single hippocampal neurons with high resolution in both space (location of the animal) and time of spike firing. Computer based methods are described which improve the accuracy of chronic hippocampal recording. Experiments using these methods reveal differences, from previous studies, in the characteristics of hippocampal single neurons and the relationship between extracellular electrical activity and the animal's spatial location. In particular, the receptive field of each putative hippocampal pyramidal cell (place cell) is smaller and more localised. Smaller firing regions improve the performance of an associative memory for places, perhaps located in the hippocampus (region CA3). During displacement behaviours in the rat, hippocampal EEG has a striking sinusoidal activity pattern, the theta rhythm. In contrast to published data the frequency of the theta rhythm is shown to be correlated with the speed of locomotion of the rat. In addition the frequency of the theta rhythm is shown to predict the speed of the animal's movement. The correlation between speed and EEG frequency may explain how place cell firing maintains spatial specificity with changes in movement parameters. Experiments were conducted to study the detailed firing relationship between the theta rhythm and the activity of single hippocampal neurons in the freely moving rat. The preferential firing phase of single place cells was found to shift systematically as the animal ran through the place field. This firing phase is shown to be spatially coded to the location in the place field. These results change the way in which place cell firing is interpreted

An Embedded System for Extracting Keystroke Patterns using Pressure Sensors

Popular biometric security technologies include fingerprint and iris recognition systems. These technologies are extremely accurate because the patterns associated with an individual\u27s finger or eye are very unique and static. However, when these technologies are used for physical access control they inform the potential adversary that specific characteristics are required to gain access. Behaviometrics aims to develop new strategies to enhance physical security via covert monitoring of distinct behavioral patterns. This research presents a novel stand-alone behaviometric prototype that incorporates standard password security with unique pressure characteristics to covertly analyse individual typing patterns. The prototype is evaluated under a controlled setting with 62 human subjects and nine classification algorithms. The kNN algorithm produced the highest classification rate of 94%. This research is one of the few papers that empirically substantiates biometric performance with a large-scale human subject trial, and also identifies several critical design considerations that impact classifier performance

Memory for places: A navigational model in support of Marr's theory of hippocampal function

In this paper we describe a model that applies Marr's theory of hippocampal function to the problem of map based navigation. Like many others we attribute a spatial memory function to the hippocampus, but we suggest that the additional functional components required for map based navigation are located elsewhere in the brain. One of the key functional components in this model is an egocentric map of space, located in the neocortex, that is continuously updated using ideothetic (self motion) information. The hippocampus stores snapshots of this egocentric map. The modelled activity pattern of head direction cells is used to set the best egocentric map rotation to match the snapshots stored in the hippocampus, resulting in place cells with a non-directional firing pattern. We describe an evaluation of this model using a mobile robot, and demonstrate that with this model the robot can recognise an environment and find a hidden goal. This model is discussed in the context of prior experime..