Spatial Analysis of Expression Patterns Predicts Genetic Interactions at the Mid-Hindbrain Boundary

The isthmic organizer mediating differentiation of mid- and hindbrain during vertebrate development is characterized by a well-defined pattern of locally restricted gene expression domains around the mid-hindbrain boundary (MHB). This pattern is established and maintained by a regulatory network between several transcription and secreted factors that is not yet understood in full detail. In this contribution we show that a Boolean analysis of the characteristic spatial gene expression patterns at the murine MHB reveals key regulatory interactions in this network. Our analysis employs techniques from computational logic for the minimization of Boolean functions. This approach allows us to predict also the interplay of the various regulatory interactions. In particular, we predict a maintaining, rather than inducing, effect of Fgf8 on Wnt1 expression, an issue that remained unclear from published data. Using mouse anterior neural plate/tube explant cultures, we provide experimental evidence that Fgf8 in fact only maintains but does not induce ectopic Wnt1 expression in these explants. In combination with previously validated interactions, this finding allows for the construction of a regulatory network between key transcription and secreted factors at the MHB. Analyses of Boolean, differential equation and reaction-diffusion models of this network confirm that it is indeed able to explain the stable maintenance of the MHB as well as time-courses of expression patterns both under wild-type and various knock-out conditions. In conclusion, we demonstrate that similar to temporal also spatial expression patterns can be used to gain information about the structure of regulatory networks. We show, in particular, that the spatial gene expression patterns around the MHB help us to understand the maintenance of this boundary on a systems level

Stoat trap tunnel location : GIS predictive modelling to identify the best tunnel location : a thesis submitted in fulfillment of the requirements for the degree of Master of Philosophy in Geographic Information Systems in Massey University

Stoats are recognised as one of the biggest threats to New Zealand's threatened species. They are difficult to control because of their biological characteristics. Currently trapping is the most common type of control technique that has a proven success rate. Research studies have shown that some traps catch more stoats than others. However the reason for this is not well documented. The effectiveness of a trap set is difficult to determine because not all trap locations are the same and not all people have the same ability to select the best location for a trap. This study uses GIS to spatially analyse stoat capture data from a control operation on Secretary Island in conjunction with commonly available vegetation, habitat, diet and home range spatial data to see if there are consistent patterns that could be used as variables in a model that would predict the best place to locate a stoat trap tunnel. The model would then be tested against a similar dataset from Resolution Island. The Department of Conservation supplied the stoat capture data from the control operations on both islands. Standard spatial analysis techniques were used to generate surfaces that combined the capture data with the vegetation, habitat, diet and home range surfaces to produce predictive surfaces. The key finding from the research was that it is possible to produce a predictive model, although one was not created because the spatial datasets were not of a high enough resolution to provide conclusive evidence that could be confidently used as a variable in a model. The spatial analysis also indicated that stoats on both islands were caught mainly in the warmer northwestern parts of the islands although the study could not determine why there was a preference for these areas. In rugged terrain like that found on both islands the location of the track network will influence where the majority of stoats will be caught

Effect of fuzzy discretization in the association performance with continuous attributes

Flood is one of the natural disasters caused by complex factors such as natural, breeding and environmental.The variability of such factors on multiple heterogeneous spatial scales may cause difficulties in finding correlation or association between regions.The interaction between these factors has resulted in provision of either diverse or repeated information which can be detrimental to prediction accuracy.The complex and diverse available database has triggered this study to incorporate multi source heterogeneous data source in finding association between regions.Bayesian Network based method has been used to quantify dependency patterns in spatial data.However, a group of variables may be relevant for a particular region but may not be relevant to other region.To overcome the weakness of Bayesian network in handling continuous variable, this study has proposed data discretization technique to produce spatial correlation model.The effect of the proposed fuzzy discretization on the association performance is investigated.The comparison between different data discretization techniques proved that the proposed fuzzy discretization method gives better result with high precision, good F-measure, and a better receiver operating characteristic area compared with other methods.The results of correlation between the spatial patterns gives detailed information that may help the government, planners, decision makers, and researchers to perform actions that help to prevent and mitigate flood events in the future

New Methods for Network Traffic Anomaly Detection

In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel matrix. The Hankel matrix is spatio-temporal correlation matrix and has been used in many other domains including climate data analysis and econometrics. Using our approach we can seamlessly integrate the discovery of both spatial and temporal anomalies. Comparison with other state of the art methods in the networks community confirms that our approach can discover both DoS and port scan attacks. The spectral decomposition of the Hankel matrix is closely tied to the problem of inference in Linear Dynamical Systems (LDS). We introduce a new problem, the Online Selective Anomaly Detection (OSAD) problem, to model the situation where the objective is to report new anomalies in the system and suppress know faults. For example, in the network setting an operator may be interested in triggering an alarm for malicious attacks but not on faults caused by equipment failure. In order to solve OSAD we combine techniques from machine learning and control theory in a unique fashion. Machine Learning ideas are used to learn the parameters of an underlying data generating system. Control theory techniques are used to model the feedback and modify the residual generated by the data generating state model. Experiments on synthetic and real data sets confirm that the OSAD problem captures a general scenario and tightly integrates machine learning and control theory to solve a practical problem

A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges