Spatial analysis for the distribution of cells in tissue sections

Spatial analysis, playing an essential role in data mining, is applied in a considerable number of fields. It is because of its broad applicability that dealing with the interdisciplinary issues is becoming more prevalent. It aims at exploring the underlying patterns of the data. In this project, we will employ the methodology that we utilize to tackle spatial problems to investigate how the cells distribute in the infected tissue sections and if there are clusters existing among the cells. The cells that are neighboring to the viruses are of interest. The data were provided by the Medetect Company in the form of 2-dimensional point data. We firstly adopted two common spatial analysis methods, clustering methods and proximity methods. In addition, a method for constructing a 2-dimensional hull was developed in order to delineate the compartments in tissue sections. A binomial test was conducted to evaluate the results. It is detectable that the clusters do exist among cells. The immune cells would accumulate around the viruses. We also found different patterns near and far away from viruses. This study implicates that the cells are interactive with each other and thus present the spatial patterns. However, our analyses are restricted in a planar circumstance instead of treating them in 3-dimensional space. For the further study, the spatial analysis could be carried out in three dimensions.It has been popular to utilize the heuristic methods or the existing methods to discover new findings and explain the mysterious phenomena in other subjects. And it is known that everything in nature relates to each other. In this sense, we could assume that the entire distribution of objects is relative to the locations of individuals. The idea of my work is attempting to explore this spatial relationship existing among cells. In my project, the relationships between individual cells or groups of cells are interesting. Our data is presented like the point cloud. It is doubted that if there are any groups existing among these points and if the viruses have neighbors. The methods are mainly categorized into three parts. The first method is to integrate the similar objects into groups. Here the similar objects could be the ones that are close to each other. The second method analyzes the degree of closeness between objects and looks for the neighbors of viruses. The last method can be used to draw the border of a point cloud, which seems like constructing the boundary of districts. Within each method, we carried out the corresponding case studies. Since similar objects can be grouped together, it is interesting to look into the details of each group. Thus we can know which two objects are similar in the same group. Basically, different types of cells in the same group can be checked and studied. In the closeness analysis, we found that some cells are indeed closer to each other. The constructed border could help us know the shape of point clouds. It can be concluded that the spatial relationship does exist among the cells. Groups of cells can be identified at a large extent. And one certain type of cells could be more attracted by some cells from a local level. However, this study is carried out in a 2D space. Actually, we neglect the real shape of cells which have heights. This could be a more interesting topic in the future

Incremental Learning with Large Datasets

This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector machines find the hyper-plane that maximizes the margin between two classes, and the decision boundary is represented with a few training samples it becomes a favorable choice for incremental learning. The dissertation presents a novel method Geometric Incremental Support Vector Machines (GISVMs) to address both efficiency and accuracy issues in handling massive data sets. In GISVM, skin of convex hulls is defined and an efficient method is designed to find the best skin approximation given available examples. The set of extreme points are found by recursively searching along the direction defined by a pair of known extreme points. By identifying the skin of the convex hulls, the incremental learning will only employ a much smaller number of samples with comparable or even better accuracy. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This results in a small number of instances used in incremental steps of the training process. Based on the experimental results with synthetic data sets, public benchmark data sets from UCI and endoscopy videos, it is evident that the GISVM achieved satisfactory classifiers that closely model the underlying data distribution. GISVM improves the performance in sensitivity in the incremental steps, significantly reduced the demand for memory space, and demonstrates the ability of recovery from temporary performance degradation

Knowledge discovery from trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAs a newly proliferating study area, knowledge discovery from trajectories has attracted more and more researchers from different background. However, there is, until now, no theoretical framework for researchers gaining a systematic view of the researches going on. The complexity of spatial and temporal information along with their combination is producing numerous spatio-temporal patterns. In addition, it is very probable that a pattern may have different definition and mining methodology for researchers from different background, such as Geographic Information Science, Data Mining, Database, and Computational Geometry. How to systematically define these patterns, so that the whole community can make better use of previous research? This paper is trying to tackle with this challenge by three steps. First, the input trajectory data is classified; second, taxonomy of spatio-temporal patterns is developed from data mining point of view; lastly, the spatio-temporal patterns appeared on the previous publications are discussed and put into the theoretical framework. In this way, researchers can easily find needed methodology to mining specific pattern in this framework; also the algorithms needing to be developed can be identified for further research. Under the guidance of this framework, an application to a real data set from Starkey Project is performed. Two questions are answers by applying data mining algorithms. First is where the elks would like to stay in the whole range, and the second is whether there are corridors among these regions of interest

Novel applications of spectroscopy to characterize soil variation

This thesis embodies a collection of novel studies related to the use of multivariate information provided by spectroscopic tools such as Visible and Near Infrared (Vis-NIR) spectrometers to represent soil variation. The general structure is organized following the increasing levels of soil complexity, starting from the characterization of soil aggregates and the identification of soil colloids, to the recognition of soil horizons and their boundaries in the soil profile, to finally the depiction of soil type’s distribution in the landscape. Briefly, Chapter 1 is written as a rationale, emphasising the need for up-to-date methodologies for making effective use of the increasing amount of soil information produced worldwide. Chapter 2 presents the development of a new methodology for the measure of soil aggregate stability and the further use of spectroscopic information to predict its values. Chapter 3 gives examples of the use of Vis-NIR spectral libraries for the prediction of soil properties. Chapter 4 presents the development of a new method for the identification of soil horizons and their boundaries using fuzzy clustering of Vis-NIR spectra. Chapter 5 expands into a new way of measuring the diversity of soils into the landscape, introducing two new indices for measuring soil diversity or “Functional Pedodiversity” inspired in previous studies in Functional Ecology. Finally Chapter 6 discusses the main findings of this thesis and foresees issues, challenges and opportunities in the area of spectroscopy and multivariate soil data analysis

The Importance of Generalizability to Anomaly Detection

In security-related areas there is concern over novel “zero-day” attacks that penetrate system defenses and wreak havoc. The best methods for countering these threats are recognizing “nonself” as in an Artificial Immune System or recognizing “self” through clustering. For either case, the concern remains that something that appears similar to self could be missed. Given this situation, one could incorrectly assume that a preference for a tighter fit to self over generalizability is important for false positive reduction in this type of learning problem. This article confirms that in anomaly detection as in other forms of classification a tight fit, although important, does not supersede model generality. This is shown using three systems each with a different geometric bias in the decision space. The first two use spherical and ellipsoid clusters with a k-means algorithm modified to work on the one-class/blind classification problem. The third is based on wrapping the self points with a multidimensional convex hull (polytope) algorithm capable of learning disjunctive concepts via a thresholding constant. All three of these algorithms are tested using the Voting dataset from the UCI Machine Learning Repository, the MIT Lincoln Labs intrusion detection dataset, and the lossy-compressed steganalysis domain

Context based detection of urban land use zones

This dissertation proposes an automated land-use zoning system based on the context of an urban scene. Automated zoning is an important step toward improving object extraction in an urban scene