Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Integrated assessment of ecosystem connectivity and functioning: coastal forest avifauna of northeast Australia

The extraordinary diversity of species-environment relationships that occur across space and time can engender a deep curiosity of their mechanistic underpinnings. Moreover, the rapid rate of ecosystem change associated with anthropogenic and climatic pressures makes information regarding species' landscape and resource use ever more important. Without this information, we will be unable to effectively protect landscapes and their constituent species. The coastal ecosystem mosaic of northeast Australia, which is comprised of a high diversity of habitat types, provides a suitable region for investigating how species respond to heterogeneity in habitat and resource availability. The present thesis examined ecosystem functioning in heterogeneous coastal landscapes of northeast Australia for forest avifauna. An array of analytical approaches were employed to establish a comprehensive understanding: 1) spatial assessment to determine relationships between regional landscape connectivity and coastal forest bird assemblages, 2) isotopic assessment to evaluate the local foraging ecology of mangrove bird assemblages, and 3) nutrient assessment of cross-ecosystem connectivity provided by a migratory coastal forest bird species (i.e. the Pied Imperial-Pigeon (Ducula bicolor)). Within the coastal ecosystem mosaic, mangrove forests sit at the land-sea interface. Therefore, to effectively 'set the scene' I review how mangrove birds require and facilitate connectivity through their use of the broader coastal landscape. Next, to specifically assess regional landscape patterns and processes influencing northeast Australia's coastal forest avifauna, I surveyed the composition of bird assemblages in four of the major coastal forest types occurring throughout the region (i.e. Eucalypt, Melaleuca, mangrove, and rainforest). Following this, spatial patterns of habitat configuration within the coastal landscape (i.e. structural connectivity) were quantified to understand broad relationships between coastal forest bird assemblage composition and landscape heterogeneity at multiple spatial scales. Most bird species in coastal northeast Australia occurred in multiple forest types. Spatial assessment suggested that Melaleuca woodlands are a keystone structure that supports use of the entire coastal landscape mosaic by coastal forest generalist species. However, the species composition of mangrove bird assemblages was distinct relative to other coastal forest types. Therefore, to provide more detailed information regarding the response of coastal forest generalists and mangrove specialists to specific forest attributes, functionally connected forest networks were developed to assess the relative importance of forest area, availability, and connectivity to their compositional turnover. This revealed that mangrove specialists and coastal generalists differ in the forest attributes they require (i.e. area vs. availability) to maintain regional beta diversity. Understanding landscape pattern-process relationships that drive bird assemblage composition and turnover can inform the prioritization of regional-scale landscape features for protection. However, species' responses to local-scale spatiotemporal variability in resource availability may also play a role in these relationships. I used isotopic analysis to better understand the foraging ecology of coastal forest birds in a highly dynamic mangrove forest environment. This demonstrated that flexible and opportunistic foraging strategies were prevalent among coastal forest generalist species. However, specialized foraging strategies were employed by some species, primarily for resources that were uniquely available in mangrove forests (i.e. estuarine fish and crabs). Mobile species not only respond to landscape patterns and processes, but can also facilitate connectivity processes through their movement (e.g. nutrient transfer, pollination, genetic linking, etc.). To determine the implications of avian mobility for ecosystem functioning in northeast Australia, I focused on a migratory coastal forest bird species, the Pied Imperial-Pigeon (Ducula bicolor). Nutrient measurements demonstrated that Pied Imperial-Pigeons provide mainland-derived nutrient subsidies to island forests, highlighting their important role as an avian mobile-link species. The integrated analytical approach used in this thesis has provided insight to the complexity of coastal landscapes and their use by forest avifauna. This has broadened our understanding of coastal ecosystem functioning to include a hierarchy of ecosystem components that exist at local and regional scales. The ecosystem properties that emerge from interactions across coastal ecosystem components include: vegetative connectivity, compositional turnover, avian foraging strategy, and nutrient transfer. Results from this thesis can inform the holistic conservation and management strategies that are required to maintain coastal ecosystem functioning in regional northeast Australia

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Photonics simulation and modelling of skin for design of spectrocutometer

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Prediction of Early Vigor from Overhead Images of Carinata Plants

Breeding more resilient, higher yielding crops is an essential component of ensuring ongoing food security. Early season vigor is signi cantly correlated with yields and is often used as an early indicator of tness in breeding programs. Early vigor can be a useful indicator of the health and strength of plants with bene ts such as improved light interception, reduced surface evaporation, and increased biological yield. However, vigor is challenging to measure analytically and is often rated using subjective visual scoring. This traditional method of breeder scoring becomes cumbersome as the size of breeding programs increase. In this study, we used hand-held cameras tted on gimbals to capture images which were then used as the source for automated vigor scoring. We have employed a novel image metric, the extent of plant growth from the row centerline, as an indicator of vigor. Along with this feature, additional features were used for training a random forest model and a support vector machine, both of which were able to predict expert vigor ratings with an 88:9% and 88% accuracies respectively, providing the potential for more reliable, higher throughput vigor estimates

Comparison of different algorithms for exploting the hidden trends in data sources

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2003Includes bibliographical references (leaves: 92-97)Text in English; Abstract: Turkish and English97 leavesThe growth of large-scale transactional databases, time-series databases and other kinds of databases has been giving rise to the development of several efficient algorithms that cope with the computationally expensive task of association rule mining.In this study, different algorithms, Apriori, FP-tree and CHARM, for exploiting the hidden trends such as frequent itemsets, frequent patterns, closed frequent itemsets respectively, were discussed and their performances were evaluated. The perfomances of the algorithms were measured at different support levels, and the algorithms were tested on different data sets (on both synthetic and real data sets). The algorihms were compared according to their, data preparation performances, mining performance, run time performances and knowledge extraction capabilities.The Apriori algorithm is the most prevalent algorithm of association rule mining which makes multiple passes over the database aiming at finding the set of frequent itemsets for each level. The FP-Tree algorithm is a scalable algorithm which finds the crucial information as regards the complete set of prefix paths, conditional pattern bases and frequent patterns by using a compact FP-Tree based mining method. The CHARM is a novel algorithm which brings remarkable improvements over existing association rule mining algorithms by proving the fact that mining the set of closed frequent itemsets is adequate instead of mining the set of all frequent itemsets.Related to our experimental results, we conclude that the Apriori algorithm demonstrates a good performance on sparse data sets. The Fp-tree algorithm extracts less association in comparison to Apriori, however it is completelty a feasable solution that facilitates mining dense data sets at low support levels. On the other hand, the CHARM algorithm is an appropriate algorithm for mining closed frequent itemsets (a substantial portion of frequent itemsets) on both sparse and dense data sets even at low levels of support