Towards Enhanced Diagnosis of Diseases using Statistical Analysis of Genomic Copy Number Data

Genomic copy number data are a rich source of information about the biological systems they are collected from. They can be used for the diagnoses of various diseases by identifying the locations and extent of aberrations in DNA sequences. However, copy number data are often contaminated with measurement noise which drastically affects the quality and usefulness of the data. The objective of this project is to apply some of the statistical filtering and fault detection techniques to improve the accuracy of diagnosis of diseases by enhancing the accuracy of determining the locations of such aberrations. Some of these techniques include multiscale wavelet-based filtering and hypothesis testing based fault detection. The filtering techniques include Mean Filtering (MF), Exponentially Weighted Moving Average (EWMA), Standard Multiscale Filtering (SMF) and Boundary Corrected Translation Invariant filtering (BCTI). The fault detection techniques include the Shewhart chart, EWMA and Generalized Likelihood Ratio (GLR). The performance of these techniques is illustrated using Monte Carlo simulations and through their application on real copy number data. Based on the Monte Carlo simulations, the non-linear filtering techniques performed better than the linear techniques, with BCTI performing with the least error . At an SNR of 1, BCTI technique had an average mean squared error of 2.34% whereas mean filtering technique had the highest error of 5.24%. As for the fault detection techniques, GLR had the lowest missed detection rate of 1.88% at a fixed false alarm rate of around 4%. At around the same false alarm rate, the Shewhart chart had the highest missed detection of 67.4%. Furthermore, these techniques were applied on real genomic copy number data sets. These included data from breast cancer cell lines (MPE600) and colorectal cancer cell lines (SW837)

Multiscale statistical process control with multiresolution data

An approach is presented for conducting multiscale statistical process control that adequately integrates data at different resolutions (multiresolution data), called MR-MSSPC. Its general structure is based on Bakshi's MSSPC framework designed to handle data at a single resolution. Significant modifications were introduced in order to process multiresolution information. The main MR-MSSPC features are presented and illustrated through three examples. Issues related to real world implementations and with the interpretation of the multiscale covariance structure are addressed in a fourth example, where a CSTR system under feedback control is simulated. Our approach proved to be able to provide a clearer definition of the regions where significant events occur and a more sensitive response when the process is brought back to normal operation, when it is compared with previous approaches based on single resolution data. © 2006 American Institute of Chemical Engineers AIChE J, 200

(Un)naturally low? Sequential Monte Carlo tracking of the US natural interest rate

Following the 2000 stockmarket crash, have US interest rates been held "too low" in relation to their natural level? Most likely, yes. Using a structural neo-Keynesian model, this paper attempts a real-time evaluation of the US monetary policy stance while ensuring consistency between the specification of price adjustments and the evolution of the econ- omy under flexible prices. To do this, the model's likelihood function is evaluated using a Sequential Monte Carlo algorithm providing inference about the time-varying distribution of structural parameters and unobservable, nonstationary state variables. Tracking down the evolution of underlying stochastic processes in real time is found crucial (i) to explain postwar Fed's policy and (ii) to replicate salient features of the data. JEL Classification: E43, C11, C15Bayesian Analysis, DSGE Models, Natural Interest Rate, Particle Filters

Effective Use Methods for Continuous Sensor Data Streams in Manufacturing Quality Control

This work outlines an approach for managing sensor data streams of continuous numerical data in product manufacturing settings, emphasizing statistical process control, low computational and memory overhead, and saving information necessary to reduce the impact of nonconformance to quality specifications. While there is extensive literature, knowledge, and documentation about standard data sources and databases, the high volume and velocity of sensor data streams often makes traditional analysis unfeasible. To that end, an overview of data stream fundamentals is essential. An analysis of commonly used stream preprocessing and load shedding methods follows, succeeded by a discussion of aggregation procedures. Stream storage and querying systems are the next topics. Further, existing machine learning techniques for data streams are presented, with a focus on regression. Finally, the work describes a novel methodology for managing sensor data streams in which data stream management systems save and record aggregate data from small time intervals, and individual measurements from the stream that are nonconforming. The aggregates shall be continually entered into control charts and regressed on. To conserve memory, old data shall be periodically reaggregated at higher levels to reduce memory consumption

Self-service business intelligence data analytics

Information systems bring competitive advantages to companies when they know how to use them to their full potential. Business intelligence systems can garner large volumes of data and convert it into information but have the disadvantage of having associated high implementation costs both structurally and qualified people. Then comes Self Service Business Intelligence which provides companies with much lower costs of extracting information from their systems. This work arises from Tecmic’s need to extract information in the form of reports and dashboards from the incident system it has currently implemented. This system has limited information extraction and handling capabilities to help company managers make informed decisions about customer reported incidents. In order to address this problem, it was proposed to implement the Self-Service Business Intelligence software from Microsoft, Power BI. This work details all the preparatory steps to import the data into Power BI as well as an analysis of the results obtained. Additionally, through reports created in Power BI, it is explored what inefficiencies can be tackled in order to improve company performance and drive down the number of services created.Os sistemas de informação trazem vantagens competitivas para as empresas que retiram máximo proveito deles. Os sistemas de business intelligence podem reunir grandes volumes de dados e convertê-los em informação, mas têm a desvantagem de ter elevados custos associados, estruturalmente e de trabalhadores qualificados. Surge então o Self Service Business Intelligence, que possibilita às empresas custos muito mais baixos de extrair informação de seus sistemas de informação. Este trabalho surge da necessidade da Tecmic de extrair informação sob forma de relatórios e dashboards do sistema de incidentes que tem atualmente implementado. Este sistema tem capacidades limitadas no que diz respeito à extração e tratamento de informação o que dificulta os gestores da empresa de tomar decisões informadas sobre incidentes relatados pelos clientes. Para resolver esse problema, foi proposto implementar o software de Self-Service Business Intelligence da Microsoft, o Power BI. Este trabalho detalha todas as etapas preparatórias para importar os dados para o Power BI, bem como uma análise sobre os resultados obtidos. Adicionalmente, por meio de relatórios criados no Power BI, são exploradas as ineficiências que podem ser enfrentadas para melhorar o desempenho da empresa e diminuir o número de serviços criados

PRIORITIZING CHEMICAL CONSTITUENTS IN TOBACCO PRODUCTS AND SMOKE TO PREDICT DEVELOPMENTAL OSTEOTOXICITY IN HUMAN EMBRYONIC STEM CELLS

Though it is well known that tobacco related products can cause prenatal maldevelopment, very little is known on how tobacco products affect bone tissue as it develops in the embryo. Identifying which chemicals can induce the greatest harm to the prenatal skeletal system is an improbable task as there are over 7,000 chemicals in tobacco smoke alone. We hypothesized that the Toxicological Priority Index (ToxPI) program can be used to rank osteogenic cytotoxicity potential to aid in the assessment of what chemicals out of the thousands can cause osteogenic differentiation inhibition. ToxPI aggregates information from various assays and incorporates them into visual “pie charts” which allow chemicals to be ranked against each other by given parameters. The larger the pie chart the greater likelihood of potential effects and vice versa. Seventeen tobacco chemical constituents were ranked using ToxPI and those chemicals with pie charts (0 To assess the ability of ToxPI to correctly predict maldevelopment in silico eight compounds were then tested in vitro: four of them being ToxPi positive and the other four having null predicted effects. To verify the predictions, human embryonic stem cells were differentiated into osteoblasts and exposed to various concentrations of each compound. Cell viability was measured via MTT assay in conjunction with a calcium assay to measure osteogenic differentiation. In addition, adult human feeder fibroblasts cell viability in response to exposure was measured. ToxPI positive predictions (xin vitro, caused differentiation inhibition. Together our data suggests that ToxPi might be useful to identify strongly inhibitory chemicals based on their cytotoxicity but might also give false negative results for chemicals that cause differentiation inhibition at sub-toxic levels

Patch-based Denoising Algorithms for Single and Multi-view Images

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels

Digital Image-Based Frameworks for Monitoring and Controlling of Particulate Systems

Particulate processes have been widely involved in various industries and most products in the chemical industry today are manufactured as particulates. Previous research and practise illustrate that the final product quality can be influenced by particle properties such as size and shape which are related to operating conditions. Online characterization of these particles is an important step for maintaining desired product quality in particulate processes. Image-based characterization method for the purpose of monitoring and control particulate processes is very promising and attractive. The development of a digital image-based framework, in the context of this research, can be envisioned in two parts. One is performing image analysis and designing advanced algorithms for segmentation and texture analysis. The other is formulating and implementing modern predictive tools to establish the correlations between the texture features and the particle characteristics. According to the extent of touching and overlapping between particles in images, two image analysis methods were developed and tested. For slight touching problems, image segmentation algorithms were developed by introducing Wavelet Transform de-noising and Fuzzy C-means Clustering detecting the touching regions, and by adopting the intensity and geometry characteristics of touching areas. Since individual particles can be identified through image segmentation, particle number, particle equivalent diameter, and size distribution were used as the features. For severe touching and overlapping problems, texture analysis was carried out through the estimation of wavelet energy signature and fractal dimension based on wavelet decomposition on the objects. Predictive models for monitoring and control for particulate processes were formulated and implemented. Building on the feature extraction properties of the wavelet decomposition, a projection technique such as principal component analysis (PCA) was used to detect off-specification conditions which generate particle mean size deviates the target value. Furthermore, linear and nonlinear predictive models based on partial least squares (PLS) and artificial neural networks (ANN) were formulated, implemented and tested on an experimental facility to predict particle characteristics (mean size and standard deviation) from the image texture analysis