Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Visual data mining using principled projection algorithms and information visualization techniques

We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.

Analysis of variables measured in the visual screening of University of Birmingham freshmen and a critical review of visual screening methods and approach

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Detection of foveation windows and analysis of foveation sequences in congenital nystagmus

Congenital nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations, that can arise since the first months of life. Pathogenesis of congenital nystagmus is still under investigation. In general, CN patients show a considerable decrease of their visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations, mainly horizontal. However, image stabilisation is still achieved during the short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals). To quantify the extent of nystagmus, eye movement recording are routinely employed, allowing physicians to extract and analyse nystagmus main features such as shape, amplitude and frequency. Using eye movement recording, it is also possible to compute estimated visual acuity predictors: analytical functions which estimates expected visual acuity using signal features such as foveation time and foveation position variability. Use of those functions add information to typical visual acuity measurement (e.g. Landolt C test) and could be a support for therapy planning or monitoring. This study focus on robust detection of CN patients' foveations. Specifically, it proposes a method to recognize the exact signal tracts in which a subject foveates, This paper also analyses foveation sequences. About 50 eyemovement recordings, either infrared-oculographic or electrooculographic, from different CN subjects were acquired. Results suggest that an exponential interpolation for the slow phases of nystagmus could improve foveation time computing and reduce influence of breaking saccades and data noise. Moreover a concise description of foveation sequence variability can be achieved using non-fitting splines. © 2009 Springer Berlin Heidelberg.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Exploring the integration of data mining and data visualization

Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be effective, it is important to include the visualization techniques in the mining process and to generate the discovered patterns for a more comprehensive visual view. In this dissertation, four related problems: dimensionality reduction for visualizing high dimensional datasets, visualization-based clustering evaluation, interactive document mining, and multiple clusterings exploration are studied to explore the integration of data mining and data visualization. In particular, we 1) propose an efficient feature selection method (reliefF + mRMR) for preprocessing high dimensional datasets; 2) present DClusterE to integrate cluster validation with user interaction and provide rich visualization tools for users to examine document clustering results from multiple perspectives; 3) design two interactive document summarization systems to involve users efforts and generate customized summaries from 2D sentence layouts; and 4) propose a new framework which organizes the different input clusterings into a hierarchical tree structure and allows for interactive exploration of multiple clustering solutions.

A phenomenological exploration of Black male law enforcement officers' perspectives of racial profiling and their law enforcement career exploration and commitment

This phenomenological study explored Black male law enforcement officers' perspectives of how racial profiling shaped their decisions to explore and commit to a law enforcement career. Criterion and snow ball sampling was used to obtain the 17 participants for this study. Super's (1990) archway model was used as the theoretical framework. The archway model "is designed to bring out the segmented but unified and developmental nature of career development, to highlight the segments, and to make their origin clear" (Super, 1990, p. 201). Interview data were analyzed using inductive, deductive, and comparative analyses. Three themes emerged from the inductive analysis of the data: (a) color and/or race does matter, (b) putting on the badge, and (c) too black to be blue and too blue to be black. The deductive analysis used a priori coding that was based on Super's (1990) archway model. The deductive analysis revealed the participants' career exploration was influenced by their knowledge of racial profiling and how others view them. The comparative analysis between the inductive themes and deductive findings found the theme "color and/or race does matter" was present in the relationships between and within all segments of Super's (1990) model. The comparative analysis also revealed an expanded notion of self-concept for Black males – marginalized and/or oppressed individuals. Self-concepts, "such as self-efficacy, self-esteem, and role self-concepts, being combinations of traits ascribed to oneself" (Super, 1990, p. 202) do not completely address the self-concept of marginalized and/or oppressed individuals. The self-concept of marginalized and/or oppressed individuals is self-efficacy, self-esteem, traits ascribed to oneself expanded by their awareness of how others view them. (DuBois, 1995; Freire, 1970; Sheared, 1990; Super, 1990; Young, 1990). Ultimately, self-concept is utilized to make career and life decisions. Current human resource policies and practices do not take into consideration that negative police contact could be the result of racial profiling. Current human resource hiring guidelines penalize individuals who have had negative police contact. Therefore, racial profiling is a discriminatory act that can effectively circumvent U.S. Equal Employment Opportunities Commission laws and serve as a boundary mechanism to employment (Rocco & Gallagher, 2004).

Options to advance co-ordination and analysis of climate data in Europe

Systematic, high-quality observations of the atmosphere, oceans and terrestrial environments are required to improve understanding of climate characteristics and the consequences of climate change. The overall aim of this report is to carry out a comparative assessment of approaches taken to addressing the state of European observations systems and related data analysis by some leading actors in the field. This research reports on approaches to climate observations and analyses in Ireland, Switzerland, Germany, The Netherlands and Austria and explores options for a more coordinated approach to national responses to climate observations in Europe. The key aspects addressed are: an assessment of approaches to develop GCOS and provision of analysis of GCOS data; an evaluation of how these countries are reporting development of GCOS; highlighting best practice in advancing GCOS implementation including analysis of Essential Climate Variables (ECVs); a comparative summary of the differences and synergies in terms of the reporting of climate observations; an overview of relevant European initiatives and recommendations on how identified gaps might be addressed in the short to medium term.

Content and discontent: a qualitative exploration of obstacles to elearning engagement in medical students

BACKGROUND: Elearning is ubiquitous in healthcare professions education. Its equivalence to 'traditional' educational delivery methods is well established. There is a research imperative to clarify when and how to use elearning most effectively to mitigate the potential of it becoming merely a 'disruptive technology.' Research has begun to broadly identify challenges encountered by elearning users. In this study, we explore in depth the perceived obstacles to elearning engagement amongst medical students. Sensitising concepts of achievement emotions and the cognitive demands of multi-tasking highlight why students' deeply emotional responses to elearning may be so important in their learning.

METHODS: This study used focus groups as a data collection tool. A purposeful sample of 31 participated. Iterative data gathering and analysis phases employed a constant comparative approach to generate themes firmly grounded in participant experience.

RESULTS: Key themes that emerged from the data included a sense of injustice, passivity and a feeling of being 'lost at sea'. The actual content of the elearning resource provided important context.

CONCLUSIONS: The identified themes have strong emotional foundations. These responses, interpreted through the lens of achievement emotions, have not previously been described. Appreciation of their importance is of benefit to educators involved in curriculum development or delivery.

RICH AND EFFICIENT VISUAL DATA REPRESENTATION

Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.

A Phenomenological Exploration of Black Male Law Enforcement Officers' Perspectives of Racial Profiling and Their Law Enforcement Career Exploration and Commitment

This phenomenological study explored Black male law enforcement officers’ perspectives of how racial profiling shaped their decisions to explore and commit to a law enforcement career. Criterion and snow ball sampling was used to obtain the 17 participants for this study. Super’s (1990) archway model was used as the theoretical framework. The archway model “is designed to bring out the segmented but unified and developmental nature of career development, to highlight the segments, and to make their origin clear” (Super, 1990, p. 201). Interview data were analyzed using inductive, deductive, and comparative analyses. Three themes emerged from the inductive analysis of the data: (a) color and/or race does matter, (b) putting on the badge, and (c) too black to be blue and too blue to be black. The deductive analysis used a priori coding that was based on Super’s (1990) archway model. The deductive analysis revealed the participants’ career exploration was influenced by their knowledge of racial profiling and how others view them. The comparative analysis between the inductive themes and deductive findings found the theme “color and/or race does matter” was present in the relationships between and within all segments of Super’s (1990) model. The comparative analysis also revealed an expanded notion of self-concept for Black males – marginalized and/or oppressed individuals. Self-concepts, “such as self-efficacy, self-esteem, and role self-concepts, being combinations of traits ascribed to oneself” (Super, 1990, p. 202) do not completely address the self-concept of marginalized and/or oppressed individuals. The self-concept of marginalized and/or oppressed individuals is self-efficacy, self-esteem, traits ascribed to oneself expanded by their awareness of how others view them. (DuBois, 1995; Freire, 1970; Sheared, 1990; Super, 1990; Young, 1990). Ultimately, self-concept is utilized to make career and life decisions. Current human resource policies and practices do not take into consideration that negative police contact could be the result of racial profiling. Current human resource hiring guidelines penalize individuals who have had negative police contact. Therefore, racial profiling is a discriminatory act that can effectively circumvent U.S. Equal Employment Opportunities Commission laws and serve as a boundary mechanism to employment (Rocco & Gallagher, 2004).

Power spectrum model of visual masking: simulations and empirical data

In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channel's shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channel's shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise cutoffs around the spatial frequency of the signal match the shape of the visual channel (symmetric or asymmetric) involved in the detection. In order to test the explanatory power of the model with empirical data, we performed six visual masking experiments. We show that this model, with only two free parameters, fits the empirical masking data with high precision. Finally, we provide equations of the power spectrum model for six masking noises used in the simulations and in the experiments.

Exploration of polymetallic nodules in the Area: Reporting practices, data management and transparency

The International Seabed Authority (ISA) regulates the activities related with the exploration and exploitation of seabed mineral resources in the Area, which are considered as the "common heritage of mankind" under the United Nations Convention on the Law of the Sea.The ISA has also the mandate to ensure the protection of the marine environment.The development of good practices for the annual reporting and data submission by Contractors is crucial for the ISA to comply with the sustainable development of the mineral marine resources. In 2015,the ISA issued a new template for reporting on exploration activities, which includes the definition of the format for all geophysical, geological and environmental data to be collected and analysed during exploration. The availability of reliable data contributes to improve the assessment of the ISA on the activities in the Area while promoting transparency, which is considered as a major principle of industry bestpractices.

NGC 7097: THE ACTIVE GALACTIC NUCLEUS AND ITS MIRROR, REVEALED BY PRINCIPAL COMPONENT ANALYSIS TOMOGRAPHY

Three-dimensional spectroscopy techniques are becoming more and more popular, producing an increasing number of large data cubes. The challenge of extracting information from these cubes requires the development of new techniques for data processing and analysis. We apply the recently developed technique of principal component analysis (PCA) tomography to a data cube from the center of the elliptical galaxy NGC 7097 and show that this technique is effective in decomposing the data into physically interpretable information. We find that the first five principal components of our data are associated with distinct physical characteristics. In particular, we detect a low-ionization nuclear-emitting region (LINER) with a weak broad component in the Balmer lines. Two images of the LINER are present in our data, one seen through a disk of gas and dust, and the other after scattering by free electrons and/or dust particles in the ionization cone. Furthermore, we extract the spectrum of the LINER, decontaminated from stellar and extended nebular emission, using only the technique of PCA tomography. We anticipate that the scattered image has polarized light due to its scattered nature.