A regional analysis of contraction rate in written Standard American English

The goal of this study is to determine if various measures of contraction rate are regionally patterned in written Standard American English. In order to answer this question, this study employs a corpus-based approach to data collection and a statistical approach to data analysis. Based on a spatial autocorrelation analysis of the values of eleven measures of contraction across a 25 million word corpus of letters to the editor representing the language of 200 cities from across the contiguous United States, two primary regional patterns were identified: easterners tend to produce relatively few standard contractions (not contraction, verb contraction) compared to westerners, and northeasterners tend to produce relatively few non-standard contractions (to contraction, non-standard not contraction) compared to southeasterners. These findings demonstrate that regional linguistic variation exists in written Standard American English and that regional linguistic variation is more common than is generally assumed.

A spatial pattern analysis of beta-amyloid (Abeta) deposition in the temporal lobe in Alzheimer's disease

To determine the factors influencing the distribution of -amyloid (Abeta) deposits in Alzheimer's disease (AD), the spatial patterns of the diffuse, primitive, and classic A deposits were studied from the superior temporal gyrus (STG) to sector CA4 of the hippocampus in six sporadic cases of the disease. In cortical gyri and in the CA sectors of the hippocampus, the Abeta deposits were distributed either in clusters 200-6400 microm in diameter that were regularly distributed parallel to the tissue boundary or in larger clusters greater than 6400 microm in diameter. In some regions, smaller clusters of Abeta deposits were aggregated into larger 'superclusters'. In many cortical gyri, the density of Abeta deposits was positively correlated with distance below the gyral crest. In the majority of regions, clusters of the diffuse, primitive, and classic deposits were not spatially correlated with each other. In two cases, double immunolabelled to reveal the Abeta deposits and blood vessels, the classic Abeta deposits were clustered around the larger diameter vessels. These results suggest a complex pattern of Abeta deposition in the temporal lobe in sporadic AD. A regular distribution of Abeta deposit clusters may reflect the degeneration of specific cortico-cortical and cortico-hippocampal pathways and the influence of the cerebral blood vessels. Large-scale clustering may reflect the aggregation of deposits in the depths of the sulci and the coalescence of smaller clusters.

A morphometric study of the spatial patterns of TDP-43 immunoreactive neuronal inclusions in frontotemporal lobar degeneration (FTLD) with progranulin (GRN) mutation

Mutations of the progranulin (GRN) gene are a major cause of familial frontotemporal lobar degeneration with transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) proteinopathy (FTLD-TDP). We studied the spatial patterns of TDP-43 immunoreactive neuronal cytoplasmic inclusions (NCI) and neuronal intranuclear inclusions (NII) in histological sections of the frontal and temporal lobe in eight cases of FTLD-TDP with GRN mutation using morphometric methods and spatial pattern analysis. In neocortical regions, the NCI were clustered and the clusters were regularly distributed parallel to the pia mater; 58% of regions analysed exhibiting this pattern. The NII were present in regularly distributed clusters in 35% of regions but also randomly distributed in many areas. In neocortical regions, the sizes of the regular clusters of NCI and NII were 400-800 µm, approximating to the size of the modular columns of the cortico-cortical projections, in 31% and 36% of regions respectively. The NCI and NII also exhibited regularly spaced clustering in sectors CA1/2 of the hippocampus and in the dentate gyrus. The clusters of NCI and NII were not spatially correlated. The data suggest degeneration of the cortico-cortical and cortico-hippocampal pathways in FTLD-TDP with GRN mutation, the NCI and NII affecting different clusters of neurons.

Analysis of magnetoencephaoographic data as a nonlinear dynamical system

This thesis presents the results from an investigation into the merits of analysing Magnetoencephalographic (MEG) data in the context of dynamical systems theory. MEG is the study of both the methods for the measurement of minute magnetic flux variations at the scalp, resulting from neuro-electric activity in the neocortex, as well as the techniques required to process and extract useful information from these measurements. As a result of its unique mode of action - by directly measuring neuronal activity via the resulting magnetic field fluctuations - MEG possesses a number of useful qualities which could potentially make it a powerful addition to any brain researcher's arsenal. Unfortunately, MEG research has so far failed to fulfil its early promise, being hindered in its progress by a variety of factors. Conventionally, the analysis of MEG has been dominated by the search for activity in certain spectral bands - the so-called alpha, delta, beta, etc that are commonly referred to in both academic and lay publications. Other efforts have centred upon generating optimal fits of "equivalent current dipoles" that best explain the observed field distribution. Many of these approaches carry the implicit assumption that the dynamics which result in the observed time series are linear. This is despite a variety of reasons which suggest that nonlinearity might be present in MEG recordings. By using methods that allow for nonlinear dynamics, the research described in this thesis avoids these restrictive linearity assumptions. A crucial concept underpinning this project is the belief that MEG recordings are mere observations of the evolution of the true underlying state, which is unobservable and is assumed to reflect some abstract brain cognitive state. Further, we maintain that it is unreasonable to expect these processes to be adequately described in the traditional way: as a linear sum of a large number of frequency generators. One of the main objectives of this thesis will be to prove that much more effective and powerful analysis of MEG can be achieved if one were to assume the presence of both linear and nonlinear characteristics from the outset. Our position is that the combined action of a relatively small number of these generators, coupled with external and dynamic noise sources, is more than sufficient to account for the complexity observed in the MEG recordings. Another problem that has plagued MEG researchers is the extremely low signal to noise ratios that are obtained. As the magnetic flux variations resulting from actual cortical processes can be extremely minute, the measuring devices used in MEG are, necessarily, extremely sensitive. The unfortunate side-effect of this is that even commonplace phenomena such as the earth's geomagnetic field can easily swamp signals of interest. This problem is commonly addressed by averaging over a large number of recordings. However, this has a number of notable drawbacks. In particular, it is difficult to synchronise high frequency activity which might be of interest, and often these signals will be cancelled out by the averaging process. Other problems that have been encountered are high costs and low portability of state-of-the- art multichannel machines. The result of this is that the use of MEG has, hitherto, been restricted to large institutions which are able to afford the high costs associated with the procurement and maintenance of these machines. In this project, we seek to address these issues by working almost exclusively with single channel, unaveraged MEG data. We demonstrate the applicability of a variety of methods originating from the fields of signal processing, dynamical systems, information theory and neural networks, to the analysis of MEG data. It is noteworthy that while modern signal processing tools such as independent component analysis, topographic maps and latent variable modelling have enjoyed extensive success in a variety of research areas from financial time series modelling to the analysis of sun spot activity, their use in MEG analysis has thus far been extremely limited. It is hoped that this work will help to remedy this oversight.

Density and spatial pattern of the neuropathological changes in the cerebellum in the prion disease variant Creutzfeldt-Jakob disease (vCJD)

The objective of this chapter is to quantify the neuropathology of the cerebellar cortex in cases of the prion disease variant Creutzfeldt-Jakob disease (vCJD). Hence, sequential sections of the cerebellum of 15 cases of vCJD were stained with H/E, or immunolabelled with a monoclonal antibody 12F10 against prion protein (PrP) and studied using quantitative techniques and spatial pattern analysis. A significant loss of Purkinje cells was evident in all cases. Densities of the vacuolation and the protease resistant form of prion protein (PrPSc) in the form of diffuse and florid plaques were greater in the granule cell layer (GL) than the molecular layer (ML). In the ML, vacuoles and PrPSc plaques, occurred in clusters which were regularly distributed along the folia, larger clusters of vacuoles and diffuse plaques being present in the GL. There was a negative spatial correlation between the vacuoles and the surviving Purkinje cells in the ML and a positive spatial correlation between the clusters of vacuoles and the diffuse PrPSc plaques in the ML and GL in five and six cases respectively. A canonical variate analysis (CVA) suggested a negative correlation between the densities of the vacuolation in the GL and the diffuse PrPSc plaques in the ML. The data suggest: 1) all laminae of the cerebellar cortex were affected by the pathology of vCJD, the GL more severely than the ML, 2) the pathology was topographically distributed especially in the Purkinje cell layer and GL, 3) pathological spread may occur in relation to a loop of anatomical projections connecting the cerebellum, thalamus, cerebral cortex, and pons, and 4) there are differences in the pathology of the cerebellum in vCJD compared with the M/M1 subtype of sporadic CJD (sCJD).

A penny for your thoughts! Patterns of fMRI activity reveal the content and the spatial topography of visual mental images

Visual mental imagery is a complex process that may be influenced by the content of mental images. Neuropsychological evidence from patients with hemineglect suggests that in the imagery domain environments and objects may be represented separately and may be selectively affected by brain lesions. In the present study, we used functional magnetic resonance imaging (fMRI) to assess the possibility of neural segregation among mental images depicting parts of an object, of an environment (imagined from a first-person perspective), and of a geographical map, using both a mass univariate and a multivariate approach. Data show that different brain areas are involved in different types of mental images. Imagining an environment relies mainly on regions known to be involved in navigational skills, such as the retrosplenial complex and parahippocampal gyrus, whereas imagining a geographical map mainly requires activation of the left angular gyrus, known to be involved in the representation of categorical relations. Imagining a familiar object mainly requires activation of parietal areas involved in visual space analysis in both the imagery and the perceptual domain. We also found that the pattern of activity in most of these areas specifically codes for the spatial arrangement of the parts of the mental image. Our results clearly demonstrate a functional neural segregation for different contents of mental images and suggest that visuospatial information is coded by different patterns of activity in brain areas involved in visual mental imagery. Hum Brain Mapp 36:945-958, 2015.

The carbon dioxide emissions of firms:a spatial analysis

In order to gain a greater understanding of firms' 'environmental behaviour' this paper explores the factors that influence firms' emissions intensities and provides the first analysis of the determinants of firm level carbon dioxide (CO2) emissions. Focussing on Japan, the paper also examines whether firms' CO2 emissions are influenced by the emissions of neighbouring firms and other possible sources of spatial correlation. Results suggest that size, the capital-labour ratio, R&D expenditure, the extent of exports and concern for public profile are the key determinants of CO2 emissions. Local lobbying pressure, as captured by regional community characteristics, does not appear to play a role, however emissions are found to be spatially correlated. This raises implications for the manner in which the environmental performance of firms is modelled in future. © 2013 Elsevier Inc.

EU-ASEAN relations in the post-Cold War era:an analysis of the impact of normative factors on interregionalism

This thesis examines the relationship between the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) with a focus on why their normative elements, e.g. values and norms, affect their ties in the post-Cold War era. Since the end of the Cold War, policy-makers and academics have become interested in region-to-region interaction, termed interregionalism. Though interregionalism is considered to have become an indelible feature of post-Cold War international politics, there are question marks over its importance. It is often argued that interregionalism reinforces the collective identity of the regional organisations involved. It is also maintained that its overall relevance to the international system depends on the level of actorness, which is primarily measured in institutional and material terms, of the participant regional organisations. This thesis contends that the normative components of the EU and ASEAN are also fundamental constituents of their actorness and, consequently, define significantly their interregionalism. This is based on a crucial observation that normative factors are of importance to the regional and international relations of the EU and ASEAN. Yet, while they strongly espouse norms and values to guide their internal and external activities, their normative premises radically differ from each other. Furthermore, these normative differences jeopardise their cooperation. Building on this observation the inquiry takes the normative components of the EU and ASEAN as the criterion as well as the focus for investigating their interregionalism. In doing so, it hypothesises that the EU and ASEAN are two different regional actors that adopt two dissimilar sets of norms to conduct their regional and international affairs and that such normative differences hinder their relations. Within this hypothesis, it seeks to address three central questions. First, what are the normative features that constitute the EU and ASEAN as actors in world politics and that make them different from each other? Second, what are the main sources of their normative differences? Finally, why do their normative differences become an obstructive factor in their relationship? To address these issues, the inquiry adopts a constructivist interpretation (of International Relations) and opts for a narrative and empirical inquiry, which is based on information and data acquired from official documents, scholarly works and interviews and questionnaires. In doing so, it finds that as they were born and evolved in two dissimilar temporal and spatial settings, the EU and ASEAN are two different norm entrepreneurs and normative powers. The former advocates a set of liberal cosmopolitan norms whereas the latter champions a set of traditional communitarian principles. Their normative differences become a major obstacle to their cooperation, especially when one regional organisation’s norms are refused or violated by the other. Thus, a key lesson drawn from these findings is that in order to explain more fully EU-ASEAN interregionalism, it is essential to consider their norms, the reasons behind their normative differences and the implication of those differences to their relations

Complexity analysis of electroencephalographic data

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Application of the Artificial Intelligence Algorithms for System Analysis of Multi Dimension Physiological Data for Developing Polyparametric Information System of Public Health Diagnostics

The polyparametric intelligence information system for diagnostics human functional state in medicine and public health is developed. The essence of the system consists in polyparametric describing of human functional state with the unified set of physiological parameters and using the polyparametric cognitive model developed as the tool for a system analysis of multitude data and diagnostics of a human functional state. The model is developed on the basis of general principles geometry and symmetry by algorithms of artificial intelligence systems. The architecture of the system is represented. The model allows analyzing traditional signs - absolute values of electrophysiological parameters and new signs generated by the model – relationships of ones. The classification of physiological multidimensional data is made with a transformer of the model. The results are presented to a physician in a form of visual graph – a pattern individual functional state. This graph allows performing clinical syndrome analysis. A level of human functional state is defined in the case of the developed standard (“ideal”) functional state. The complete formalization of results makes it possible to accumulate physiological data and to analyze them by mathematics methods.

Technological innovation in electronic inventory solutions:an analysis of patent data

The purpose of this paper is to investigate the technological development of electronic inventory solutions from perspective of patent analysis. We first applied the international patent classification to classify the top categories of data processing technologies and their corresponding top patenting countries. Then we identified the core technologies by the calculation of patent citation strength and standard deviation criterion for each patent. To eliminate those core innovations having no reference relationships with the other core patents, relevance strengths between core technologies were evaluated also. Our findings provide market intelligence not only for the research and development community, but for the decision making of advanced inventory solutions.

Using the framework method for the analysis of qualitative data in multi-disciplinary health research

Background: The Framework Method is becoming an increasingly popular approach to the management and analysis of qualitative data in health research. However, there is confusion about its potential application and limitations. Discussion. The article discusses when it is appropriate to adopt the Framework Method and explains the procedure for using it in multi-disciplinary health research teams, or those that involve clinicians, patients and lay people. The stages of the method are illustrated using examples from a published study. Summary. Used effectively, with the leadership of an experienced qualitative researcher, the Framework Method is a systematic and flexible approach to analysing qualitative data and is appropriate for use in research teams even where not all members have previous experience of conducting qualitative research. © 2013 Gale et al.; licensee BioMed Central Ltd.

Analysis and Data Mining of Lead-Zinc Ore Data

This paper presents the results of our data mining study of Pb-Zn (lead-zinc) ore assay records from a mine enterprise in Bulgaria. We examined the dataset, cleaned outliers, visualized the data, and created dataset statistics. A Pb-Zn cluster data mining model was created for segmentation and prediction of Pb-Zn ore assay data. The Pb-Zn cluster data model consists of five clusters and DMX queries. We analyzed the Pb-Zn cluster content, size, structure, and characteristics. The set of the DMX queries allows for browsing and managing the clusters, as well as predicting ore assay records. A testing and validation of the Pb-Zn cluster data mining model was developed in order to show its reasonable accuracy before beingused in a production environment. The Pb-Zn cluster data mining model can be used for changes of the mine grinding and floatation processing parameters in almost real-time, which is important for the efficiency of the Pb-Zn ore beneficiation process. ACM Computing Classification System (1998): H.2.8, H.3.3.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.