Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

Organizations as distinction generating and processing systems: Niklas Luhmann's contribution to organization studies

Niklas Luhmann's theory of social systems has been widely influential in the German-speaking countries in the past few decades. However, despite its significance, particularly for organization studies, it is only very recently that Luhmann's work has attracted attention on the international stage as well. This Special Issue is in response to that. In this introductory paper, we provide a systematic overview of Luhmann's theory. Reading his work as a theory about distinction generating and processing systems, we especially highlight the following aspects: (i) Organizations are processes that come into being by permanently constructing and reconstructing themselves by means of using distinctions, which mark what is part of their realm and what not. (ii) Such an organizational process belongs to a social sphere sui generis possessing its own logic, which cannot be traced back to human actors or subjects. (iii) Organizations are a specific kind of social process characterized by a specific kind of distinction: decision, which makes up what is specifically organizational about organizations as social phenomena. We conclude by introducing the papers in this Special Issue. Copyright © 2006 SAGE.

Review of 3D model development with application to groundwater studies in New Zealand 1996 – 2011

Three dimensional geological modelling techniques have been applied since 1996 with an aim to characterise the lithological and chronological units of New Zealand’s many diverse aquifers. Models of property-scattered data have also been applied to assess physical properties of aquifers and the distribution of groundwater chemistry, including groundwater age, to inform an understanding of groundwater systems. These models, fundamental to understanding groundwater recharge, flow and discharge have found many uses as outlined in this paper.

Socio-Spatial Approaches To Literacy Studies: Rethinking the Social Constitution and Politics of Space

Literacy studies have begun to examine the spatial dimension of literacy practices in a way that foregrounds space, and that considers space as constitutive to human relations and practices. This chapter provides an introduction to spatial literacy research, providing a guide to key theorists, themes, and studies that have shaped historical and new developments in spatial approaches to literacy practice and pedagogy. It begins by reconceptualising socio-spatial approaches to literacy research and defines terms. Intersections with related social theories are examined, with an emphasis on critical approaches and the politics of space. It clarifies the relationship between socio-spatial and socio-cultural paradigms, revisiting the spatial in seminal socio-cultural research. It covers new ground,including networks, flows, and deterritorialisation of literacy practice. The chapter concludes with challenges and recommendations for future language research and educational practice.

Simultaneous determination of 6,7-dimethylesculetin and geniposide in rat plasma and its application to pharmacokinetic studies of Yin Chen Hao Tang preparation

A method for the rapid and simultaneous determination of 6,7-dimethylesculetin (CAS 120-08-1) and geniposide (CAS 24512-63-8) in rat plasma has been developed, using validated high performance liquid chromatography (HPLC) with solid phase extraction (SPE). The HPLC analysis was performed on a commercially available column (200 mm x 4.6 mm, 5 microm) with acetonitrile-methanol-0.1% aqueous formic acid as mobile phase and the UV detection at 343 nm and 238 nm for 6,7-dimethylesculetin and geniposide, respectively. The calibration curves for 6,7-dimethylesculetin and geniposide were linear over the range 0.4-25.6 microg/mL and 1.12-71.68 microg/mL, respectively. The lower limits of quantitation were 0.40 microg/ mL and 1.12 microg/mL, and the lower limits of detection were 0.06 microg/mL and 0.09 microg/ mL, respectively. The intra-day and inter-day precision for 6,7-dimethylesculetin and geniposide were < 5%, whereas the absolute recovery percentages were > 74%. A successful application of the developed HPLC analysis was demonstrated for the pharmacokinetic study of a Traditional Chinese Medicine formula of Yin Chen Hao Tang preparation.

Organizations as distinction generating and processing systems : Niklas Luhmann's contribution to organization studies

Niklas Luhmann's theory of social systems has been widely influential in the German-speaking countries in the past few decades. However, despite its significance, particularly for organization studies, it is only very recently that Luhmann's work has attracted attention on the international stage as well. This Special Issue is in response to that. In this introductory paper, we provide a systematic overview of Luhmann's theory. Reading his work as a theory about distinction generating and processing systems, we especially highlight the following aspects: (i) Organizations are processes that come into being by permanently constructing and reconstructing themselves by means of using distinctions, which mark what is part of their realm and what not. (ii) Such an organizational process belongs to a social sphere sui generis possessing its own logic, which cannot be traced back to human actors or subjects. (iii) Organizations are a specific kind of social process characterized by a specific kind of distinction: decision, which makes up what is specifically organizational about organizations as social phenomena. We conclude by introducing the papers in this Special Issue. Copyright © 2006 SAGE.

Application of XRF and correspondence analysis to provenance studies of coastal and inland archaeological pottery from the Mngeni river area, South Africa

XRF spectrometry was applied to provenance studies of Iron Age pottery specimens that originated from the Mngeni river area in South Africa. Ten transition metals (Sc to Zn) mere determined in 107 potsherds, excavated from four different sites. The data were subjected to a computerized mathematical technique (correspondence analysis), which was used to group the samples according to the similarity of their elemental distributions. The groupings were interpreted in terms of social or cultural interaction between the sites. (C) 1997 by John Wiley & Sons, Ltd.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

Prioritization of candidate cancer genes-an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.

Application of the Schwinger multichannel method to multichannel studies of electronically inelastic electron-molecule collisions

We have applied the Schwinger Multichannel Method(SMC) to the study of electronically inelastic, low energy electron-molecule collisions. The focus of these studies has been the assessment of the importance of multichannel coupling to the dynamics of these excitation processes. It has transpired that the promising quality of results realized in early SMC work on such inelastic scattering processes has been far more difficult to obtain in these more sophisticated studies.

We have attempted to understand the sources of instability of the SMC method which are evident in these multichannel studies. Particular instances of such instability have been considered in detail, which indicate that linear dependence, failure of the separable potential approximation, and difficulties in converging matrix elements involving recorrelation or Q-space terms all conspire to complicate application of the SMC method to these studies. A method involving singular value decomposition(SVD) has been developed to, if not resolve these problems, at least mitigate their deleterious effects on the computation of electronically inelastic cross sections.

In conjunction with this SVD procedure, the SMC method has been applied to the study of the H_2 , H_2O, and N_2 molecules. Rydberg excitations of the first two molecules were found to be most sensitive to multichannel coupling near threshold. The (3σ_g → 1π_g ) and (1π_u → 1π_g) valence excitations of the N_2 molecule were found to be strongly influenced by the choice of channel coupling scheme at all collision energies considered in these studies.

Extending noise robust structured support vector machines to larger vocabulary tasks

This paper describes a structured SVM framework suitable for noise-robust medium/large vocabulary speech recognition. Several theoretical and practical extensions to previous work on small vocabulary tasks are detailed. The joint feature space based on word models is extended to allow context-dependent triphone models to be used. By interpreting the structured SVM as a large margin log-linear model, illustrates that there is an implicit assumption that the prior of the discriminative parameter is a zero mean Gaussian. However, depending on the definition of likelihood feature space, a non-zero prior may be more appropriate. A general Gaussian prior is incorporated into the large margin training criterion in a form that allows the cutting plan algorithm to be directly applied. To further speed up the training process, 1-slack algorithm, caching competing hypothesis and parallelization strategies are also proposed. The performance of structured SVMs is evaluated on noise corrupted medium vocabulary speech recognition task: AURORA 4. © 2011 IEEE.