Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Linking client and employee perceptions of the organization: A study of client satisfaction with health care services

Client satisfaction with health care services has usually been researched in terms of socio-demographic and predispositional characteristics associated with the client. The present study included organizational characteristics as predictors of client satisfaction with health care services. Participants in the research were clients and employees of an Australian public-sector health care organization who responded to separate client and employee questionnaires. Hierarchical regression analyses indicated that, after controlling for a number of client characteristics, organizational characteristics, as perceived by employees, accounted for a significant proportion of additional variance in client satisfaction with health care services. Results of the present study provided some support for the proposition that employee perceptions of the working environment should be considered in a more comprehensive understanding of client satisfaction with health care services. Limitations of the study highlight practical difficulties in the assessment of client outcomes and methodological complexities in linking individual and organizational processes.

A longitudinal investigation of coping processes during a merger: Implications for job satisfaction and organizational identification

This study tested the utility of a stress and coping model of employee adjustment to a merger. Two hundred and twenty employees completed both questionnaires (Time 1: 3 months after merger implementation; Time 2: 2 years later). Structural equation modeling analyses revealed that positive event characteristics predicted greater appraisals of self-efficacy and less stress at Time 1. Self-efficacy, in turn, predicted greater use of problem-focused coping at Time 2, whereas stress predicted a greater use of problem-focused and avoidance coping. Finally, problem-focused coping predicted higher levels of job satisfaction and identification with the merged organization (Time 2), whereas avoidance coping predicted lower identification.

Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods

This paper presents the results of a research project aimed at examining the capabilities and challenges of two distinct but not mutually exclusive approaches to in-service bridge assessment: visual inspection and installed monitoring systems. In this study, the intended functionality of both approaches was evaluated on its ability to identify potential structural damage and to provide decision-making support. Inspection and monitoring are compared in terms of their functional performance, cost, and barriers (real and perceived) to implementation. Both methods have strengths and weaknesses across the metrics analyzed, and it is likely that a hybrid evaluation technique that adopts both approaches will optimize efficiency of condition assessment and ultimately lead to better decision making.

Value congruence and job-related attitudes in a nonprofit organization: A competing values approach

Researchers have highlighted the importance of the nonprofit sector, its continued growth, and a relative lack of literature particularly related to nonprofit organizational values. Therefore, this study investigates organizational culture in a human services nonprofit organization. The relationship between person-organization value congruence and employee and volunteer job-related attitudes is examined (N = 227). Following initial qualitative enquiry, confirmatory factor analyses of the Competing Values Framework and additional values revealed five dimensions of organizational values. The relationship between value congruence, and employee and volunteers' job-related attitudes was examined using polynomial regression techniques. Analyses revealed that for employees, job-related attitudes were influenced strongly by organization values ratings, particularly when exceeding person ratings of the same values. For volunteers, person value ratings exceeding organization value ratings were especially detrimental to their job-related attitudes. Findings are discussed in terms of their theoretical and practical implications.

Single layer lead iodide: Computational exploration of structural, electronic and optical properties, strain induced band modulation and the role of spin-orbital-coupling

Graphitic like layered materials exhibit intriguing electronic structures and thus the search for new types of two-dimensional (2D) monolayer materials is of great interest for developing novel nano-devices. By using density functional theory (DFT) method, here we for the first time investigate the structure, stability, electronic and optical properties of monolayer lead iodide (PbI2). The stability of PbI2 monolayer is first confirmed by phonon dispersion calculation. Compared to the calculation using generalized gradient approximation, screened hybrid functional and spin–orbit coupling effects can not only predicts an accurate bandgap (2.63 eV), but also the correct position of valence and conduction band edges. The biaxial strain can tune its bandgap size in a wide range from 1 eV to 3 eV, which can be understood by the strain induced uniformly change of electric field between Pb and I atomic layer. The calculated imaginary part of the dielectric function of 2D graphene/PbI2 van der Waals type hetero-structure shows significant red shift of absorption edge compared to that of a pure monolayer PbI2. Our findings highlight a new interesting 2D material with potential applications in nanoelectronics and optoelectronics.

Conserved gene structure and function of interleukin-10 in teleost fish

Interleukin-10 (IL-10) is an important immunoregulatory cytokine produced by various types of cells. Researchers describe here the isolation and characterization of olive flounder IL-10 (ofIL-10) cDNA and genomic organization. The ofIL-10 gene encodes a 187 amino acid protein and is composed of a five exon/four intron structure, similar to other known IL-10 genes. The ofIL-10 promoter sequence analysis shows a high level of homology in putative binding sites for transcription factors which are sufficient for transcriptional regulation ofIL-10. Important structural residues are maintained in the ofIL-10 protein including the four cysteines responsible for the two intra-chain disulfide bridges reported for human IL-10 and two extra cysteine residues that exist only in fish species. The phylogenetic analysis clustered ofIL-10 with other fish IL-10s and apart from mammalian IL-10 molecules. Quantitative real-time Polymerase Chain Reaction (PCR) analysis demonstrated ubiquitous ofIL-10 gene expression in the 13 tissues examined. Additionally, the induction of ofIL-10 gene expression was observed in the kidney tissue from olive flounder infected with bacteria (Edawardsiella tarda) or virus (Viral Hemorrhagic Septicemia Virus; VHSV). These data indicate that IL-10 is an important immune regulator that is conserved strictly genomic organization and function during the evolution of vertebrate immunity.

QUT structural health monitoring system and the capture of recent Queensland earthquakes

This article reports the main features of an innovative full-scale Structural Health Monitoring (SHM) system which has been implemented onto a landmark building on QUT Gardens Point Campus and its efficacy in capturing the recent Queensland earthquakes although they occurred almost 300 km away from where the system is located.

Influence of structural sealant joints on the blast performance of laminated glass panels

This paper investigates the influence of structural sealant joints on the blast performance of laminated glass (LG) panels, using a comprehensive numerical procedure. A parametric study was carried out by varying the width, thickness and the Young’s modulus (E) of the structural silicone sealant joints and the behavior of the LG panel was studied under two different blast loads. Results show that these parameters influence the blast response of LG panels, especially under the higher blast load. Sealant joints that are thicker, have smaller widths and lower E values increase the flexibility at the supports and hence increase the energy absorption of the LG panel while reducing the support reactions. Results also confirmed that sealant joints designed according to current standards perform well under blast loads. Modeling techniques presented in this paper could be used to complement and supplement the guidance in existing design standards. The new information generated in this paper will contribute towards safer and more economical designs of entire facade systems including window glazing, frames and supporting structures.

Meta-analysis of structural imaging findings in Attention-Deficit/Hyperactivity Disorder

Background Although there are many structural neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) in children, there are inconsistencies across studies and no consensus regarding which brain regions show the most robust area or volumetric reductions relative to control subjects. Our goal was to statistically analyze structural imaging data via a meta-analysis to help resolve these issues. Methods We searched the MEDLINE and PsycINFO databases through January 2005. Studies must have been written in English, used magnetic resonance imaging, and presented the means and standard deviations of regions assessed. Data were extracted by one of the authors and verified independently by another author. Results Analyses were performed using STATA with metan, metabias, and metainf programs. A meta-analysis including all regions across all studies indicated global reductions for ADHD subjects compared with control subjects, standardized mean difference equal to .408, p less than .001. Regions most frequently assessed and showing the largest differences included cerebellar regions, the splenium of the corpus callosum, total and right cerebral volume, and right caudate. Several frontal regions assessed in only two studies also showed large significant differences. Conclusions This meta-analysis provides a quantitative analysis of neuroanatomical abnormalities in ADHD and information that can be used to guide future studies.

Encouraging Generosity: The Practice and organization of Fundraising Across Nations

The international collaboration in this book creates a unique opportunity to establish, discuss and draw conclusions about fundraising across nations. Based on the 26-country dataset provided by the authors in this volume, this chapter describes and analyzes for the first time the diverse fundraising environments around the world that are shaped by different historical, cultural, social, religious, political and economic conditions. It begins by noting the lack of research on fundraisers and fundraising in contrast to the extensive studies undertaken of donors, and argues that the demand side of charitable transactions is worthy of greater attention if a complete and dynamic understanding of giving is to be achieved. It then presents and discusses key themes related to fundraising in the countries represented in this book. A typology is suggested to impose order on the huge variety of fundraising approaches and stages of development in the organization of this activity around the world; this typology also strengthens understanding of the connection between asking and giving. After offering suggestions for future research in this area of study, the chapter ends by noting that despite global differences in the evolution of fundraising as a profession and the diversity of current contexts, fundraisers in every country face shared challenges that would benefit from greater exchange of knowledge and best practices.

Mathematical modeling for improved greenhouse gas balances, agro-ecosystems, and policy development: Lessons from the Australian experience

If the land sector is to make significant contributions to mitigating anthropogenic greenhouse gas (GHG) emissions in coming decades, it must do so while concurrently expanding production of food and fiber. In our view, mathematical modeling will be required to provide scientific guidance to meet this challenge. In order to be useful in GHG mitigation policy measures, models must simultaneously meet scientific, software engineering, and human capacity requirements. They can be used to understand GHG fluxes, to evaluate proposed GHG mitigation actions, and to predict and monitor the effects of specific actions; the latter applications require a change in mindset that has parallels with the shift from research modeling to decision support. We compare and contrast 6 agro-ecosystem models (FullCAM, DayCent, DNDC, APSIM, WNMM, and AgMod), chosen because they are used in Australian agriculture and forestry. Underlying structural similarities in the representations of carbon flows though plants and soils in these models are complemented by a diverse range of emphases and approaches to the subprocesses within the agro-ecosystem. None of these agro-ecosystem models handles all land sector GHG fluxes, and considerable model-based uncertainty exists for soil C fluxes and enteric methane emissions. The models also show diverse approaches to the initialisation of model simulations, software implementation, distribution, licensing, and software quality assurance; each of these will differentially affect their usefulness for policy-driven GHG mitigation prediction and monitoring. Specific requirements imposed on the use of models by Australian mitigation policy settings are discussed, and areas for further scientific development of agro-ecosystem models for use in GHG mitigation policy are proposed.

Fractional diffusion models of cardiac electrical propagation: Role of structural heterogeneity in dispersion of repolarization

Impulse propagation in biological tissues is known to be modulated by structural heterogeneity. In cardiac muscle, improved understanding on how this heterogeneity influences electrical spread is key to advancing our interpretation of dispersion of repolarization. We propose fractional diffusion models as a novel mathematical description of structurally heterogeneous excitable media, as a means of representing the modulation of the total electric field by the secondary electrical sources associated with tissue inhomogeneities. Our results, analysed against in vivo human recordings and experimental data of different animal species, indicate that structural heterogeneity underlies relevant characteristics of cardiac electrical propagation at tissue level. These include conduction effects on action potential (AP) morphology, the shortening of AP duration along the activation pathway and the progressive modulation by premature beats of spatial patterns of dispersion of repolarization. The proposed approach may also have important implications in other research fields involving excitable complex media.

Spatially explicit structural equation modeling

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.

Secondary structure element alignment kernel method for prediction of protein structural classes

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.