Delirium in early-stage Alzheimer's disease : enhancing cognitive reserve as a possible preventive measure

Delirium is a disorder of acute onset with fluctuating symptoms and is characterized by inattention, disorganized thinking, and altered levels of consciousness. The risk for delirium is greatest in individuals with dementia, and the incidence of both is increasing worldwide because of the aging of our population. Although several clinical trials have tested interventions for delirium prevention in individuals without dementia, little is known about the mechanisms for the prevention of delirium in early-stage Alzheimer’s disease (AD). The purpose of this article is to explore ways of preventing delirium and slowing the rate of cognitive decline in early-stage AD by enhancing cognitive reserve. An agenda for future research on interventions to prevent delirium in individuals with early-stage AD is also presented.

Effectiveness of community-based nonpharmacological interventions for early-stage dementia : conclusions and recommendations

In 2007, a comprehensive review of the extant research on nonpharmacological interventions for persons with early-stage dementia was conducted. More than 150 research reports, centered on six major domains, were included: early-stage support groups, cognitive training and enhancement programs, exercise programs, exemplar programs, health promotion programs, and “other” programs not fitting into previous categories. Theories of neural regeneration and plasticity were most often used to support the tested interventions. Recommendations for practice, research, and health policy are outlined, including evidence-based, nonpharmacological treatment protocols for persons with mild cognitive impairment and early-stage dementia. A tested, community-based, multimodal treatment program is also described. Overall, findings identify well-supported nonpharmacological treatments for persons with early-stage dementia and implications for a national health care agenda to optimize outcomes for this growing population of older adults.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

A closer look at business planning : early stage outcome effects of how it is prepared and used

The widespread use of business planning in combination with the mixed theoretical and empirical support for its effect suggest research is needed that takes a deeper into the quality of plans and how they are used. In this study we longitudinally examine use vs. non-use; degree of formalizations; revision of plans, and moderation of planning effects by product novelty,among nascent firms. We relate these to attainment of profitability after 12 months. We find that business planning is negatively related to profitability, but that revising plans is positively related to profitability. Both these effects are stronger under conditions of high product novelty.

A framework for life cycle cost estimation of a product family at the early stage of product development

A cost estimation method is required to estimate the life cycle cost of a product family at the early stage of product development in order to evaluate the product family design. There are difficulties with existing cost estimation techniques in estimating the life cycle cost for a product family at the early stage of product development. This paper proposes a framework that combines a knowledge based system and an activity based costing techniques in estimating the life cycle cost of a product family at the early stage of product development. The inputs of the framework are the product family structure and its sub function. The output of the framework is the life cycle cost of a product family that consists of all costs at each product family level and the costs of each product life cycle stage. The proposed framework provides a life cycle cost estimation tool for a product family at the early stage of product development using high level information as its input. The framework makes it possible to estimate the life cycle cost of various product family that use any types of product structure. It provides detailed information related to the activity and resource costs of both parts and products that can assist the designer in analyzing the cost of the product family design. In addition, it can reduce the required amount of information and time to construct the cost estimation system.

A method for systematically pooling data in very early stage construction price forecasting

Client owners usually need an estimate or forecast of their likely building costs in advance of detailed design in order to confirm the financial feasibility of their projects. Because of their timing in the project life cycle, these early stage forecasts are characterized by the minimal amount of information available concerning the new (target) project to the point that often only its size and type are known. One approach is to use the mean contract sum of a sample, or base group, of previous projects of a similar type and size to the project for which the estimate is needed. Bernoulli’s law of large numbers implies that this base group should be as large as possible. However, increasing the size of the base group inevitably involves including projects that are less and less similar to the target project. Deciding on the optimal number of base group projects is known as the homogeneity or pooling problem. A method of solving the homogeneity problem is described involving the use of closed form equations to compare three different sampling arrangements of previous projects for their simulated forecasting ability by a cross-validation method, where a series of targets are extracted, with replacement, from the groups and compared with the mean value of the projects in the base groups. The procedure is then demonstrated with 450 Hong Kong projects (with different project types: Residential, Commercial centre, Car parking, Social community centre, School, Office, Hotel, Industrial, University and Hospital) clustered into base groups according to their type and size.

Early stage start-ups : evidence from the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE)

In this chapter, the picture of Australian small business is supplemented by using data from the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) . This data tracks large numbers of on-going business start-ups over time. The Australian Centre of Entrepreneurship Research at Queensland University of Technology collected data in four annual waves. (Wave 1 to Wave 4) from 2007 to 2011. CAUSEE allows the analysis of entrepreneurial entrants at two stages of development, i.e. nascent and young firms. Nascent firms are defined as firms in the process of being created, but not yet established in the market, and young firms are defined as having been operational for up to four years. An analysis of nascent firms provides unique insights, as no other known Australian database captures and follows the development of business start-ups at the pre-operational stage. In addition, the project captured judgment over samples of high-potential start-ups.

What it means to be green : an examination of early stage environmental and sustainability focused firms

This paper examines ‘green’ entrepreneurial nascent and young firms in Australia. Findings of interest in this paper include: • Green entrepreneurs are more likely to be highly educated and have an extended depth of experience within their industry and are more likely to have started a business prior to their current venture. • Green entrepreneurs exhibit increased levels of innovation, with an increased focus on new & high technology, R&D and the development of proprietary technology. • Green entrepreneurs are most likely to be based upon a product rather than a service and have a higher emphasis upon growth when compared with non-green entrepreneurs. • Green entrepreneurial firms tend to have a longer venture creation process and draw financial resources from a larger number of sources and rely more upon equity as a means of financing their venture.

An activity and resource advisory system for manufacturing process chains selection at the early stage of product development

Designers need to consider both the functional and production process requirements at the early stage of product development. A variety of the research works found in the literature has been proposed to assist designers in selecting the most viable manufacturing process chain. However, they do not provide any assistance for designers to evaluate the processes according to the particular circumstances of their company. This paper describes a framework of an Activity and Resource Advisory System (ARAS) that generates advice about the required activities and the possible resources for various manufacturing process chains. The system provides more insight, more flexibility, and a more holistic and suitable approach for designers to evaluate and then select the most viable manufacturing process chain at the early stage of product development.

Computational simulation of the early stage of bone healing under different configurations of locking compression plates

Flexible fixation or the so-called ‘biological fixation’ has been shown to encourage the formation of fracture callus, leading to better healing outcomes. However, the nature of the relationship between the degree of mechanical stability provided by a flexible fixation and the optimal healing outcomes has not been fully understood. In this study, we have developed a validated quantitative model to predict how cells in fracture callus might respond to change in their mechanical microenvironment due to different configurations of locking compression plate (LCP) in clinical practice, particularly in the early stage of healing. The model predicts that increasing flexibility of the LCP by changing the bone–plate distance (BPD) or the plate working length (WL) could enhance interfragmentary strain in the presence of a relatively large gap size (.3 mm). Furthermore, conventional LCP normally results in asymmetric tissue development during early stage of callus formation, and the increase of BPD or WL is insufficient to alleviate this problem.

The effects of flexible fixation on early stage bone fracture healing

The mechanical microenvironment at a fracture site could potentially influence the outcomes of bone fracture healing. It is known that, should the fixation construct be too stiff, or the gap between the fracture ends be too large, bones are less likely to heal. Flexible fixation or so-called “biological fixation” has been shown to encourage the formation of fracture callus, and therefore result in better healing outcomes. However, till date the nature of the relationship between the degree of mechanical stability provided by a flexible fixation and optimal healing fracture healing outcomes has not been fully understood. This paper presents a computational model that can predict healing out-comes from early stage healing data under various fixation configurations. The results of the simulations demonstrate that the change of mechanical microenvironment of fracture site resulting from the different fixation configurations is of importance for the healing outcomes.

Bricolage and early stage firm performance

Bricolage is one way firms innovate in the midst of resource constraints. In this thesis bricolage in entrepreneurial firms is investigated as is its impact on firm performance. The research provides empirical tests of bricolage and firm performance and tests environmental dynamism and team compositions as moderating effects that impact the bricolage-performance relationship. The limits of bricolage during firm creation are also explored. This research offers several novel theoretical contributions and some suggestions for entrepreneurs who typically use bricolage in attempts of firm creation.

Determination of coefficient of consolidation from early stage of log t plot

The standard curve-fitting methods, Casagrande's log t method and Taylor's root t method, for the determination of the coefficient of consolidation use the later part of the consolidation curve and are influenced by secondary compression effects. Literature shows that secondary compression is concurrent with primary consolidation and that its effect is to decrease the value of the coefficient of consolidation. If the early part of the time-compression data is used, the values obtained will be less influenced by secondary compression effects. A method that uses the early part of the log t plot is proposed in this technical note. As the influence of secondary compression is reduced, the value obtained by this method is greater than that yielded by both the standard methods. The permeability values computed from C-v (obtained from the proposed method) rue more in agreement with the measured values than the standard methods showing that the effects of secondary compression are minimized. Time-compression data for a shorter duration is sufficient for the determination of C-v if the coefficient of secondary compression is not required.