A Cultural Tourism Strategy: Enriching Culture and Building Tourism in Buffalo Niagara

Their continued dedication to the region\u27s cultural, arts and heritage organizations and the development of cultural tourism has been, and will continue to be, essential to attaining the vision of “A Cultural Tourism Strategy”. The cultural tourism mission is to strengthen cultural, artistic and heritage organizations; expand individual opportunities for creativity and interpretation; help our regional economy grow; enhance the quality of life in our communities; advance the image and identity of the region; and build the region\u27s reputation as a world-class tourism destination. These benefits reinforce one another and can be achieved together

Arts and Economic Prosperity 5: The Economic Impact of Nonprofit Arts & Cultural Organizations & their Audiences in Western New York

[Excerpt] Arts & Economic Prosperity 5 provides evidence that the nonprofit arts and culture sector is a significant industry in Western New York—one that generates $352.1 million in total economic activity. This spending—$156 million by nonprofit arts and cultural organizations and an additional $196.1 million in event-related spending by their audiences—supports 10,160 full-time equivalent jobs, generates$208.2 million in household income to local residents, and delivers $40.3 million in local and state government revenue. This economic impact study sends a strong signal that when we support the arts, we not only enhance our quality of life, but we also invest in Western New York’s economic well-being

Creating Jobs in Samoa Through Public-Private Partnerships

Samoa’s achievement in contracting out to the private sector the functions of its Public Works Department (PWD) is one of the most successful reforms to upgrade infrastructure; improve the effectiveness of public expenditure; and increase the overall employment, productivity, and capacity in a Pacific island economy in the past 20 years. The reform has resulted in the establishment of nearly 30 new Samoan road construction and maintenance companies. Directly and indirectly, the reform has led to the creation of more than 2,000 new jobs, making this a prime example of the power of public–private partnerships to promote economic development and increase employment.1 Prior to the reform, much of this work was undertaken inefficiently by the PWD, or by foreign companies under contract. All construction and maintenance in Samoa is now outsourced to Samoan companies, which are sufficiently productive and cost-effective that foreign firms now struggle to compete

Alzheimer's Disease Prediction Using Longitudinal and Heterogeneous Magnetic Resonance Imaging

Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While traditional MRI-based diagnosis uses images acquired at a single time point, a longitudinal study is more sensitive and accurate in detecting early pathological changes of the AD. Two main difficulties arise in longitudinal MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects (i.e., different scanning time and different total number of scans); (2) the heterogeneous progressions of high-dimensional regions of interest (ROIs) in MRI. In this work, we propose a novel feature selection and estimation method which can be applied to extract features from the heterogeneous longitudinal MRI. A key ingredient of our method is the combination of smoothing splines and the $l_1$-penalty. We perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results corroborate the advantages of the proposed method for AD prediction in longitudinal studies

Rethinking Impact: Understanding the complexity of poverty and change

Summary of workshop on Rethinking Impact: Understanding the complexity of poverty and change. March 26–28, 2008, Cali, ColombiaImpact, CGIAR, Workshop, Agricultural and Food Policy, Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies,

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps.

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small

Random forest prediction of Alzheimer's disease using pairwise selection from time series data

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a machine learning method to learn the relationship between pairs of data points at different time separations. The input vector comprises a summary of the time series history and includes both demographic and non-time varying variables such as genetic data. The dataset used is from the 2017 TADPOLE grand challenge which aims to predict the onset of Alzheimer's disease using including demographic, physical and cognitive data. The challenge is a three-fold diagnosis classification into AD, MCI and control groups, the prediction of ADAS-13 score and the normalised ventricle volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73. The results show that the method is effective and comparable with other methods.Comment: 6 pages, 1 figure, 6 table