A Human Capital Approach to Reduce Health Disparities

Objective: To introduce a human capital approach to reduce health disparities in South Carolina by increasing the number and quality of trained minority professionals in public health practice and research. Methods: The conceptual basis and elements of Project EXPORT in South Carolina are described. Project EXPORT is a community based participatory research (CBPR) translational project designed to build human capital in public health practice and research. This project involves Claflin University (CU), a Historically Black College University (HBCU) and the African American community of Orangeburg, South Carolina to reduce health disparities, utilizing resources from the University of South Carolina (USC), a level 1 research institution to build expertise at a minority serving institution. The elements of Project EXPORT were created to advance the science base of disparities reduction, increase trained minority researchers, and engage the African American community at all stages of research. Conclusion: Building upon past collaborations between HBCU’s in South Carolina and USC, this project holds promise for a public health human capital approach to reduce health disparities

Cryptanalysis and improvement of a biometrics-based multi-server authentication with key agreement scheme

In 2010, Yoon et al. proposed a robust biometrics- based multi-server authentication with key agreement scheme for smart cards on elliptic curve cryptosystem. In this letter, however, we show that Yoon et al.’s scheme is vulnerable to off-line password guessing attack and propose an improved scheme to prevent the attack

Construction and Evaluation of a Korean Native Microbial Consortium for the Bioremediation of Diesel Fuel-Contaminated Soil in Korea

A native microbial consortium for the bioremediation of soil contaminated with diesel fuel in Korea was constructed and its biodegradation ability was assessed. Microbial strains isolated from Korean terrestrial environments, with the potential to biodegrade aliphatic hydrocarbons, PAHs, and resins, were investigated and among them, eventually seven microbial strains, Acinetobacter oleivorans DR1, Corynebacterium sp. KSS-2, Pseudomonas sp. AS1, Pseudomonas sp. Neph5, Rhodococcus sp. KOS-1, Micrococcus sp. KSS-8, and Yarrowia sp. KSS-1 were selected for the construction of a microbial consortium based on their biodegradation ability, hydrophobicity, and emulsifying activity. Laboratory- and bulk-scale biodegradation tests showed that in diesel fuel-contaminated soil supplemented with nutrients (nitrogen and phosphorus), the microbial consortium clearly improved the biodegradation of total petroleum hydrocarbons, and all microbial strains constituting the microbial consortium, except for Yarrowia survived and grew well, which suggests that the microbial consortium can be used for the bioremediation of diesel fuel-contaminated soil in Korea

A Human Capital Approach to Reduce Health Disparities

Objective: To introduce a human capital approach to reduce health disparities in South Carolina by increasing the number and quality of trained minority professionals in public health practice and research. Methods: The conceptual basis and elements of Project EXPORT in South Carolina are described. Project EXPORT is a community based participatory research (CBPR) translational project designed to build human capital in public health practice and research. This project involves Claflin University (CU), a Historically Black College University (HBCU) and the African American community of Orangeburg, South Carolina to reduce health disparities, utilizing resources from the University of South Carolina (USC), a level 1 research institution to build expertise at a minority serving institution. The elements of Project EXPORT were created to advance the science base of disparities reduction, increase trained minority researchers, and engage the African American community at all stages of research. Conclusion: Building upon past collaborations between HBCU\u27s in South Carolina and USC, this project holds promise for a public health human capital approach to reduce health disparities

PDE-guided reservoir computing for image denoising with small data

While network-based techniques have shown outstanding performance in image denoising in the big data regime requiring massive datasets and expensive computation, mathematical understanding of their working principles is very limited. Not to mention, their relevance to traditional mathematical approaches has not attracted much attention. Therefore, we suggest how reservoir computing networks can be strengthened in combination with conventional partial differential equation (PDE) methods for image denoising, especially in the small data regime. Given image data, PDEs generate sequential datasets enhancing desired image features, which provide the network with a better guideline for training in reservoir computing. The proposed procedure, reservoir computing in collaboration with PDEs (RCPDE), offers a synergetic combination of data-driven network-based methods and mathematically well-established PDE methods. It turns out that RCPDE outperforms both the usual reservoir computing and existing PDE approaches in image denoising. Furthermore, RCPDE also excels deep neural networks such as a convolutional neural network both in quality and in time in the small data regime. We believe that RCPDE reveals the great potential of reservoir computing in collaboration with various mathematically justifiable dynamics for better performance as well as for better mathematical understanding