A hydro-environmental optimization for assessing sustainable carrying capacity

The present study proposes an applicable method to determine the population carrying capacity of urban areas in which ecological impacts of river ecosystem as the source of water supply and sustainable population growth are linked. A multiobejctive optimization method was developed in which two objectives were considered: 1) minimizing the fish population loss as the environmental index of the river ecosystem and 2) minimizing the difference between initial population carrying capacity and the sustainable population carrying capacity. The ecological impacts of the river ecosystem were assessed through the potential fish population as an environmental index using several artificial intelligence and regression models. Based on case study results, the initial plan of development is not reliable because ecological impacts on the river ecosystem are remarkable. The proposed method is able to reduce the ecological impacts. However, the sustainable population carrying capacity is considerably lower than the initial planned population. It is needed to reduce the planned population more than 45% in the case study. Habitat loss is less than 35% which means the optimization model is able to find an optimal solution for balancing environmental requirements and humans’ needs. In other words, the optimization model balances the needs of environment and water supply by reducing 45% of population and decreasing habitat loss to 35%

Study designs and statistical methods for pharmacogenomics and drug interaction studies

Indiana University-Purdue University Indianapolis (IUPUI)Adverse drug events (ADEs) are injuries resulting from drug-related medical interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI). In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain pharmacovigilance databases for detecting novel drug-ADE associations. However, pharmacovigilance databases usually contain a significant portion of false associations due to their nature structure (i.e. false drug-ADE associations caused by co-medications). Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to genetic factors and synergistic effects between drugs. During the past decade, pharmacogenomics studies have successfully identified several predictive markers to reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample size and budget. In this dissertation, we develop statistical methods for pharmacovigilance and pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to identify significant drug-ADE associations. The proposed approach can be used for both signal generation and ranking. Following this approach, the portion of false associations from the detected signals can be well controlled. Secondly, we propose a mixture dose response model to investigate the functional relationship between increased dimensionality of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a significantly low local false discovery rates. Finally, we proposed a cost-efficient design for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed design involves both DNA pooling and two-stage design approach. Compared to traditional design, the cost under the proposed design will be reduced dramatically with an acceptable compromise on statistical power. The proposed methods are examined by extensive simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance databases are applied to the FDA’s Adverse Reporting System database and a local electronic medical record (EMR) database. For different scenarios of pharmacogenomics study, optimized designs to detect a functioning rare allele are given as well

Measurement and mathematical modelling of endoplasmic reticulum stress in human adipocytes

Obesity is the single biggest risk factor for type 2 diabetes mellitus (T2DM). Weight gain chronically induces endoplasmic reticulum (ER) stress in adipocytes, which activates the unfolded protein response (UPR) and leads to inflammation and insulin resistance. Therefore, investigating ways to combat inflammation through ER stress may reduce obesity mediated T2DM. One candidate to improve metabolic health may be cruciferous vegetables, such as broccoli, known to reduce inflammation and the risk of various cancers. Mathematical modelling may be a useful tool in order to identify how time and other factors may impact the UPR, however little experimental time series data exist to support the parameterisation and validation process. The aim was therefore to determine whether perturbation of human adipocytes with tunicamycin and broccoli extract can lead to significant changes in the UPR over time, deriving a novel mathematical model to characterise the relevant components of the UPR. Chub-S7 pre-adipocyte cells were grown, differentiated and treated with broccoli extract, tunicamycin, or a combination of the two (17 time points over 72 hours). Western blotting and qRT-PCR were used to assess ER stress markers, whilst the influence of broccoli on other cellular functions was analysed through appropriate assays. A qualitatively realistic mathematical model of part of the UPR was developed using nonlinear ordinary differential equations (ODEs), utilising experimental time series data for parameterisation and validation. The time series data identified novel expression profiles of proteins involved in the UPR, and highlighted that broccoli extract could significantly reduce the impact of tunicamycin on ER stress over time (p<0.05), as well as reactive oxygen species (ROS) (p<0.05) and mitochondrial dysfunction (p<0.05). These findings identify that broccoli extract appears to reduce the impact of tunicamycin on human adipocytes, and highlight the importance of modelling changes within the UPR to understand its response over time