A biometrical inheritance model for heritability under the presence of environmental exposures: application to Michigan fisheater data

Master of ScienceDepartment of StatisticsWei-Wen HsuPolychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (DDE) are endocrine disrupting chemicals which can imbalance the hormonal system in the human body and lead to deleterious diseases such as diabetes, irregular menstrual cycles, endometriosis, and breast cancer. These chemicals as environmental exposures still exist in the environment and food chains and can be accumulated in human fatty tissues for many years. These chemicals can also be passed from mothers to their children through placental transfer or breastfeeding; therefore, their offspring may be at increased risk of adverse health outcomes from these inherited chemicals. However, it is still unclear how the parental association with offspring health outcomes and the inter-generational phenotypic inheritance could be affected by these chemical compounds. In this study, we mainly focus on how PCBs and DDE can affect the inheritance of Body Mass Index (BMI) across generations, as BMI is the primary health outcome (or phenotype) linked to diabetes. We propose a biometrical inheritance model to investigate the effects of PCBs and DDE on the heritability of BMI over two generations. Technically, a linear mixed effects model is developed based on the decomposition of phenotypic variance and assuming the variance of the environmental effect depends on parental exposures. The proposed model is evaluated extensively by simulations and then is applied to Michigan Fisheater Cohort data for answering the research question of interest

Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network

The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ``Prediction-Gradient-Optimization'' (PGO) framework to solve it, where the ``Prediction'' means that the objective function without a closed-form is approximated and predicted by a neural network, the ``Gradient'' is calculated based on the former approximation, and the ``Optimization'' procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management

Chemical composition, antioxidant and antimicrobial activities of essential oil from Wedelia prostrata

The following study deals with the chemical composition, antioxidant and antimicrobial activity of essential oils of Wedelia prostrata and their main constituents in vitro. A total of 70 components representing 99.26 % of the total oil were identified. The main compounds in the oil were limonene (11.38 %) and α-pinene (10.74 %). Antioxidant assays (1,1-diphenyl-2-picrylhydrazyl, superoxide anion radical, and reducing power test) demonstrate moderate activities for the essential oil and its main components (limonene and α-pinene). The essential oil (1000 μg/disc) exhibited promising antimicrobial activity against 10 strains of test microorganisms as a diameter of zones of inhibition (20.8 to 22.2 mm) and MIC values (125 to 250 μg/ml). The activities of limonene and α-pinene were also determined as main components of the oil. α-Pinene showed higher antimicrobial activity than the essential oil with a diameter of zones of inhibition (20.7 to 22.3 mm) and MIC values (62.5 to 125 μg/ml). The antioxidant and antimicrobial properties of the essential oil may be attributed to the synergistic effects of its diverse major and minor components

Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.This research was funded in part by the National Natural Science Foundation of China (Grant No. 41,804,004, 41,820,104,005, 41,531,068, 41,904,004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P