Materials Science at Xiamen University: A Special Issue Dedicated to the 100th Anniversary of Xiamen University

Abstract

In cardiac Magnetic Resonance Imaging (MRI) analysis, T2 mapping is a tissue quantification technology to measure water and inflammation levels and has been recognized as an important versatile index for myocardial pathologies. The T2 quantification is a highly relevant task with the myocardial segmentation but they only have been explored separately in deep learning. Here, we proposed a simultaneous dual-task network for myocardial segmentation and T2 quantification, called SQNet. It integrates Transformer and Convolutional Neural Network (CNN) components. SQNet has a T2-refine fusion decoder for quantitative analysis, incorporating global features extracted by the Transformer, and employs a segmentation decoder with multiple local region supervision to enhance segmentation accuracy. Additionally, SQNet proposes a tight coupling module that aligns and fuses CNN and Transformer branch features. The mutual promotion of the two tasks enables the SQNet to focus on the myocardium regions. The dual-task performance is validated on healthy controls (HC, N=17) and acute myocardial infarction patients (AMI, N=12). The segmentation dice of SQNet on HC/AMI is 89.3/89.2, which is higher than that (87.7/87.9) of the compared state-of-the-art segmentation method. The Pearson correlation coefficient of the T2 value on HC/AMI is 0.84 and 0.93 to the label value, indicating a strong linear correlation in myocardial T2 mapping. From the clinical diagnostic perspective, eight experienced radiologists have evaluated the image quality (Score standard: Good is 4.0 and excellent is 5.0) of SQNet on HC/AMI with the segmentation score 4.60/4.58 and the T2 quantification score 4.32/4.42, which is higher than the compared state-of-the-art segmentation score (4.50/4.44) and the T2 quantification score (3.59/4.37). Thus, SQNet provides an accurate simultaneous segmentation and quantification, providing the more accurate diagnosis of cardiac disease, such as AMI.

Online prediction based on the Copula function for the intrinsic viscosity of polyester fibre

“…C x reflects the correlation structure between the random vectors X 1 ,X 2 , ÁÁÁ, X N ð Þ , which is independent of the marginal distribution. [31] In multivariate analysis, it is often necessary to construct multivariate distributions from a given dependence structure as well as marginal distributions. And multivariate distributions with given margins come not only from statistical theory but also from practical problems.…”

Fish Price Determination Around Lake Victoria, Tanzania: Analysis of Factors Affecting Fish Landing Price

“…Apart from the above studies done by Ibengwe (2013), Chandrashekar (2014), Bergman (2012), Roheim et al (2007), Abila (2015) and Hou and Westbrook (2014), this article objectively focused on determination of fish landing price in Lake Victoria, Tanzania, specifically, examined methods for fish price determination and analysed factors that affect fish landing price in Lake Victoria.…”

“…It facilitated installation of ice plants, refrigeration cold storage for fish, provision of vehicles and rehabilitation of fish markets in 20 centres surrounding Lake Victoria to enhance the market and influence better price (URT, 2014b). According to Hou and Westbrook (2014) the market enables fishermen to identify, communicate and maintain contact with buyers at a right price, thus attaining better per capital income. Besides construction of marketing centres, the government has also made some effort to ensure fishery sustainable markets in Lake Victoria (Roangead, 2013).…”

The mushroom body development and learning ability of adult honeybees are influenced by cold exposure during their early pupal stage

“…These cold damaged broods will be groomed out of the colony consequently. The development of honeybee broods determines the health level of colonies, which affects the quality and yield of colony products ( Zhou, 2002 ).…”