REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

Motivational Factors behind Second-hand Luxury Fashion Purchasing of Chinese Consumers

Second-hand luxury market has been undergoing a makeover, and the consumption concepts have been redefined during recent years. The rapid market growth has shown increasing recognition of the new trend, indicating tremendous potential underlying the emerging market. With the growing interest in the alternative consumption patterns of conventional retailing, the insights into the motivations that drive Chinese consumers to purchase the pre-loved luxury will constitute a meaningful research. In the previous research, Roux and Guiot (2010) proposed a scale, consisting of hedonic, economic and critical motivations. In addition to those, I identified another motivation of symbolic social value that features Chinese market. Therefore, the goal is to examine the four variables in Chinese secondary luxury market and the main field is fashion goods. The initial literature review focused on the theoretical framework, which lay a foundation for the study. After defining the concept, I moved on to the four motivations in different contexts. Then, with the instrument of self-completion questionnaires, the primary quantitative data was gathered from 184 respondents who had purchased the secondary luxury items before. The findings revealed that in the Chinese market, the hedonic, recreational and critical motivations could exert a positive relationship on the purchasing decisions, whereas the economic incentive had no impact. Based on the conclusion, I also presented several of research implications and managerial implications. In terms of research contributions, it can supplement the research gap and contribute to underdeveloped studies exploring the emerging Chinese market. Besides, second-hand retailers were provided with some marketing strategies to exploit the identified motivations and expand the customer base. In the last place, the research concluded with some limitations and suggestions for further research

Synthesis and characterization of aligned ZnO/BeO core/shell nanocable arrays on glass substrate

By sequential hydrothermal growth of ZnO nanowire arrays and thermal evaporation of Be, large-scale vertically aligned ZnO/BeO core/shell nanocable arrays on glass substrate have been successfully synthesized without further heat treatment. Detailed characterizations on the sample morphologies, compositions, and microstructures were systematically carried out, which results disclose the growth behaviors of the ZnO/BeO nanocable. Furthermore, incorporation of BeO shell onto ZnO core resulted in distinct improvement of optical properties of ZnO nanowire, i.e., significant enhancement of near band edge (NBE) emission as well as effective suppression of defects emission in ZnO. In particular, the NBE emission of nanocable sample shows a noticeable blue-shift compared with that of pristine ZnO nanowire, which characteristics most likely originate from Be alloying into ZnO. Consequently, the integration of ZnO and BeO into nanoscale heterostructure could bring up new opportunities in developing ZnO-based device for application in deep ultraviolet region

GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs

Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support training on graph streams. In contrast, many real-world graphs are dynamic and contain time domain information. We introduce GNNFlow, a distributed framework that enables efficient continuous temporal graph representation learning on dynamic graphs on multi-GPU machines. GNNFlow introduces an adaptive time-indexed block-based data structure that effectively balances memory usage with graph update and sampling operation efficiency. It features a hybrid GPU-CPU graph data placement for rapid GPU-based temporal neighborhood sampling and kernel optimizations for enhanced sampling processes. A dynamic GPU cache for node and edge features is developed to maximize cache hit rates through reuse and restoration strategies. GNNFlow supports distributed training across multiple machines with static scheduling to ensure load balance. We implement GNNFlow based on DGL and PyTorch. Our experimental results show that GNNFlow provides up to 21.1x faster continuous learning than existing systems

Involvement of C2H2 zinc finger proteins in the regulation of epidermal cell fate determination in Arabidopsis

Cell fate determination is a basic developmental process during the growth of multicellular organisms. Trichomes and root hairs of Arabidopsis are both readily accessible structures originating from the epidermal cells of the aerial tissues and roots respectively, and they serve as excellent models for understanding the molecular mechanisms controlling cell fate determination and cell morphogenesis. The regulation of trichome and root hair formation is a complex program that consists of the integration of hormonal signals with a large number of transcriptional factors, including MYB and bHLH transcriptional factors. Studies during recent years have uncovered an important role of C2H2 type zinc finger proteins in the regulation of epidermal cell fate determination. Here in this minireview we briefly summarize the involvement of C2H2 zinc finger proteins in the control of trichome and root hair formation in Arabidopsis .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109574/1/jipb12221.pd

Functional variant of the carboxypeptidase M (CPM) gene may affect silica-related pneumoconiosis susceptibility by its expression: a multistage case-control study.

ObjectivesIn a genome-wide association study, we discovered chromosome 12q15 (defined as rs73329476) as a silica-related pneumoconiosis susceptibility region. However, the causal variants in this region have not yet been reported.MethodsWe systematically screened eight potentially functional single-neucleotide polymorphism (SNPs) in the genes near rs73329476 (carboxypeptidase M (CPM) and cleavage and polyadenylation specific factor 6 (CPSF6)) in a case-control study including 177 cases with silicosis and 204 healthy controls, matched to cases with years of silica dust exposure. We evaluated the associations between these eight SNPs and the development of silicosis. Luciferase reporter gene assays were performed to test the effects of selected SNP on the activity of CPM in the promoter. In addition, a two-stage case-control study was performed to investigate the expression differences of the two genes in peripheral blood leucocytes from a total of 64 cases with silicosis and 64 healthy controls with similar years of silica dust exposure as the cases.ResultsWe found a strong association between the mutant rs12812500 G allele and the susceptibility of silicosis (OR=1.45, 95% CI 1.03 to 2.04, p=0.034), while luciferase reporter gene assays indicated that the mutant G allele of rs12812500 is strongly associated with increased luciferase levels compared with the wild-type C allele (p<0.01). Moreover, the mRNA (peripheral blood leucocytes) expression of the CPM gene was significantly higher in subjects with silicosis compared with healthy controls.ConclusionsThe rs12812500 variant of the CPM gene may increase silicosis susceptibility by affecting the expression of CPM, which may contribute to silicosis susceptibility with biological plausibility

DOA Estimation of Cylindrical Conformal Array Based on Geometric Algebra

Due to the variable curvature of the conformal carrier, the pattern of each element has a different direction. The traditional method of analyzing the conformal array is to use the Euler rotation angle and its matrix representation. However, it is computationally demanding especially for irregular array structures. In this paper, we present a novel algorithm by combining the geometric algebra with Multiple Signal Classification (MUSIC), termed as GA-MUSIC, to solve the direction of arrival (DOA) for cylindrical conformal array. And on this basis, we derive the pattern and array manifold. Compared with the existing algorithms, our proposed one avoids the cumbersome matrix transformations and largely decreases the computational complexity. The simulation results verify the effectiveness of the proposed method

Solar activity of the past 100 years inferred from 10Be in ice cores – implications for long-term solar activity reconstructions

Differences between 10Be records from Greenland and Antarctica over the last 100 years have led to different conclusions about past changes in solar activity. The reasons for this disagreement remain unresolved. We analyze a seasonally resolved 10Be record from a firn core (NEEM ice core project) in Northwestern Greenland for 1887-2002. By comparing the NEEM data to 10Be data from the NGRIP and Dye3 ice cores, we find that the Dye3 data after 1958 are significantly lower. These low values lead to a normalization problem in solar reconstructions when connecting 10Be variations to modern observations. Excluding these data strongly reduces the differences between solar reconstructions over the last 2000 years based on Greenland and Antarctic 10Be data. Furthermore, 10Be records from polar regions and group sunspot numbers do not support a substantial increase in solar activity for the 1937-1950 period as proposed by previous extensions of the neutron monitor data.This article is protected by copyright. All rights reserved

wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7T

White matter hyperintensity (WMH) remains the top imaging biomarker for neurodegenerative diseases. Robust and accurate segmentation of WMH holds paramount significance for neuroimaging studies. The growing shift from 3T to 7T MRI necessitates robust tools for harmonized segmentation across field strengths and artifacts. Recent deep learning models exhibit promise in WMH segmentation but still face challenges, including diverse training data representation and limited analysis of MRI artifacts' impact. To address these, we introduce wmh_seg, a novel deep learning model leveraging a transformer-based encoder from SegFormer. wmh_seg is trained on an unmatched dataset, including 1.5T, 3T, and 7T FLAIR images from various sources, alongside with artificially added MR artifacts. Our approach bridges gaps in training diversity and artifact analysis. Our model demonstrated stable performance across magnetic field strengths, scanner manufacturers, and common MR imaging artifacts. Despite the unique inhomogeneity artifacts on ultra-high field MR images, our model still offers robust and stable segmentation on 7T FLAIR images. Our model, to date, is the first that offers quality white matter lesion segmentation on 7T FLAIR images

A method for improving the crack resistance of aluminum alloy aircraft skin inspired by plant leaf

Abstract(#br)Aircraft skins are likely to experience cracks and fracture failure due to the combined action of shear, bending, and torsional load. Inspired by the crack resistance exhibited by plant leaf, a method is proposed to improve the crack resistance of aluminum alloy aircraft skin. The characteristic parameters of main and secondary leaf veins are extracted by image edge detection and analysis methods. According to a constructed collection of self-similar fractal sets, a bio-inspired residual stress field with fractal characteristics extracted from leaf veins is applied to specimens ahead of crack tip by using laser peening. The effects of fractal parameters on crack retardation are analyzed using interaction integral. The results show that the stress intensity factor ahead of crack tip is reduced by applying a bio-inspired residual stress field, whereas the plastic zone area ahead of crack tip is enlarged. The correlation between these two trends reveals the mechanism of stress intensity decrease after the introduction of bio-inspired residual stress field. The optimal crack retardation effect is achieved at a fractal angle of 55°, at which the residual fatigue life is increased by up to 203.0%. Compared with square-shaped laser peening, full-coverage laser peening, square criss-cross pattern method, and single-edge notched tensile (SENT) specimen repair method, the proposed method achieves the longest residual fatigue life, which is almost three times that of the square-shaped laser peening method. Therefore, this theoretical study presents a potential method for improving the crack resistance of aluminum alloy aircraft skin