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(1998)Journal ArticleThe first results obtained using a SOI device for microdosimetry applications are presented. Microbeam and broadbeam spectroscopy methods are used for determining minority carrier lifetime and radiation damage constants. A spectroscopy model is presented which includes the majority of effects that impact spectral resolution. Charge collection statistics were found to substantially affect spectral resolution. Lateral diffusion effects significantly complicate charge collection
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(1996)Conference PaperThe Mungi single address space operating system provides a protected procedure call mechanism named protection domain extension (PDX). The PDX call executes in a protection domain which is the union of (a subset of) the caller`s, and a fixed domain associated with the procedure. On return, the caller`s original protection domain is reestablished. Extensive caching of validation data allows amortisation of setup costs over a possibly large number of invocations. The PDX mechanism forms the basis for object support in Mungi, particularly encapsulation. It is also used for accessing devices, and to implement user-level page fault handlers.
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(2008)Journal ArticleDespite rapid advancements in molecular genetics research, little is known about the psychological experiences of individuals with a family history of melanoma. The present study aimed to identify factors contributing to psychological distress among affected and unaffected individuals with a strong family history of melanoma. A total of 121 adults who had recently been informed of the identification of a family-specific mutation in the CDKN2A melanoma susceptibility gene, completed a self-report questionnaire assessing cancer-specific and generalized distress, and a variety of potential predictors. Having a personal history of melanoma (OR = 3.37, p = 0.033), perceiving greater family implications of melanoma (OR = 2.52, p < 0.0001), and the tendency to monitor for threatening information (OR = 3.12, p = 0.008) were associated with melanoma-specific distress. Being childless (beta = 2.09, p = 0.007), perceiving sun exposure as an important cause of melanoma (beta = 1.15, p = 0.015), and perceiving greater family implications of melanoma (beta = 1.02, p = 0.002) were associated with greater generalized anxiety, while monitoring moderated the relationship between endorsement of a genetic model of melanoma and generalized anxiety (p = 0.005). As in other common familial cancers, distress was relatively uncommon in this familial melanoma cohort, even after notification of the presence of a family mutation. Participants do not contemplate their melanoma risk in isolation, but evaluate their risk vis-a-vis the experiences of their relatives.
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(2008)Journal ArticleObjectives: This study assesses interest in genetic testing for gene variations associated with bipolar disorder and associated information needs. Methods: Two hundred individuals (95 unaffected and 105 affected with either bipolar disorder, schizoaffective disorder-manic type, or recurrent major depression) from families with multiple cases of bipolar disorder were assessed, using mailed, self-administered questionnaires. Results: The percentage of participants reporting interest in genetic testing was associated with the degree of certainty with which any test would indicate the development of bipolar disorder. Interest in genetic testing, given a 25% lifetime risk scenario, was lowest (with 77% of participants indicating interest), and highest for the 100% lifetime risk scenario (92%). Eighty percent of participants indicated interest in genetic testing of their own children; of these 30% reported wanting their children tested at birth, and 33% in early childhood. Forty-one percent of participants reported that they would be interested in preimplantation genetic diagnosis, and 54% in prenatal testing. Limitations: The possibility of ascertainment bias cannot be ruled out. Interest in hypothetical genetic testing for bipolar disorder may not necessarily translate into actual utilization. Conclusions: These results indicate that uptake of genetic testing for genotyping for low-risk alleles related to bipolar disorder is likely to be lower than for testing for high-penetrance gene mutations that follow Mendelian inheritance. The discrepancy between the desired age of testing children and the accepted current practice may be a source of distress and conflict for parents and health professionals alike.
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(2006)Journal ArticleWe have used a bioinformatics approach to evaluate the completeness and functionality of the reported human immunoglobulin heavy-chain IGHD gene repertoire. Using the hidden Markov-model-based iHMMune-align program, 1,080 relatively unmutated heavy-chain sequences were aligned against the reported repertoire. These alignments were compared with alignments to 1,639 more highly mutated sequences. Comparisons of the frequencies of gene utilization in the two databases, and analysis of features of aligned IGHD gene segments, including their length, the frequency with which they appear to mutate, and the frequency with which specific mutations were seen, were used to determine the reliability of alignments to the less commonly seen IGHD genes. Analysis demonstrates that IGHD4-23 and IGHD5-24, which have been reported to be open reading frames of uncertain functionality, are represented in the expressed gene repertoire; however, the functionality of IGHD6-25 must be questioned. Sequence similarities make the unequivocal identification of members of the IGHD1 gene family problematic, although all genes except IGHD1-14*01 appear to be functional. On the other hand, reported allelic variants of IGHD2-2 and of the IGHD3 gene family appear to be nonfunctional, very rare, or nonexistent. Analysis also suggests that the reported repertoire is relatively complete, although one new putative polymorphism (IGHD3-10*p03) was identified. This study therefore confirms a surprising lack of diversity in the available IGHD gene repertoire, and restriction of the germline sequence databases to the functional set described here will substantially improve the accuracy of IGHD gene alignments and therefore the accuracy of analysis of the V-D-J junction.
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(2008)Journal ArticleThe concept that macrophages can become foam cells as a result of a disturbed balance between the uptake of cholesterol from lipoproteins and cholesterol efflux is generally accepted. ABCA1 and ABCG1 are two cholesterol transporters that may act sequentially to remove cellular cholesterol, but currently their combined role in vivo is unknown. We report here that targeted disruption of both ABCA1 and ABCG1 in mice, despite severe plasma hypocholesterolemia, leads to massive lipid accumulation and foam cell formation of tissue macrophages. A complete ablation of cellular cholesterol efflux in vitro is observed, whereas in vivo macrophage-specific reverse cholesterol transport to the feces is markedly decreased. Despite the massive foam cell formation of tissue macrophages, no lipid accumulation was observed in the vascular wall, even in mice of 1 year old, indicating that the double knockout mice, possibly because of their hypocholesterolemia, lack the trigger to attract macrophages to the vessel wall. In conclusion, even under hypocholesterolemic conditions macrophages can be converted into foam cells, and ABCA1 and ABCG1 play an essential role in the prevention of foam cell formation.
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(2008)Journal ArticleThe designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system. © 2008 IEEE.
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(2022)ThesisWith the rapid development of various geospatial technologies including remote sensing, mobile devices, and Global Position System (GPS), spatio-temporal data are abundantly available nowadays. Extracting valuable knowledge from spatio-temporal data is of crucial importance for many real-world applications such as intelligent transportation, social services, and intelligent distribution. With the fast increase of the amount and resolution of spatio-temporal data, traditional data mining methods are becoming obsolete. In recent years, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made promising achievements in many fields based on the strong ability in automated feature extraction and have been broadly used in different spatio-temporal data mining tasks. Many methods have been developed, and more diverse data were collected in recent decades, however, the existing methods have faced challenges from multi-source geospatial data. This thesis investigates four efficient techniques in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. This study investigates spatio-temporal data mining from four different perspectives. Firstly, a multi-elemental geolocation inference method is proposed to predict the location of tweets without geo-tags. Secondly, an optimization model is proposed to detect multiple Areas-of-Interest (AOIs) simultaneously and solve the multi-AOIs detection problem. Thirdly, a multi-task Res-U-Net model with attention mechanism is developed for the extraction of the building roofs and the whole building shapes from remote sensing images, then an offset vector method is used to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. Lastly, a novel decoder fusion model is introduced to extract interior road network from remote sensing images and GPS trajectory data. And this method is effective for multi-source data mining. The proposed four methods use different techniques for spatio-temporal data mining to improve the detection performance. Numerous experiments show that the techniques developed in this thesis can detect ground features efficiently and effectively and overcome the limitations of conventional algorithms. The studies demonstrate that exploiting spatial information from multi-source geospatial data can improve the detection accuracy in comparison with single-source geospatial data.
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(2022)ThesisClinical data are highly complex and pose challenges to machine learning that can introduce bias or negatively affect performance. Clinical data are typically high-dimensional and of mixed types, they may contain correlated values and missing information and a large proportion of the data is often irrelevant. Clinical measurements are often repeated over time, and the data may be censored, meaning the disease of interest has not yet been observed. Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is ultimately fatal and has no cure. There are more than 55 million people worldwide living with dementia today, with AD thought to account for 60-70% of those cases, and numbers are forecast to triple by 2050. The pathological processes leading to AD begin decades before overt symptoms appear, presenting an opportunity to determine early biomarkers that might help identify individuals at risk of developing AD. Traditionally the Cox proportional hazards model has been used to analyse censored data. But the Cox model does not scale well to high dimensions and is limited by some strict assumptions. Consequently, machine learning algorithms have been adapted to handle censored data. This thesis performs a thorough comparison of the performance and stability of the available machine learning and feature selection methods for survival analysis, identifying their strengths and weaknesses. Some of these methods can be unstable in the presence of high-dimensional or correlated data. This thesis examines the reasons for these instabilities and develops new ensemble feature selection frameworks to improve the stability of feature selection. Data-driven thresholds are also developed to automatically separate the important from the redundant features, and clustering is used to handle correlated features. Improvements in stability of up to 40% are achieved. Clinical data is often collected repeatedly over time. A novel temporal pattern mining algorithm is developed to analyse this temporal data and is combined with temporal abstraction to find patterns common to those who develop AD. Survival analysis shows that these patterns are predictive of AD, with a C-Index of up to 0.74, and a novel visualisation module displays the clinically relevant results in an easily interpretable way.