Applications of Next-Generation Sequencing in Cancer Research and Molecular Diagnosis

Next-generation sequencing (NGS) technologies including DNA sequencing and RNA sequencing provide “omics” approaches to reveal genomic, transcriptomic, and epigenomic landscapes of individual cancers. A variety of genomic aberrations can be screened simultaneously, such as common and rare variants, structural variations (e.g. insertions and deletions), copy-number variation, and fusion transcripts. NGS technologies together with bioinformatics analysis, which expand our knowledge, are increasingly used to simultaneously analyze multiple genes in a cost and time-effective manner and have been applied in analyzing clinical cancer samples and offering NGS-based molecular diagnosis. Therefore, NGS is increasingly valuable as a tool for diagnosis for a number of cancers. Here we briefly introduce NGS technologies and summarize the recent applications in cancer research and molecular diagnosis in breast and prostate cancers

A linear mixed model analysis of the APOE4 gene with the logical memory test total score in Alzheimer’s disease

Linear mixed model (LMM) has the advantage of modeling the corelated data. Alzheimer’s disease (AD) is a chronic neurogenerative disease that affects the brain of the subject. No study was found to study the longitudinal effect of apolipoprotein E epsilon 4 (APOE4) genotype on the logical memory test total score in AD. A longitudinal data of 844 with AD, 2167 with cognitive normal (CN), and 4472 with mild cognitive impairment (MCI) participants who underwent logical memory examination test in the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) were investigated. Episodic memory of the study participants was monitored based on a short story told to the participants and then participants asked to recall what was told. The multivariate LMM was used to determine the longitudinal changes in the logical memory test total score adjusting for age and sex. The Akaike information criterion (AIC) statistic and the Bayesian information criterion (BIC) statistic were used to select the best covariance structure. The repeated measures longitudinal analysis was performed using PROC MIXED in SAS 9.4. Both AIC and BIC statistics favor the unstructured correlated structure (UN). Using a UN model in the LMM, the APOE gene was is significantly associated with logical memory test total score (pUN covariance structure is the best. This study provided the first evidence of the effect of APOE4 genotype on the logical memory related to AD

Age and Gender Differences in the Association between Serious Psychological Distress and Cancer: Findings from the 2003, 2005, and 2007 Health Information National Trends Surveys (HINTS)

Background: Little is known about the association of serious psychological distress (SPD) with cancer.Aims: This study examined the association between SPD and cancer, and tested whether such association differed by age and gender.Methods: Data came from the 2003, 2005, and 2007 Health Information National Trends Surveys (HINTS) (2,637 cancer cases and 16,581 controls). Weighted univariate and multiple logistic regression analyses were used to estimate the odds ratios (ORs) with 95% confidence intervals (CIs).Results: The overall prevalence of SPD was 6.7% (5.4% for males and 7.9 % for females; 7.1% for cancers and 6.4% for controls). The prevalence of SPD decreased with age (7.8%, 5.8% and 3.9% for age groups 18-49, 50-64 and 65+ years, respectively). After adjusting for other factors, being female, elder (65+ years), SPD, and poor general health were positively associated with cancer (p0.05). Gender-stratified analyses showed that SPD was associated with cancer only in women. Stratified by age groups, SPD and obesity were associated with cancer only in elderly.Conclusions: Older age, being female, SPD and poor general health were associated with increased likelihood of cancer. Stratified by age groups and gender, SPD was significantly associated with cancer in women and elder group. It is important to develop effective strategies to manage SPD among patients with cancer, especially in women and elder adults

Approaches to the improvement of order tracking techniques for vibration based diagnostics in rotating machines

Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to variations of the rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique is introduced. One of main advantages of order tracking over traditional vibration monitoring lies in its ability to clearly identify non-stationary vibration data and to a large extent exclude the influences of varying rotational speed. In recent years, different order tracking techniques have been developed. Each of these has their own pros and cons in analyzing rotating machinery vibration signals. In this research, three existing order tracking techniques are extensively investigated and combined to further explore their abilities in the context of condition monitoring. Firstly, computed order tracking is examined. This allows non-stationary effects due to the variation of rotational speed to be largely excluded. However, this technique was developed to deal with the entire raw signal and therefore looses the ability to focus on each individual order of interest. Secondly, Vold-Kalman filter order tracking is considered. It is widely reported that this technique overcomes many of the limitations of other order tracking methods and extracts order signals into the time domain. However because of the adaptive nature of the Vold-Kalman filter, the non-stationary effects due to the rotational speed will remain in the extracted order waveform, which is not ideal for conventional signal processing methods such as Fourier analysis. Yet, the strict mathematical filter (the Vold-Kalman filter is based upon two rigorous mathematical equations, namely the data equation and the structural equation, to realize the filter) gives this technique an excellent ability to focus on the orders of interest. Thirdly, the empirical mode decomposition method is studied. In the literature, this technique is claimed to be an effective diagnostic tool for various kinds of applications including diagnosis of rotating machinery faults. Its unique empirical way of extracting non-stationary and non-linear signals allows it to capture machine fault information which is intractable by other order tracking methods. But since there is no precise mathematical definition for an intrinsic mode function in empirical mode decomposition and – as far as could be ascertained – no published assessment of the relationship between an order and an intrinsic mode function, this technique has not been properly considered by analysts in terms of order tracking. As a result, its abilities have not really been explored in the context of order related vibrations in rotating machinery. In this research, the relationship between an order and an intrinsic mode function is discussed and it is treated as a special kind of order tracking method. In stead of focusing individually on each order tracking technique, the current work synthesizes different order tracking techniques. Through combination, exchange and reconciliation of ideas between these order tracking techniques, three improved order tracking techniques are developed for the purpose of enhancing order tracking analysis in condition monitoring. The techniques are Vold-Kalman filter and computed order tracking (VKC-OT), intrinsic mode function and Vold-Kalman filter order tracking (IVK-OT) and intrinsic cycle re-sampling (ICR). Indeed, these improved approaches contribute to current order tracking practice, by providing new order tracking methods with new capabilities for condition monitoring of systems which are intractable by traditional order tracking methods, or which enhances results obtained by these traditional methods. The work commences with a discussion of the inter-relationship between the order tracking methods which are considered in the thesis, and exposition of the scope of the work and an explanation of the way these independent order tracking techniques are integrated in the thesis. To demonstrate the abilities of the improved order tracking techniques, two simulation models are established. One is a simple single-degree-of-freedom (SDOF) rotor model with which VKC-OT and IVK-OT techniques are demonstrated. The other is a simplified gear mesh model through which the effectiveness of the ICR technique is proved. Finally two experimental set-ups in the Sasol Laboratory for Structural Mechanics at the University of Pretoria are used for demonstrating the improved approaches for real rotating machine signals. One test rig was established to monitor an automotive alternator driven by a variable speed motor. A stator winding inter-turn short was artificially introduced. Advantages of the VKC-OT technique are presented and features clear and clean order components under non-stationary conditions. The diagnostic ability of the IVK-OT technique of further decomposing an intrinsic mode function is also demonstrated via signals from this test rig, so that order signals and vibrations that modulate orders in IMFs can be separated and used for condition monitoring purposes. The second experimental test rig is a transmission gearbox. Artificially damaged gear teeth were introduced. The ICR technique provides a practical alternative tool for fault diagnosis. It proves to be effective in diagnosing damaged gear teeth.Thesis (PhD)--University of Pretoria, 2011.Mechanical and Aeronautical Engineeringunrestricte

Vibration monitoring on electrical machine using Vold-Kalman filter order tracking

Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to changing rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique was introduced. One of main advantages of order tracking over traditional vibration monitoring techniques, lies in its ability to clearly identify non-stationary vibration data, and to a large extent exclude the influences from varying rotational speed. Several order tracking techniques have been developed and researched during the past 20 years. Among these techniques, Fourier Transform Based Order Tracking (FT-OT), Angle Domain Sampling Based Order Tracking (AD-OT) and Vold-Kalman Filter Order Tracking (VKF-OT) are the three most popular techniques and have been commercialised in software. While the VKF-OT is comparatively new, and both its theory and application are different from the other two techniques, the unique advantages of this technique has led to increased research attention in this field. This growing interest in research on the application of the VKF-OT technique on real machines, and its comparative advantages with respect to other order tracking techniques, inspired the present research. With this work, a comprehensive literature of electrical machine condition monitoring was surveyed, which gives a broad perspective of electrical machine monitoring methods ranging through electrical techniques, vibration techniques, temperature techniques and chemical techniques. To simply the process of applying VKF-OT in initial investigations, simulated single-degree-of freedom and two-degree-of freedom rotor models were established, and the application of the VKF-OT technique on these simulated models was explored. Because most of the current research draws significantly on an understanding of the VKF-OT theory, it was also necessary to review and summarize the current status of VKF-OT theory from previous work, as well as explore the procedures for selection of its filter bandwidth when dealing with real data. An experimental set-up for monitoring an electrical alternator was constructed. Real experimental data were subsequently used to compare the advantages and disadvantages of the three popular order tracking techniques. The unique time domain advantage of VKF-OT was implemented, using crest factor and kurtosis values as indictors of the fault condition of the machine. This gave encouraging results.Dissertation (MSc)--University of Pretoria, 2008.Mechanical and Aeronautical Engineeringunrestricte

Associations of Anxiety and Psychological Distress with Cancer in US Adults: Results from the 2012 National Health Interview Survey

Background: Little is known about age differences in the associations of anxiety, depression, and psychological distress (PD) with cancer.Objectives: We estimated the prevalence of cancer in the United States (US) adults and examined the associations between mental health problems and cancer, and tested the related age differences.Materials and Methods: This was a cross-sectional study (n=34,505, 3,118 had cancer) from the 2012 National Health Interview Survey (NHIS) data. Weighted univariate and multiple logistic regression analyses were used to estimate the odds ratios (ORs) with 95% confidence intervals (CIs).Results: The overall prevalence of cancer is 8.6% (7.6% for males and 9.4 % for females). The prevalence increased with age (2.0%, 9.3% and 24.3% for age groups 18-49, 50-64 and 65+ years, respectively). The prevalence of anxiety, depression, and PD was significantly higher in cancer patients than in non-cancers (26% vs. 18%, 20% vs.13%, and 13% vs. 9%, respectively). Multiple logistic regression analyses showed that being female, aging, anxiety, and PD were positively associated with cancer (p0.05). Age group revealed significant interactions with anxiety and PD, in relation to cancer. Stratified by age groups, PD was positively associated with cancer just in young adults (18-49 years) while anxiety showed a stronger association with cancer in young adults and elderly (65+ years).Conclusions: The prevalence of mental health problems was higher among US adults who had cancer. The associations between mental health problems and cancer varied across ages. Effective strategies may be needed to manage these mental health conditions among patients with cancer at each age

Family Size and Risk of Juvenile Idiopathic Arthritis: A Cross-Sectional Study

Background: Juvenile idiopathic arthritis (JIA) refers to a group of auto-immune conditions involving joint inflammation that first appears before the age of 16. In the United States, about 294,000 children are affected. Although JIA can be widely attributed to genetic factors, the consensus is that environmental factors also play a role. Attempts to assess the role of environmental factors, though scarce, have focused on the role of infections, smoking exposure, and breastfeeding. Hygiene hypothesis, which suggests that adaptive immunological response improves with higher frequencies of pathogen exposure in early childhood, has been used to try to explain the risk of JIA. Common markers of microbe exposure in early life include sibling number, pet number, and maternal parity. Some prior studies conducted outside the U.S. suggests that increasing sibling number is protective against the risk of JIA. This study aimed to evaluate prior findings, using data from the U.S. Methods: The study used data from the 2017 Centers for Disease Control and Prevention National Survey for Child Health. The survey used a sample size of 21599 children to estimate the number of children in the U.S. Descriptive statistics was carried out, and logistic regression was used to determine the association between family number and the odds of developing JIA, while adjusting for sociodemographic variables. Family number was used as a proxy for sibling number. SAS v 9.4 was used for analysis. Results: Complete data on all the variables of interest were available for 17618 children, of which 67 had JIA. Although there was a marginal association between sibling number and JIA in the unadjusted model (OR [95% CI] 0.983-1.602) (P=0.068), in the adjusted model, there was no significant association between JIA and sibling number ([OR 95% CI] 0.8985-1.447) (P=0.29). There was a significant association between JIA and age, low birth weight, highest education level in the family, while sex had a marginal association. Conclusion: There was no association between family size and the development of JIA in this study. While some prior results have supported the observed significant effect of low birth weight, the disparity in results between this study and the Australian study could be due to the use of family number instead of sibling number. Further studies should assess the association of sibling number and developing JIA in the U.S

Relationship between Chronic Disease Conditions and Colorectal Cancer Screening: Results from the 2012 National Health Interview Survey Data

Background: Uptake of screening remains crucial in the prevention of both the incidence of colorectal cancer (CRC) and its mortality.Objectives: To estimate the prevalence of CRC screening and identify chronic conditions that predict CRC screening uptake among US adults using the 2012 National Health Interview Survey (NHIS) data.Materials and Methods: A cross-sectional analysis of the 2012 NHIS data. Chronic conditions examined were hypertension, cancer history, arthritis, ulcer, and high cholesterol level. A total of 21,511 participants were included in the analysis. Weighted univariate and multiple logistic regression analyses in SAS ver. 9.2 were used to estimate the odds ratios (ORs) with 95% confidence intervals (CIs).Results: The overall prevalence of CRC screening was 19%. The prevalence of CRC screening in adults with cancer history, hypertension, ulcer, high cholesterol, and arthritis was significantly higher than those without the chronic conditions (26% vs.18%, 23% vs.16%, 25% vs.18%, 23% vs. 16%, and 23% vs. 17%, respectively). After adjusting for potential factors, hypertension (OR=1.18, 95%CI=1.08-1.30), ulcer (OR=1.28, 95%CI=1.10-1.48), high cholesterol (OR=1.25, 95%CI=1.14-1.39), and arthritis (OR=1.24, 95%CI=1.12-1.37) were all positively associated with CRC screening (p0.05). Females were less likely to screen for CRC than to males (OR=0.72; 95% CI=0.65-0.80). Compared to young adults (18-44 years), screening was significantly higher in middle-aged (45-64 years) and elder adults (65+) (OR=2.60, 95%CI=2.11-3.21 and OR=2.67, 95%CI=2.13-3.33, respectively). African Americans were more likely to screen for CRC compared to their white counterparts (OR=1.61, 95% CI=1.44-1.81).  Conclusions: We have found significant associations between chronic conditions and CRC screening uptake. We also found higher uptake of CRC screen in African Americans than Whites, in contrast to earlier findings

Multivariate analyses of social-behavioral factors with health insurance coverage among Asian Americans in California

This study aimed to estimate the prevalence of uninsurance among California adults and Asian Americans, and to examine the associations of social-behavioral variables with uninsurance. A total of 24,136 adults (aged 18–64) including 2,060 Asian Americans were selected from the combined 2013–2014 California Health Interview Survey. Weighted univariate and multivariate logistic regression analyses were used to estimate the associations of potential factors with uninsurance. To evaluate the relationship of independent variables, the oblique principal component cluster analysis (OPCCA) was used to classify 9 variables into disjoint clusters. For Whites, African Americans, Latinos, and Asians, the prevalence of uninsurance was 8.5%, 10.3%, 24.7%, and 12.6%, respectively. Among Asians, the prevalence of uninsurance was 15.5%, 9.2%, 6.2%, 20.8% and 12.1% for Chinese, Filipinos, Japanese, Koreans, and Vietnamese, respectively. In the whole sample, multivariate logistic regression analysis revealed that being male, non-citizen, lower education, higher poverty, and current smoking were associated with uninsurance. Among Asians, compared to Koreans, being Filipinos and Vietnamese were associated with lower odds of being uninsured; meanwhile being male, non-citizen, lower education, and higher poverty were significantly associated with increased odds of uninsurance. Elder age groups and current smoking were significantly associated with increased odds of uninsurance in bivariate analysis; however, such associations disappeared after adjusting for other factors. Nine independent variables were divided into 2 clusters, where the variables in the same cluster were strongly correlated but had weak correlations with the variables in the other cluster. In conclusion, there are differences in the prevalence of uninsurance between Asians and Whites, and among Asian subgroups. Being male, non-citizen, lower education, higher poverty and current smoking were positively significantly associated with uninsurance