Konzeption, Implementierung und Anwendung eines automatisierten aerothermodynamischen Vorentwurfsprozesses für Axialturbinen

In dieser Arbeit wird ein Prozess für den frühen aerothermodynamischen Entwurf von Axialturbinen konzipiert und durch Kopplung einzelner Computerprogramme im DLR Göttingen realisiert. Speziell für die Erstauslegung von Geometrien und die Vorhersage von globalen Leistungsdaten beliebiger Axialturbinen wurde ein neues Programm erzeugt. Dessen effiziente Anwendung wird mit einer zu diesem Zweck konzipierten grafischen Entwurfsumgebung ausgeführt. Kennzeichnend für den Vorentwurfsprozess in dieser Arbeit ist die Anwendung von ein- und zweidimensionaler Strömungssimulation sowie der hohe Grad an Verknüpfung der verwendeten Programme sowohl auf prozesstechnischer wie auch auf datentechnischer Ebene. Dabei soll dem sehr frühen Entwurf eine deutlich stärkere Rolle zukommen als bisher üblich und im Gegenzug die Entwurfszeit mit höher auflösenden Vorentwurfsprogrammen reduziert werden. Die Anwendung der einzelnen Programme im Rahmen von Subprozessen wird anhand von exemplarischen Turbinenkonfigurationen in der Arbeit ebenso dargestellt, wie die Validierung des gesamten Entwurfsprozesses anhand der Auslegung einer folgend realisierten und erfolgreich operierenden Axialturbine eines Triebwerkssimulators für Flugzeug-Windkanalmodelle (TPS). Neben der Erleichterung von manueller Entwurfstätigkeit durch grafische Benutzerinteraktion kommt in einzelnen Subprozessen eine automatisierte Mehrziel-Optimierung zum Einsatz.

Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.

Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

Raw data for "Linkage disequilibrium estimation of effective population size with immigrants from divergent populations: a case study on Spanish mackerel (Scomberomorus commerson)."

Abstract of Macbeth, G. M., Broderick, D., Buckworth, R. & Ovenden, J. R. (In press, Feb 2013). Linkage disequilibrium estimation of effective population size with immigrants from divergent populations: a case study on Spanish mackerel (Scomberomorus commerson). G3: Genes, Genomes and Genetics. Estimates of genetic effective population size (Ne) using molecular markers are a potentially useful tool for the management of endangered through to commercial species. But, pitfalls are predicted when the effective size is large, as estimates require large numbers of samples from wild populations for statistical validity. Our simulations showed that linkage disequilibrium estimates of Ne up to 10,000 with finite confidence limits can be achieved with sample sizes around 5000. This was deduced from empirical allele frequencies of seven polymorphic microsatellite loci in a commercially harvested fisheries species, the narrow barred Spanish mackerel (Scomberomorus commerson). As expected, the smallest standard deviation of Ne estimates occurred when low frequency alleles were excluded. Additional simulations indicated that the linkage disequilibrium method was sensitive to small numbers of genotypes from cryptic species or conspecific immigrants. A correspondence analysis algorithm was developed to detect and remove outlier genotypes that could possibly be inadvertently sampled from cryptic species or non-breeding immigrants from genetically separate populations. Simulations demonstrated the value of this approach in Spanish mackerel data. When putative immigrants were removed from the empirical data, 95% of the Ne estimates from jacknife resampling were above 24,000.

Large panel-survey data demonstrated country-level and ethnic minority variation in consent for health record linkage

Objectives
To investigate individual, household and country variation in consent to health record linkage.

Study Design and Setting
Data from 50,994 individuals aged 16-74 years recruited to wave 1 of a large UK general purpose household survey (January 2009 – December 2010) were analysed using multi-level logistic regression models.

Results
Overall, 70.7% of respondents consented to record linkage. Younger age, marriage, tenure, car ownership and education were all significantly associated with consent, though there was little deviation from 70% in subgroups defined by these variables. There were small increases in consent rates in individuals with poor health when defined by self-reported long term limiting illness (adjusted OR 1.11; 95%CIs 1.06, 1.16), less so when defined by General Health Questionnaire score (adjusted OR=1.05; 95%CIs 1.00, 1.10), but the range in absolute consent rates between categories was generally less than 10%. Larger differences were observed for those of non-white ethnicity who were 38% less likely to consent (adjusted OR 0.62; 95%CIs 0.59, 0.66). Consent was higher in Scotland than England (adjusted OR 1.17; 95%CIs 1.06, 1.29) but lower in Northern Ireland (adjusted OR 0.56; 95%CIs 0.50, 0.63).

Conclusion
The modest overall level of systematic bias in consent to record linkage provides reassurance for record linkage potential in general purpose household surveys. However, the low consent rates amongst non-white ethnic minority survey respondents will further compound their low survey participation rates. The reason for the country-level variation requires further study.

Linkage disequilibrium levels in Bos indicus and Bos taurus cattle using medium and high density SNP chip data and different minor allele frequency distributions

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Trauma registry record linkage: methodological approach to benefit from complementary data using the example of the German Pelvic Injury Register and the TraumaRegister DGU(®)

In Germany, hospitals can deliver data from patients with pelvic fractures selectively or twofold to two different trauma registries, i.e. the German Pelvic Injury Register (PIR) and the TraumaRegister DGU(®) (TR). Both registers are anonymous and differ in composition and content. We describe the methodological approach of linking these registries and reidentifying twofold documented patients. The aim of the approach is to create an intersection set that benefit from complementary data of each registry, respectively. Furthermore, the concordance of data entry of some clinical variables entered in both registries was evaluated.

HIV-related malignancies: A community-based study using a linkage of cancer registry and HIV registry data

Background. Increased incidence of cancer is documented in immunosuppressed transplant patients. Likewise, as survival increases for persons infected with the Human Immunodeficiency Virus (HIV), we expect their incidence of cancer to increase. The objective of this study was to examine the current gender specific spectrum of cancer in an HIV infected cohort (especially malignancies not currently associated with Acquired Immunodeficiency Syndrome (AIDS)) in relation to the general population.^ Methods. Cancer incidence data was collected for residents of Harris County, Texas who were diagnosed with a malignancy between 1975 and 1994. This data was linked to HIV/AIDS registry data to identify malignancies in an HIV infected cohort of 14,986 persons. A standardized incidence ratio (SIR) analysis was used to compare incidence of cancer in this cohort to that in the general population. Risk factors such as mode of HIV infection, age, race and gender, were evaluated for contribution to the development of cancer within the HIV cohort, using Cox regression techniques.^ Findings. Of those in the HIV infected cohort, 2289 persons (15%) were identified as having one or more malignancies. The linkage identified 29.5% of these malignancies (males 28.7% females 60.9%). HIV infected men and women had incidences of cancer that were 16.7 (16.1, 17.3) and 2.9 (2.3, 3.7) times that expected for the general population of Harris County, Texas, adjusting for age. Significant SIR's were observed for the AIDS-defining malignancies of Kaposi's sarcoma, non-Hodgkin's lymphoma, primary lymphoma of the brain and cancer of the cervix. Additionally, significant SIR's for non-melanotic skin cancer in males, 6.9 (4.8, 9.5) and colon cancer in females, 4.0 (1.1, 10.2) were detected. Among the HIV infected cohort, race/ethnicity of White (relative risk 2.4 with 95% confidence intervals 2.0, 2.8) or Spanish Surname, 2.2 (1.9, 2.7) and an infection route of male to male sex, with, 3.0 (1.9, 4.9) or without, 3.4 (2.1, 5.5) intravenous drug use, increased the risk of having a diagnosis of an incident cancer.^ Interpretation. There appears to be an increased risk of developing cancer if infected with the HIV. In addition to the malignancies routinely associated with HIV infection, there appears to be an increased risk of being diagnosed with non-melanotic skin cancer in males and colon cancer in females. ^

Privacy Preserving Probabilistic Record Linkage (P3RL): a novel method for linking existing health-related data and maintaining participant confidentiality.

BACKGROUND Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information. Unfortunately, encrypted hash codes of two names differ completely if the plain names differ only by a single character. Therefore, standard encryption methods cannot be applied. To overcome these challenges, we developed the Privacy Preserving Probabilistic Record Linkage (P3RL) method. METHODS In this Privacy Preserving Probabilistic Record Linkage method we apply a three-party protocol, with two sites collecting individual data and an independent trusted linkage center as the third partner. Our method consists of three main steps: pre-processing, encryption and probabilistic record linkage. Data pre-processing and encryption are done at the sites by local personnel. To guarantee similar quality and format of variables and identical encryption procedure at each site, the linkage center generates semi-automated pre-processing and encryption templates. To retrieve information (i.e. data structure) for the creation of templates without ever accessing plain person identifiable information, we introduced a novel method of data masking. Sensitive string variables are encrypted using Bloom filters, which enables calculation of similarity coefficients. For date variables, we developed special encryption procedures to handle the most common date errors. The linkage center performs probabilistic record linkage with encrypted person identifiable information and plain non-sensitive variables. RESULTS In this paper we describe step by step how to link existing health-related data using encryption methods to preserve privacy of persons in the study. CONCLUSION Privacy Preserving Probabilistic Record linkage expands record linkage facilities in settings where a unique identifier is unavailable and/or regulations restrict access to the non-unique person identifiable information needed to link existing health-related data sets. Automated pre-processing and encryption fully protect sensitive information ensuring participant confidentiality. This method is suitable not just for epidemiological research but also for any setting with similar challenges.

Direct support and general support maintenance manual (including repair parts and special tools list for direct support, general support, and depot maintenance) : periscope, tank, M50 (1240-00-950-2805) and associated equipment items, wiper assembly, data transmission linkage assembly, guard and shield assembly, and components of washer pump and reservoir.

Includes index.

To link or not to link: a test of the case linkage principles using serial car theft data

The purpose of the present study is to test the case linkage principles of behavioural consistency and behavioural distinctiveness using serial vehicle theft data. Data from 386 solved vehicle thefts committed by 193 offenders were analysed using Jaccard's, regression and Receiver Operating Characteristic analyses to determine whether objectively observable aspects of crime scene behaviour could be used to distinguish crimes committed by the same offender from those committed by different offenders. The findings indicate that spatial behaviour, specifically the distance between theft locations and between dump locations, is a highly consistent and distinctive aspect of vehicle theft behaviour; thus, intercrime and interdump distance represent the most useful aspects of vehicle theft for the purpose of case linkage analysis. The findings have theoretical and practical implications for understanding of criminal behaviour and for the development of decision-support tools to assist police investigation and apprehension of serial vehicle theft offenders.

Mapping area variability in social and behavioural difficulties among Glasgow pre-schoolers : linkage of a survey of pre-school staff with routine monitoring data

Funded by The Scottish Government

Mapping area variability in social and behavioural difficulties among Glasgow pre-schoolers : linkage of a survey of pre-school staff with routine monitoring data

Funded by The Scottish Government

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.