Forensic biology encompasses the examination of evidentiary items from crime scenes for biological fluids, often identifying the specific biological fluid present and developing a DNA profile that can be used to link a suspect to a crime. Blood identification consists of visual examination, presumptive tests based on the catalytic activity of hemoglobin, and confirmatory tests based on antigen-antibody interactions. Issues encountered in blood identification include the occurrence of false positive and false negative results. Many causes of these results are well-known but more recently three substances resulting in false negatives with catalytic color tests, chemiluminescent reagents, and immunoassays have been explored. Quebracho extract (a common leather tannin), sodium percarbonate (the main component of detergents containing active oxygen) and vitamin C-containing beverages were all found to produce false negative results at varying degrees with each of the tests mentioned. Increased knowledge of potential negative interfering agents by forensic investigators can help ensure that probative evidence is properly collected and thoroughly analyzed from a crime scene.

Diese Arbeit untersucht das Auftreten falschpositiver Mannan- und Galactomannanergebnisse bei hämatologischen Patienten nach allogener Stammzelltransplantation, die parenteral ernährt werden müssen oder eine antibiotische Therapie mit Piperacillin-Tazobactam erhalten. Die Kenntnis von Quellen, die die Spezifität des Platelia™ Candida Ag plus und den Platelia™ Aspergillus EIA beeinflussen, ist ein wichtiger Beitrag zur Diagnostik systemischer Pilzinfektionen. / This study investigates the occurrence of false-positive mannan and galactomannan results in hematological patients undergoing allogeneic hematopoietic cell transplantation with current parenteral nutrition or antibiotic treatment with piperacillin-tazobactam. The knowledge of sources influencing specificity of Platelia™ Candida Ag plus and Platelia™ Aspergillus EIA contributes to the diagnosis of invasive fungal infections.

Randomized experiments are regarded as the gold standard for estimating causal effects. Commonly, a single test is performed using a fixed sample size. However, observations may also be observed sequentially and because of economical and ethical reasons, it may be desirable to terminate the trial early. The group sequential design allows for interim analyses and early stopping of a trial without the need for continuous monitoring of the accumulating data. The implementation of a group sequential procedure requires that the sampling distribution of the test statistic observed at each wave of testing to have a known or asymptotically known sampling distribution. This thesis investigates an approach for finding a general approximation to the group sequential bootstrap test for test statistics with unknown or analytically intractable sampling distributions. There is currently no bootstrap version of the group sequential test. The approach implies approximating the covariance structure of the test statistics over time, but not the marginal sampling distribution, with that of a normal test statistic. The evaluation is performed with a Monte Carlo simulation study where the achieved significance level is compared to the nominal. Evidence from the Monte Carlo simulations suggests that the approach performs well for test statistics with sampling distributions close to a normal distribution.

The term "teachable moment" (TM) has been used to describe a life transition or event which motivates an individual to change a behavior or presents an opportunity to intervene to prompt behavior change. We examined whether receipt of a false positive ovarian cancer (OC) screening result may represent a TM. 403 women participating in an OC screening program completed questionnaires assessing demographic, clinical, behavioral, and psychosocial information. The TM was operationalized as expressed interest in receiving health-related information. We hypothesized that among women receiving a false positive screening test result, those women who had experienced greater personal perceived risk for OC as well as distress would be more interested in receiving health-related information than women receiving a normal result. Analyses revealed that women receiving a false positive screening result were less interested in receiving health-related information than women receiving a normal screening result. For women receiving a false positive result, expressed interest in receipt of health-related information was only modestly related to distress and related even less to perceptions of OC risk. Our data do not support viewing a false positive OC screening result as a TM. Potential explanations for the current findings as well as recommendations for future research investigating the TM are discussed.

Fire occurrence was widespread in the central Appalachians pre-European settlement due to Native American ignition and occasional lightning strikes, and continued through European settlement. During this time, low to mixed severity burns supported a suite of ecological communities that were fire adapted. In the mid-20th century, the frequency and intensity of fire decreased regionally, resulting in profound forest composition shifts. Land managers now are prioritizing prescribed fire as a restoration tool in current and transitioning fire dependent communities. However, it is unclear how the re-introduction of fire will affect bat community assemblages, particularly after the severe White-nose Syndrome related population declines of many cave-hibernating bat species. To address this concern we used acoustic detectors to sample bat activity levels in burned and unburned environments to examine habitat and temporal effects of fire on bat species in a repeatedly burned landscape. We found evidence for weak positive fire effects on the northern long-eared bat, Indiana bat, little brown bat, big brown bat/silver-haired bat group, high frequency phonic group, and total bat activity. Temporal effects of fire were only apparent for the big brown bat, where we observed a negative relationship between activity and time since fire. Additionally, historic wildfires may offer a suitable surrogate to assess long-term burn impacts on bats, which in turn can be used to better inform bat and prescribed fire relationships. To examine effects of historic fire on bats, we assessed bat presence using acoustic detections at 16 paired burned and unburned forest stands in Shenandoah National Park. Overall, we found few or mostly equivocal relationships of bat occupancy across species relative to burn condition or time since fire at SNP, indicating there is little evidence to support the concept that fire has a significant ecological effect on bats in this portion of the central Appalachians. Riparian areas are particularly important for bats, and serve as foraging and drinking areas, roost sites, and travel corridors. Because fire impacts dry upland and mesic riparian areas differently, is possible that fire will impact bats differently in burned and riparian habitats. To examine fire effects on bats in riparian and upland habitats, we used paired sampling to monitor bat activity in burned, unburned, riparian, and non-riparian areas. Burn and riparian variables had empirical support to explain activity of all bat species. However, coefficients for these species were small and confidence intervals overlapped zero indicating that differences between habitat configurations were marginal. Our results suggest bats have somewhat species-specific responses to fire that differ between upland and riparian habitats, but that large landscape level prescribed fire has a slightly positive to neutral impact on all bats species identified in at our study site post-fire suppression. / Master of Science

DNA methylation is an epigenetic mechanism that usually occurs at CpG sites in the genome. Both sequencing and array-based techniques are available to detect methylation patterns. Whole-genome bisulfite sequencing is the most comprehensive but cost-prohibitive approach, and microarrays represent an affordable alternative approach. Array-based methods are generally cheaper but assess a specific number of genomic loci, such as Illumina methylation arrays. Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple CpG sites that associate with a phenotype. Methylation at nearby sites tends to be correlated, therefore it may be more powerful to study sets of sites to detect methylation differences as well as reduce the multiple testing burden, compared to utilizing individual sites. Several statistical approaches exist for identifying DMRs, and a few prior publications compared the performance of several commonly used DMR methods. However, as far as we know, no comprehensive comparisons have been made based on genome-wide simulation studies. This dissertation provides some comprehensive suggestions for DMR analysis based on genome-wide evaluations of existing DMR tools and presents the development of a novel approach to increase the power to identify DMRs with clinical value in genomic research. The second chapter presents genome-wide null simulations to compare five commonly used array-based DMR methods (Bumphunter, comb-p, DMRcate, mCSEA and coMethDMR) and identifies coMethDMR as the only approach that consistently yields appropriate Type I error control. We suggest that a genome-wide evaluation of false positive (FP) rates is critical for DMR methods. The third chapter develops a novel Principal Component Analysis based DMR method (denoted as DMRPC), which demonstrates its ability to identify DMRs using genome-wide methylation arrays with well-controlled FP rates at the level of 0.05. Compared to coMethDMR, DMRPC is a robust and powerful novel DMR tool that can examine more genomic regions and extract signals from low-correlation regions. The fourth chapter applies the new DMR approach DMRPC in two “real-world” datasets and identifies novel DMRs that are associated with several inflammatory markers.

Binary measurement systems (BMS) are widely used in both manufacturing industry and medicine. In industry, a BMS is often used to measure various characteristics of parts and then classify them as pass or fail, according to some quality standards. Good measurement systems are essential both for problem solving (i.e., reducing the rate of defectives) and to protect customers from receiving defective products. As a result, it is desirable to assess the performance of the BMS as well as to separate the effects of the measurement system and the production process on the observed classifications. In medicine, BMSs are known as diagnostic or screening tests, and are used to detect a target condition in subjects, thus classifying them as positive or negative. Assessing the performance of a medical test is essential in quantifying the costs due to misclassification of patients, and in the future prevention of these errors. In both industry and medicine, the most commonly used characteristics to quantify the performance a BMS are the two misclassification rates, defined as the chance of passing a nonconforming (non-diseased) unit, called the consumer's risk (false positive), and the chance of failing a conforming (diseased) unit, called the producer's risk (false negative). In most assessment studies, it is also of interest to estimate the conforming (prevalence) rate, i.e. probability that a randomly selected unit is conforming (diseased). There are two main approaches for assessing the performance of a BMS. Both approaches involve measuring a number of units one or more times with the BMS. The first one, called the "gold standard" approach, requires the use of a gold-standard measurement system that can determine the state of units with no classification errors. When a gold standard does not exist, is too expensive or time-consuming, another option is to repeatedly measure units with the BMS, and then use a latent class approach to estimate the parameters of interest. In industry, for both approaches, the standard sampling plan involves randomly selecting parts from the population of manufactured parts. In this thesis, we focus on a specific context commonly found in the manufacturing industry. First, the BMS under study is nondestructive. Second, the BMS is used for 100% inspection or any kind of systematic inspection of the production yield. In this context, we are likely to have available a large number of previously passed and failed parts. Furthermore, the inspection system typically tracks the number of parts passed and failed; that is, we often have baseline data about the current pass rate, separate from the assessment study. Finally, we assume that during the time of the evaluation, the process is under statistical control and the BMS is stable. Our main goal is to investigate the effect of using sampling plans that involve random selection of parts from the available populations of previously passed and failed parts, i.e. conditional selection, on the estimation procedure and the main characteristics of the estimators. Also, we demonstrate the value of combining the additional information provided by the baseline data with those collected in the assessment study, in improving the overall estimation procedure. We also examine how the availability of baseline data and using a conditional selection sampling plan affect recommendations on the design of the assessment study. In Chapter 2, we give a summary of the existing estimation methods and sampling plans for a BMS assessment study in both industrial and medical settings, that are relevant in our context. In Chapters 3 and 4, we investigate the assessment of a BMS in the case where we assume that the misclassification rates are common for all conforming/nonconforming parts and that repeated measurements on the same part are independent, conditional on the true state of the part, i.e. conditional independence. We call models using these assumptions fixed-effects models. In Chapter 3, we look at the case where a gold standard is available, whereas in Chapter 4, we investigate the "no gold standard" case. In both cases, we show that using a conditional selection plan, along with the baseline information, substantially improves the accuracy and precision of the estimators, compared to the standard sampling plan. In Chapters 5 and 6, we investigate the case where we allow for possible variation in the misclassification rates within conforming and nonconforming parts, by proposing some new random-effects models. These models relax the fixed-effects model assumptions regarding constant misclassification rates and conditional independence. As in the previous chapters, we focus on investigating the effect of using conditional selection and baseline information on the properties of the estimators, and give study design recommendations based on our findings. In Chapter 7, we discuss other potential applications of the conditional selection plan, where the study data are augmented with the baseline information on the pass rate, especially in the context where there are multiple BMSs under investigation.

Previous research into the accuracy of referrals for glaucoma has shown that a large number of referrals to the Hospital Eye Service are false positive. Research in areas of healthcare other than ophthalmology has shown that psychological distress can be caused by false positive referrals. The present study aimed to evaluate the quality of referrals to the HES for all ocular pathologies, and also to quantify the proportion of these referrals that were false positive. Any commonality between false positive referrals was investigated. The psychological effect of being referred to the HES was also evaluated using the Hospital Anxiety and Depression Scale (HADS) and State-Trait Anxiety Inventory (STAI). Both scales were validated in this population with Rasch analysis before use. A final aim was to develop an improvement to the present referral pathway in order to reduce numbers of false positive referrals. The accuracy of referrals to the HES appears to improve as clinicians become more experienced, and greater numbers of false positive referrals are generated by female clinicians. Optometrists refer patients with a wide range of ocular diseases and in most cases include both fundus observations and visual acuity measurements in their referrals. GPs mainly refer patients with anterior segment disorders, particularly lid lesions, based on direct observation and symptoms. Illegibility and missing clinical information reduce the quality of many optometric referrals. Patients referred to the HES experience raised levels of anxiety as measured by the STAI and raised levels of depression as measured by the HADS-Depression subscale. As a method of assessing psychological distress, the questionnaires HADS-T (all items), STAI-S (State subscale) and STAI-T (Trait subscale) show good discrimination between patients when administered to a population of new ophthalmic outpatients, despite all having a floor effect. Subsequently a referral refinement service was developed which reduced numbers of unnecessary referrals and reduced costs for the NHS.

Binary measurement systems (BMS) are widely used in both manufacturing industry and medicine. In industry, a BMS is often used to measure various characteristics of parts and then classify them as pass or fail, according to some quality standards. Good measurement systems are essential both for problem solving (i.e., reducing the rate of defectives) and to protect customers from receiving defective products. As a result, it is desirable to assess the performance of the BMS as well as to separate the effects of the measurement system and the production process on the observed classifications. In medicine, BMSs are known as diagnostic or screening tests, and are used to detect a target condition in subjects, thus classifying them as positive or negative. Assessing the performance of a medical test is essential in quantifying the costs due to misclassification of patients, and in the future prevention of these errors. In both industry and medicine, the most commonly used characteristics to quantify the performance a BMS are the two misclassification rates, defined as the chance of passing a nonconforming (non-diseased) unit, called the consumer's risk (false positive), and the chance of failing a conforming (diseased) unit, called the producer's risk (false negative). In most assessment studies, it is also of interest to estimate the conforming (prevalence) rate, i.e. probability that a randomly selected unit is conforming (diseased). There are two main approaches for assessing the performance of a BMS. Both approaches involve measuring a number of units one or more times with the BMS. The first one, called the "gold standard" approach, requires the use of a gold-standard measurement system that can determine the state of units with no classification errors. When a gold standard does not exist, is too expensive or time-consuming, another option is to repeatedly measure units with the BMS, and then use a latent class approach to estimate the parameters of interest. In industry, for both approaches, the standard sampling plan involves randomly selecting parts from the population of manufactured parts. In this thesis, we focus on a specific context commonly found in the manufacturing industry. First, the BMS under study is nondestructive. Second, the BMS is used for 100% inspection or any kind of systematic inspection of the production yield. In this context, we are likely to have available a large number of previously passed and failed parts. Furthermore, the inspection system typically tracks the number of parts passed and failed; that is, we often have baseline data about the current pass rate, separate from the assessment study. Finally, we assume that during the time of the evaluation, the process is under statistical control and the BMS is stable. Our main goal is to investigate the effect of using sampling plans that involve random selection of parts from the available populations of previously passed and failed parts, i.e. conditional selection, on the estimation procedure and the main characteristics of the estimators. Also, we demonstrate the value of combining the additional information provided by the baseline data with those collected in the assessment study, in improving the overall estimation procedure. We also examine how the availability of baseline data and using a conditional selection sampling plan affect recommendations on the design of the assessment study. In Chapter 2, we give a summary of the existing estimation methods and sampling plans for a BMS assessment study in both industrial and medical settings, that are relevant in our context. In Chapters 3 and 4, we investigate the assessment of a BMS in the case where we assume that the misclassification rates are common for all conforming/nonconforming parts and that repeated measurements on the same part are independent, conditional on the true state of the part, i.e. conditional independence. We call models using these assumptions fixed-effects models. In Chapter 3, we look at the case where a gold standard is available, whereas in Chapter 4, we investigate the "no gold standard" case. In both cases, we show that using a conditional selection plan, along with the baseline information, substantially improves the accuracy and precision of the estimators, compared to the standard sampling plan. In Chapters 5 and 6, we investigate the case where we allow for possible variation in the misclassification rates within conforming and nonconforming parts, by proposing some new random-effects models. These models relax the fixed-effects model assumptions regarding constant misclassification rates and conditional independence. As in the previous chapters, we focus on investigating the effect of using conditional selection and baseline information on the properties of the estimators, and give study design recommendations based on our findings. In Chapter 7, we discuss other potential applications of the conditional selection plan, where the study data are augmented with the baseline information on the pass rate, especially in the context where there are multiple BMSs under investigation.

The aim of this study was to validate the two presumptive blood tests LMG, LCV and the three visualising blood methods Bluestar Forensics, Lumiscene and the Ruhoff method. The methods’ sensitivity, durability, matrices effects, false positive results and the methods effect on subsequent DNA analysis were studied. DNA analyses were also performed to assess the detection limit of the forensic DNA analysis. Drops of diluted blood were applied on different absorptive matrices and the sensitivity was investigated. The solutions were also placed under different conditions to investigate the durability of the solutions. The solutions were applied upon panels using different chemicals and materials and the false positive results were studied. The DNA analyses were performed by diluting the blood with Bluestar Forensics, the hydrogen peroxide method, the Ruhoff method and deionised water. The study showed that the LMG with a 3 % H2O2 concentration performs the best and it is suited for practical casework. The positive results of LMG was easier to interpret than those of LCV, this is probably due to the fixative agent of the used LCV solution. Bluestar Forensics and Lumiscene did perform similar on the different matrices tested, but the Lumiscene solution had a slightly higher durability. The results strongly indicate that the Ruhoff method can be used without luminol, hence only as a hydrogen peroxide solution (the hydrogen peroxide method). All three visualising blood methods decreases chances of retrieving a positive DNA profile, however the visualising blood methods could be used if the blood cannot be found in any other way. A DNA profile was obtained from the one blood sample analysed at dilution of 1:256 in deionized water.