Systems Modeling of Lung Cancer Screening Programs to Improve Quality

Thesis (Master's)--University of Washington, 2016-08

CFAR Adaptive PN Code acquisition for DSSS Systems.

Constant false alarm rate (CFAR) techniques can be used in Pseudo-Noise (PN) code acquisition in Spread Spectrum (SS) communication systems, and all the CFAR techniques perform well in homogeneous background PN code acquisition. However, in non-homogeneous background, some CFAR techniques suffer rapid degradation. GO/SO (Greatest-of/Smallest-of) CFAR and adaptive censored mean level detector (ACMLD) are two adaptive CFAR techniques, which are analyzed and compared with other CFAR techniques. The simulation results show that GO/SO CFAR is superior to other CFAR techniques, it maintains short mean acquisition time (MAT) even at environment with strong clutter noise, and ACMLD is suitable for background with strong interfering targets

A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.

Estrutura e dinâmica de populações de Hydrochoerus hydrochaeris (Linnaeus, 1766) (Rodentia) em duas áreas urbanas no norte do Paraná, Brasil

This study aimed to evaluate the population ecology of Hydrochoerus hydrochaeris (Linnaeus, 1766) in two urban areas in the north of Paraná, an open and other protected by assessing the effects of these two different types of environments on the structure and dynamics of the population and its implications for conservation of the species. The monitoring of the populations were performed monthly between June 2014 and March 2015 in Jaboti Lake Park (Apucarana / PR) and Conservation Unit Parque Arthur Thomas (Londrina / PR). To conduct the survey population data was used the methodology of total census (direct counting of individuals).They were evaluated ecological parameters of the populations of capybaras, such as ecological density, abundance, age distribution and birth rate, as well as performed the analysis of the landscape. It was applied to analysis of variance (ANOVA) to test the differences between the averages of the abundance of each age group per year of observation, as well as the differences between the average ecological density over the years of observation. The standard relationship between the use of classes and land use in the landscape and the ecological parameters of the capybara populations was evaluated by Principal Component Analysis (PCA). The trend of variation of average abundance over time to Thomas Arthur Park revealed abrupt decrease of the population in a short period of time (2014 16 ± 9, 14 ± 1 and 2015 7±1) as well as ecological density in 2015 (0.05 ind./ha). On the other hand, the Jaboti Lake Park, showed an increase in absolute abundance, with marked recovery of the population in the same period (2014 38±8,30 and 2015 45±1,73) and coming up with an ecological density in 2015 (2 ind ./ha). The birth rate found Lake Park Jaboti was superior to Arthur Thomas Park, which had a negative rate between 2012 and 2015. The use and land cover analysis showed significant differences from the point of view of the relative contribution of landscape elements in the spatial heterogeneity. Arthur Thomas Park shows areas of dense vegetation and urban areas relatively higher than those observed in Jaboti Lake Park that revealed relatively higher proportions of the areas of agriculture / field and exposed soil. Thus, the present study revealed that the local population structure is directly related to the spatial characteristics of both studied landscapes, as can be seen by the greater abundance and density seen in Jaboti Lake Park compared to Arthur Thomas Park in recent years of study.

A more reliable PCR for detection of Mycobacterium tuberculosis in clinical samples

Diagnostic techniques based on PCR have two major problems: false-positive reactions due to contamination with DNA fragments from previous PCRs (amplicons) and false-negative reactions caused by inhibitors that interfere with the PCR. We have improved our previously reported PCR based on the amplification of a fragment of the Mycobacterium tuberculosis complex-specific insertion element IS6110 with respect to both problems. False-positive reactions caused by amplicon contamination were prevented by the use of uracil-N-glycosylase and dUTP instead of dTTP. We selected a new set of primers outside the region spanned by the formerly used primers to avoid false-positive reactions caused by dTTP-containing amplicons still present in the laboratory. With this new primer set, 16 copies of the IS6110 insertion element, the equivalent of two bacteria, could be amplified 10(10) times in 40 cycles, resulting in a mean efficiency of 77% per cycle. To detect the presence of inhibitors of the Taq polymerase, which may cause false-negative reactions, part of each sample was spiked with M. tuberculosis DNA. The DNA purification method using guanidinium thiocyanate and diatoms effectively removed most or all inhibitors of the PCR. However, this was not suitable for blood samples, for which we developed a proteinase K treatment followed by phenol-chloroform extraction. This method permitted detection of 20 M. tuberculosis bacteria per ml of whole blood. Various laboratory procedures were introduced to reduce failure or inhibition of PCR and avoid DNA cross contamination. We have tested 218 different clinical specimens obtained from patients suspected of having tuberculosis. The samples included sputum (n=145), tissue biopsy samples (n=25), cerebrospinal fluid (n=15), blood (n=14), pleural fluid (n=9), feces, (n=7), fluid from fistulae (n=2), and pus from a wound (n=1). The results obtained by PCR were consistent with those obtained with culture, which is the "gold standard." We demonstrate that PCR is a useful technique for the rapid diagnosis of tuberculosis at various sites.

Hierarchically grouped 2D local features applied to edge contour localisation

One of the most significant research topics in computer vision is object detection. Most of the reported object detection results localise the detected object within a bounding box, but do not explicitly label the edge contours of the object. Since object contours provide a fundamental diagnostic of object shape, some researchers have initiated work on linear contour feature representations for object detection and localisation. However, linear contour feature-based localisation is highly dependent on the performance of linear contour detection within natural images, and this can be perturbed significantly by a cluttered background. In addition, the conventional approach to achieving rotation-invariant features is to rotate the feature receptive field to align with the local dominant orientation before computing the feature representation. Grid resampling after rotation adds extra computational cost and increases the total time consumption for computing the feature descriptor. Though it is not an expensive process if using current computers, it is appreciated that if each step of the implementation is faster to compute especially when the number of local features is increasing and the application is implemented on resource limited ”smart devices”, such as mobile phones, in real-time. Motivated by the above issues, a 2D object localisation system is proposed in this thesis that matches features of edge contour points, which is an alternative method that takes advantage of the shape information for object localisation. This is inspired by edge contour points comprising the basic components of shape contours. In addition, edge point detection is usually simpler to achieve than linear edge contour detection. Therefore, the proposed localization system could avoid the need for linear contour detection and reduce the pathological disruption from the image background. Moreover, since natural images usually comprise many more edge contour points than interest points (i.e. corner points), we also propose new methods to generate rotation-invariant local feature descriptors without pre-rotating the feature receptive field to improve the computational efficiency of the whole system. In detail, the 2D object localisation system is achieved by matching edge contour points features in a constrained search area based on the initial pose-estimate produced by a prior object detection process. The local feature descriptor obtains rotation invariance by making use of rotational symmetry of the hexagonal structure. Therefore, a set of local feature descriptors is proposed based on the hierarchically hexagonal grouping structure. Ultimately, the 2D object localisation system achieves a very promising performance based on matching the proposed features of edge contour points with the mean correct labelling rate of the edge contour points 0.8654 and the mean false labelling rate 0.0314 applied on the data from Amsterdam Library of Object Images (ALOI). Furthermore, the proposed descriptors are evaluated by comparing to the state-of-the-art descriptors and achieve competitive performances in terms of pose estimate with around half-pixel pose error.

Caracterización de señales sísmicas del Volcán Cotopaxi utilizando estimadores espectrales clásicos y de máxima entropía

Se presenta un estudio de detección y caracterización de eventos sísmicos del tipo volcano tectónicos y largo periodo de registros sísmicos generados por el volcán Cotopaxi. La estructura secuencial de detección propuesta permite en un registro sísmico maximizar la probabilidad de presencia de un evento y minimizar la ausencia de este. La detección se la realiza en el dominio del tiempo en cuasi tiempo real manteniendo una tasa constante de falsa alarma para posteriormente realizar un estudio del contenido espectral de los eventos mediante el uso de estimadores espectrales clásicos como el periodograma y paramétricos como el método de máxima entropía de Burg, logrando así, categorizar a los eventos detectados como volcano tectónicos, largo periodo y otros cuando no poseen características pertenecientes a los otros dos tipos como son los rayos.

Chilean IPNV isolates: Robustness analysis of PCR detection

Background: The genomes of several infectious pancreatic necrosis viruses (IPNVs) isolated in Chile were sequenced with a single amplification approach for both segments A and B. The resulting sequences were then used to determine the conservation of the primer-binding regions used in polymerase chain reaction (PCR)-based diagnostic methods proposed in the literature. Thus, the robustness of each technique was studied, particularly the eventual effect of further mutations within the primer-binding sites. Results: On analysis, most methods currently used to detect Chilean IPNV varieties were deemed adequate. However, the primers were designed to be genogroup specific, implying that most detection methods pose some risk of detecting all strains prevalent in the country, due to the coexistence of genogroups 1 and 5. Conclusions: Negative resultsmust be interpreted carefully given the high genomic variability of IPNVs. Detection techniques (quantitative reverse transcription (qRT)-PCR) based on degenerate primers can be used to minimize the possibilities of false-negative detections.

Development and evaluation of the polymerase chain reaction (PCR) method for diagnosis of spring viremia of carp virus (SVCV)

Aquaculture has been expanded rapidly to become a major commercial and food-producing sector worldwide in recent decade. In parallel, viral diseases rapidly spread among farms causing enormous economic losses. The accurate detection of pathogens at early stages of infection is a key point for disease control in aquaculture. Spring Viraemia of Carp Virus (SVCV) is a very severe pathogen of carp fishes in different parts of the world and is categorized as a reportable listed disease in the annual published list of World Organization for animal Health (OIE). The objective of this study was to develop and evaluate RT- PCR test for detecting SVC virus and also the sensitivity and specificity of this test. A semi nested RT- PCR was designed using combination of three primers: two external (SVCF , SVCR) and one internal (SVCS) primers which based on conserved region of G gen. The specificity of designed primers (only external ones) by examination on Viral Hemorrhagic Septicemia Virus (VHSV) and Infectious Hematopoietic Necrosis Virus (IHNV) was confirmed. For optimizing of the PCR test, primer concentration, primer annealing temperature, cycle number and Mgcl2 concentration were surveyed. Also for validity test, prevention of false negative and Assurance of its accuracy, a competitive internal control (mimic) designed and its suitable concentration was defined. Evaluation of the sensitivity of designed test were conducted first by comparing the different commercially available RNA isolation guidelines, two guidelines: isotiocyanate phenol–chloroform based protocols (RNX–Plus Iran, Iq2000 kit Taiwan ) and two column based protocols (Cinna pure RNA Iran , high pure viral RNA kit, Roche Germany ). The results indicated that the column based protocols (Roche method and Cinna pure), yield 36.77 ng/μl and 16/47 ng/μl RNA concentration respectively, which were significantly higher than other protocols(P<0.05). Then for evaluation of extracted RNA sensitivity, Serial dilution of SVCV strain 56.70 grown in EPC (1.9×105 TCID50/ml) was examined To compare sensitivity. Extracted RNA from serial dilution with stone's primers and commercial IQ-2000 kit were examined simultaneously. The result indicated that designed semi- nested RT- PCR was able to recognize SVC virus to 10-4 dilution and stone's primer recognize to 10-3 dilution whereas Iq-2000 commercial kit did not recognized in any dilution. In high virus titer in designed test two DNA band (462 bp and 266 bp) produced, and by decreasing virus titer 462 bp was omitted. In low virus titer or lack of virus, just DNA band (mimic) 729 bp can propagate. After designing and optimizing PCR test, a total of 400 suspected cultured Cyprinus carpio with high mortality from 4 aquaculture zone of Khuzestan province were collected and tested for SVCV during 2012- 2013 using developed PCR method and IQ- 2000. The results indicated that SVC virus was not observed in samples using both methods.

Estadiamento do carcinoma do colo do útero por ressonância magnética – comparação com os achados anátomo-patológicos

Objectivo: Avaliar a acuidade da Ressonância Magnética (RM) no estadiamento do carcinoma do colo do útero, comparando os achados em RM com os resultados Anátomo-Patológicos da peça operatória. Material e Métodos: Foi efectuado um estudo retrospectivo que incluiu 41 doentes operadas com o diagnóstico de carcinoma do colo do útero e previamente submetidas a RM para estadiamento, entre Janeiro de 2007 e Dezembro de 2009. Foram analisados os seguintes factores de estadiamento e prognóstico: dimensão do tumor, invasão dos paramétrios, invasão da vagina e metástases ganglionares. A dimensão do tumor determinada por RM foi comparada com a medição na peça operatória através da análise do declive e ordenada na origem de uma recta de regressão entre os dois métodos. Resultados: O tumor foi visualizado por RM na maioria dos casos (35 doentes, 85.4%). Nas restantes 6 doentes a avaliação anátomo-patológica revelou um tumor com menos de 6 mm de diâmetro. A dimensão do tumor foi adequadamente avaliada por RM, sem diferenças estatisticamente significativas entre a medição por RM e na peça operatória. Foi confirmado o elevado valor preditivo negativo da RM na exclusão de invasão dos paramétrios previamente reportado, com apenas 2 falsos negativos em que a anatomia patológica demonstrou apenas invasão microscópica focal. A invasão da vagina foi correctamente avaliada em 30 doentes (85.7%), tendo-se verificado nos restantes casos 2 falsos negativos e 3 falsos positivos. Em relação às metástases ganglionares verificaram-se 4 falsos negativos, no total das 41 doentes avaliadas. Conclusão: A dimensão do tumor, invasão dos paramétrios, invasão da vagina e metástases ganglionares foram adequadamente avaliadas por RM, confirmando a capacidade da RM no estadiamento do carcinoma do colo do útero.

Evaluation of faecal sampling methods for the analysis of Giardia sp. in companion animals

Dissertação de Mestrado Integrado em Medicina Veterinária

Filtering redundant data from RFID data streams

Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.

Reliability evaluation for clustered WSNs under malware propagation

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.

Detection and classification of anomaly intrusion using hierarchy clustering and SVM

Anomaly detection as a kind of intrusion detection is good at detecting the unknown attacks or new attacks, and it has attracted much attention during recent years. In this paper, a new hierarchy anomaly intrusion detection model that combines the fuzzy c-means (FCM) based on genetic algorithm and SVM is proposed. During the process of detecting intrusion, the membership function and the fuzzy interval are applied to it, and the process is extended to soft classification from the previous hard classification. Then a fuzzy error correction sub interval is introduced, so when the detection result of a data instance belongs to this range, the data will be re-detected in order to improve the effectiveness of intrusion detection. Experimental results show that the proposed model can effectively detect the vast majority of network attack types, which provides a feasible solution for solving the problems of false alarm rate and detection rate in anomaly intrusion detection model.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.