False positive dipstick for urinary blood in childhood

BACKGROUND: In view of the obvious practical advantages, the most common test for hematuria is currently a reagent strip. METHODS: A standardized microscopic examination of the sediment was performed in 20 asymptomatic children referred for evaluation of chronic isolated microhematuria detected by means of a reagent strip. RESULTS: In 6 of the 20 children the microscopic examination failed to confirm the result of the dipstick test. CONCLUSIONS: Confirmation for the presence of hematuria by microscopy is the most important step in children with a positive dipstick for urinary blood.

Predictors of emotional well-being following a 'false positive' breast cancer screening result

Using a short-term longitudinal design, and consistent with a stress and coping perspective, this study examined the main and stress-buffering effects of social support and coping on emotional well-being following a 'false positive' breast cancer screening result. Immediately prior to obtaining results of follow-up assessment, 178 women completed measures of emotional well-being, stress appraisal, coping strategies and social support. Six weeks later, 85 women found to be cancer free completed a measure of well-being. Hierarchical regression analyses were used to examine the effects of social support and coping on well-being after controlling for initial well-being and stress appraisal. Consistent with predictions, avoidant coping was associated with higher levels of emotional well-being and social support was found to have a stress buffering effect on well-being. Active-cognitive coping strategies had a stress-buffering effect on well-being. Findings suggest that social support and coping do influence emotional well-being following recall for follow-up assessment of a 'false positive' breast cancer screening result.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Gene network effects on brain microstructure and intellectual performance identified in 472 twins

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 2.1 SD years; 193 male/279 female).Wecombined clustering with genome-wide scanning to find brain systems withcommongenetic determination.Wethen filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus.

Guaiac versus immunochemical tests: Faecal occult blood test screening for colorectal cancer in a rural community

Objective: To describe patient participation and clinical performance in a colorectal cancer (CRC) screening program utilising faecal occult blood test (FOBT). Methods: A community-based intervention was conducted in a small, rural community in north Queensland, 2000/01. One of two FOBT kits – guaiac (Hemoccult-ll) or immunochemical (Inform) – was assigned by general practice and mailed to participants (3,358 patients aged 50–74 years listed with the local practices). Results: Overall participation in FOBT screening was 36.3%. Participation was higher with the immunochemical kit than the guaiac kit (OR=1.9, 95% Cl 1.6-2.2). Women were more likely to comply with testing than men (OR=1.4, 95% Cl 1.2-1.7), and people in their 60s were less likely to participate than those 70–74 years (OR=0.8, 95% Cl 0.6-0.9). The positivity rate was higher for the immunochemical (9.5%) than the guaiac (3.9%) test (χ2=9.2, p=0.002), with positive predictive values for cancer or adenoma of advanced pathology of 37.8% (95% Cl 28.1–48.6) for !nform and 40.0% (95% Cl 16.8–68.7) for Hemoccult-ll. Colonoscopy follow-up was 94.8% with a medical complication rate of 2–3%. Conclusions: An immunochemical FOBT enhanced participation. Higher positivity rates for this kit did not translate into higher false-positive rates, and both test types resulted in a high yield of neoplasia. Implications: In addition to type of FOBT, the ultimate success of a population-based screening program for CRC using FOBT will depend on appropriate education of health professionals and the public as well as significant investment in medical infrastructure for colonoscopy follow-up.

Evaluation of ELISA coupled with Western blot as a surveillance tool for Trichinella infection in wild boar (Sus scrofa)

Trichinella surveillance in wildlife relies on muscle digestion of large samples which are logistically difficult to store and transport in remote and tropical regions as well as labour-intensive to process. Serological methods such as enzyme-linked immunosorbent assays (ELISAs) offer rapid, cost-effective alternatives for surveillance but should be paired with additional tests because of the high false-positive rates encountered in wildlife. We investigated the utility of ELISAs coupled with Western blot (WB) in providing evidence of Trichinella exposure or infection in wild boar. Serum samples were collected from 673 wild boar from a high- and low-risk region for Trichinella introduction within mainland Australia, which is considered Trichinella-free. Sera were examined using both an 'in-house' and a commercially available indirect-ELISA that used excretory secretory (E/S) antigens. Cut-off values for positive results were determined using sera from the low-risk population. All wild boar from the high-risk region (352) and 139/321 (43.3%) of the wild boar from the low-risk region were tested by artificial digestion. Testing by Western blot using E/S antigens, and a Trichinella-specific real-time PCR was also carried out on all ELISA-positive samples. The two ELISAs correctly classified all positive controls as well as one naturally infected wild boar from Gabba Island in the Torres Strait. In both the high- and low-risk populations, the ELISA results showed substantial agreement (k-value = 0.66) that increased to very good (k-value = 0.82) when WB-positive only samples were compared. The results of testing sera collected from the Australian mainland showed the Trichinella seroprevalence was 3.5% (95% C.I. 0.0-8.0) and 2.3% (95% C.I. 0.0-5.6) using the in-house and commercial ELISA coupled with WB respectively. These estimates were significantly higher (P < 0.05) than the artificial digestion estimate of 0.0% (95% C.I. 0.0-1.1). Real-time PCR testing of muscle from seropositive animals did not detect Trichinella DNA in any mainland animals, but did reveal the presence of a second larvae-positive wild boar on Gabba Island, supporting its utility as an alternative, highly sensitive method in muscle examination. The serology results suggest Australian wildlife may have been exposed to Trichinella parasites. However, because of the possibility of non-specific reactions with other parasitic infections, more work using well-defined cohorts of positive and negative samples is required. Even if the specificity of the ELISAs is proven to be low, their ability to correctly classify the small number of true positive sera in this study indicates utility in screening wild boar populations for reactive sera which can be followed up with additional testing. (C) 2013 Elsevier B.V. All rights reserved.

Mineração de dados para previsão de eventos extremos de precipitação

No presente trabalho foram desenvolvidos modelos de classificação aplicados à mineração de dados climáticos para a previsão de eventos extremos de precipitação com uma hora de antecedência. Mais especificamente, foram utilizados dados observacionais registrados pela estação meteorológica de superfície localizada no Instituto Politécnico da Universidade do Estado do Rio de Janeiro em Nova Friburgo RJ, durante o período de 2008 a 2012. A partir desses dados foi aplicado o processo de Descoberta de Conhecimento em Banco de Dados (KDD Knowledge Discovery in Databases), composto das etapas de preparação, mineração e pós processamento dos dados. Com base no uso de algoritmos de Redes Neurais Artificiais e Árvores de Decisão para a extração de padrões que indicassem um acúmulo de precipitação maior que 10 mm na hora posterior à medição das variáveis climáticas, pôde-se notar que a utilização da observação meteorológica de micro escala para previsões de curto prazo é suscetível a altas taxas de alarmes falsos (falsos positivos). Para contornar este problema, foram utilizados dados históricos de previsões realizadas pelo Modelo Eta com resolução de 15 km, disponibilizados pelo Centro de Previsão de Tempo e Estudos Climáticos do Instituto Nacional de Pesquisas Espaciais CPTEC/INPE. De posse desses dados, foi possível calcular os índices de instabilidade relacionados à formação de situação convectiva severa na região de Nova Friburgo e então armazená-los de maneira estruturada em um banco de dados, realizando a união entre os registros de micro e meso escala. Os resultados demonstraram que a união entre as bases de dados foi de extrema importância para a redução dos índices de falsos positivos, sendo essa uma importante contribuição aos estudos meteorológicos realizados em estações meteorológicas de superfície. Por fim, o modelo com maior precisão foi utilizado para o desenvolvimento de um sistema de alertas em tempo real, que verifica, para a região estudada, a possibilidade de chuva maior que 10 mm na próxima hora.

On Combining Morphological Component Analysis and Concentric Morphology Model for Mammographic Mass Detection

Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively preprocess mammogram to preserve both the intensity distribution and morphological characteristics of regions. In this paper, morphological component analysis is first introduced to decompose a mammogram into a piecewise-smooth component and a texture component. The former is utilized in our detection scheme as it effectively suppresses both structural noises and effects of blood vessels. Then, we propose two novel concentric layer criteria to detect different types of suspicious regions in a mammogram. The combination is evaluated based on the Digital Database for Screening Mammography, where 100 malignant cases and 50 benign cases are utilized. The sensitivity of the proposed scheme is 99% in malignant, 88% in benign, and 95.3% in all types of cases. The results show that the proposed detection scheme achieves satisfactory detection performance and preferable compromises between sensitivity and false positive rates.

Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.

Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.

Repeated measures screening for down's syndrome

Objective To demonstrate the potential value of screening for Down's Syndrome using highly correlated repeated measures of serum markers taken in the first and second trimesters of pregnancy. Design A Monte Carlo simulation study. Population Detection rates and false positive rates relating to the maternal age distribution of England and Wales for the period 1996 to 1998 were obtained using marker distributions from the SURUSS study. Results Screening using first trimester nuchal translucency and repeated measures of uE3 and PAPP-A in the first and second trimester has an estimated false positive rate of 0.3% for an 85% detection rate. This should be compared with the integrated test with an estimated false positive rate of 1.2% for the same detection rate. Conclusionsâ?? The performance of repeated measures screening tests, and their acceptability to women, should be assessed in further prospective studies.