False-positive PCR results linked to administration of seasonal influenza vaccine

False-positive PCR results usually occur as a consequence of specimen-to-specimen or amplicon-to-specimen contamination within the laboratory. Evidence of contamination at time of specimen collection linked to influenza vaccine administration in the same location as influenza sampling is described. Clinical, circumstantial and laboratory evidence was gathered for each of five cases of influenza-like illness (ILI) with unusual patterns of PCR reactivity for seasonal H1N1, H3N2, H1N1 (2009) and influenza B viruses. Two 2010 trivalent influenza vaccines and environmental swabs of a hospital influenza vaccination room were also tested for influenza RNA. Sequencing of influenza A matrix (M) gene amplicons from the five cases and vaccines was undertaken. Four 2009 general practitioner (GP) specimens were seasonal H1N1, H3N2 and influenza B PCR positive. One 2010 GP specimen was H1N1 (2009), H3N2 and influenza B positive. PCR of 2010 trivalent vaccines showed high loads of detectable influenza A and B RNA. Sequencing of the five specimens and vaccines showed greatest homology with the M gene sequence of Influenza A/Puerto Rico/8/1934 H1N1 virus (used in generation of influenza vaccine strains). Environmental swabs had detectable influenza A and B RNA. RNA detection studies demonstrated vaccine RNA still detectable for at least 66 days. Administration of influenza vaccines and clinical sampling in the same room resulted in the contamination with vaccine strains of surveillance swabs collected from patients with ILI. Vaccine contamination should therefore be considered, particularly where multiple influenza virus RNA PCR positive signals (e.g. H1N1, H3N2 and influenza B) are detected in the same specimen.

Optimizing the balance between false positive and false negative error probabilities of confirmatory methods for the detection of veterinary drug residues

GC-MS data on veterinary drug residues in bovine urine are used for controlling the illegal practice of fattening cattle. According to current detection criteria, peak patterns of preferably four ions should agree within 10 or 20% from a corresponding standard pattern. These criteria are rigid, rather arbitrary and do not match daily practice. A new model, based on multivariate modeling of log peak abundance ratios, provides a theoretical basis for the identification of analytes and optimizes the balance between the avoidance of false positives and false negatives. The performance of the model is demonstrated on data provided by five laboratories, each supplying GC-MS measurements on the detection of clenbuterol, dienestrol and 19 beta-nortestosterone in urine. The proposed model shows a better performance than confirmation by using the current criteria and provides a statistical basis for inspection criteria in terms of error probabilities.

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.

Economic modelling of antenatal screening and ultrasound scanning programmes for identification of fetal abnormalities

Objective Within the framework of a health technology assessment and using an economic model, to determine the most clinically and cost effective policy of scanning and screening for fetal abnormalities in early pregnancy. Design A discrete event simulation model of 50,000 singleton pregnancies. Setting Maternity services in Scotland. Population Women during the first 24 weeks of their pregnancy. Methods The mathematical model was populated with data on uptake of screening, prevalence, detection and false positive rates for eight fetal abnormalities and with costs for ultrasound scanning and serum screening. Inclusion of abnormalities was based on the relative prevalence and clinical importance of conditions and the availability of data. Six strategies for the identification of abnormalities prenatally including combinations of first and second trimester ultrasound scanning and first and second trimester screening for chromosomal abnormalities were compared. Main outcome measures The number of abnormalities detected and missed, the number of iatrogenic losses resulting from invasive tests, the total cost of strategies and the cost per abnormality detected were compared between strategies. Results First trimester screening for chromosomal abnormalities costs more than second trimester screening but results in fewer iatrogenic losses. Strategies which include a second trimester ultrasound scan result in more abnormalities being detected and have lower costs per anomaly detected. Conclusions The preferred strategy includes both first and second trimester ultrasound scans and a first trimester screening test for chromosomal abnormalities. It has been recommended that this policy is offered to all women in Scotland.

Three-stage contingent screening for down syndrome

Objective To demonstrate the potential value of three-stage sequential screening for Down syndrome. Methods Protocols were considered in which maternal serum pregnancy associated plasma protein-A (PAPP-A) and free -human chorionic gonadotropin (hCG) measurements were taken on all women in the first trimester. Those women with very low Down syndrome risks were screened negative at that stage and nuchal translucency (NT) was measured on the remainder and the risk reassessed. Those with very low risk were then screened negative and those with very high risk were offered early diagnostic testing. Those with intermediate risks received second-trimester maternal serum -fetoprotein, free -hCG, unconjugated estriol and inhibin-A. Risk was then reassessed and those with high risk were offered diagnosis. Detection rates and false-positive rates were estimated by multivariate Gaussian modelling using Monte-Carlo simulation. Results The modelling suggests that, with full adherence to a three-stage policy, overall detection rates of nearly 90% and false-positive rates below 2.0% can be achieved. Approximately two-thirds of pregnancies are screened on the basis of first-trimester biochemistry alone, five out of six women complete their screening in the first trimester, and the first-trimester detection rate is over 60%. Conclusion Three-stage contingent sequential screening is potentially highly effective for Down syndrome screening. The acceptability of this protocol and its performance in practice, should be tested in prospective studies. Copyright © 2006 John Wiley & Sons, Ltd.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Contingent screening for down syndrome is an efficient alternative to non-disclosure sequential screening

Objective To present a first and second trimester Down syndrome screening strategy, whereby second-trimester marker determination is contingent on the first-trimester results. Unlike non-disclosure sequential screening (the Integrated test), which requires all women to have markers in both trimesters, this allows a large proportion of the women to complete screening in the first trimester. Methods Two first-trimester risk cut-offs defined three types of results: positive and referred for early diagnosis; negative with screening complete; and intermediate, needing second-trimester markers. Multivariate Gaussian modelling with Monte Carlo simulation was used to estimate the false-positive rate for a fixed 85% detection rate. The false-positive rate was evaluated for various early detection rates and early test completion rates. Model parameters were taken from the SURUSS trial. Results Completion of screening in the first trimester for 75% of women resulted in a 30% early detection rate and a 55% second trimester detected rate (net 85%) with a false-positive rate only 0.1% above that achievable by the Integrated test. The screen-positive rate was 0.1% in the first trimester and 4.7% for those continuing to be tested in the second trimester. If the early detection rate were to be increased to 45% or the early completion rate were to be increased to 80%, there would be a further 0.1% increase in the false-positive rate. Conclusion Contingent screening can achieve results comparable with the Integrated test but with earlier completion of screening for most women. Both strategies need to be evaluated in large-scale prospective studies particularly in relation to psychological impact and practicability.

The Paediatric Rheumatology International Trials Organisation provisional criteria for the evaluation of response to therapy in juvenile dermatomyositis.

Objective To develop a provisional definition for the evaluation of response to therapy in juvenile dermatomyositis (DM) based on the Paediatric Rheumatology International Trials Organisation juvenile DM core set of variables. Methods Thirty-seven experienced pediatric rheumatologists from 27 countries achieved consensus on 128 difficult patient profiles as clinically improved or not improved using a stepwise approach (patient's rating, statistical analysis, definition selection). Using the physicians' consensus ratings as the “gold standard measure,” chi-square, sensitivity, specificity, false-positive and-negative rates, area under the receiver operating characteristic curve, and kappa agreement for candidate definitions of improvement were calculated. Definitions with kappa values >0.8 were multiplied by the face validity score to select the top definitions. Results The top definition of improvement was at least 20% improvement from baseline in 3 of 6 core set variables with no more than 1 of the remaining worsening by more than 30%, which cannot be muscle strength. The second-highest scoring definition was at least 20% improvement from baseline in 3 of 6 core set variables with no more than 2 of the remaining worsening by more than 25%, which cannot be muscle strength (definition P1 selected by the International Myositis Assessment and Clinical Studies group). The third is similar to the second with the maximum amount of worsening set to 30%. This indicates convergent validity of the process. Conclusion We propose a provisional data-driven definition of improvement that reflects well the consensus rating of experienced clinicians, which incorporates clinically meaningful change in core set variables in a composite end point for the evaluation of global response to therapy in juvenile DM.

Comparison of two polyclonal antibodies for the detection of 19-nortestosterone in bovine bile by ELISA

19-Nortestosterone (beta-NT) is banned for use as a growth promoter in food animals within the European Union. For regulatory control purposes, urine and bile samples are routinely screened by immunoassay. The aim of the present study was to compare the ability of two immunoassays, using two rabbit polyclonal antibodies raised against two different NT derivatives, to detect NT residues in bovine bile. One antiserum cross-reacted with both alpha-NT and beta-NT (alpha/beta-NT), whereas the other was specific for alpha-NT. Bile samples from 266 slaughtered cattle were deconjugated and analyzed using both antibodies, with all screening positives (>2 ng ml(-1)) confirmed by high resolution gas chromatography mass spectrometry. The alpha/beta-NT and alpha-NT antibody-based ELISAs screened 39 and 44 samples positive, respectively, with NT confirmed in 22 and 39, respectively. The alpha/beta-NT antibody-based ELISA produced a false-negative rate of 44% compared to 0% for the alpha-NT antibody-based ELISA. Supplementary investigations concluded that a matrix effect was a major cause of the marked differences in false-negative rates. This result underlines the necessity to validate immunoassays in the sample matrix.

Detecting Packed Executables using Steganalysis

This paper proposes a novel method of detecting packed executable files using steganalysis, primarily targeting the detection of obfuscated malware through packing. Considering that over 80% of malware in the wild is packed, detection accuracy and low false negative rates are important properties of malware detection methods. Experimental results outlined in this paper reveal that the proposed approach achieving an overall detection accuracy of greater than 99%, a false negative rate of 1% and a false positive rate of 0%.

The comparative utility of fluorescence in situ hybridization and reverse transcription-polymerase chain reaction in the diagnosis of alveolar rhabdomyosarcoma.

Fluorescence in situ hybridization (FISH) for FOXO1 gene rearrangement and reverse transcription-polymerase chain reaction (PCR) for PAX3/7-FOXO1 fusion transcripts have become routine ancillary tools for the diagnosis of alveolar rhabdomyosarcomas (ARMS). Here we summarize our experience of these adjunct diagnostic modalities at a tertiary center, presenting the largest comparative series of FISH and PCR for suspected or possible ARMS to date. All suspected or possible ARMS tested by FISH or PCR for FOXO1 rearrangement or PAX3/7-FOXO1 fusion transcripts over a 7-year period were included. FISH and PCR results were correlated with clinical and histologic findings. One hundred samples from 95 patients had FISH and/or PCR performed. FISH had higher rates of technical success (96.8 %) compared with PCR (88 %). Where both tests were utilized successfully, there was high concordance rate between them (94.9 %). In 24 histologic ARMS tested for FISH or PCR, 83.3 % were translocation-positive (all for PAX3-FOXO1 by PCR) and included 3 histologic solid variants. In 76 cases where ARMS was excluded, there were 3 potential false-positive cases with FISH but none with PCR. PCR had similar sensitivity (85.7 %) and better specificity (100 %) in aiding the diagnosis of ARMS, compared with FISH (85 and 95.8 %, respectively). All solid variant ARMS harbored FOXO1 gene rearrangements and PAX3-FOXO1 ARMS were detected to the exclusion of PAX7-FOXO1. In comparative analysis, both FISH and PCR are useful in aiding the diagnosis of ARMS and excluding its sarcomatous mimics. FISH is more reliable technically but has less specificity than PCR. In cases where ARMS is in the differential diagnosis, it is optimal to perform both PCR and FISH: both have similar sensitivities for detecting ARMS, but FISH may confirm or exclude cases that are technically unsuccessful with PCR, while PCR can detect specific fusion transcripts that may be useful prognostically.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Comparison of porcine urine and bile as matrices to screen for the residues of two sulfonamides using a semi-automated enzyme immunoassay.

Porcine urine enzyme immunoassays for sulfamethazine and sulfadiazine have previously been employed as screening tests to predict the concentrations of the drugs in the corresponding tissues (kidneys), If a urine was found positive (> 800 ng ml(-1)) the corresponding kidney was then analysed by an enzyme immunoassay and, if found positive, a confirmatory analysis by HPLC was performed. Urine was chosen as the screening matrix since sulfonamides are mainly eliminated through this body fluid, However, after obtaining a number of false positive predictions, an investigation was carried out to assess the possibility of using an alternative body fluid which would act as a superior indicator of the presence of sulfonamides in porcine kidney, An initial study indicated that serum, plasma and bile could all be used as screening matrices. From these, bile was chosen as the preferred sample matrix and an extensive study followed to compare the efficiencies of sulfonamide positive bile and urine at predicting sulphonamide positive kidneys, Bile was found to be 17 times more efficient than urine at predicting a sulfamethazine positive kidney and 11 times more efficient at predicting a sulfadiazine positive kidney, With this enhanced performance of the initial screening test, the need for the costly and time consuming kidney enzyme immunoassay, prior to HPLC analysis, was eliminated

Towards accurate estimation of the proportion of true null hypotheses in multiple testing

Background

Biomedical researchers are now often faced with situations where it is necessary to test a large number of hypotheses simultaneously, eg, in comparative gene expression studies using high-throughput microarray technology. To properly control false positive errors the FDR (false discovery rate) approach has become widely used in multiple testing. The accurate estimation of FDR requires the proportion of true null hypotheses being accurately estimated. To date many methods for estimating this quantity have been proposed. Typically when a new method is introduced, some simulations are carried out to show the improved accuracy of the new method. However, the simulations are often very limited to covering only a few points in the parameter space.

Results

Here I have carried out extensive in silico experiments to compare some commonly used methods for estimating the proportion of true null hypotheses. The coverage of these simulations is unprecedented thorough over the parameter space compared to typical simulation studies in the literature. Thus this work enables us to draw conclusions globally as to the performance of these different methods. It was found that a very simple method gives the most accurate estimation in a dominantly large area of the parameter space. Given its simplicity and its overall superior accuracy I recommend its use as the first choice for estimating the proportion of true null hypotheses in multiple testing.