Showing papers on "False positive paradox published in 2011"

Estimating Missing Heritability for Disease from Genome-wide Association Studies

Abstract: Genome-wide association studies are designed to discover SNPs that are associated with a complex trait. Employing strict significance thresholds when testing individual SNPs avoids false positives at the expense of increasing false negatives. Recently, we developed a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously. Here we develop this method further for case-control studies. We use a linear mixed model for analysis of binary traits and transform the estimates to a liability scale by adjusting both for scale and for ascertainment of the case samples. We show by theory and simulation that the method is unbiased. We apply the method to data from the Wellcome Trust Case Control Consortium and show that a substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs.

Comparison of F ST outlier tests for SNP loci under selection

Abstract: Genome scans with many genetic markers provide the opportunity to investigate local adaptation in natural populations and identify candidate genes under selection. In particular, SNPs are dense throughout the genome of most organisms and are commonly observed in functional genes making them ideal markers to study adaptive molecular variation. This approach has become commonly employed in ecological and population genetics studies to detect outlier loci that are putatively under selection. However, there are several challenges to address with outlier approaches including genotyping errors, underlying population structure and false positives, variation in mutation rate and limited sensitivity (false negatives). In this study, we evaluated multiple outlier tests and their type I (false positive) and type II (false negative) error rates in a series of simulated data sets. Comparisons included simulation procedures (FDIST2, ARLEQUIN v.3.5 and BAYESCAN) as well as more conventional tools such as global F(ST) histograms. Of the three simulation methods, FDIST2 and BAYESCAN typically had the lowest type II error, BAYESCAN had the least type I error and Arlequin had highest type I and II error. High error rates in Arlequin with a hierarchical approach were partially because of confounding scenarios where patterns of adaptive variation were contrary to neutral structure; however, Arlequin consistently had highest type I and type II error in all four simulation scenarios tested in this study. Given the results provided here, it is important that outlier loci are interpreted cautiously and error rates of various methods are taken into consideration in studies of adaptive molecular variation, especially when hierarchical structure is included.

Improving occupancy estimation when two types of observational error occur: non‐detection and species misidentification

Abstract: Efforts to draw inferences about species occurrence frequently account for false negatives, the common situation when individuals of a species are not detected even when a site is occupied. However, recent studies suggest the need to also deal with false positives, which occur when species are misidentified so that a species is recorded as detected when a site is unoccupied. Bias in estimators of occupancy, colonization, and extinction can be severe when false positives occur. Accordingly, we propose models that simultaneously account for both types of error. Our approach can be used to improve estimates of occupancy for study designs where a subset of detections is of a type or method for which false positives can be assumed to not occur. We illustrate properties of the estimators with simulations and data for three species of frogs. We show that models that account for possible misidentification have greater support (lower AIC for two species) and can yield substantially different occupancy estimates than those that do not. When the potential for misidentification exists, researchers should consider analytical techniques that can account for this source of error, such as those presented here.

Statistical Properties of the Branch-Site Test of Positive Selection

Abstract: The branch-site test is a likelihood ratio test to detect positive selection along prespecified lineages on a phylogeny that affects only a subset of codons in a protein-coding gene, with positive selection indicated by accelerated nonsynonymous substitutions (with ω = d(N)/d(S) > 1). This test may have more power than earlier methods, which average nucleotide substitution rates over sites in the protein and/or over branches on the tree. However, a few recent studies questioned the statistical basis of the test and claimed that the test generated too many false positives. In this paper, we examine the null distribution of the test and conduct a computer simulation to examine the false-positive rate and the power of the test. The results suggest that the asymptotic theory is reliable for typical data sets, and indeed in our simulations, the large-sample null distribution was reliable with as few as 20-50 codons in the alignment. We examined the impact of sequence length, the strength of positive selection, and the proportion of sites under positive selection on the power of the branch-site test. We found that the test was far more powerful in detecting episodic positive selection than branch-based tests, which average substitution rates over all codons in the gene and thus miss the signal when most codons are under strong selective constraint. Recent claims of statistical problems with the branch-site test are due to misinterpretations of simulation results. Our results, as well as previous simulation studies that have demonstrated the robustness of the test, suggest that the branch-site test may be a useful tool for detecting episodic positive selection and for generating biological hypotheses for mutation studies and functional analyses. The test is sensitive to sequence and alignment errors and caution should be exercised concerning its use when data quality is in doubt.

MODELING KEPLER TRANSIT LIGHT CURVES AS FALSE POSITIVES: REJECTION OF BLEND SCENARIOS FOR KEPLER-9, AND VALIDATION OF KEPLER-9 d, A SUPER-EARTH-SIZE PLANET IN A MULTIPLE SYSTEM

Abstract: Light curves from the Kepler Mission contain valuable information on the nature of the phenomena producing the transit-like signals. To assist in exploring the possibility that they are due to an astrophysical false positive, we describe a procedure (BLENDER) to model the photometry in terms of a “blend” rather than a planet orbiting a star. A blend may consist of a background or foreground eclipsing binary (or star–planet pair) whose eclipses are attenuated by the light of the candidate and possibly other stars within the photometric aperture. We apply BLENDER to the case of Kepler-9 (KIC 3323887), a target harboring two previously confirmed Saturn-size planets (Kepler-9 b and Kepler-9 c) showing transit timing variations, and an additional shallower signal with a 1.59 day period suggesting the presence of a super-Earth-size planet. Using BLENDER together with constraints from other follow-up observations we are able to rule out all blends for the two deeper signals and provide independent validation of their planetary nature. For the shallower signal, we rule out a large fraction of the false positives that might mimic the transits. The false alarm rate for remaining blends depends in part (and inversely) on the unknown frequency of small-size planets. Based on several realistic estimates of this frequency, we conclude with very high confidence that this small signal is due to a super-Earth-size planet (Kepler-9 d) in a multiple system, rather than a false positive. The radius is determined to be 1.64 +0.19 −0.14 R⊕, and current spectroscopic observations are as yet insufficient to establish its mass.

A novel computer-aided lung nodule detection system for CT images.

Abstract: Purpose: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. Methods: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. Results: The overall performancemore » of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. Conclusions: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.« less

Parametric methods for anomaly detection in aggregate traffic

Abstract: This paper develops parametric methods to detect network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phase or manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit-rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less.

High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes.

Abstract: We investigate the effect of aligner choice on inferences of positive selection using site-specific models of molecular evolution. We find that independently of the choice of aligner, the rate of false positives is unacceptably high. Our study is a whole-genome analysis of all protein-coding genes in 12 Drosophila genomes annotated in either all 12 species (~6690 genes) or in the six melanogaster group species. We compare six popular aligners: PRANK, T-Coffee, ClustalW, ProbCons, AMAP, and MUSCLE, and find that the aligner choice strongly influences the estimates of positive selection. Differences persist when we use (1) different stringency cutoffs, (2) different selection inference models, (3) alignments with or without gaps, and/or additional masking, (4) per-site versus per-gene statistics, (5) closely related melanogaster group species versus more distant 12 Drosophila genomes. Furthermore, we find that these differences are consequential for downstream analyses such as determination of over/under-represented GO terms associated with positive selection. Visual analysis indicates that most sites inferred as positively selected are, in fact, misaligned at the codon level, resulting in false positive rates of 48%–82%. PRANK, which has been reported to outperform other aligners in simulations, performed best in our empirical study as well. Unfortunately, PRANK still had a high, and unacceptable for most applications, false positives rate of 50%–55%. We identify misannotations and indels, many of which appear to be located in disordered protein regions, as primary culprits for the high misalignment-related error levels and discuss possible workaround approaches to this apparently pervasive problem in genome-wide evolutionary analyses.

The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)

Abstract: We revisit the adaptive Lasso as well as the thresholded Lasso with refitting, in a high-dimensional linear model, and study prediction error, lq-error (q∈{1,2}), and number of false positive selections. Our theoretical results for the two methods are, at a rather fine scale, comparable. The differences only show up in terms of the (minimal) restricted and sparse eigenvalues, favoring thresholding over the adaptive Lasso. As regards prediction and estimation, the difference is virtually negligible, but our bound for the number of false positives is larger for the adaptive Lasso than for thresholding. We also study the adaptive Lasso under beta-min conditions, which are conditions on the size of the coefficients. We show that for exact variable selection, the adaptive Lasso generally needs more severe beta-min conditions than thresholding. Both the two-stage methods add value to the one-stage Lasso in the sense that, under appropriate restricted and sparse eigenvalue conditions, they have similar prediction and estimation error as the one-stage Lasso but substantially less false positives. Regarding the latter, we provide a lower bound for the Lasso with respect to false positive selections.

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

Abstract: According to signal detection theoretical analyses, visual signals occurring at a cued location are detected more accurately, whereas frequently occurring ones are reported more often but are not better distinguished from noise. However, conventional analyses that estimate sensitivity and bias by comparing true- and false-positive rates offer limited insights into the mechanisms responsible for these effects. Here, we reassessed the prior influences of signal probability and relevance on visual contrast detection using a reverse-correlation technique that quantifies how signal-like fluctuations in noise predict trial-to-trial variability in choice discarded by conventional analyses. This approach allowed us to estimate separately the sensitivity of true and false positives to parametric changes in signal energy. We found that signal probability and relevance both increased energy sensitivity, but in dissociable ways. Cues predicting the relevant location increased primarily the sensitivity of true positives by suppressing internal noise during signal processing, whereas cues predicting greater signal probability increased both the frequency and the sensitivity of false positives by biasing the baseline activity of signal-selective units. We interpret these findings in light of “predictive-coding” models of perception, which propose separable top-down influences of expectation (probability driven) and attention (relevance driven) on bottom-up sensory processing.

Pooled Testing for HIV Screening: Capturing the Dilution Effect

Abstract: We study pooled or group testing as a cost-effective alternative for screening donated blood products sera for HIV; rather than test each sample individually, this method combines various samples into a pool, and then tests the pool. A group testing policy specifies an initial pool size, and based on the HIV test result, either releases all samples in the pool for transfusion, discards all samples in the pool, or divides the pool into subpools for further testing. We develop a hierarchical statistical model that relates the HIV test output to the antibody concentration in the pool, thereby capturing the effect of pooling together different samples. The model is validated using data from a variety of field studies. The model is embedded into a dynamic programming algorithm that derives a group testing policy to minimize the expected cost due to false negatives, false positives, and testing. Because the implementation of the dynamic programming algorithm is cumbersome, a simplified version of the model is used to develop near optimal heuristic policies. A simulation study shows that significant cost savings can be achieved without compromising the accuracy of the test. However, the efficacy of group testing depends upon the use of a classification rule that is, discard the samples in the pool, transfuse them or test them further that is dependent on pool size, a characteristic that is lacking in currently implemented pooled testing procedures.

False positive circumsporozoite protein ELISA: a challenge for the estimation of the entomological inoculation rate of malaria and for vector incrimination

Abstract: The entomological inoculation rate (EIR) is an important indicator in estimating malaria transmission and the impact of vector control. To assess the EIR, the enzyme-linked immunosorbent assay (ELISA) to detect the circumsporozoite protein (CSP) is increasingly used. However, several studies have reported false positive results in this ELISA. The false positive results could lead to an overestimation of the EIR. The aim of present study was to estimate the level of false positivity among different anopheline species in Cambodia and Vietnam and to check for the presence of other parasites that might interact with the anti-CSP monoclonal antibodies. Mosquitoes collected in Cambodia and Vietnam were identified and tested for the presence of sporozoites in head and thorax by using CSP-ELISA. ELISA positive samples were confirmed by a Plasmodium specific PCR. False positive mosquitoes were checked by PCR for the presence of parasites belonging to the Haemosporidia, Trypanosomatidae, Piroplasmida, and Haemogregarines. The heat-stability and the presence of the cross-reacting antigen in the abdomen of the mosquitoes were also checked. Specimens (N = 16,160) of seven anopheline species were tested by CSP-ELISA for Plasmodium falciparum and Plasmodium vivax (Pv210 and Pv247). Two new vector species were identified for the region: Anopheles pampanai (P. vivax) and Anopheles barbirostris (Plasmodium malariae). In 88% (155/176) of the mosquitoes found positive with the P. falciparum CSP-ELISA, the presence of Plasmodium sporozoites could not be confirmed by PCR. This percentage was much lower (28% or 5/18) for P. vivax CSP-ELISAs. False positive CSP-ELISA results were associated with zoophilic mosquito species. None of the targeted parasites could be detected in these CSP-ELISA false positive mosquitoes. The ELISA reacting antigen of P. falciparum was heat-stable in CSP-ELISA true positive specimens, but not in the false positives. The heat-unstable cross-reacting antigen is mainly present in head and thorax and almost absent in the abdomens (4 out of 147) of the false positive specimens. The CSP-ELISA can considerably overestimate the EIR, particularly for P. falciparum and for zoophilic species. The heat-unstable cross-reacting antigen in false positives remains unknown. Therefore it is highly recommended to confirm all positive CSP-ELISA results, either by re-analysing the heated ELISA lysate (100°C, 10 min), or by performing Plasmodium specific PCR followed if possible by sequencing of the amplicons for Plasmodium species determination.

False Discovery Rate.

Abstract: In hypothesis testing, statistical significance is typically based on calculations involving p-values and Type I error rates. A p-value calculated from a single statistical hypothesis test can be used to determine whether there is statistically significant evidence against the null hypothesis. The upper threshold applied to the p-value in making this determination (often 5% in the scientific literature) determines the Type I error rate; i.e., the probability of making a Type I error when the null hypothesis is true. Multiple hypothesis testing is concerned with testing several statistical hypotheses simultaneously. Defining statistical significance is a more complex problem in this setting. A longstanding definition of statistical significance for multiple hypothesis tests involves the probability of making one or more Type I errors among the family of hypothesis tests, called the family-wise error rate. However, there exist other well established formulations of statistical significance for multiple hypothesis tests. The Bayesian framework for classification naturally allows one to calculate the probability that each null hypothesis is true given the observed data (Efron et al. 2001, Storey 2003), and several frequentist definitions of multiple hypothesis testing significance are also well established (Shaffer 1995). Soric (1989) proposed a framework for quantifying the statistical significance of multiple hypothesis tests based on the proportion of Type I errors among all hypothesis tests called statistically significant. He called statistically significant hypothesis tests discoveries and proposed that one be concerned about the rate of false discoveries1 when testing multiple hypotheses. This false discovery rate is robust to the false positive paradox and is particularly useful in exploratory analyses, where one is more concerned with having mostly true findings among a set of statistically significant discoveries rather than guarding against one or more false positives. Benjamini & Hochberg (1995) provided the first implementation of false discovery rates with known operating characteristics. The idea of quantifying the rate of false discoveries is directly related to several pre-existing ideas, such as Bayesian misclassification rates and the positive predictive value (Storey 2003).

Distinguishing Positive Selection From Neutral Evolution: Boosting the Performance of Summary Statistics

Abstract: Summary statistics are widely used in population genetics, but they suffer from the drawback that no simple sufficient summary statistic exists, which captures all information required to distinguish different evolutionary hypotheses. Here, we apply boosting, a recent statistical method that combines simple classification rules to maximize their joint predictive performance. We show that our implementation of boosting has a high power to detect selective sweeps. Demographic events, such as bottlenecks, do not result in a large excess of false positives. A comparison to other neutrality tests shows that our boosting implementation performs well compared to other neutrality tests. Furthermore, we evaluated the relative contribution of different summary statistics to the identification of selection and found that for recent sweeps integrated haplotype homozygosity is very informative whereas older sweeps are better detected by Tajima's π. Overall, Watterson's θ was found to contribute the most information for distinguishing between bottlenecks and selection.

A new alert correlation algorithm based on attack graph

Abstract: Intrusion Detection Systems (IDS) are widely deployed in computer networks. As modern attacks are getting more sophisticated and the number of sensors and network nodes grows, the problem of false positives and alert analysis becomes more difficult to solve. Alert correlation was proposed to analyze alerts and to decrease false positives. Knowledge about the target system or environment is usually necessary for efficient alert correlation. For representing the environment information as well as potential exploits, the existing vulnerabilities and their Attack Graph (AG) is used. It is useful for networks to generate an AG and to organize certain vulnerabilities in a reasonable way. In this paper, we design a correlation algorithm based on AGs that is capable of detecting multiple attack scenarios for forensic analysis. It can be parameterized to adjust the robustness and accuracy. A formal model of the algorithm is presented and an implementation is tested to analyze the different parameters on a real set of alerts from a local network.

False-positive and false-negative assignments of topological insulators in density functional theory and hybrids

Abstract: Density-functional theory (DFT) approaches have been used recently to judge the topological order of various materials despite DFT’s well-known band-gap underestimation. Use of the more accurate quasi-particle GW approach reveals few cases where DFT identifications are false positive, which can possibly misguide experimental searches for materials that are topological insulators (TIs) in DFT but not expected to be TIs in reality. We also present the case of false positives due to the incorrect choice of crystal structures and address the relevance of choice of crystal structure with respect to the ground-state one and thermodynamical instability with respect to binary competing phases. We conclude that it is necessary to consider both the correct ground-state crystal structure and the correct Hamiltonian in order to predict new TIs.

Are Women More at Risk of False-Positive Primary Aldosteronism Screening and Unnecessary Suppression Testing than Men?

Abstract: Aldosterone/renin ratios fluctuate during the menstrual cycle; avoiding the luteal phase during screening premenopausal women for primary aldosteronism may minimize the risk of false positives.

Direct Genetic Analysis of Ten Cancer Cells: Tuning Sensor Structure and Molecular Probe Design for Efficient mRNA Capture†

Abstract: In order to facilitate the use of biomolecular markers in clinical medicine, devices are urgently needed that are highly sensitive, specific, cost effective, and automated. [1–3] Great strides have been made in this area with elegant sensing systems employing nanomaterials, microfluidics, and increasingly sophisticated device design. [4–7] The direct analysis of most types of clinical samples requires femtomolar detection limits to sense scarce analytes with an acceptably low level of false negatives. Very high levels of specificity are required to ensure low levels of false positives. From a practical perspective, equally important is a streamlined approach to sample workup, since the need for extensive sample process

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

Abstract: Image processing of a fundus image is performed for the early detection of diabetic retinopathy. Recently, several studies have proposed that the use of a morphological filter may help extract hemorrhages from the fundus image; however, extraction of hemorrhages using template matching with templates of various shapes has not been reported. In our study, we applied hue saturation value brightness correction and contrast-limited adaptive histogram equalization to fundus images. Then, using template matching with normalized cross-correlation, the candidate hemorrhages were extracted. Region growing thereafter reconstructed the shape of the hemorrhages which enabled us to calculate the size of the hemorrhages. To reduce the number of false positives, compactness and the ratio of bounding boxes were used. We also used the 5 × 5 kernel value of the hemorrhage and a foveal filter as other methods of false positive reduction in our study. In addition, we analyzed the cause of false positive (FP) and false negative in the detection of retinal hemorrhage. Combining template matching in various ways, our program achieved a sensitivity of 85% at 4.0 FPs per image. The result of our research may help the clinician in the diagnosis of diabetic retinopathy and might be a useful tool for early detection of diabetic retinopathy progression especially in the telemedicine.

Precise Detection and Automatic Mitigation of False Sharing

Abstract: False sharing is an insidious problem for multithreaded programs running on multicore processors, where it can silently degrade performance and scalability. Previous tools for detecting false sharing are severly limited: they cannot distinguish false sharing from true sharing, have high false positive rates, and provide limited assistance to help programmers locate and resolve false sharing. This paper presents two tools that attack the problem of false sharing: FS-DETECTIVE and FS-PATROL. Both tools leverage a framework we introduce here called SHERIFF. SHERIFF breaks out threads into separate processes, and exposes an API that allows programs to perform per-thread memory isolation and tracking on a per-page basis. We believe SHERIFF is of independent interest. FS-DETECTIVE finds instances of false sharing by comparing updates within the same cache lines by different threads, and uses sampling to rank them by performance impact. FS-DETECTIVE is precise (no false positives), runs with low overhead (on average, 20%), and is accurate, pinpointing the exact objects involved in false sharing. We present a case study demonstrating FS-DETECTIVE’s effectiveness at locating false sharing in a variety of benchmarks. Rewriting a program to fix false sharing can be infeasible when source is unavailable, or undesirable when padding objects would unacceptably increase memory consumption or further worsen runtime performance. FS-PATROL mitigates false sharing by adaptively isolating shared updates from different threads into separate physical addresses, effectively eliminating most of the performance impact of false sharing. We show that FS-PATROL can improve performance for programs with catastrophic false sharing by up to 9 , without programmer intervention.

SHERIFF: precise detection and automatic mitigation of false sharing

Abstract: False sharing is an insidious problem for multithreaded programs running on multicore processors, where it can silently degrade performance and scalability. Previous tools for detecting false sharing are severely limited: they cannot distinguish false sharing from true sharing, have high false positive rates, and provide limited assistance to help programmers locate and resolve false sharing.This paper presents two tools that attack the problem of false sharing: Sheriff-Detect and Sheriff-Protect. Both tools leverage a framework we introduce here called Sheriff. Sheriff breaks out threads into separate processes, and exposes an API that allows programs to perform per-thread memory isolation and tracking on a per-page basis. We believe Sheriff is of independent interest.Sheriff-Detect finds instances of false sharing by comparing updates within the same cache lines by different threads, and uses sampling to rank them by performance impact. Sheriff-Detect is precise (no false positives), runs with low overhead (on average, 20%), and is accurate, pinpointing the exact objects involved in false sharing. We present a case study demonstrating Sheriff-Detect's effectiveness at locating false sharing in a variety of benchmarks.Rewriting a program to fix false sharing can be infeasible when source is unavailable, or undesirable when padding objects would unacceptably increase memory consumption or further worsen runtime performance. Sheriff-Protect mitigates false sharing by adaptively isolating shared updates from different threads into separate physical addresses, effectively eliminating most of the performance impact of false sharing. We show that Sheriff-Protect can improve performance for programs with catastrophic false sharing by up to 9×, without programmer intervention.

Detecting cycles in stratigraphic data: Spectral analysis in the presence of red noise

Abstract: [1] We discuss the detection of cyclic signals in stratigraphic ‘time series’ using spectral methods. The dominant source of variance in the stratigraphic record is red noise, which greatly complicates the process of searching for weak periodic signals. We highlight two issues that are more significant than generally appreciated. The first is the lack of a correction for ‘multiple tests’ – many independent frequencies are examined for periods but using a significance test appropriate for examination of a single frequency. The second problem is the poor choice of null hypothesis used to model the spectrum of non-periodic variations. Stratigraphers commonly assume the noise is a first-order autoregressive process – the AR(1) model – which in practice often gives a very poor match to real data; a fact that goes largely unnoticed because model checking is rarely performed. These problems have the effect of raising the number of false positives far above the expected rate, to the extent that the literature on spatial stratigraphic cycles is dominated by false positives. In turn these will distort the construction of astronomically calibrated timescales, lead to inflated estimates of the physical significance of deterministic forcing of the climate and depositional processes in the pre-Neogene, and may even bias models of solar system dynamics on very long timescales. We make suggestions for controlling the false positive rate, and emphasize the value of Monte Carlo simulations to validate and calibrate analysis methods.

Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification.

Abstract: Purpose: The authors presented a novel system for automated nodule detection in lungCT exams Methods: The approach is based on (1) a lungtissue segmentation preprocessing step, composed of histogram thresholding, seeded region growing, and mathematical morphology; (2) a filtering step, whose aim is the preliminary detection of candidate nodules (via 3D fast radial filtering) and estimation of their geometrical features (via scale space analysis); and (3) a false positive reduction (FPR) step, comprising a heuristic FPR, which applies thresholds based on geometrical features, and a supervised FPR, which is based on support vector machines classification, which in turn, is enhanced by a feature extraction algorithm based on maximum intensity projection processing and Zernike moments Results: The system was validated on 154 chest axial CT exams provided by the lungimagedatabase consortium public database The authors obtained correct detection of 71% of nodules marked by all radiologists, with a false positive rate of 65 false positives per patient (FP/patient) A higher specificity of 25 FP/patient was reached with a sensitivity of 60% An independent test on the ANODE09 competition database obtained an overall score of 0310 Conclusions: The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness

EFindBugs: Effective Error Ranking for FindBugs

Abstract: Static analysis tools have been widely used to detect potential defects without executing programs. It helps programmers raise the awareness about subtle correctness issues in the early stage. However, static defect detection tools face the high false positive rate problem. Therefore, programmers have to spend a considerable amount of time on screening out real bugs from a large number of reported warnings, which is time-consuming and inefficient. To alleviate the above problem during the report inspection process, we present EFindBugs to employ an effective two-stage error ranking strategy that suppresses the false positives and ranks the true error reports on top, so that real bugs existing in the programs could be more easily found and fixed by the programmers. In the first stage, EFindBugs initializes the ranking by assigning predefined defect likelihood for each bug pattern and sorting the error reports by the defect likelihood in descending order. In the second stage, EFindbugs optimizes the initial ranking self-adaptively through the feedback from users. This optimization process is executed automatically and based on the correlations among error reports with the same bug pattern. Our experiment on three widely-used Java projects (AspectJ, Tomcat, and Axis) shows that our ranking strategy outperforms the original ranking in Find Bugs in terms of precision, recall and F1-score.

False negative rates in Drosophila cell-based RNAi screens: a case study.

Abstract: High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention. We performed a meta-analysis of several genome-wide, cell-based Drosophila RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene. RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.

Mining Gold Dust under the Genome Wide Significance Level: A Two-Stage Approach to Analysis of GWAS

Abstract: We propose a two-stage approach to analyze genome-wide association (GWA) data in order to identify a set of promising single-nucleotide polymorphisms (SNPs). In stage one, we select a list of top signals from single SNP analyses by controlling false discovery rate (FDR). In stage two, we use the least absolute shrinkage and selection operator (LASSO) regression to reduce false positives. The proposed approach was evaluated using simulated quantitative traits based on genome-wide SNP data on 8,861 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC) Study. Our first stage, targeted at controlling false negatives, yields better power than using Bonferroni corrected significance level. The LASSO regression reduces the number of significant SNPs in stage two: it reduces false positive SNPs and it reduces true positive SNPs also at simulated causal loci due to linkage disequilibrium. Interestingly, the LASSO regression preserves the power from stage one, i.e., the number of causal loci detected from the LASSO regression in stage two is almost the same as in stage one, while reducing false positives further. Real data on systolic blood pressure in the ARIC study was analyzed using our two-stage approach which identified two significant SNPs, one of which was reported to be genome-significant in a meta-analysis containing a much larger sample size. On the other hand, a single SNP association scan did not yield any significant results.

Dynamic Cascades with Bidirectional Bootstrapping for Action Unit Detection in Spontaneous Facial Behavior

Abstract: Automatic facial action unit detection from video is a long-standing problem in facial expression analysis. Research has focused on registration, choice of features, and classifiers. A relatively neglected problem is the choice of training images. Nearly all previous work uses one or the other of two standard approaches. One approach assigns peak frames to the positive class and frames associated with other actions to the negative class. This approach maximizes differences between positive and negative classes, but results in a large imbalance between them, especially for infrequent AUs. The other approach reduces imbalance in class membership by including all target frames from onsets to offsets in the positive class. However, because frames near onsets and offsets often differ little from those that precede them, this approach can dramatically increase false positives. We propose a novel alternative, dynamic cascades with bidirectional bootstrapping (DCBB), to select training samples. Using an iterative approach, DCBB optimally selects positive and negative samples in the training data. Using Cascade Adaboost as basic classifier, DCBB exploits the advantages of feature selection, efficiency, and robustness of Cascade Adaboost. To provide a real-world test, we used the RU-FACS (a.k.a. M3) database of nonposed behavior recorded during interviews. For most tested action units, DCBB improved AU detection relative to alternative approaches.