Occurrence Of False Positives Research Articles

On the occurrence of false positives in tests of migration under an isolation-with-migration model.

The population genetic study of divergence is often carried out using a Bayesian genealogy sampler, like those implemented in ima2 and related programs, and these analyses frequently include a likelihood ratio test of the null hypothesis of no migration between populations. Cruickshank and Hahn (2014, Molecular Ecology, 23, 3133-3157) recently reported a high rate of false-positive test results with ima2 for data simulated with small numbers of loci under models with no migration and recent splitting times. We confirm these findings and discover that they are caused by a failure of the assumptions underlying likelihood ratio tests that arises when using marginal likelihoods for a subset of model parameters. We also show that for small data sets, with little divergence between samples from two populations, an excellent fit can often be found by a model with a low migration rate and recent splitting time and a model with a high migration rate and a deep splitting time.

Classification of beaked whale and dolphin clicks measured by environmental acoustic recording system buoys in the northern Gulf of Mexico

The Littoral Acoustic Demonstration Center (LADC) has used its Environmental Acoustic Recording System (EARS) buoys to record sperm and beaked whales and dolphins, with frequencies up to 96 kHz, in the northern Gulf of Mexico in 2007 and 2010. The 2007 experiment was the first to record beaked whales in the Gulf. It has been found that there is considerable overlap in the band of beaked whale signals from 20 to 60 kHz with deep-water dolphin clicks, so traditional energy-band detectors have a high occurrence of false positives. Although acoustic measurements in this frequency range validated by visual observations have been limited, for the Gulf of Mexico species, progress is being made in automatically delineating clicks that belong to beaked whale species observed in the Gulf and those originating from dolphins. Spectrograms of the classified clicks are shown and compared to known spectrograms for beaked whale and dolphin species. Many of the spectrograms show an upsweep in the observed spectrum but others do not. Improved classifiers can provide higher accuracy estimates of the regional abundance trends and effects of environmental changes on both beaked whale and dolphin groups. [Research supported by BP/GOMRI, SPAWAR, ONR, NSF, and Greenpeace.]

Enhancing Product Detection With Multicue Optimization for TV Shopping Applications

Smart TVs allow consumers to watch TV, interact with applications, and access the Internet, thus enhancing the consumer experience. However, the consumers are still unable to seamlessly interact with the contents being streamed, as it is highlighted by TV-enabled shopping. For example, if a consumer is watching a TV show and is interested in purchasing a product being displayed, the consumer can only go to a store or access the Web to make the purchase. It would be more convenient if the consumer could interact with the TV to purchase interesting items. To realize this use case, products in the content stream must be detected so that the TV system notifies consumers of possibly interesting ones. A practical solution must address the detection of complex products, i.e., those that do not have a rigid form and can appear in various poses, which poses a significant challenge. To this end, a multicue product detection framework is proposed for TV shopping. The framework is generic as it is not tied to specific object detection approaches. Instead, it utilizes appearance, topological, and spatio-temporal cues that make use of a related, easier to detect object class to improve the detection results of the target, more difficult product class. The three cues are jointly considered to select the best path that occurrences of the target product class can follow in the video and thus eliminate false positive occurrences. The empirical results demonstrate the advantages of the proposed approach in improving the precision of the results.

Role of GLCM Features in Identifying Abnormalities in the Retinal Images

Accurate detection of exudates in the diabetic retinal images is a challenging task. The images can have varying contrast and color characteristics. In this paper authors present the performance comparison of two feature extraction methods namely color intensity features and second order texture features based on GLCM. Authors have proposed and implemented new approach for GLCM feature calculation in which the input image is divided into number smaller blocks and GLCM features are computed on these blocks. The performance of each feature extraction method is evaluated using Back Propagation Neural Network (BPNN) classifier that is classifying the blocks as either abnormal block or normal block. With GLCM features, an accuracy of 76.6% was obtained and with color features an accuracy of 100% was obtained. It was found that color features are better in identifying true positives than GLCM based texture features. However use of GLCM features reduces the occurrence of false positives. Index Terms—Texture features, Hard exudates, GLCM features, Back propagation neural network.

Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets genome-wide association studies.

A novel recommendation-based drug repositioning strategy is presented to simultaneously determine novel drug indications and side effects in one integrated framework. This strategy provides a complementary method to medical genetics-based drug repositioning, which reduces the occurrence of false positives in medical genetics-based drug repositioning, resulting in a ranked list of new candidate indications and/or side effects with different confidence levels. Several new drug indications and side effects are reported with high prediction confidences.

Tile-based Fisher ratio analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) data using a null distribution approach.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a versatile instrumental platform capable of collecting highly informative, yet highly complex, chemical data for a variety of samples. Fisher-ratio (F-ratio) analysis applied to the supervised comparison of sample classes algorithmically reduces complex GC × GC-TOFMS data sets to find class distinguishing chemical features. F-ratio analysis, using a tile-based algorithm, significantly reduces the adverse effects of chromatographic misalignment and spurious covariance of the detected signal, enhancing the discovery of true positives while simultaneously reducing the likelihood of detecting false positives. Herein, we report a study using tile-based F-ratio analysis whereby four non-native analytes were spiked into diesel fuel at several concentrations ranging from 0 to 100 ppm. Spike level comparisons were performed in two regimes: comparing the spiked samples to the nonspiked fuel matrix and to each other at relative concentration factors of two. Redundant hits were algorithmically removed by refocusing the tiled results onto the original high resolution pixel level data. To objectively limit the tile-based F-ratio results to only features which are statistically likely to be true positives, we developed a combinatorial technique using null class comparisons, called null distribution analysis, by which we determined a statistically defensible F-ratio cutoff for the analysis of the hit list. After applying null distribution analysis, spiked analytes were reliably discovered at ∼1 to ∼10 ppm (∼5 to ∼50 pg using a 200:1 split), depending upon the degree of mass spectral selectivity and 2D chromatographic resolution, with minimal occurrence of false positives. To place the relevance of this work among other methods in this field, results are compared to those for pixel and peak table-based approaches.

TDMの落とし穴

Therapeutic drug monitoring (TDM) is an essential tool for the optimal use of drugs. Although rare, the drug concentration in blood determined by TDM can be higher or lower than that predicted from the dosage amount and patient information. In such cases, it is necessary to consider the occurrence of false positives and false negatives in the measurement process. Dose adjustment based on the measured blood drug concentration poses potential risks of underdosing and overdosing in patients suspected of false positive and false negative data, respectively. Therefore, pharmacists involved in TDM are required to become familiar with false positives and false negatives in TDM. In particular, the possibility of false positives in TDM of immunosuppressants by the affinity column-mediated immunoassay (ACMIA) method should be considered, while TDM of vancomycin or phenytoin requires attention to false negatives during measurement by the particle-enhanced turbidimetric inhibition immunoassay (PETINIA) method. Moreover, interference from IgM in the measurement process may be of concern in false-negative results; therefore, the measured blood drug concentration needs to be verified when the blood IgM level is high. This mini-review outlines false positives and false negatives that should be considered in TDM and presents a discussion of the relevant literature.

New possibilities of accurate particle characterisation by applying direct boundary models to analytical centrifugation.

Analytical centrifugation (AC) is a powerful technique for the characterisation of nanoparticles in colloidal systems. As a direct and absolute technique it requires no calibration or measurements of standards. Moreover, it offers simple experimental design and handling, high sample throughput as well as moderate investment costs. However, the full potential of AC for nanoparticle size analysis requires the development of powerful data analysis techniques. In this study we show how the application of direct boundary models to AC data opens up new possibilities in particle characterisation. An accurate analysis method, successfully applied to sedimentation data obtained by analytical ultracentrifugation (AUC) in the past, was used for the first time in analysing AC data. Unlike traditional data evaluation routines for AC using a designated number of radial positions or scans, direct boundary models consider the complete sedimentation boundary, which results in significantly better statistics. We demonstrate that meniscus fitting, as well as the correction of radius and time invariant noise significantly improves the signal-to-noise ratio and prevents the occurrence of false positives due to optical artefacts. Moreover, hydrodynamic non-ideality can be assessed by the residuals obtained from the analysis. The sedimentation coefficient distributions obtained by AC are in excellent agreement with the results from AUC. Brownian dynamics simulations were used to generate numerical sedimentation data to study the influence of diffusion on the obtained distributions. Our approach is further validated using polystyrene and silica nanoparticles. In particular, we demonstrate the strength of AC for analysing multimodal distributions by means of gold nanoparticles.

A Tool Set to Map Dynamic Allosteric Networks through the NMR Chemical Shift Covariance Analysis (CHESCA)

Allostery is an essential regulatory mechanism of biological function and allosteric sites are also pharmacologically relevant, as they are typically targeted with higher selectivity than orthosteric sites. However, obtaining a comprehensive map of allosteric sites poses experimental challenges because allostery is driven not only by structural changes, but also by modulations in dynamics that often remain elusive to classical structure determination methods. An avenue to overcome these challenges is provided by the covariance analysis of NMR chemical shift [1, 2], which are exquisitely sensitive to redistributions in dynamic conformational ensembles. Here, we propose a set of complementary algorithms for the NMR chemical shift covariance analysis (CHESCA) designed to reliably detect allosteric networks with minimal occurrences of false positives or negatives. The proposed CHESCA toolset was tested for two allosteric proteins (Protein Kinase A, PKA, and the Exchange Protein directly Activated by cAMP, EPAC) and is expected to complement traditional comparative structural analyses in the comprehensive identification of functionally relevant allosteric sites, including those in otherwise elusive partially unstructured regions.[1] Signaling through dynamic linkers as revealed by PKA. Akimoto M, Selvaratnam R, McNicholl ET, Verma G, Taylor SS, Melacini G. Proc Natl Acad Sci U S A. 2013;110(35):14231-6.[2] Mapping allostery through the covariance analysis of NMR chemical shifts. Selvaratnam R, Chowdhury S, VanSchouwen B, Melacini G. Proc Natl Acad Sci U S A. 2011;108(15):6133-8.

A tool set to map allosteric networks through the NMR chemical shift covariance analysis.

Allostery is an essential regulatory mechanism of biological function. Allosteric sites are also pharmacologically relevant as they are often targeted with higher selectivity than orthosteric sites. However, a comprehensive map of allosteric sites poses experimental challenges because allostery is driven not only by structural changes, but also by modulations in dynamics that typically remain elusive to classical structure determination methods. An avenue to overcome these challenges is provided by the NMR chemical shift covariance analysis (CHESCA), as chemical shifts are exquisitely sensitive to redistributions in dynamic conformational ensembles. Here, we propose a set of complementary CHESCA algorithms designed to reliably detect allosteric networks with minimal occurrences of false positives or negatives. The proposed CHESCA toolset was tested for two allosteric proteins (PKA and EPAC) and is expected to complement traditional comparative structural analyses in the comprehensive identification of functionally relevant allosteric sites, including those in otherwise elusive partially unstructured regions.

Detecting molecular signatures of natural selection in Morus alba populations from trans‐Himalaya

AbstractGenome scan was done with 439 dominant marker loci to identify outlier loci in three populations of Morus alba from the trans‐Himalayan region. Employing finite and hierarchical island population genetic models, we detected 30 (6.85%) loci under selection, of which 3.64% were under directional selection and 3.19% under balancing selection. The hierarchical island model was efficient in detecting signatures of natural selection with low occurrence of false positives. Data showed significant spatial genetic structure (SGS), especially in population from Nubra valley with significantly high SGS. Bayesian approach revealed high genetic differentiation and inbreeding in Nubra population. Indus and Suru valleys populations showed comparatively low SGS, genetic differentiation, and inbreeding. The study showed that M. alba falls in the range of mixed mating and outcrossing species. However, the population of Nubra valley showed selfing with high SGS. Significant SGS strengthens the presence of natural selection in M. alba natural populations.

Blind separation of analytes in nuclear magnetic resonance spectroscopy: Improved model for nonnegative matrix factorization

We introduce an improved model for sparseness-constrained nonnegative matrix factorization (sNMF) of amplitude nuclear magnetic resonance (NMR) spectra of mixtures into a greater number of component spectra. In the proposed method, the selected sNMF algorithm is applied to the square of the amplitude of the NMR spectrum of the mixture instead of to the amplitude spectrum itself. Afterwards, the square roots of separated squares of the component spectra and the concentration matrix yield estimates of the true component amplitude spectrum and of the concentration matrix. The proposed model remains linear on average when the number of overlapping components is increasing, while the model based on the amplitude spectra of the mixtures deviates from the linear one when the number of overlapping components is increased. This is demonstrated through the conducted sensitivity analysis. Thus, the proposed model improves the capability of the sparse NMF algorithms to separate correlated (overlapping) component spectra from the smaller number of mixture NMR spectra. This is demonstrated in two experimental scenarios: extraction of three correlated component spectra from two 1H NMR mixture spectra and extraction of four correlated component spectra from three COSY NMR mixture spectra. The proposed method can increase efficiency in a spectral library search by reducing the occurrence of false positives and false negatives. That, in turn, can yield better accuracy in biomarker identification studies, which makes the proposed method important for natural product research and the field of metabolic studies.

False positive localization of primary mesenteric neuroendocrine tumor to the pancreas with Ga-DOTATATE PET–CT changing the surgical management

Neuroendocrine tumors (NETs) are a group of neoplasms that arise from the neural crest. They can be predominantly found in the gastrointestinal tract, pancreas, lung and rarely in the ovary and thymus.The introduction of β emitting 68Ga-radiolabelled-DOTA peptides in PET–CT for the imaging of NETs has been giving promising results. But, it is worthwhile to know the limitations of using Ga-DOTA peptides to minimize the occurrence of false positives. We report an interesting case of a primary mesenteric neuroendocrine tumor that was misdiagnosed as a nodal secondary from a falsely localized primary pancreatic NET as a result of non-specific physiologic Ga-DOTA uptake in the pancreas. This resulted in an unnecessary distal pancreatectomy.

An Effective Procedure for MNP-Enhanced Breast Cancer Microwave Imaging

Magnetic nanoparticles-enhanced microwave imaging has been recently proposed to overcome the limitations of conventional microwave imaging methods for breast cancer monitoring. In this paper, we discuss how to tackle the linear inverse scattering problem underlying this novel technique in an effective way. In particular, our aim is to minimize the required a priori patient-specific information, avoid occurrence of false positives, and keep the computational burden low. By relying on an extensive numerical analysis in realistic conditions, we show that the method can provide accurate and reliable images without information on the inner structure of the inspected breast and with an only rough knowledge of its shape. Notably, this allows moving to an offline stage the computationally intensive part of the image formation procedure. In addition, we show how to appraise the total amount of magnetic contrast agent targeted in the tumor.

Comments to Prediction of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: An Enhanced Model of BARD Score

Dear Editor, I read with interest this paper which introduces an enhanced scoring model to predict advanced fibrosis in nonalcoholic fatty liver disease. The new model added the international normalized ratio (INR) as a risk factor apart from the three factors of body mass index, aspartate transaminase/alanine aminotransferase ratio, and diabetes mellitus (BARD score).1 INR is a measure of the extrinsic pathway of coagulation and status of liver damage. It is influenced by vitamin K intake and availability, tobacco consumption, and other variables.2,3 Common digestive disorders, such as vomiting and malabsorption, create fluctuations in INR. Warfarin resistance has been reported with rapid increase in INR due to interaction with prednisone, vitamin C, leflunomide, and herbal-drug interaction.4-6 And there are several case reports about Warfarin-cranberry juice interaction.7 In addition, value of INR increases significantly with time from plasma stored at -40℃ or -20℃.8 The new model presented here maintains the simplicity and strength of the BARD score, and the combination of INR and BARD score reduces the occurrence of false positives. Nevertheless, the INR is influenced by multiple variables as stated above, especially the use of warfarin, which is prescribed worldwide. More studies have been suggested to assess the INR, which is influenced by many variables, before the establishment of a new model. Additionally, the authors state in the introduction section the importance of the diagnosis of fibrosis in the early stage, rather than the advanced stage as mentioned in the title.

False‐Positive Results with Amylase Testing of Citrus Fruits

In a case of robbery in which the criminals passed through the garden adorned with calamondin trees (Citrus madurensis), the investigators found in the grass six calamondin fruits, some undamaged, while others apparently bitten. The fruits were collected and sent to the laboratory for DNA analysis to verify the presence of saliva and robbers' DNA profile. A specific immunochromatographic strip test for saliva confirmed the presence of human salivary α-amylase, but similar positive results were also observed for intact calamondin and other citrus fruits. Further analysis with a specific automated amylase test confirmed the absence of amylase activity. DNA quantification and typing using a specific forensic kit revealed no human DNA presence in any fruits. This case report demonstrates for the first time the occurrence of false positives when human saliva is sought on citrus fruits.

Localization of Contamination Sources in Drinking Water Distribution Systems: A Method Based on Successive Positive Readings of Sensors

This paper addresses the problem of the localization of contamination sources after deliberate contaminations in drinking water distribution systems (DWDS). The proposed methodology is based on the information given by successive positive readings of sensors. Thus, it is possible to estimate the localization of the contamination sources based on only the first sensor that detected a contamination, and then update the results when more information is available. From the tests performed on a real drinking water distribution system, it was possible to observe that as new sensors detect changes in contaminant concentration, other possible contaminations may be detected and the location of contamination sources may be more restricted. The results achieved for the two set of sensors considered in the study contained the correct locations and the instants of contaminations previously simulated. Two case studies were also analysed to study the effect of the occurrence of false positives. It was concluded that it is not always possible to verify the occurrence of those anomalies and when it is verified, it is not possible to distinguish between a false positive and a false negative. The occurrence of false positives did not affect also the results related with the real detections.

PERFORMANCE ANALYSIS OF EFFECT RATE OF CROSS LAYER BASED INTRUSION DETECTION FOR WIRELESS LAN

Wireless ad-hoc networks are vulnerable to various kinds of security threats and attacks due to relative ease of access to wireless medium and lack of a centralized infrastructure. Security is an alarming concern, as everything being transmitted is available in the air. The current paper deals with Study of effect of rate on performance of cross layer based intrusion detection for WLAN reflects the significance of cross layer technique in detecting intruder on WLAN. Exploiting the information available across different layers of the protocol stack by triggering multiple levels of detection enhances the accuracy of detection. We validate our design through simulations and also demonstrate lower occurrence of false positives.

A Dual-Mode Single-Molecule Fluorescence Assay for the Detection of Expanded CGG Repeats in Fragile X Syndrome

Fragile X syndrome is the leading cause of inherited mental impairment and is associated with expansions of CGG repeats within the FMR1 gene. To detect expanded CGG repeats, we developed a dual-mode single-molecule fluorescence assay that allows acquisition of two parallel, independent measures of repeat number based on (1) the number of Cy3-labeled probes bound to the repeat region and (2) the physical length of the electric field-linearized repeat region, obtained from the relative position of a single Cy5 dye near the end of the repeat region. Using target strands derived from cell-line DNA with defined numbers of CGG repeats, we show that this assay can rapidly and simultaneously measure the repeats of a collection of individual sample strands within a single field of view. With a low occurrence of false positives, the assay differentiated normal CGG repeat lengths (CGG( N ), N = 23) and expanded CGG repeat lengths (CGG( N ), N = 118), representing a premutation disease state. Further, mixtures of these DNAs gave results that correlated with their relative populations. This strategy may be useful for identifying heterozygosity or for screening collections of individuals, and it is readily adaptable for screening other repeat disorders.

How long must a firm be great to rule out chance? Benchmarking sustained superior performance without being fooled by randomness

AbstractAlthough sustained superior firm performance may arise from skillful management or other valuable, rare, and inimitable resources, it can also result from randomness. Studying U.S. companies from 1965–2008, we benchmark how long a firm must perform at a high level to be confident that it is something other than the outcome of a time‐homogeneous stationary Markov chain defined on the state space of percentiles. We find (a) the number of sustained superior performers in Compustat, measured by ROA and Tobin's q, exceeds the number of false positives we would expect to be generated by such a process; yet (b) the occurrence of false positives is often enough to fool many observers, so (c) the identification of sustained superior performers requires particularly stringent benchmarks to enable valid study. Copyright © 2011 John Wiley & Sons, Ltd.