Acceptable False Positive Rate Research Articles

Use of Morphological Early Response Data to Predict Relapse in Teenage and Young Adult (TYA) Patients with Acute Lymphoblastic Leukaemia (ALL)

IntroductionTreatment outcomes for teenage and young adult (TYA) patients with Acute Lymphoblastic Leukaemia (ALL) lag behind those of younger patients when treated on equivalent protocols, with higher rates of toxicity and relapse reported for those aged >10 years. Outcomes of relapsed ALL are poor so more accurate identification of those destined to relapse is required. Whilst minimal residual disease (MRD) is a powerful tool to predict those at low risk and in whom treatment can be de-escalated, improved strategies are required for early identification of high risk patients. This study aimed to determine if the morphological early response assessment could be used as a predictor of relapse in TYA patients with ALL.MethodsAll patients treated on the national prospective UKALL2003 trial aged 10 - 24 years who had not relapsed by day 35 (i.e. could potentially have treatment escalated) were included in the study. Early response in patients aged >10 years was defined as percentage of blasts by morphology at day 8: rapid early responders had ≤ 25% blasts and slow early responders >25% blasts. We analysed the following patient characteristics which were previously associated with differential outcomes: age at diagnosis, presenting white cell count, rapid or slow early response (RER or SER); MRD status at day 28; immunophenotype (B/T/other) and cytogenetic risk group (high risk, good risk, poor risk, T-ALL, other). Cox regression was used to assess the association of these factors with time to relapse and logistic regression (LR) was used to assess their association with relapse by the end of treatment (EOT: 2.5 years for girls and 3.5 years for boys). Using backwards selection (cut off for inclusion: p=0.1) a multivariable LR model was established and a receiver operating characteristic curve (ROC) was used to assess how well it could predict relapse before the EOT.Results820 consecutive patients were included in the analysis of whom 597 were aged 10 - 15 years and 223 were aged 16 - 24 years. 332 patients were classified as MRD high risk and 184 as SER on the basis of morphology. RER was significantly associated with a reduced risk of relapse even after adjustment for other factors including MRD, age, WBC and risk status (Hazard Ratio (HR) 0.64 (95% CI 0.42 - 0.97, p<0.001).697 patients with an outcome available at the EOT could be included in the LR analysis. The final multivariable model included day 28 MRD, day 8 response, age, WBC and cytogenetic risk group. The predictor generated by this model gives a ROC area under the curve of 81.2% (95% CI: 76.8 - 85.6) but there were no cut points which resulted in acceptable detection (DR) and false positive rates (FPR). As the intervention for the poor prognosis group would potentially include an allogeneic transplant with associated high mortality and morbidity a low FPR would be key to clinical utility. In order to achieve a clinically useful DR of 79.5% we would have to accept a false positive rate of 30.5%. As only 11% pf patients relapsed by EOT but 36% of patients fell into our bad prognosis group this would mean that for every 100 patients, ~27 would be transplanted unnecessarily. In the subgroup analysis of those aged 16 - 24 early response was not significantly associated with either time to relapse (HR RER: 0.74 (0.36 - 1.51, p = 0.4) or relapse by the EOT (Odds ratio RER: 0.63 (0.26 - 1.49, p =0.3)ConclusionsSlow early response is significantly associated with both time to relapse and relapse by EOT in this patient cohort aged 10 - 24 years and this effect is independent of other prognostic factors including MRD. However, even when over-fit the prognostic model generated (which included SER) resulted in FPRs which were too high to be clinically useful. Future work should focus on novel alternative methods of relapse risk prediction for the adolescent patient with ALL. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Accurate mobile malware detection and classification in the cloud.

As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox’s features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.

Cardiac Arrest and Postanoxic Encephalopathy.

Neuroprognostication following cardiac arrest is a common and challenging dilemma for neurologists and intensivists, complicated by the use of therapeutic hypothermia and targeted temperature management. Great advances have been made in understanding the mechanisms of disorders of consciousness in hypoxic-ischemic brain injury, and new diagnostic and therapeutic avenues are arising. In the era of therapeutic hypothermia and targeted temperature management, traditional clinical signs, electrophysiologic findings, and chemical biomarkers have now become questionable or variable, and the proper time for diagnostic and prognostic testing has become murky. Although the value of absent pupillary and corneal reflexes remains fairly robust, the presence of myoclonic status epilepticus and a motor response of extensor posturing or worse no longer appear to have acceptable false-positive rates. Somatosensory evoked potentials (SSEPs), thought to be the most reliable ancillary test for predicting prognosis, have recently been the subject of several reports showing unexpectedly good outcome in the face of absent cortical responses. The presence or absence of reactivity on EEG appears to be a promising prognostic sign, and aggressive treatment of seizures (and even status epilepticus) may lead to better than expected outcomes. Serum biomarkers that were previously felt to be valid, such as elevated neuron-specific enolase, have been drawn into question owing to multiple reports of patient survival with good outcome despite highly elevated levels. Advanced EEG and neuroimaging techniques, particularly looking at functional connectivity and underlying neuronal pathways, are giving great insights to the underlying mechanisms of disease and potential therapeutic targets. Predicting neurologic prognosis following cardiac arrest is an evolving field, with new prognostic methods and reevaluation of older techniques holding great promise for advancing our ability to predict outcome and improve patient care.

Toward Predicting Drug-Induced Liver Injury: Parallel Computational Approaches to Identify Multidrug Resistance Protein 4 and Bile Salt Export Pump Inhibitors

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.

Optimization and Practical Use of Composition Based Approaches Towards Identification and Collection of Genomic Islands and Their Ontology in Prokaryotes

Abstract Motivation Horizontally transferred genomic islands (islands, GIs) have been referred to as important factors which contribute towards bacterial evolution in general and particularly towards the emergences of pathogens and outbreak instances. The development of tools for identification of such elements and retracing their distribution will help to understand how such cases arise. Sequence composition has been used to identify GIs, infer their phylogeny; and determine their relative time of insertion. Collection of metadata on known GIs will enhance insight into horizontal gene transfer ontology and flow. Results This paper introduces the merger of SeqWord Genomic Islands Sniffer (SWGIS), which utilizes composition based approaches for identification of GIs in bacterial genomic sequences, and the Predicted Genomic Islands (Pre_GI) database, which houses 26,744 islands found in 2,407 bacterial plasmids and chromosomes. SWGIS is a standalone program that detects GIs using a set of optimized parametric measures with estimates of acceptable false positive and false negative rates. Pre_GI is a novel repository that includes island ontology and flux. This study furthermore illustrates the need for parametric optimization towards the prediction of GIs to minimize false negative and false positive predictions. In addition Pre_GI emphasizes the practicality of the compounded knowledge that the database affords in detection and visualization of ontological links between GIs. Availability SWGIS is freely available on the web at http://www.bi.up.ac.za/SeqWord/sniffer/index.html , and Pre_GI is freely accessible at http://pregi.bi.up.ac.za/index.php.

Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.

BackgroundPharmacovigilance signal detection largely relies on individual case reports, but longitudinal health data are being explored as complementary information sources. Research to date has focused on the ability of epidemiological methods to distinguish established adverse drug reactions (ADRs) from unrelated adverse events.ObjectiveThe aim of this study was to evaluate a process for structured clinical and epidemiological assessment of temporally associated drugs and medical events in electronic medical records.MethodsPairs of drugs and medical events were selected for review on the basis of their temporal association according to a calibrated self-controlled cohort analysis in The Health Improvement Network. Six assessors trained in pharmacovigilance and/or epidemiology evaluated seven drugs each, with up to 20 medical events per drug. A pre-specified questionnaire considered aspects related to the nature of the temporal pattern, demographic features of the cohort, concomitant medicines, earlier signs and symptoms, and possible confounding by underlying disease. This informed a classification of drug–event pairs as known ADRs, meriting further evaluation, or dismissed.ResultsThe number of temporally associated medical events per drug ranged from 11 to 307 (median 50) for the 42 selected drugs. Out of the 509 relevant drug–event combinations subjected to the assessment, 127 (25 %) were classified as known ADRs. Ninety-one (24 %) of the remaining pairs were classified as potential signals meriting further evaluation and 291 (76 %) were dismissed. Suggestive temporal patterns and lack of clear alternative explanations were the most common reasons that drug–event pairs were classified as meriting further evaluation. Earlier signs and symptoms and confounding by the underlying disease were the most common reasons that drug–event pairs were dismissed.ConclusionsExploratory analysis of electronic medical records can detect important potential safety signals. However, effective signal detection requires that statistical signal detection be combined with clinical and epidemiological review to achieve an acceptable false positive rate.

Commentary on 'Performance of ultrasound as a second line test to serum CA125 in ovarian cancer screening'.

Commentary on 'Performance of ultrasound as a second line test to serum CA125 in ovarian cancer screening'.

Using Two-Level Factor Analysis to Test for Cluster Bias in Ordinal Data

The test for cluster bias is a test of measurement invariance across clusters in 2-level data. This article examines the true positive rates (empirical power) and false positive rates of the test for cluster bias using the likelihood ratio test (LRT) and the Wald test with ordinal data. A simulation study indicates that the scaled version of the LRT that accounts for nonnormality of the data gives untrustworthy results, whereas the unscaled LRT and the Wald test have acceptable false positive rates and perform well in terms of empirical power rate if the amount of cluster bias is large. The test for cluster bias is illustrated with data from research on teacher-student relations.

Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction

Patient monitors in modern hospitals have become ubiquitous but they generate an excessive number of false alarms causing alarm fatigue. Our previous work showed that combinations of frequently co-occurring monitor alarms, called SuperAlarm patterns, were capable of predicting in-hospital code blue events at a lower alarm frequency. In the present study, we extend the conceptual domain of a SuperAlarm to incorporate laboratory test results along with monitor alarms so as to build an integrated data set to mine SuperAlarm patterns. We propose two approaches to integrate monitor alarms with laboratory test results and use a maximal frequent itemsets mining algorithm to find SuperAlarm patterns. Under an acceptable false positive rate FPRmax, optimal parameters including the minimum support threshold and the length of time window for the algorithm to find the combinations of monitor alarms and laboratory test results are determined based on a 10-fold cross-validation set. SuperAlarm candidates are generated under these optimal parameters. The final SuperAlarm patterns are obtained by further removing the candidates with false positive rate>FPRmax. The performance of SuperAlarm patterns are assessed using an independent test data set. First, we calculate the sensitivity with respect to prediction window and the sensitivity with respect to lead time. Second, we calculate the false SuperAlarm ratio (ratio of the hourly number of SuperAlarm triggers for control patients to that of the monitor alarms, or that of regular monitor alarms plus laboratory test results if the SuperAlarm patterns contain laboratory test results) and the work-up to detection ratio, WDR (ratio of the number of patients triggering any SuperAlarm patterns to that of code blue patients triggering any SuperAlarm patterns). The experiment results demonstrate that when varying FPRmax between 0.02 and 0.15, the SuperAlarm patterns composed of monitor alarms along with the last two laboratory test results are triggered at least once for [56.7–93.3%] of code blue patients within an 1-h prediction window before code blue events and for [43.3–90.0%] of code blue patients at least 1-h ahead of code blue events. However, the hourly number of these SuperAlarm patterns occurring in control patients is only [2.0–14.8%] of that of regular monitor alarms with WDR varying between 2.1 and 6.5 in a 12-h window. For a given FPRmax threshold, the SuperAlarm set generated from the integrated data set has higher sensitivity and lower WDR than the SuperAlarm set generated from the regular monitor alarm data set. In addition, the McNemar’s test also shows that the performance of the SuperAlarm set from the integrated data set is significantly different from that of the SuperAlarm set from the regular monitor alarm data set. We therefore conclude that the SuperAlarm patterns generated from the integrated data set are better at predicting code blue events.

Detection of extracolonic pathologic findings with CT colonography: a discrete choice experiment of perceived benefits versus harms.

To determine the maximum rate of false-positive diagnoses that patients and health care professionals were willing to accept in exchange for detection of extracolonic malignancy by using computed tomographic (CT) colonography for colorectal cancer screening. After obtaining ethical approval and informed consent, 52 patients and 50 health care professionals undertook two discrete choice experiments where they chose between unrestricted CT colonography that examined intra- and extracolonic organs or CT colonography restricted to the colon, across different scenarios. The first experiment detected one extracolonic malignancy per 600 cases with a false-positive rate varying across scenarios from 0% to 99.8%. One experiment examined radiologic follow-up generated by false-positive diagnoses while the other examined invasive follow-up. Intracolonic performance was identical for both tests. The median tipping point (maximum acceptable false-positive rate for extracolonic findings) was calculated overall and for both groups by bootstrap analysis. The median tipping point for radiologic follow-up occurred at a false-positive rate greater than 99.8% (interquartile ratio [IQR], 10 to >99.8%). Participants would tolerate at least a 99.8% rate of unnecessary radiologic tests to detect an additional extracolonic malignancy. The median tipping-point for invasive follow-up occurred at a false-positive rate of 10% (IQR, 2 to >99.8%). Tipping points were significantly higher for patients than for health care professionals for both experiments (>99.8 vs 40% for radiologic follow-up and >99.8 vs 5% for invasive follow-up, both P < .001). Patients and health care professionals are willing to tolerate high rates of false-positive diagnoses with CT colonography in exchange for diagnosis of extracolonic malignancy. The actual specificity of screening CT colonography for extracolonic findings in clinical practice is likely to be highly acceptable to both patients and health care professionals. Online supplemental material is available for this article.

A simplified approach to minimally invasive parathyroidectomy

To assess the feasibility of a simplified approach for the use of a rapid intraoperative parathyroid hormone (IOPTH) assay based on a single 10-minute post-excision level using the workup parathyroid hormone level (wPTH) as the baseline in minimally invasive parathyroidectomy (MIP) and to compare the predictive value of this criterion with other recommended criteria. Case series with chart review. A single surgeon's prospectively maintained parathyroidectomy database at an academic center was reviewed over a 2-year period from June 2009 through June 2011. A total of 102 patients undergoing MIP met the inclusion criteria. An IOPTH threshold of a ≥ 50% drop at 10 minutes post-excision from the wPTH baseline resulted in acceptable false positive (1.9%) and false negative (0.9%) rates. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of this modified criterion was 98.9%, 71.4%, 98%, 83.3%, and 97%, respectively. In our patient cohort, the pre-incision and pre-excision IOPTH levels did not seem to change the overall accuracy of predicting surgical success in MIP if a single 10-minute post-excision IOPTH level is used along with the wPTH, and is commensurate with the commonly used Miami and Vienna criteria. A single intraoperative blood sample demonstrating a ≥50% drop from the wPTH at 10 minutes post-excision should be explored further as a feasible simplified criterion that avoids multiple IOPTH samples.

Technical variability of 2D gel electrophoresis – Application to soybean allergens

Two-dimensional gel electrophoresis (2-DE) technique is used as a performing technique to assess the variability of protein expression in crops, and especially soybean endogenous food allergens, which are a subset of proteins of interest for assessing whether genetically modified (GM) soybean has a different allergenic profile compared to its non-GM counterpart.On top of the biological variability of the 2-DE, which has already been studied by several laboratories, technical variability has to be evaluated. In this study, several sources of variability (number of gel replicates, protein extracts, study timings and operators) were assessed qualitatively and quantitatively on all detectable polypeptide spots as well as on food allergen spots. Results showed that the major source of variability was the number of gel replicates. Other sources were minor.This has a direct practical impact on the laboratory work as this supports the utilization of three or four gel replicates to get robust results. Furthermore, this implies that the study can be run over several days, and be performed by several trained operators, without impacting its reproducibility.Furthermore, 2-DE could detect a 2-fold change between two samples with an acceptable rate of false positives (below 7%). This level of sensitivity is acceptable in the context of safety assessment of GM soybean as the biological variability of proteins in soybean is higher than the technical variability shown in this study.Overall, the 2-DE technique is suitable for investigating endogenous food allergen variability between several soybean seeds, including GM and non-GM counterpart.

Point of care (POC) troponin testing in the peripheral hospital

While large city hospitals have 24 hour laboratories attached that can provide reasonable turnaround times for troponin testing to the hospital Emergency Department and for other critical areas of the hospital, the situation is much more difficult in the peripheral or country hospitals. Frequently the laboratories are only open during daytime hours and might not be open during the weekend. POC assays for troponin T and I have been developed to help clinicians in this setting, however, there are a number of issues with the successful implementation. Drawbacks are the high reagent cost, occasionally inaccurate results and lack of interfacing. Participation in external quality assurance programs is crucial. Two POC troponin assays were inferior to routine laboratory assays in a Swedish study in predicting shorter term and longer term death and could lead to missing these patients or to delay diagnosis. Similarly we confirmed that the sensitivity of the POC assays was inferior to the routine laboratory assay. After reducing the cut-off level from the recommended 99 percentile of normals to half, we increased the sensitivity of these assays significantly with an acceptable false positive rate. Clinical review of the patients who had elevated troponin I result showed that the majority of the patients had either myocardial infarction, or exacerbation of congestive heart failure. Sepsis, hypotension and collapse with fall were other common clinical scenarios. We believe that while we currently cannot compare the sensitivity of the point of care assay to the routine laboratory assays, we can improve the relative sensitivity and achieve earlier and better diagnosis of patients with cardiac damage.

First trimester combined test for Down syndrome screening in unselected pregnancies — A report of a 13-year experience

ObjectiveTo analyze the performance of the first trimester Down syndrome screening in a single medical center in Northern Taiwan. Materials and methodsFrom April 1999 to June 2012, a total of 25,104 pregnant women at gestational age of 10 weeks to 13 weeks 6 days received first trimester “combined test” for Down syndrome screening. The test combines the ultrasound scan of nuchal translucency thickness and maternal biochemical serum levels of pregnancy-associated plasma protein A (PAPP-A) and free beta-human chorionic gonadotropin (β-hCG). A positive screen was defined as an estimated Down syndrome risk ≥1/270, and either chorionic villous sampling or amniocentesis was performed for fetal chromosomal analyses. ResultsSeventy-eight of the 25,104 pregnancies were proven to have fetal chromosome anomalies. The detection rates for trisomy 21, trisomy 18, Turner syndrome, and other chromosome anomalies were 87.5% (21/24), 69.2% (9/13), 81.8% (9/11), and 60% (18/30), respectively, with a false positive rate (FPR) of 5.4% (1353/25,026). Further evaluation of the detection rates for trisomy 21, by gestational age at 11, 12, and 13 weeks, were 92.3%, 87.5%, and 66.7%, respectively. ConclusionThe first trimester combined test is an effective screening tool for Down syndrome detection with an acceptable low false positive rate. The best timing of screening will be between 11 and 12 weeks' gestation.

Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data

Gut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple. Using L1 and L2 regularizations, we developed a new variable selection method that identifies important features at multiple taxonomy levels. The regularization parameters are chosen by a new, data-adaptive, repeated cross-validation approach, which performed well. In simulation studies, our method outperformed competing methods: it more often selected significant variables, and had small false discovery rates and acceptable false-positive rates. Applying our method to gut microbiota data, we found which taxonomic levels were most altered by specific interventions or physiological status. The new approach is implemented in an R package, which is freely available from the corresponding author. tpgarcia@srph.tamhsc.edu Supplementary data are available at Bioinformatics online.

From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality.

Detection of disruptions in the high-β spherical torus NSTX

This paper describes the prediction of disruptions based on diagnostic data in the high-β spherical torus NSTX (Ono et al 2000 Nucl. Fusion 40 557). The disruptive threshold values on many signals are examined. In some cases, raw diagnostic data can be used as a signal for disruption prediction. In others, the deviations of the plasma data from simple models provides the information used to determine the proximity to disruption. However, no single signal or calculation and associated threshold value can form the basis for disruption prediction in NSTX; thresholds that produce an acceptable false-positive rate have too large a missed or late-warning rate, while combinations that produce an acceptable rate of missed or late warnings have an unacceptable false-positive rate. To solve this problem, a novel means of combining multiple threshold tests has been developed. After being properly tuned, this algorithm can produce a false-positive rate of 2.8%, with a late + missed warning rate of 3.7% and thus a total failure rate of 6.5%, when applied to a database of ∼2000 disruptions during the IP flat top collected from three run campaigns. Furthermore, many of these false positives are triggered by near-disruptive magnetohydrodynamic (MHD) events that might indeed be disruptive in larger plasmas with more stored energy. However, the algorithm is less efficient at detecting the MHD event that prompts the disruption process.

C2Detector: a covert channel detection framework in cloud computing

ABSTRACTCloud computing is becoming increasingly popular because of the dynamic deployment of computing service. Another advantage of cloud is that data confidentiality is protected by the cloud provider with the virtualization technology. However, a covert channel can break the isolation of the virtualization platform and leak confidential information without letting it known by virtual machines. In this paper, the threat model of covert channels is analyzed. The channels are classified into three categories, and only the category that is new to cloud computing is concerned, for example, CPU load‐based, cache‐based, and shared memory‐based covert channels. The covert channel scenario is modeled into an error‐corrected four‐state automaton, and two error‐corrected algorithms are designed. A new detection framework termed C2Detector is presented. C2Detector includes a captor located in the hypervisor and a two‐phase synthesis algorithm implemented as Markov and Bayesian detectors. A prototype of C2Detector is implemented on Xen hypervisor, and its performance of detecting the covert channels is demonstrated. The experiment results show that C2Detector can detect the three types of the covert channels with an acceptable false positive rate by using a pessimistic threshold. Moreover, C2Detector is a plug‐in framework and can be easily extended. It is believed that new covert channels can be detected by C2Detector in the future. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Effectiveness of the model for prenatal control of severe thalassemia

The aim of the research was to determine effectiveness of the model for prenatal control in reducing new cases of severe thalassemia. Pregnant women at six tertiary centers were recruited to follow the model, consisting of (1) carrier screening using mean corpuscular volume (for alpha-thal-1 and beta-thal) and CMU-E screen (for HbE trait), (2) carrier diagnosis, (3) the couples at risk were counseled and offered prenatal diagnosis, and (4) termination of affected pregnancy. All neonates were evaluated for thalassemia. Of the 12,874 recruited pregnancies, 7008 were valid for analysis. Of them, 281 couples were identified to be at risk, Of the 281, 58 affected fetuses were identified and 55 pregnancies were terminated, whereas three did not accept pregnancy termination. All 6727 neonates at no risk were proven to be unaffected. The model had sensitivity and positive predictive value of 100% and 20%, respectively. The model could detect all of affected fetuses. The model could prenatally identify affected fetuses with a detection rate and negative predictive value of 100%. The model was highly effective to prenatally detect affected fetuses with an acceptable false positive rate.

Estimating Empirical Codon Hidden Markov Models

Empirical codon models (ECMs) estimated from a large number of globular protein families outperformed mechanistic codon models in their description of the general process of protein evolution. Among other factors, ECMs implicitly model the influence of amino acid properties and multiple nucleotide substitutions (MNS). However, the estimation of ECMs requires large quantities of data, and until recently, only few suitable data sets were available. Here, we take advantage of several new Drosophila species genomes to estimate codon models from genome-wide data. The availability of large numbers of genomes over varying phylogenetic depths in the Drosophila genus allows us to explore various divergence levels. In consequence, we can use these data to determine the appropriate level of divergence for the estimation of ECMs, avoiding overestimation of MNS rates caused by saturation. To account for variation in evolutionary rates along the genome, we develop new empirical codon hidden Markov models (ecHMMs). These models significantly outperform previous ones with respect to maximum likelihood values, suggesting that they provide a better fit to the evolutionary process. Using ECMs and ecHMMs derived from genome-wide data sets, we devise new likelihood ratio tests (LRTs) of positive selection. We found classical LRTs very sensitive to the presence of MNSs, showing high false-positive rates, especially with small phylogenies. The new LRTs are more conservative than the classical ones, having acceptable false-positive rates and reduced power.