False Positives Caused by Single Primer in DDRT Analysis of Soybean

[Objective] The aim was to explore the reasons of false positives in Different Display Reverse Transcription（DDRT）analysis.[Method] Soybean varieties ＂Jilin 30＂ and ＂Tongnong 13＂ were used as materials to carry out analysis on false positives in DDRT analysis.[Result] An important origin of false positives appeared in DDRT analysis was the non-specific amplification caused by the combination of single primer and cDNA.The parallel PCR test of single primer should be set so as to verify whether the obtained fragments were the false positives or the PCR productions combined with single primer.[Conclusion] This study had provided basis for improving the success rate of DDRT experiment.

False Positive HIV “Combo” Screening Test in a Hemodialysis Patient in Saudi Arabia: A Case Report and Review of the Literature

Introduction: HIV screening tests are routinely conducted on dialysis patients as the constant exposure of their blood during the dialysis process makes them a reasonable risk for blood-borne infections. However, in low prevalence settings, where HIV rates are <0.1% of the population, false positive results are more likely. This results in apprehension in the dialysis unit as breaches in infectious disease protocols could be presumed. This is illustrated in the case report below. Case Summary: A 62-year-old male Saudi end-stage kidney disease patient secondary to DM nephropathy began dialysis a year before presentation in a hemodialysis center in Saudi Arabia. Routine screening tests done at the start of dialysis revealed negative Hepatitis C, HIV 1 and 2 screening but a positive Hepatitis B surface antigen screen. The patient went for holiday dialysis at another facility and had a routine fourth-generation HIV test done which was positive. A confirmatory HIV PCR test was negative. Conclusion: This case highlights the need for caution in interpreting highly sensitive and specific HIV screening tests in a low-prevalence setting. Routine screening beyond the national recommendation may not be necessary in low-prevalence areas.

Mitochondrial DNA as a Risk Factor for False Positives in Case-Control Association Studies

During the last decade, hundreds of studies have been pub- lished examining whether significant associations exist be- tween mitochondrial DNA （mtDNA） variants and/or haplogroups （clades） and particular diseases （generally com- mon/complex diseases） （Fig. 1）. However, several authors have gathered evidence indicating a high incidence of false positive findings in mtDNA case-control association studies. Raule et al. （2007） and Herrnstadt and Howell （2004） showed various problems affecting mtDNA case-control association studies. Salas et al.

False positive detection of serum cryptococcal antigens due to insufficient sample dilution:A case series

At present,with the development of technology,the detection of cryptococcal antigen(CRAG)plays an increasingly important role in the diagnosis of cryptococcosis.However,the three major CRAG detection technologies,latex agglutination test(LA),lateral flow assay(LFA)and Enzyme-linked Immunosorbent Assay,have certain limitations.Although these techniques do not often lead to false-positive results,once this result occurs in a particular group of patients(such as human immunodeficiency virus patients),it might lead to severe consequences.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Diagnostic challenges from conflicting results of tests and imaging

Accurate diagnosis is the foundation of clinical care but accurate diagnosis is not easily reached in some cases.In rare instances,even a sophisticated multidisciplinary team at an academic medical center cannot reliably reach an accurate diagnosis after extensive testing and imaging,and has to wait until histological diagnosis or even autopsy results are available.The underlying reason of challenging diagnoses is mostly conflicting data from history,tests,and imaging that point to different diagnoses.In this issue of World Journal of Clinical Cases,Huffaker et al reported such a challenging case of a tricuspid mass in a patient with Li-Fraumeni syndrome.The case by Huffaker et al powerfully illustrates the occasional diagnostic challenges inherent in our current diagnostic approach and the current technology.Clinicians should realize that in rare situations,agnosticism in diagnosis is unavoidable but a treatment has to be initiated so long as the principle of primum non nocere is upheld.

False Human Immunodeficiency Virus Test Results Associated with Rheumatoid Factors in Rheumatoid Arthritis

Objective To investigate if immunological factors associated with rheumatoid arthritis(RA) affect the result of human immunodeficiency virus(HIV) screening by electrochemiluminescence immunoassay(ECLIA) and enzyme-linked immunosorbent assay(ELISA). Methods 100 RA cases were enrolled from January 2012 to February 2013 into this study. HIV screening was conducted with ECLIA detecting both HIV-1 p24 antigen, HIV-1 and HIV-2 antibodies, with ELISA and colloidal gold method detecting HIV-1 and HIV-2 antibodies. The samples producing positive results were submitted to the Center for Disease Control for confirmation using Western blotting method. The antibody titers of rheumatoid factors(RF) including RF-IgG, RF-IgM, RF-IgA, and CCP-IgG were analyzed by ELISA. Results The HIV positive-rate determined by ECLIA was significantly higher than that by ELISA and colloidal gold method(P<0.01). The false-positive rate of HIV screening was associated with antibody titers of RF-IgG, RF-IgM, RF-IgA, and CCP-IgG in RA(P<0.01). Conclusion Immunological factors, including RF and anti-CCP antibody, may influence the screening of HIV by ECLIA, producing false-positive result.

A new modification of false position method based on homotopy analysis method

A new modification of false position method for solving nonlinear equations is presented by applying homotopy analysis method （HAM）. Some numerical illustrations are given to show the efficiency of algorithm.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

Benchmarking Approach to Compare Web Applications Static Analysis Tools Detecting OWASP Top Ten Security Vulnerabilities

To detect security vulnerabilities in a web application,the security analyst must choose the best performance Security Analysis Static Tool(SAST)in terms of discovering the greatest number of security vulnerabilities as possible.To compare static analysis tools for web applications,an adapted benchmark to the vulnerability categories included in the known standard Open Web Application Security Project(OWASP)Top Ten project is required.The information of the security effectiveness of a commercial static analysis tool is not usually a publicly accessible research and the state of the art on static security tool analyzers shows that the different design and implementation of those tools has different effectiveness rates in terms of security performance.Given the significant cost of commercial tools,this paper studies the performance of seven static tools using a new methodology proposal and a new benchmark designed for vulnerability categories included in the known standard OWASP Top Ten project.Thus,the practitioners will have more precise information to select the best tool using a benchmark adapted to the last versions of OWASP Top Ten project.The results of this work have been obtaining using widely acceptable metrics to classify them according to three different degree of web application criticality.

Effects of 105 traditional Chinese medicines on the detection ofβ-agonists in medicine extracts and swine urine based on colloidal gold immunochromatographic assay

Colloidal gold immunochromatographic assay(CGIA)is commonly used for the on-site detection ofβ-agonists that are sometimes used illegally as feed additives in swine diets.However,few studies have evaluated the causes of false-positive results that sometimes occur when applying CGIA in agricultural settings.In this study,we investigated if this false-positive phenomenon is related to the addition of certain traditional Chinese medicines(TCMs)to swine feed.We established and verified an extraction method for TCMs,and then applied CGIA to detectβ-agonists in the extracts of 105 TCMs and in the urine of swine dosed with TCMs,respectively.Liquid chromatography-tandem mass spectrometry was used to validate the results of the urine samples tested positive forβ-agonists using CGIA.The results were also verified using TCMs and colloidal gold test strips produced by different manufacturers.The extracts of Citri Reticulatae Pericarpium Viride,Citri Reticulatae Pericarpium,Magnoliae Officinalis Cortex,Chaenomelis Fructus,and Rhodiolae Crenulatae Radix Et Rhizoma were tested positive forβ-agonists.Meanwhile,the addition of Citri Reticulatae Pericarpium Viride and Citri Reticulatae Pericarpium to swine feed resulted in false-positive results forβ-agonists in swine urine.The results provide a new way to explain false-positive CGIA results and provide valuable information for livestock feeding programs.

Abnormal Event Correlation and Detection Based on Network Big Data Analysis

With the continuous development of network technology,various large-scale cyber-attacks continue to emerge.These attacks pose a severe threat to the security of systems,networks,and data.Therefore,how to mine attack patterns from massive data and detect attacks are urgent problems.In this paper,an approach for attack mining and detection is proposed that performs tasks of alarm correlation,false-positive elimination,attack mining,and attack prediction.Based on the idea of CluStream,the proposed approach implements a flow clustering method and a two-step algorithm that guarantees efficient streaming and clustering.The context of an alarm in the attack chain is analyzed and the LightGBM method is used to perform falsepositive recognition with high accuracy.To accelerate the search for the filtered alarm sequence data to mine attack patterns,the PrefixSpan algorithm is also updated in the store strategy.The updated PrefixSpan increases the processing efficiency and achieves a better result than the original one in experiments.With Bayesian theory,the transition probability for the sequence pattern string is calculated and the alarm transition probability table constructed to draw the attack graph.Finally,a long-short-term memory network and embedding word-vector method are used to perform online prediction.Results of numerical experiments show that the method proposed in this paper has a strong practical value for attack detection and prediction.

Significance of inferior wall ischemia in non-dominant right coronary artery anatomy

AIM To investigate the relationship of inferior wall ischemia on myocardial perfusion imaging in patients with nondominant right coronary artery anatomy.METHODS This was a retrospective observational analysis of consecutive patients who presented to the emergency department with primary complaint of chest pain.Only patients who underwent single photon emission computed tomography(SPECT)myocardial perfusion imaging(MPI)were included.Patients who showed a reversible defect on SPECT MPI and had coronary angiography during the same hospitalization was analyzed.Patients with prior history of coronary artery disease(CAD)including history of percutaneous coronary intervention and coronary artery bypass graft surgerys were excluded.True positive and false positive results were identified on the basis of hemodynamically significant CAD on coronary angiography,in the same territory as identified on SPECT MPI.Coronary artery dominance was determined on coronary angiography.Patients were divided into group 1 and group 2.Group1 included patients with non-dominant right coronary artery(RCA)(left dominant and codominant).Group2 included patients with dominant RCA anatomy.Demographics,baseline characteristics and positive predictive value(PPV)were analyzed for the two groups.RESULTS The mean age of the study cohort was 57.6 years.Sixtyone point seven percent of the patients were males.The prevalence of self-reported diabetes mellitus,hypertension and dyslipidemia was 36%,71.9%and 53.9%respectively.A comparison of baseline characteristics between the two groups showed that patients with a non-dominant RCA were more likely to be men.For inferior wall ischemia on SPECT MPI,patients in study group 2 had a significantly higher PPV,32/42(76.1%),compared to patients in group 1,in which only 3 out of the 29 patients(10.3%)had true positive results(P value<0.001 Z test).The difference remained statistically significant even when only patients with left dominant coronary system(without co-dominant)were compared to patients with right dominant system(32/40,76.1%in right dominant group,3/19,15.8%in left dominant group,P value<0.001 Z test).There was no significant difference in mean hospital stay,re-hospitalization,and in-hospital mortality between the two groups.CONCLUSION The positive predictive value of SPECT MPI for inferior wall ischemia is affected by coronary artery dominance.More studies are needed to explain this phenomenon.

Do the benefits outweigh the side effects of colorectal cancer surveillance? A systematic review

Most patients treated with curative intent for colorectal cancer(CRC) are included in a follow-up program involving periodic evaluations. The survival benefits of a follow-up program are well delineated, and previous meta-analyses have suggested an overall survival improvement of 5%-10% by intensive follow-up. However, in a recent randomized trial, there was no survival benefit when a minimal vs an intensive follow-up program was compared. Less is known about the potential side effects of follow-up. Well-known side effects of preventive programs are those of somatic complications caused by testing, negative psychological conse-quences of follow-up itself, and the downstream impact of false positive or false negative tests. Accordingly, the potential survival benefits of CRC follow-up must be weighed against these potential negatives. The present review compares the benefits and side effects of CRC follow-up, and we propose future areas for research.

L-priorities Bloom Filter: A New Member of the Bloom Filter Family

A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.

Evaluation of genetically modified rice detection methods 2011/884/EU and 2008/289/EC proposed by the European Union

Increases in the number of cases of identified genetically modified （GM） rice contamination can be traced back to the first Rapid Alert System for Food and Feed （RASFF） in 2006. In response to the lack of reliable detection methods, Decision 2011/884/EU proposed that new screening methods replace Decision 2008/289/EC, to identify all possible GM rice products originating in China. However, the synergy brands （SYBR） Green real-time PCR assay proposed by Decision 2011/884/EU has been shown to lack conformity with other TaqMan methods currently in use. To evaluate the specificity and repeatability of the methods recommended in Decision 2011/884/EU and Decision 2008/289/EC, we collected 74 rice products originating from six countries or districts. The 74 rice samples were tested using the Decision 2011/884/EU and Decision 2008/289/ EC methods. The parallel use of different instruments and reagents were used for testing in parallel, and the results were analyzed statistically. To avoid the limitations of specific laboratories, eight GM organism detection laboratories in China participated in a collaborative trial. In our tests, 24.3% （18/74） of the samples tested were positive with the SYBR Green real-time PCR assay using the Decision 2011/884/EU method, but were negative with the TaqMan real-time PCR assay using the Decision 2011/884/EU and Decision 2008/289/EC methods. Sequencing the PCR-amplified CrylA（b/c） genes in three samples （6, 30 and 43） showed that the products consisted of primer dimers rather than the targeted sequence. The combined experimental results showed that testing for the nopaline synthase gene （NOS） of Agrobacterium tumefasciens terminator and CrylA（b/c） produced false-positive results when the Decision 2011/884/EU method was used. Because of the high rate of false-positive results, the Decision 2011/884/EU SYBR Green method to detect GM rice requires improvement.

Analysis of Pesticide Residues in Vegetables by Enzyme Inhibition Colorimetric Kit

[Objectives]The paper was to understand the detection effect of commercial enzyme inhibition colorimetric kit.[Methods]Six brands of kits were used to detect pesticide residues in vegetables.The detection results were compared with those of 50 kinds of organophosphorus pesticides and 10 kinds of carbamate pesticides detected by chromatography and mass spectrometry.According to different thresholds,the test results of different kits were evaluated,and the false positive rate,false negative rate and coincidence rate of each kit were obtained.The test results of commercial enzyme inhibition colorimetric kits were compared and analyzed.[Results]The detection effect of kit D was the best among the 6 brands of kits,and the coincidence rate was the highest under the 7 thresholds.There was a certain relationship between the detection effect of commercial enzyme inhibition colorimetric kit and the determination threshold of positive samples.The false positive rate decreased with the increase of determination threshold,and the false negative rate increased with the increase of determination threshold,but the coincidence rate with chromatography and mass spectrometry can not reach 100%.When the threshold was set to 20%,the effect was the best.The coincidence rate of 3 brands of kits with the results of chromatography and mass spectrometry was the highest,and none of the 6 kits involved in the comparison had the lowest coincidence rate under this threshold.[Conclusions]It is suggested to modify the threshold values in national standard and trade standard.

High Speed and Low Power Architecture for Network Intrusion Detection System

The tremendous growth in the field of modern communication and network systems places demands on the security. As the network complexity grows, the need for the automated detection and timely alert is required to detect the abnormal activities in the network. To diagnose the system against the malicious signatures, a high speed Network Intrusion Detection System is required against the attacks. In the network security applications, Bloom Filters are the key building block. The packets from the high speed link can be easily processed by Bloom Filter using state- of-art hardware based systems. As Bloom Filter and its variant Counting Bloom Filter suffer from False Positive Rate, Multi Hash Counting Bloom Filter architecture is proposed. The proposed work, constitute parallel signature detection improves the False Positive Rate, but the throughput and hardware complexity suffer. To resolve this, a Multi-Level Ranking Scheme is introduced which deduces the 13% - 16% of the power and increases the throughput to 23% - 30%. This work is best suited for signature detection in high speed network.

An early recognition algorithm for BitTorrent traffic based on improved K-means

In response to the deficiencies of BitTorrent, the concept of density radius was proposed, and the distance from the maximum point of radius density to cluster center as a cluster radius was taken to solve the too large cluster radius resulted from the discrete points and to reduce the false positive rate of early recognition algorithms. Simulation results show that in the actual network environment, the improved algorithm, compared with K-means, will reduce the false positive rate of early identification algorithm from 6.3% to 0.9% and has a higher operational efficiency.