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.

Revisiting the false positive rate in detecting recent positive selection

There is increasing interest in studying the molecular mechanisms of recent adaptations caused by positive selection in the genomics era. Such endeavors to detect recent positive selection, however, have been severely handicapped by false positives due to the confounding impact of demography and the population structure. To reduce false positives, it is critical to conduct a functional analysis to identify the true candidate genes/mutations from those that are filtered through neutrality tests. However, the extremely high cost of such functional analysis may restrict studies within a small number of model species. In particular, when the false positive rate of neutrality tests is high, the efficiency of the functional analysis will also be very low. Therefore, although the recent improvements have been made in the （joint） inference of demography and selection, our ultimate goal, which is to understand the mechanism of adaptation generally in a wide variety of natural populations, may not be achieved using the currently available approaches. More attention should thus be spent on the development of more reliable tests that could not only free themselves from the confounding impact of demography and the population structure but also have reasonable power to detect selection.

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.

Current dilemma in photocatalytic CO_(2) reduction:real solar fuel production or false positive outcomings?

Solar driven carbon dioxide(CO_(2))recycling into hydrocarbon fuels using semiconductor photocatalysts offers an ideal energy conversion pathway to solve both the energy crisis and environmental degradation problems.However,the ubiquitous presence of carbonaceous contaminants in photocatalytic CO_(2) reduction system and the inferior yields of hydrocarbon fuels raise serious concerns about the reliability of the reported experimental results.Here in this perspective,we focus on the accurate assessment of the CO_(2) reduction products,systemically discuss the possible sources of errors in the product quantification,elaborate the common mistakes spread in the analysis of reaction products obtained in 13CO_(2) labelling experiments,and further propose reliable protocols for reporting the results of these isotopic tracing experiments.Moreover,the challenges and cautions in the precise measurement of O_(2) evolution rate are also depicted,and the amplification of the concentration of O_(2) in photoreactors well above the limit of detection is still demonstrated to be the most effective solution to this troublesome issue.We hope the viewpoints raised in this paper will help to assessment the reliability of the reported data in future,and also benefit the beginners that intend to dive in the photocatalytic CO_(2) reduction area.

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.

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 Enhanced Automated Signature Generation Algorithm for Polymorphic Malware Detection

Polymorphic malware is a secure menace for application of computer network systems because hacker can evade detection and launch stealthy attacks. In this paper, a novel enhanced automated signature generation （EASG） algorithm to detect polymorphic malware is proposed. The EASG algorithm is composed of enhanced-expectation maximum algorithm and enhanced K-means clustering algorithm. In EASG algorithm, the fixed threshold value is replaced by the decision threshold of interval area. The false positive ratio can be controlled at low level, and the iterative operations and the execution time are effectively reduced. Moreover, the centroid updating is realized by application of similarity metric of Mahalanobis distance and incremental learning. Different malware group families are partitioned by the centroid updating.

Methods for Increasing Creditability of Anomaly Detection System

Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.

Renewed Focus on the Threat of Cross-Contamination in Molecular Laboratories: Notes from the COVID-19 Testing Experience in Bangladesh

Rapid and accurate laboratory diagnosis of SARS-CoV-2 infection is crucial for the management of COVID-19 patients and control of the spread of the virus. At the start of the COVID-19 pandemic, Bangladesh had only one government molecular laboratory where real-time RT-PCR would be performed to diagnose SARS-CoV-2 infection. With the increasing number of suspected cases requiring confirmation diagnostic testing, there is a requirement to expand capacity for large-scale testing quickly. The government of Bangladesh established over 100 molecular laboratories within one year to test COVID-19. To expand the testing capacity, the government was compelled to recruit laboratory staff with limited experience and technical expertise, especially in molecular assays, to process specimens, interpret results, troubleshoot. As a result, the risk of diagnostic errors, such as cross-contamination, increased, potentially undermining the efficacy of public health policies, public health response, surveillance programs, and restrictive measures aimed toward containing the outbreak. In this piece, we discuss the different sources of cross-contamination in the COVID-19 RT-PCR laboratories and proffer practical preventive measures to avoid them.

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.

Pseudomonas aeruginosa infections and improper storage conditions influence the performance of 1,3‐β‐D‐glucan in diagnosis of invasive fungal infections

Background:The association between 1,3-β-D-glucan(BDG)levels and in-fections caused by Pseudomonas aeruginosa or Streptococcus pneumoniae,and the stability of BDG under different storage conditions are unclear.Methods:Strains of Pseudomonas aeruginosa and S.pneumoniae were grown in medium and human serum.The BDG concentrations in culture superna-tants were measured.The specificity and stability of BDG were also evaluated.Results:P.aeruginosa produced high levels of BDG in Luria-Bertani medium(>4×10^(4)pg/mL)and human serum(527.0 pg/mL),whereas S.pneumoniae produced low levels of BDG in THY medium(175.6 pg/mL)and human serum(78.3 pg/mL).The BDG produced by these two bacteria was specifically degraded by 1,3-β-D-glucanase.BDG was degraded when stored at different temperatures,decreasing by 22.5%and 9.3%at−20℃and−70℃,respectively,for 63 days;by 30.7%at 4℃for 12 days;and by 12.6%and 22.0%at 37℃for 6 and 12 h.Conclusion:BDG false-positivity must be considered in patients with bacteremia caused by P.aeruginosa when diagnosing invasive fungal infection.Human serum samples for the BDG test in medical facilities should be tested as soon as possible or stored at low temperatures before testing.

Computer-Aided Detection for CT Colonography

CT colonography(CTC) is a non-invasive screening technique for the detection of colorectal polyps,as an alternative to optical colonoscopy in clinical practice. Computer-aided detection(CAD) for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists' detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.