False Negative Detection Rates Research Articles

Network Attack Classification with a Shallow Neural Network for Internet and Internet of Things (IoT) Traffic

In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems.

Improved Elman Deep Learning Model for Intrusion Detection System in Internet of Things

Many researchers have developed intrusion detection systems in the past using conventional techniques such as artificial neural networks, fuzzy clustering, evolutionary algorithms, association rule mining, and support vector machines. However, in terms of false negative rates and detection rates, these methods did not yield the best outcomes. To address these problems, we proposed a hybrid deep learning model (HDLM) based on intrusion detection and prevention in IoT devices. Initially, the data are collected from KDDCup-99 and NSL-KDD datasets. Then, the important features are extracted from the dataset using the Forward Feature Selection Algorithm (FFSA). Finally, the extracted features are given to the HDLM classifier. The proposed HDLM is a combination of Elman Recurrent Neural Network (ERNN) and Subtraction-Average-Based Optimizer (SABO). The performance of the suggested method is assessed using performance metrics including precision, recall, accuracy, sensitivity, specificity, and F_Measure. The experimental results show that the proposed method attained the maximum intrusion detection accuracy of 98.52%.

SAT575 Can We Miss Diagnosis Of Tall Cell Variant Papillary Thyroid Carcinoma?

Abstract Disclosure: J. Huynh: None. A. Makawi: None. G. Elshimy: None. Introduction Large thyroid nodules have a high rate of evolving into malignancy. Fine needle aspiration biopsy (FNAB) is a key diagnostic tool with a caveat of a high false-negative rate for cancer detection ranging from 0.7 to 13%. Key to maximizing the odds of a successful FNAB are the utilization of ultrasound (US) guidance, visualization of the needle tip, preprocedural consideration of the size of the target nodule, and expertise of the cytopathologist. Case presentation 40-year-old African American female who presented to endocrinology clinic given concern for a large left-side thyroid nodule with tracheal deviation seen on a CT scan of the chest. The US revealed a large left hypoechoic, spongiform nodule >5cm with grade 2 vascularity with no calcification measuring 5.1 x 4.3 cm. The thyroid labs were within normal range and she denied any compressive symptoms. FNAB of the nodule showed benign colloid. Given the large size of the nodule, we recommended surgical resection but the patient declined and preferred conservative management. Five months later, she started to experience dysphagia with positional neck pain and shortness of breath. Due to the progression of the compressive signs, the patient opted for left thyroidectomy which revealed multifocal papillary thyroid carcinoma (PTC) tall cell variant (TCV) with the largest foci of 1.7 cm with extrathyroidal extension and vascular invasion. She then underwent completion thyroidectomy followed by postoperative radioactive iodine ablation later on as the second surgery pathology was also consistent with PTC with metastasis to the lymph nodes. She was treated with levothyroxine with the goal to suppress TSH. Postoperatively, the patient is being monitored with biochemical testing and imaging for recurrence given the aggressive nature of her disease. Discussion There is a positive correlation between nodule size and false negative results (FNR). FNR for malignancy is known to be higher in nodules larger than 4cm. Patients with large thyroid nodules causing compressive signs should undergo resection regardless of the FNAB result. This patient had a benign FNAB initially which was misleading as the pathology of thyroidectomy was consistent with TCV PTC, a more aggressive variant. TCV PTC usually presents in larger nodules and with a higher rate of extrathyroidal extension and metastasis compared to classic. The prognosis is poorer with TCV, even without an extranodal extension. This patient received appropriate treatment with total thyroidectomy, THR therapy, sequential imaging, and biochemical testing for recurrence. A key takeaway here is that FNABs may not always reflect accurate histology of the whole lesion with larger nodules having a higher tendency for inadequate sampling. Key to successful biopsy and reducing FNAB false-negative results is an experienced radiologist, a cytopathologist familiar with these tumors, and US guidance. Presentation: Saturday, June 17, 2023

Designing of rapid assay for the detection of RdRp/Orf1ab specific to SARS-CoV-2

SARS-CoV-2 is still threat and mostly used detection method is real time reverse transcriptase polymerase chain reaction (rRT-PCR) for the open reading frame (Orf1ab), RNA-dependent RNA polymerase (RdRp), nucleocapsid (N) and envelope (E) genes of virus. However, rRT-PCR may have false negative rate for the nucleic acid detection. Since the RdRp/Orf1ab has high sensitivity for the molecular detection, two sandwich models, Model 1A-Model 1B, based on hybridization on lateral flow assay (LFA) were designed here and applied with the synthetic and clinical samples of RdRp/Orf1ab. In this purpose colloidal gold nanoparticles (AuNPs) were used as label. Membranes having different flow rate, three oligonucleotide probe concentrations and running buffers were used. Although synthetic target sequence was recognized by all the LFAs, PCR products obtained from either the synthetic plasmid DNA or oro/nasopharyngeal swabs were detected by Model 1 A using W12 membrane. Designed strip assays detected the RdRp/Orf1ab of the clinical samples as 100% sensitivity and specifity. It means that they might be used for the detection of virus and can be modified for the recognition of mutant genes of virus. These findings also demonstrated the importance of membranes, sandwich models, probe concentrations and sample contents for developing LFAs for viral detection.

Hospital encounters and associated costs of prostate evaluation for clinically important disease MRI vs. standard evaluation procedures (PRECISE) study from a provincial-payer perspective.

Systematic transrectal ultrasonography (TRUS) biopsy has been the standard diagnostic tool for prostate cancer (PCa) but is subject to limitations, such as a high false-negative rate of cancer detection. Multiparametric magnetic resonance imaging (mpMRI) prior to biopsy is emerging as an alternative diagnostic procedure for PCa. The PRECISE study found that MRI followed by a targeted biopsy was more accurately able to identify clinically significant cancer than TRUS biopsy. PRECISE study patients recruited in Ontario between January 2017 and November 2019 were linked to various Ontario provincial administrative databases available at the Institute for Clinical and Evaluative Sciences (ICES ) to determine health resources used, associated costs, and hospitalizations in the 14 days after biopsy. Costs are presented in 2021 CAD. A total of 281 males were included in this study, with 48.4% of the patients in the TRUS biopsy group, 28.1% in the MRI+, and 23.5% in the MRI- group. Twenty-one patients (15%) from the TRUS biopsy group were seen at a hospital in the 14 days after their biopsy compared to fewer than five patients (6%) from the MRI+ group. The mean per person per year (PPPY) costs for the TRUS and all MRI groups (MRI- and MRI+) were $7828 and $8525, respectively. Patients in the TRUS biopsy group experienced more hospital encounters compared to patients who received an MRI prior to their biopsy. This economic analysis suggests that MRI imaging prior to biopsy is not associated with a significant increase in costs.

Assessment of existing cyber-attack detection models for web-based systems

In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

Quantification of the mobility potential of antibiotic resistance genes through multiplexed ddPCR linkage analysis.

There is a clear need for global monitoring initiatives to evaluate the risks of antibiotic resistance genes (ARGs) towards human health. Therefore, not only ARG abundances within a given environment, but also their potential mobility, hence their ability to spread to human pathogenic bacteria needs to be quantified. We developed a novel, sequencing-independent method for assessing the linkage of an ARG to a mobile genetic element by statistical analysis of multiplexed droplet digital PCR (ddPCR) carried out on environmental DNA sheared into defined, short fragments. This allows quantifying the physical linkage between specific ARGs and mobile genetic elements, here demonstrated for the sulfonamide ARG sul1 and the Class 1 integron integrase gene intI1. The method's efficiency is demonstrated using mixtures of model DNA fragments with either linked and unlinked target genes: Linkage of the two target genes can be accurately quantified based on high correlation coefficients between observed and expected values (R2) as well as low mean absolute errors (MAE) for both target genes, sul1 (R2 = 0.9997, MAE = 0.71%, n = 24) and intI1 (R2 = 0.9991, MAE = 1.14%, n = 24). Furthermore, we demonstrate that adjusting the fragmentation length of DNA during shearing allows controlling rates of false positives and false negative detection of linkage. The presented method allows rapidly obtaining reliable results in a labor- and cost-efficient manner.

AFP-Mask: Anchor-Free Polyp Instance Segmentation in Colonoscopy.

Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. The most effective way to prevent CRC is through using colonoscopy to identify and remove precancerous growths at an early stage. The detection and removal of colorectal polyps have been found to be associated with a reduction in mortality from colorectal cancer. However, the false negative rate of polyp detection during colonoscopy is often high even for experienced physicians. With recent advances in deep learning based object detection techniques, automated polyp detection shows great potential in helping physicians reduce false positive rate during colonoscopy. In this paper, we propose a novel anchor-free instance segmentation framework that can localize polyps and produce the corresponding instance level masks without using predefined anchor boxes. Our framework consists of two branches: (a) an object detection branch that performs classification and localization, (b) a mask generation branch that produces instance level masks. Instead of predicting a two-dimensional mask directly, we encode it into a compact representation vector, which allows us to incorporate instance segmentation with one-stage bounding-box detectors in a simple yet effective way. Moreover, our proposed encoding method can be trained jointly with object detector. Our experiment results show that our framework achieves a precision of 99.36% and a recall of 96.44% on public datasets, outperforming existing anchor-free instance segmentation methods by at least 2.8% in mIoU on our private dataset.

PROBABILISTIC-BASED CROWDSOURCING TECHNIQUE FOR ROAD SURFACE ANOMALY DETECTION

Abstract. Road surface monitoring is a critical key factor to serve the purpose of road safety and driving comfort. Recently, many efforts have been made in developing approaches to detect road surface anomalies using smartphone sensors. However, detecting road surface anomalies from smartphone sensors face considerable number of challenges due to the various factors affecting detection rate. By aggregating data from a large number of users (i.e., concept of crowdsourcing), the accuracy of detection can be increased, and the potential false positive and false negative detection rates raised from every single source (i.e., user) can be detected and filtered. In this paper, a novel probabilistic-based crowdsourcing technique is proposed to classify and combine road surface anomalies (i.e., dynamic events) detected from various smartphones on-board vehicles. The proposed approach can integrate detected events from multiple users which are not an absolute binary scenario primarily caused by different sensing capabilities of various participators’ smartphone sensors and diversity in mechanical properties of vehicles. Furthermore, this approach considers the spatiotemporal behaviour of reported road surface anomalies from different users in different times and locations. The experimental results show that the proposed crowdsourcing method improves the accuracy and rate for detecting road surface anomalies.

Discrete R-Contiguous bit Matching mechanism appropriateness for anomaly detection in Wireless Sensor Networks

Resource exhaustion is one of the main challenges for the security of Wireless Sensor Networks (WSNs). The challenge can be addressed by using algorithms that are light weighted. In this paper use of light-weighted R-Contiguous Bit matching for attack detection in WSNs has been evaluated. Use of R-Contiguous bit matching in Negative Selection Algorithm (NSA) has improved the performance of anomaly detection resulting in low false positive, false negative and high detection rates. The proposed model has been tested against some of the attacks. The high detection rate has proved the appropriateness of R-Contiguous bit matching mechanism for anomaly detection in WSNs.

Testing for Lynch Syndrome in Endometrial Carcinoma: From Universal to Age-Selective MLH1 Methylation Analysis.

Simple SummaryInternational guidelines recommend universal screening of endometrial carcinoma patients for Lynch syndrome, a hereditary cancer predisposition syndrome. Screening is based on mismatch repair protein immunohistochemistry and reflex MLH1 methylation analysis to exclude the likely sporadic cases of MMR deficiency. As sporadic MLH1 protein loss is common in endometrial carcinoma, the ability to target methylation testing would save efforts and costs. We discovered that limiting methylation testing to patients under 65 years would have significantly reduced the testing effort while maintaining a low false negative rate for MLH1-LS detection (0% and 3% in our clinic and registry-based cohorts, respectively).International guidelines recommend universal screening of endometrial carcinoma (EC) patients for Lynch syndrome (LS). This screening is generally based on mismatch repair (MMR) protein immunohistochemistry followed by MLH1 methylation analysis of MLH1-negative cases to exclude the likely sporadic cases from germline testing. As LS-associated EC is uncommon in the elderly, age-selective methylation testing could improve cost-efficiency. We performed MMR immunohistochemistry on 821 unselected ECs (clinic-based cohort) followed by a MLH1 promoter methylation test of all MLH1/PMS2-negative tumors. Non-methylated MLH1-deficient cases underwent NGS and MLPA-based germline analyses to identify MLH1 mutation carriers. A reduction in the test burden and corresponding false negative rates for LS screening were investigated for various age cut-offs. In addition, the age distribution of 132 MLH1 mutation carriers diagnosed with EC (registry-based cohort) was examined. A germline MLH1 mutation was found in 2/14 patients with non-methylated MLH1-deficient EC. When compared to a universal methylation analysis, selective testing with a cut-off age of 65 years, would have reduced the testing effort by 70.7% with a false negative rate for LS detection of 0% and 3% in the clinic and registry-based cohorts, respectively. The use of age-selective methylation analysis is a feasible way of reducing the costs and laboratory burden in LS screening for EC patients.

Missing cases of injury death: use of quantitative methods and case reviews to inform future improvements in case definition

BackgroundKnowledge of fatal injuries is required to inform prevention activities. Where hospital patients with an injury principal diagnosis (PDx) died and were certified to a medical underlying cause of death...

Accurately Locating Metastatic Foci in Lymph Nodes With Lugol's Iodine-Enhanced Micro-CT Imaging.

BackgroundAccurate evaluation of lymph node (LN) status is the key factor to determine the treatment and evaluate prognosis for patients with cancer. However, traditional pathological examination resulted in a 30% false-negative rate of detection of metastases in LNs. This study aimed to utilize Lugol’s iodine (I2-IK)-enhanced micro-CT imaging to reveal the 3-dimensional structure of regional LNs and decrease the false-negative rate in pathological examination.MethodsTo explore the feasibility of I2-IK-enhanced micro-CT imaging in locating metastatic lesion in LNs, nonmetastatic and metastatic LNs from mice were used to mimic the imaging process. Then, the LNs from oral squamous cell carcinoma (OSCC) patients were applied to verify the value of I2-IK-enhanced micro-CT imaging in revealing LN structure and locating metastatic lesions in LNs. The glycogen content in nonmetastatic and metastatic LNs was further detected by the use of a glycogen assay kit and periodic acid–Schiff (PAS) staining to explain the imaging differences between them.ResultsIn nude mice, 0.5% I2-IK staining for 4 h was the best parameter for normal LN. The metastatic foci in metastatic LNs were also clearly outlined in this condition. For nonmetastatic LNs from patients with OSCC, 1% I2-IK staining for 12 h was the best parameter. However, due to the increased volume of metastatic LNs, the image effect of 3% I2-IK staining for 12 h was superior to 1% I2-IK staining [tumor background ratio (TBR), 3% vs. 1%, 1.89 ± 0.10 vs. 1.27 ± 0.07, p < 0.001]. Compared with subsequent pathological sections, we found the CT intensity of metastatic foci in LNs and muscle tissues was significantly higher than in nonmetastatic regions. Meanwhile, the glycogen content of metastatic foci in LNs detected was also significantly higher than in nonmetastatic region.ConclusionsI2-IK-enhanced micro-CT imaging could identify the spatial location of metastatic foci in LNs. This will be an effective method to assist in decreasing the LN false-negative rate for cancer pathology.

The Mathematical Limitations of Fetal Echocardiography as a Screening Tool in the Setting of a Normal Second-Trimester Ultrasound

BackgroundThe effectiveness of screening strategies targeting pregnancies at higher risk of congenital heart disease (CHD) is reduced by the low prevalence of severe CHD, the increase in CHD detection rates by second-trimester ultrasound (U/S), and the high proportion of severe CHD in low-risk pregnancies. We aimed to determine situations in which additional screening by fetal echocardiography (FE) would result in a significant increase in sensitivity and a sizable decrease in the false-negative rate of detection of severe CHD. MethodsWe simulated the change in the numbers of detected severe CHD cases when FE is offered to women with a normal second-trimester U/S who have a higher risk of bearing a child with CHD, compared to U/S alone. The primary outcome was the increase in sensitivity. Secondary outcomes were the number needed to screen and the reduction in the rate of missed cases. ResultsFor an U/S sensitivity of 60%, the addition of FE in pregnancies at high risk of CHD (risk ratio 3.5; range: 2 to 5) increased sensitivity by 2.4 percentage points (1.1 to 7.9). The number needed to screen to detect one additional case of severe CHD was 436 (156 to 952). The rate of additional severe CHD cases detected by FE was 4 per 100,000 pregnancies (2 to 32). ConclusionsThe addition of FE to U/S for severe CHD prenatal screening in pregnancies at high risk of CHD yielded marginal benefits in terms of increased sensitivity and decreased rates of false negatives, at the expense of significant resource utilization.

Evaluation of confocal laser endomicroscopy for detection of occult gastric carcinoma in CDH1 variant carriers.

Hereditary diffuse gastric cancer syndrome, attributed to inactivating germline CDH1 variants, is associated with an elevated lifetime risk of gastric cancer. We sought to evaluate cancer detection using probe-based confocal laser endomicroscopy (pCLE) during endoscopic surveillance. A prospective, single-institution study was conducted in asymptomatic adults with pathogenic or likely pathogenic (P/LP) CDH1 variants. Subjects received endoscopic gastric surveillance using pCLE in conjunction with the Cambridge method (CM). Abnormalities visualized by pCLE were biopsied, followed by non-targeted mucosal biopsies according to the CM. Primary endpoint was to determine pCLE sensitivity for detection of occult SRC carcinoma compared to CM. Thirty-six patients with P/LP CDH1 variants underwent endoscopy using pCLE and CM. Majority were female (75%) with median age 47 years. Targeted biopsies of focal abnormalities on WLE were negative for carcinoma. Overall, 19.4% (7/36) patients had SRC detected on ≥1 biopsy. Non-targeted CM biopsies revealed SRC in 11.1% (4/36), whereas pCLE revealed SRC in 16.7% (6/36). Fifteen patients underwent total gastrectomy; all 15 explants contained occult carcinoma. In those 15 patients, the false-negative SRC detection rates for pCLE and CM were 67% and 87%, respectively. Confocal endomicroscopy alone has low sensitivity for occult cancer detection in CDH1 variant carriers, although it appeared no worse than the current recommended method and required fewer biopsies per patient. A more reliable endoscopic surveillance is needed as a viable alternative to surgery in this high-risk population (ClinicalTrials.gov, Number: NCT03648879).

Defense against flooding attacks using probabilistic thresholds in the internet of things ecosystem

AbstractThe Internet of Things (IoT) ecosystem allows communication between billions of devices worldwide that are collecting data autonomously. The vast amount of data generated by these devices must be controlled totally securely. The centralized solutions are not capable of responding to these concerns due to security challenges problems. Thus, the Average Packet Transmission RREQ (APT‐RREQ) as an effective solution, has been employed to overcome these concerns to allow for entirely secure communication between devices. In this paper, an approach called LSFA‐IoT is proposed that protects the AODV routing protocol as well as the IoT network against flooding. The proposed method is divided into two main phases; the first phase includes a physical layer intrusion and attack detection system used to detect attacks, and the second phase involves detecting incorrect events through APT‐RREQ messages. The simulation results indicated the superiority of the proposed method in terms of False Positive Rate (FPR), False Negative Rate (FPR), Detection Rate (DR), and Packet Delivery Rate (PDR) compared to REATO and IRAD. Also, the simulation results show how the proposed approach can significantly increase the security of each thing and network security.

MRI-targeted prostate biopsy: the next step forward!

AimFor decades, the gold standard technique for diagnosing prostate cancer was the 10 to 12 core systematic transrectal or transperineal biopsy, under ultrasound guidance. Over the past years, an increased rate of false negative results and detection of clinically insignificant prostate cancer has been noted, resulting into overdiagnosis and overtreatment. The purpose of the current study was to evaluate the changes in diagnosis and management of prostate cancer brought by MRI-targeted prostate biopsy.MethodsA critical review of literature was carried out using the Medline database through a PubMed search, 37 studies meeting the inclusion criteria: prospective studies published in the past 8 years with at least 100 patients per study, which used multiparametric magnetic resonance imaging as guidance for targeted biopsies.ResultsIn-Bore MRI targeted biopsy and Fusion targeted biopsy outperform standard systematic biopsy both in terms of overall and clinically significant prostate cancer detection, and ensure a lower detection rate of insignificant prostate cancer, with fewer cores needed. In-Bore MRI targeted biopsy performs better than Fusion biopsy especially in cases of apical lesions.ConclusionTargeted biopsy is an emerging and developing technique which offers the needed improvements in diagnosing clinically significant prostate cancer and lowers the incidence of insignificant ones, providing a more accurate selection of the patients for active surveillance and focal therapies.

Coagulation Testing in Acute Ischemic Stroke Patients Taking Non-Vitamin K Antagonist Oral Anticoagulants

Abstract Introduction/Objective In patients who present with acute ischemic stroke while on treatment with non-vitamin K antagonist oral anticoagulants (NOACs), coagulation testing is necessary to confirm the eligibility for thrombolytic therapy. We evaluated the current use of coagulation testing in routine clinical practice in patients who were on NOAC treatment at the time of acute ischemic stroke. Methods Prospective multicenter observational RASUNOA registry (Registry of Acute Stroke Under New Oral Anticoagulants). Results of locally performed nonspecific (international normalized ratio, activated partial thromboplastin time, and thrombin time) and specific (antifactor Xa tests, hemoclot assay) coagulation tests were documented. The implications of test results for thrombolysis decision-making were explored. Results In the 290 patients enrolled, nonspecific coagulation tests were performed in ≥95% and specific coagulation tests in 26.9% of patients. Normal values of activated partial thromboplastin time and international normalized ratio did not reliably rule out peak drug levels at the time of the diagnostic tests (false-negative rates 11%-44% [95% confidence interval 1%-69%]). Twelve percent of patients apparently failed to take the prescribed NOAC prior to the acute event. Only 5.7% (9/159) of patients in the 4.5-hour time window received thrombolysis, and NOAC treatment was documented as main reason for not administering thrombolysis in 52.7% (79/150) of patients. Conclusion NOAC treatment currently poses a significant barrier to thrombolysis in ischemic stroke. Because nonspecific coagulation test results within normal range have a high false-negative rate for detection of relevant drug concentrations, rapid drug-specific tests for thrombolysis decision-making should be established.

Fault Detection of the Wind Turbine Variable Pitch System Based on Large Margin Distribution Machine Optimized by the State Transition Algorithm

Aiming at solving the problem that the parameters of a fault detection model are difficult to be optimized, the paper proposes the fault detection of the wind turbine variable pitch system based on large margin distribution machine (LDM) which is optimized by the state transition algorithm (STA). By setting the three parameters of the LDM model as a three-dimensional vector which was searched by STA, by using the accuracy of fault detection model as the fitness function of STA, and by adopting the four state transformation operators of STA to carry out global search in the form of point, line, surface, and sphere in the search space, the global optimal parameters of LDM fault detection model are obtained and used to train the model. Compared with the grid search (GS) method, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA), the proposed model method has lower false positive rate (FPR) and false negative rate (FNR) in the fault detection of wind turbine variable pitch system in a real wind farm.

Highlighted Prospects of an IgM/IgG Antibodies Test in Identifying Individuals With Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection.

Covert severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections could be seeding new outbreaks. How to identify asymptomatic SARS-CoV-2 infections early has become a global focus. To explore the roles of immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies detection, nucleic acid tests, and computed tomography (CT) scanning to identify asymptomatic SARS-CoV-2 infection. The clinical data of 389 individuals with close contacts, including in general characteristics, SARS-CoV-2 etiology, serum-specific IgM and IgG antibody detection and CT imaging results, were systematically analyzed. The present study showed that only 89 of 389 individuals with close contacts were positive after the first nucleic acid test, while 300 individuals were still negative after 2 nucleic acid tests. Among the 300 individuals, 75 did not have pneumonia, and the other 225 individuals had pulmonary imaging changes. A total of 143 individuals were eventually diagnosed as having asymptomatic infection through IgM antibody and IgG antibody detection. The sensitivity, specificity, and false-negative rate of IgM and IgG antibody detection were approximately 97.1% (347 of 357), 95.3% (204 of 214), and 4.67% (10 of 214), respectively. It also indicated that during approximately 2 weeks, most individuals were both IgM positive and IgG positive, accounting for 68.57% (72 of 105). During approximately 3 weeks, the proportion of IgM-positive and IgG-positive individuals decreased to 8.57% (9 of 105), and the proportion of IgM-negative and IgG-positive individuals increased to 76.19% (80 of 105). There are highlighted prospects of IgM/IgG antibody detection as a preferred method in identifying the individuals with asymptomatic SARS-CoV-2 infection, especially combined with nucleic acid tests and pulmonary CT scanning.