False Values Research Articles

Measurement of calcitonin and cea in the needle washout fluid from thyroid nodule for the diagnosis of medullary thyroid carcinoma

Background. medullary thyroid carcinoma (mtc) produces serum markers including calcitonin (ct) and carcinoembryonic antigen (cea). The measurement of ct in fine-needle aspirate washout fluid (FNA-CT) improves the cytological diagnosis of mtc. However, no data are available about cut-off values for FNA-CT using currently immunoassay. The measurement of cea in the needle washout fluid (FNa-cea) in mtc has not been studied.Objective: to assess the diagnostic value and propose cut-off values for FNA-CT and FNacea in the thyroid nodule to diagnose mtc.Material and Methods. We conducted a retrospective analysis of 164 samples of fine-needle aspirate washout fluid collected from 92 patients with thyroid nodules, who underwent FNa followed by cytological examination and measurement of FNA-CT. seventeen cases with mtc and 41 with non-mtc nodules were histologically verified. one hundred and six nodules identified as non-mtc by cytology were not operated on. FNa-cea was additionally studied in 29 samples. The cut-off value was determined by Roc analysis.Results. The FNA-CT level was >2000 pg/ml in all mtc nodules, except for one, in which the FNA-CT level was 638 pg/ml. In non-mtc nodules, the FNA-CT levels were <10 pg/ml and <100 pg/ml in 81 % and 90 %, respectively, however, it was >500 pg/ml in 5 %, and >1000 pg/ml in 2 %. At a cut-off value of 590 pg/ml, the sensitivity and specificity of FNA-CT were 100 % and 96 %, and at a cut-off of 1721 pg/ml, the corresponding values were 94 % and 99 %. The false positive FNA-CT values were 3.7 % and 1.2 % at the cut-off values of 590 pg/ml and 1721 pg/ml, respectively. The median levels of FNa-cea in mtc and non-mtc nodules were 59.3 ng/ml and 1.5 ng/ml, respectively. At a cut-off value of 7.5 ng/ml, the sensitivity of FNa-cea was 86 % and specificity was 100 %. Additional measurement of FNa-cea prevented all false-positive results of FNA-CT, but did not detect 2 mtcs with low FNa-cea levels (false-negative rate of 6.9 %). Among samples with FNA-CT value of >590 pg/ml and negative cytology (n=9), FNa-cea differentiated mtc with 100 % sensitivity and specificity.Conclusion. The maximum sensitivity of FNA-CT in the thyroid nodule was at a cut-off value of 590 pg/ml (100 %), the maximum specificity was at a cut-off value of 1721 pg/ml (99 %). Risk of false positive result is the major challenge of FNA-CT. to reduce false-positive results, we recommend to measure FNa-cea in the nodules with negative cytology and high level of FNA-CT. At a cut-off value of 7.5 ng/ml, FNa-cea allows the false-positive FNA-CT to be excluded.

Metabarcoding read abundances of orchid mycorrhizal fungi are correlated to copy numbers estimated using ddPCR

Summary Quantifying the abundances of fungi is key to understanding natural variation in mycorrhizal communities in relation to plant ecophysiology and environmental heterogeneity. High‐throughput metabarcoding approaches have transformed our ability to characterize and compare complex mycorrhizal communities. However, it remains unclear how well metabarcoding read counts correlate with actual read abundances in the sample, potentially limiting their use as a proxy for species abundances. Here, we use droplet digital PCR (ddPCR) to evaluate the reliability of ITS2 metabarcoding data for quantitative assessments of mycorrhizal communities in the orchid species Neottia ovata sampled at multiple sites. We performed specific ddPCR assays for eight families of orchid mycorrhizal fungi and compared the results with read counts obtained from metabarcoding. Our results demonstrate a significant correlation between DNA copy numbers measured by ddPCR assays and metabarcoding read counts of major mycorrhizal partners of N. ovata, highlighting the usefulness of metabarcoding for quantifying the abundance of orchid mycorrhizal fungi. Yet, the levels of correlation between the two methods and the numbers of false zero values varied across fungal families, which warrants cautious evaluation of the reliability of low‐abundance families. This study underscores the potential of metabarcoding data for more quantitative analyses of mycorrhizal communities and presents practical workflows for metabarcoding and ddPCR to achieve a more comprehensive understanding of orchid mycorrhizal communities.

SECURITY MEASUREMENT IN LTE/LTE-A NETWORK BASED ON zS-LR FEATURE SELECTION TECHNIQUE AND UM-tGAN ATTACK DETECTION TECHNIQUE

Regarding raised data speeds, minimized access latency, robust resilience to multipath fading, enhanced spectral efficacy, and seamless integration, the LTE network performs great with other non-LTE (Long Term Evolution) wireless technologies. Nevertheless, there is an urgent need to execute extensive network Security Measurements (SMs) since the LTE/LTE-A (LTE-Advanced) network is hugely exposed to several security hazards. Grounded on z-Score-centric Lasso Regression (zS-LR) Feature Selection (FS) and Unified Momentum-based Tuned Generative Adversarial Network (GAN) (UM-tGAN) attack detection methodologies, the work has introduced precise SMs in LTE networks for performing accurate security measures. The effectual relevant data are gathered by the proposed system along with guarantees the quick as well as precise security attacks detection; in addition, it offers the base for network SM. By performing pre-processing like Missing Value (MV) handling, categorical features handling, scaling using MAD-PT, and imbalanced dataset handling that is succeeded by related FS employing the zS-LR technique, the gathered data are transmitted into structured data. In the end, by deploying the UM-tGAN method, the dimensional reduced features are trained and tested. Owing to heterogeneity, the proposed work is able to handle several attacks along with avoids adversarial-centric attacks. When weighed against the prevailing methodology, the proposed work acquires enhanced accuracy, precision, and sensitivity and avoids misclassification of attack by attaining a lower false prediction value.

A HYBRID MODEL FOR THE CLASSIFICATION OF PHYSIOLOGICAL AND NEURAL SIGNAL USING CNN-LSTM TECHNIQUE

Physiological and neural signal classification has many important applications in healthcare, including medical diagnosis and monitoring. For example, electrocardiogram (ECG) classification can be used to detect arrhythmias and other cardiac abnormalities; while EEG classification can be used to diagnose neurological disorders such as epilepsy and sleep apnea. This paper presents an LSTM model for the decoding of physiological and neural signals. In this paper, an electroencephalography brain signal data which was gotten from Kaggle.com was used. The dataset was pre-processed so as to remove noise from the data. The pre-processed data was used in training the LSTM model. The LSTM model was trained on fourteen (14) steps. The result of the LSTM model showed an accuracy of 85% at the first step and a validation (testing) accuracy of 90%. For the fourteenth step, the model achieved an accuracy result of 98% for training and 94% for validation (testing). We also evaluated the performance of the model using a classification report and confusion matrix. The result of the classification report showed an accuracy of 95%, which is implication that the performance of the model on the test data is efficient. The confusion matrix was used to specify how well the proposed model classified the electroencephalography signal. The result of the confusion matrix showed that the model predicted the result correctly to be neutral 151 out of 153, positive to be 127 out of 142, and negative to be 128 out of 132. The result showed that the level of false positive and negative values is minimal (0.02% and 0.05%).

Deep learning‐based robust hybrid approaches for brain tumor classification in magnetic resonance images

AbstractThe classification of brain images is an immensely challenging task and one of the most valuable and frequently employed procedures in the field of medical diagnostics. Deep Learning (DL), which falls under the umbrella of artificial intelligence, has introduced novel techniques for automated analysis of medical images. The objective of this study was to develop two hybrid DL models for the segmentation and classification of brain magnetic resonance imaging (MRI) data. The first hybrid model combines fully convolutional networks (FCNs) and residual networks (ResNets) for both segmentation and classification purposes. The second model is a hybrid of SegNet for segmentation and MobileNet for classification. Rest assured, it will provide you with a dependable and promising performance. The analysis was conducted on a labeled dataset comprising images of glioma, meningioma, pituitary, and no‐tumor cases. Python was utilized to implement the aforementioned hybrid models. The proposed models achieved remarkable accuracies of 93.9% and 91.3%, respectively, when evaluated on a brain tumor MRI dataset. Additionally, a comprehensive evaluation of the proposed models was conducted, comparing them with other existing hybrid models in terms of metrics such as the dice score, Jaccard index, positive predictive value (PPV), false predictive value (FPV), testing time, precision, recall, specificity, and F1 score. The results demonstrated that the proposed DL technique can significantly assist doctors and radiologists in the early detection of brain tumor cells.

A novel sensitive DDoS attacks against statistical test in network traffic fusion

AbstractDistributed denial of service (DDoS) attacks are common attacks that limit casual users from cracking critical network resources. A method to find the DDoS attack comes to light as an essential and hot research issue. Therefore, the increment in network usage scale demands more challenges to see the DDoS attack. Non‐real‐time collection of network traffic will make sincere complications inaccuracy; DDoS attack detection, and efficiency. Lately, sketch data structures play an excellent role in high‐speed networks to suppress and combine network traffic issues. However, the sketch data structure has issues rebuilding the reverse keys that behave abnormally due to the reversible hash function. To rectify the above difficulties, we include the Chinese Remainder Theorem‐based Reverse Sketching (CRTRS). However, Reverse Sketching is not good at suppressing and combining heavy network traffic, but it can easily detect abnormal keys. According to the network traffic records setup by CRTRS, we add the Modified Cumulative Sum Quality Control Scheme (MCSQCS) that encourages DDoS attack detection. The enforcement of the proposed work with DDoS attack detection has two kind of different data sets. The result of the experiments shows that it gives more accuracy and efficiency in detecting DDoS attacks. Therefore, comparing other DDoS detecting methods with the sketch data structure has fewer computational complications. Improving the anomalous keys is the most critical challenge in this proposed work. To illustrate the accuracy rate, we experiment with a new technique called CIC‐Friday Heavy network traffic fragment. We also explain the experimental results of the MAWI heavy network fragment with the various parameter setups. To compare our proposed method with other similar existing methods, we use a sketch data structure to manage the network traffic data. It also uses the MSPCA method to detect the DDoS attack. The information‐based analysis helps to see the DDoS attack. The result analysis of True Positive Value (TPV) and False Positive Value (FPV) with MSPCA techniques has 79% and .1% accuracy. Similarly, information‐based detection has an accuracy of about 90% and 5%. Hence, the proposed method gives more accuracy for the detection of DDoS attacks.

A study on the utility of KOH mount and HPR in the diagnosis of post COVID mucormycosis

Background: Mucormycosis is a deadly disease that mostly affects the immunocompromised. Steroid therapy following COVID-19 infection has led to an alarming rise in the rates of this rare infection. Key to management of this disease is early diagnosis. The most common modalities used for diagnosis are KOH mount and histopathological examination. In our study we compare the efficacy of these two diagnostic modalities. Methods: Nasal endoscopy was done in all suspected cases of the disease. Nasal discharge, crusts were collected in a sterile manner and sent for KOH mount examination. Specimens collected following surgical debridement was sent for HPR examination. The results of both are compared. Results: In our study, HPR was positive in 82.2% cases while KOH mount showed positive results in 43.5% cases only. Conclusions: Histopathology helps in confirming the diagnosis of mucormycosis. It also helps to demonstrate tissue invasion and identification of species of fungus. But it is time consuming. KOH mount examination is rapid and inexpensive but the test shows high false negative values. Thus, KOH mount examination can be used as a screening tool while histopathological examination can be used as a confirmation test for post covid mucormycosis.

An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study.

Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly. LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev. Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling. LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%. We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.

Discriminative performance of pancreatic stone protein in predicting ICU mortality and infection severity in adult patients with infection: a systematic review and individual patient level meta-analysis

BackgroundSeveral studies suggested pancreatic stone protein (PSP) as a promising biomarker to predict mortality among patients with severe infection. The objective of the study was to evaluate the performance of PSP in predicting intensive care unit (ICU) mortality and infection severity among critically ill adults admitted to the hospital for infection.MethodsA systematic search across Cochrane Central Register of Controlled Trials and MEDLINE databases (1966 to February 2022) for studies on PSP published in English using ‘pancreatic stone protein’, ‘PSP’, ‘regenerative protein’, ‘lithostatin’ combined with ‘infection’ and ‘sepsis’ found 46 records. The search was restricted to the five trials that measured PSP using the enzyme-linked immunosorbent assay technique (ELISA). We used Bayesian hierarchical regression models for pooled estimates and to predict mortality or disease severity using PSP, C-Reactive Protein (CRP) and procalcitonin (PCT) as main predictor. We used statistical discriminative measures, such as the area under the receiver operating characteristic curve (AUC) and classification plots.ResultsAmong the 678 patients included, the pooled ICU mortality was 17.8% (95% prediction interval 4.1% to 54.6%) with a between-study heterogeneity (I-squared 87%). PSP was strongly associated with ICU mortality (OR = 2.7, 95% credible interval (CrI) [1.3–6.0] per one standard deviation increase; age, gender and sepsis severity adjusted OR = 1.5, 95% CrI [0.98–2.8]). The AUC was 0.69 for PSP 95% confidence interval (CI) [0.64–0.74], 0.61 [0.56–0.66] for PCT and 0.52 [0.47–0.57] for CRP. The sensitivity was 0.96, 0.52, 0.30 for risk thresholds 0.1, 0.2 and 0.3; respective false positive rate values were 0.84, 0.25, 0.10.ConclusionsWe found that PSP showed a very good discriminative ability for both investigated study endpoints ICU mortality and infection severity; better in comparison to CRP, similar to PCT. Combinations of biomarkers did not improve their predictive ability.

Faculty Performance Evaluation through Multi-Criteria Decision Analysis Using Interval-Valued Fermatean Neutrosophic Sets

The Neutrosophic Set (Nset) represents the uncertainty in data with fuzzy attributes beyond true and false values independently. The problem arises when the summation of true (Tr), false (Fa), and indeterminacy In values crosses the membership value of one, that is, Tr+In+Fa<1. It becomes more crucial during decision-making processes like medical diagnoses or any data sets where Tr+In+Fa<1. To achieve this goal, the FNset is recently introduced. This study employs the Interval-Valued Fermatean Neutrosophic Set (IVFNset) as its chosen framework to address instances of partial ignorance within the domains of truth, falsehood, or uncertainty. This selection stands out due to its unique approach to managing such complexities within multi-decision processes when compared to alternative methodologies. Furthermore, the proposed method reduces the propensity for information loss often encountered in other techniques. IVFNS excels at preserving intricate relationships between variables even when dealing with incomplete or vague information. In the present work, we introduce the IVFNset, which deals with partial ignorance in true, false, or uncertain regions independently for multi-decision processes. The IVFNset contains the interval-valued Trmembership value, Inmembership value, and Famembership for knowledge representation. The algebraic properties and set theory between the interval-valued FNset have also been presented with an illustrative example.

Comparison of diagnostic performance of two ultrasound risk stratification systems for thyroid nodules: a systematic review and meta-analysis.

To assume the ideal cut-off values and diagnostic performance of two thyroid imaging reporting and data systems published by the Korean Thyroid Association/Korean Society of Thyroid Radiology (Korean TI-RADS) and the American Thyroid Association (ATA TI-RADS). Eighteen studies with 25,422 patients from PubMed, SCOPUS, Embase, Web of Science, and Cochrane Library databases up to August 2022. True and false positive and negative values with characteristics were extracted. The highest area under the receiver operating characteristic curve (AUC) was 0.893 and 0.887 for Korean and ATA TI-RADS. High suspicion was judged as the best cut-off value with the highest AUC based on optimal sensitivity and specificity. In determining the risk of malignant thyroid nodules, high suspicion in Korean and ATA TI-RADS showed sensitivity as 71.3% and 73.5%, specificity as 7.9% and 86.4%, diagnostic odds ratios as 20.0289 and 20.9076, AUC as 0.893 and 0.887. There was no significant difference when directly comparing the diagnostic accuracy of both TI-RADS. The two risk stratification systems had good diagnostic performance with high AUC and no significant differences. The ideal cut-off can depend on the medical condition or thyroid nodules, because the changes of cut-off point may reciprocally alter sensitivity and specificity.

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.

Validation of a new model of non-invasive functional assessment of coronary lesions by computer tomography fractional flow reserve

Introduction and objectivesComputed tomography of coronary arteries (CTCA) is emerging as the gold standard in the initial evaluation of chest pain. The aim of this study is to calculate the diagnostic performance and clinical usefulness of numerical models of arterial channels flow to assess non-invasive coronary fractional flow reserve by computed tomography (FFRct) compared to assessment by invasive angiography (FFR). MethodsComputation of FFR from CTCA images was performed on 39 vessels in 32 patients who followed the usual clinical protocol including invasive coronary angiography and FFR following the usual clinical protocol. From the segmentation of the CTCA images, 3D geometry is obtained, and computational fluid dynamics techniques are applied to accurately reproduce the circulation of blood considering steady and unsteady circulatory conditions. Additional parameters as wall shear stress, stenosis resistance index and ΔFFRct were also calculated. ResultsThe results obtained show a good correlation between FFRct and invasive FFR, without obtaining false positives values. In addition, no differences were observed between the simulations considering steady conditions or those considering the transient. Additional parameters as wall shear stress, stenosis resistance, and ΔFFRct also showed a good correlation with the invasive values. ConclusionsThis new model for calculating the FFRct from non-invasive 3D medical images of coronary tree has shown good agreement with those obtained using invasive techniques. Parameters as wall shear stress, stenosis resistance and ΔFFRct provide useful information that could be helpful in medical decision-making in those situations with FFR values close to the cut-off zone.

Diagnostic value of MRI radiomics in differentiating high‑grade glioma from low‑grade glioma: A meta‑analysis.

No clear conclusions have yet been reached regarding the accuracy of magnetic resonance imaging (MRI) radiomics in distinguishing high-grade glioma (HGG) from low-grade glioma (LGG). In the present study, a meta-analysis was conducted to determine the diagnostic value of MRI radiomics in differentiating between HGG and LGG, in order to guide their clinical diagnosis. PubMed, Embase and the Cochrane Library databases were searched up to November 2022. The search included studies in which true positive, false positive, true negative and false negative values for the differentiation of HGG from LGG were reported or could be calculated by retrograde extrapolation. Duplicate publications, research without full text, studies with incomplete information or unextractable data, animal studies, reviews and systematic reviews were excluded. STATA 15.1 was used to analyze the data. The meta-analysis included 15 studies, which comprised a total of 1,124 patients, of which 701 had HGG and 423 had LGG. The pooled sensitivity and specificity of the studies overall were 0.92 (95% CI: 0.89-0.95) and 0.89 (95% CI: 0.85-0.92), respectively. The positive and negative likelihood ratios of the studies overall were 7.89 (95% CI: 6.01-10.37) and 0.09 (95% CI: 0.07-0.12), respectively. The pooled diagnostic odds ratio of the studies was 85.20 (95% CI: 54.52-133.14). The area under the summary receiver operating characteristic curve was 0.91. These findings indicate that radiomics may be an accurate tool for the differentiation of glioma grades. However, further research is needed to verify the most appropriate of these technologies.

The feminist perspective from the traditional woman to the third woman and the challenges of the viod age through gille libovtsky

The history of feminism reveals three models of women starting from the first traditional woman whose tasks are summarized in raising children and kitchen work, while the second has received some attention as it has become praised in literature and the arts, and then the third independent woman emerged, but this path which women took in pursuit of liberation , led her to fall into the most severe deprivation and loss of self, due to the rule of consumerism and absolute individualism , according to the jeil libovtsky generation, to adopt the following problem: was the third woman actually liberated or alienated from her identity ? what is the fate of the new woman under the dominance of liberalism? we therefore aim to explain the disadvantages of the capitalist system and false modernist values, and to reveal the soft ideological backgrounds behind feminist modernity, and we have concluded that women are victims of this capitalist system, which aims to control selves.

Interference of red cell parameters by an endogenous substance: An interesting case report

A 6-month-old infant was brought to the hospital with fever, vomiting, loose stools, and abdominal distension. The venous blood sample was sent to the hematology laboratory for complete blood count (CBC). Analysis of the sample on the automated hematology analyzer showed a discrepancy in red blood cell count, hemoglobin, and hematocrit values. Examination of the blood sample revealed a cherry red color appearance. On centrifugation, the plasma was lactescent. This finding provided an essential clue to the patient’s diagnosis of severe hypertriglyceridemia. Turbidity of lactescent plasma interferes with CBC. Analytical errors in the CBC using an automated hematology analyzer caused by lactescent plasma and methods to resolve the false values will be discussed in this case report.

Detection and substrate portrayal on the serum phenoloxidase activity from the grub of rhinoceros beetle, Oryctes rhinoceros.

Phenoloxidase (PO) is a significant biomolecule involved in humoral defence mechanism of invertebrates. Spontaneous melanization of insect haemolymph is the major hinderance for studying PO activity, as haemolymph was collected devoid of phenylthiourea. In the study, no visible melanization was observed in crude serum from the grub of Oryctes rhinoceros up to 30 min of incubation amongst crude haemolymph, diluted haemolymph, crude serum and diluted serum that were subjected to visual observation for spontaneous melanization reaction. Accordingly, crude serum was taken for evaluating PO activity. At the same time, as PO substrates tend to auto-oxidize and provide false optical density value, tris-buffered saline devoid of any substrates were used as blank for PO assays. The ideal wavelength at which maximum PO activity occurred for each substrate, namely, tyrosine, tyramine, dopamine, L-dopa, DL-dopa, catechol, protocatechuic acid and pyrogallol was determined as 407, 410, 429, 465, 403, 466, 428 and 400 nm, respectively. Additionally, time course of oxidation for each phenolic substrate by the serum PO were examined and DL-dopa was identified as the specific substrate for serum PO in the grub of O. rhinoceros. Furthermore, maximum PO activity was observed at 5 min of incubation for 10 mM of DL-dopa that was considered as optimum concentration. The ideal pH and temperature for serum PO activity was observed as 7.5 and 20°C, respectively. These results suggested that standardizing a suitable substrate is an essential prerequisite to evaluate the real PO activity of serum which might significantly fluctuate in each insect model.

RETRACTED ARTICLE: An abnormal traffic detection method using GCN-BiLSTM-Attention in the internet of vehicles environment

In-vehicle network intrusion detection tasks, it is usually necessary to simultaneously meet the requirements of low computational power consumption, real-time response, and high detection accuracy. In response to the class imbalance problem in existing vehicle network anomaly flow detection methods, which leads to longer training convergence time and low detection accuracy, an anomaly flow detection method using GCN-BiLSTM-Attention is proposed. Firstly, Graph Convolutional Networks (GCN) is used to obtain spatial correlations between data streams. Secondly, obtaining the time correlation to predict the next time slice flow matrix by capitalizing the variant Bidirectional Long Short-Term Memory (BiLSTM) network. Last but not least, an attention mechanism is designed for extracting key information from the data stream. The results of experiment prove that the binary classification false positive rate, detection rate, and F1 value of the proposed GCN-BiLSTM-Attention-based anomaly flow detection method on the NSL-KDD dataset are 95.87%, 6.31%, and 94.25%, respectively; The false positive rate, detection rate, and F1 value on the CICID2017 dataset are 6.01%, 94.12%,and 94.36%, respectively. The proposed GCN-BiLSTM-Attention model has exceeded the compared methods in detecting abnormal traffic in the context of the Internet of Vehicles, and it can better preserve local features of traffic data.

A Real-Time Monitoring System for Cable Tension with Vibration Signals Based on an Automated Algorithm to Sieve Out Reliable Modal Frequencies

Cables or suspenders are the critical force-transmitting components of cable-supported bridges, and their timely tension monitoring is consequently the most important issue in the corresponding structural health monitoring. However, very few works regarding the full automation of vibration-based tension estimation have been reported in the literature. To develop a monitoring system of cable tension based on real-time vibration signals, this research first employs an efficient stochastic subspace identification (SSI) method with tailored parameter selection to continuously identify the three frequencies of adjacent modes for the cables of Mao-Luo-Hsi Bridge. More importantly, an automated sieving algorithm is delicately established to obtain the stable modal frequencies by making the best of the specific modal frequency distribution for cables. The ratios between the frequency values identified from SSI analysis are exhaustively checked to systematically extract the qualified cable frequencies and decide their corresponding mode orders. The tension is finally computed with one available cable frequency according to the priority order predetermined by the statistics of identification rate. Demonstrated by analyzing the vibration signals measured from the stay cable of Mao-Luo-Hsi Bridge in real time for two full years, the effectiveness and robustness of this real-time monitoring system have been extensively testified. The long-term success rates for the immediate determination of dependable tension are found to be perfect for 15 of the 18 investigated cables. As for the other three cables, their corresponding success rates are still higher than 99.99% with very few cases of absent or false tension values.

What do ‘false-positive’ stool tests really mean? Data from the New Hampshire colonoscopy registry

We utilized the population-based New Hampshire Colonoscopy Registry to calculate false discovery rates (FDR) and positive predictive values (PPVs) using three ‘positive’ colonoscopy definitions. Understanding the frequency of meaningful ‘true positive’ mt-sDNA and Fecal Immunochemical Test (FIT) results can optimize the use of these colorectal cancer (CRC) screening tests. We calculated FDR (positive stool test followed by negative colonoscopy divided by all positive stool tests) and PPV for mt-sDNA and FIT cohorts using the following definitions:1) DeeP-C Study (CRC, adenomas/serrated polyps ≥ 1 cm, villous/High Grade Dysplasia);2) < 10 year US Multi-Society Task Force (USMSTF) follow-up: DeeP-C findings & ≥1 sessile serrated polyps (SSPs) < 1 cm (with/without dysplasia) or ≥ 1 tubular adenomas < 1 cm.3) Clinically Significant: DeeP-C + USMSTF + clinically significant serrated polyps: traditional serrated adenomas, SSPs, hyperplastic polyps (HPs) > 1 cm, and 5–9 mm proximal HPs.The sample included 549 mt-sDNA + and 410 FIT + and patients (mean age 66.4, 43.0% male). Using the most limited definition of positive colonoscopy, DeeP-C, FDR was 71.9% for mt-sDNA + and 81.7% for FIT +. Using the USMSTF definition, FDR decreased substantially: mt-sDNA+:33.2% and FIT+:47.6%. Adding all CSSPs resulted in the lowest FDR: mt-sDNA+:32.2% and FIT+:47.1%. Decreasing FDRs corresponded to increasing PPVs: mt-sDNA+:28.1% and FIT+:18.3% (DeeP-C definition) and mt-sDNA+:67.8% and FIT+:52.9% (DeeP-C + USMSTF + CSSP) (Table 1).FDRs decreased substantially when the definition of positive exams included all significant precancerous findings. These data present a comprehensive understanding of false positive outcomes at colonoscopies following positive stool tests, which to our knowledge is the first such analysis.