Improved Detection Method Research Articles

Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.

Deep learning-based fast detection of apparent concrete crack in slab tracks with dilated convolution

Slab tracks exposed to complicated environmental factors over a long period can cause cracks in the concrete, and if these cracks gradually expand, the concrete’s durability and service life will be greatly impacted. How to quickly and effectively detect concrete cracks has become an urgent challenge during the maintenance and repair of high-speed railway slab tracks. In this study, a large number of images of concrete cracks were collected in a database, and STCNet Ⅰ, a fast detection network architecture using dilated convolution based on deep learning, was proposed to detect apparent concrete cracks in slab tracks. After that, the watershed algorithm was used to segment the detected cracks. The results show that: I) compared with traditional network models, the STCNet Ⅰ provides a faster calculation at lower space complexity. The number of parameters used in this network is reduced by 96.03% and 93.28%, respectively compared with that in the VGG 16 and ResNet 50, and the time complexity is lower, with the calculation time reduced by 49.94% and 73.28%, respectively; II) the average recognition accuracy on the training set and the validation set reached as high as 99.71% and 99.33%, respectively, proving the robustness of the model; III) the accuracy and F1 score in the test samples of concrete crack reached 99.54% and 99.54%, indicating the strong generalization ability of the model; and IV) the concrete crack area was accurately detected, and the crack contour was fully closed and continuous. The research results from this paper provide an improved detection method of slab tracks and promote the fine detection and maintenance of the apparent concrete of slab tracks.

Quantitative detection of parasitic ciliate Cryptocaryon irritans in seawater with an optimized sample processing method.

The protozoan Cryptocaryon irritans is one of the most important ectoparasites of marine fish, causing 'white spot disease' and mass mortality in aquaculture. To accurately predict disease outbreaks and develop prevention strategies, improved detection methods are required that are sensitive, convenient and rapid. In this study, a pair of specific primers based on the C.irritans 18S rRNA gene was developed and used in a real-time PCR (qPCR) assay. This assay was able to detect five theronts in 1L of natural seawater. Furthermore, a linear model was established to analyse the log of Ct value and parasite abundance in seawater (y=-2.9623x+24.2930), and the coefficient of determination (R2 ) value was 0.979. A lysis buffer was optimized for theront DNA extraction and used for storage sample. This method was superior to the commercial water DNA kit, and there was no significant degradation of DNA at room temperature for 24-96hr. A dilution method was developed to manage qPCR inhibitors and used to investigate natural seawater samples in a net cage farm with diseased fish, and the findings were consistent with the actual situation. This study provides a valuable tool for assisting in the early monitoring and control of cryptocaryoniasis in aquaculture.

Role of env gene and LTR sequence in the pathogenesis of subgroup K avian leukosis virus.

Avian leukosis virus (ALV) is a retrovirus that induces tumours in infected birds; ALV is divided into different subgroups according to the env gene and cellular tropism. In general, ALV subgroup J (ALV-J) is considered to be the most pathogenic and prevalent subgroup while subgroup K (ALV-K), a newly identified subgroup, only causes mild symptoms. To illuminate the roles of the env viral gene and LTR sequence in pathogenic differences between ALV-J and ALV-K, rescued ALV-J strain rSDAU1005, rescued ALV-K strain rJS11C1, and recombinant strains rENV(J)-LTR(K) and rENV(K)-LTR(J) were characterized and investigated in this study. Among rescued viruses, rSDAU1005 had the highest replication efficiency while rJS11C1 replicated the slowest (replication efficiency rankings were rSDAU1005 >rENV(K)-LTR(J)>rENV(J)-LTR(K)>rJS11 C1). The luciferase reporter gene assay results showed that the promoter activity of ALV-K LTR was lower than that of the ALV-J LTR promoter, which may have accounted for the slower replication efficiency of ALV-K. Pathogenicity of the four rescued viruses was determined via inoculating the yolk sacs of specific-pathogen-free chickens. The results demonstrated that all four viruses were pathogenic; rSDAU1005 caused the most severe growth retardation and immunosuppression. rENV(J)-LTR(K) was more pathogenic when compared to rENV(K)-LTR(J), indicating that env and the LTR sequence play important roles in pathogenicity between ALV-K and ALV-J. Additionally, env seemed to especially play a role in ALV-K pathogenesis. This study provided scientific data and insight to improve detection methods and judgement criteria in ALV clearance and surveillance.

An Adaptive Two-Stage Load Event Detection Method for Nonintrusive Load Monitoring

Load event detection is fundamental to event-based nonintrusive load monitoring (NILM) solution. It is directly related to whether the transient and steady-state signatures of appliances can be accurately extracted. In the real world, the load composition is random and complicated, the power consumption patterns of appliances are diverse, and multiple events may occur simultaneously or near each other, all these make it difficult for any one single event detection method to achieve satisfactory robustness. In coping with this, an adaptive two-stage event detection method is proposed in this article. First, an adaptive detection threshold whose value can be adjusted adaptively according to the load fluctuations is adopted, thus improving the ability to detect events of different amplitudes. Then, considering the different event geometric features, an improved edge detection method and a window-based detection method combining moving average with moving t-test are proposed for step-like events and long-transient events, respectively, and they are executed consecutively to achieve the effective capture of the complete transient process of appliances. Furthermore, an event separation step is taken to locate and separate the near-simultaneous events which often appear in low-frequency data. Specific event detection performance evaluation metrics are also designed. Comparison test results on private and public datasets show that the proposed method achieves high detection accuracy for different events of various appliances and maintains strong robustness in different operation scenarios.

Toward an exhaustive review on Machine Learning for Cybersecurity

Cyber-attacks are becoming more and more multiple and sophisticated, Causing profound consequences on humans and their organization. This has led researchers and specialists to put measures continuously to improve detection methods. With the wide scale use of machine learning (ML) in various field, lot of research has shown that the combination of this technology with traditional detection tools brings added value in cybersecurity. In this context, this paper list the most popular cybersecurity attack, outlines the frequently used machine learning algorithms for attack detection and prediction.

Virological and Parasitological Characterization of Mini-LEWE Minipigs Using Improved Screening Methods and an Overview of Data on Various Minipig Breeds.

Minipigs play an important role in biomedical research and have also been used as donor animals in xenotransplantation. To serve as a donor in xenotransplantation, the animals must be free of potential zoonotic viruses, bacteria and parasites. Porcine endogenous retroviruses (PERVs) are integrated in the genome of all pigs and cannot be eliminated as most of the other pig viruses can. PERV-A and PERV-B infect human cells in cell culture and are integrated in all pigs, whereas PERV-C infects only pig cells and it is found in many, but not all pigs. Minipigs are known for a high prevalence of recombinant PERV-A/C viruses able to infect human cells (Denner and Schuurman, Viruses, 2021;13:1869). Here, Mini-LEWE minipigs are screened for the first time for pig viruses including PERV. Peripheral blood mononuclear cells (PBMCs) from 10 animals were screened using PCR-based methods (PCR, RT-PCR, and real-time PCR). In comparison with our previous screening assays, numerous improvements were introduced, e.g., the usage of gene blocks as a PCR standard and foreign RNA to control reverse transcription in RT-PCR. Using these improved detection methods, Mini-LEWE pigs were found to be negative for porcine cytomegalovirus (PCMV), porcine lymphotropic herpesviruses (PLHV-1, -2 and -3), porcine circoviruses (PCV1, 2, 3 and 4), porcine parvovirus (PPV) and hepatitis E virus (HEV). All animals carried PERV-A, PERV-B and PERV-C in their genome. PERV-A/C was not found. In contrast to all other minipig breeds (Göttingen minipigs, Aachen minipigs, Yucatan micropig, Massachusetts General Hospital miniature pigs), Mini-LEWE minipigs have less viruses and no PERV-A/C. Parasitological screening showed that none of the Mini-LEWE minipigs harbored ecto- and gastrointestinal parasites, but at least one animal tested positive for anti-Toxoplasma gondii antibodies.

Development of the method for determining Methandienone in Toxicology and doping-analysis

Anabolic-androgenic steroids are ones of the most frequently detected drugs in amateur and professional sports. Doping control laboratories have developed numerous assays enabling the determination of administered drugs. However, it is relevant today to improve detection methods that are accurate, fast and require small amounts of reagents. The identification and quantification of methandienone from body fluids has been insufficiently developed. The gas chromatography-mass spectrometry (GC-MS) method for the determination of methandienone traces in body fluids was developed and validated in the toxicology laboratory in order to control a non-medical use of an anabolic steroid-methandienone. Extracting procedures were optimized in order to obtain entire amount of the drug and methandienone and its metabolites were determined by sensitive and specific reactions for the compound identification, as well as the system for carrying out the TLC method have been proposed, and conditions for identifying a test sample from the biomaterial by IR spectrophotometry have been selected. The GC-MS method is found to be simple, fast, sensitive, and the strategy proposed can be effortlessly and advantageously applied for the routine analysis of biological fluids for determining methandienone and its metabolites at a level less than 30 ng in toxicology and doping-analysis.

The epidemiology of meningococcal meningitis: multicenter, hospital-based surveillance of meningococcal meningitis in Iraq

ObjectivesOutbreaks of Neisseria meningitidis have reached alarming levels due to the pathogen's ability to cause severe complications, presenting as meningitis or septicemia. Our study reports the results of the first wide-scale surveillance of meningococcal meningitis in Iraq. MethodsThe study included all consecutive cases of clinically suspected meningitis between June 2018 and May 2020 at 18 major hospitals around Iraq (n = 2314). Laboratory analysis of biological samples and real-time polymerase chain reaction tests were conducted to confirm bacterial etiology. Demographical and medical data were collected for statistical analysis. ResultsIn total, 370 patients were confirmed to have bacterial meningitis (215 had N. meningitidis, 154 had Streptococcus pneumoniae, and one case had Haemophilus influenzae type b). The most common N. meningitidis serogroup was B (77.7%), followed by W (18.1%) and X (4.2%). The annual incidence rate of N. meningitidis per 100 000 population was 0.86, with the highest being in Karbala (1.52 per 100 000 population). Cases of meningococcal meningitis were more likely to occur in children younger than 15 (OR = 3.526), and in the winter (OR = 1.474). ConclusionsContinuous surveillance of N. meningitidis is necessary in Iraq, and can only be achieved through improved detection methods. The incidence of meningococcal meningitis in Iraq warrants improved vaccination programs.

Improved Fall Detection Model on GRU Using PoseNet

This paper investigate an improved detection method that estimates the acceleration of the head and shoulder key point position and position change using the skeleton key point information extracted using PoseNet from the image obtained from the low-cost 2D RGB camera, and improves the accuracy of fall judgment. This paper propose a fall detection method based on the post-fall characteristics of the post-fall, the speed of changes in the main point of the human body, and the change in the width and height ratio of the body's bounding box. The public data set was used to extract human skeletal features and train deep learning, GRU, and as a result of experiments, this paper find the following feature extraction methods. High classification accuracy can be achieved, and the proposed method showed a 99.8% fall detection success rate more effectively than the conventional method using raw skeletal data.

Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 am and 5:00 am under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 am, 1:00 pm and 3:00 pm.

An ultra‐sensitive and highly specific detection method for a newly discovered Oncotshavirus using one‐step reverse transcription droplet‐based digital PCR

Digital PCR is an improved detection method compared with conventional PCR. An ultra-sensitive and specific detection method for a newly discovered Oncotshavirus was established by using droplet-based digital PCR technology in this study. The detection system was highly specific and could detect the target virus in the presence of other aquatic viruses. Moreover, the limit of quantitation (LOQ) and the limit of detection (LOD) of the method were comprehensively evaluated based on the serial dilution of original virus samples and were four and one particle per reaction volume respectively. This was a substantial improvement over conventional real-time PCR. Our detection system achieved accurate and stable detection results in a variety of food matrices. Detection of the newly discovered Oncotshavirus based on droplet-based digital PCR was adaptable and applicable for routine testing.

Development of Lectin Modified Fluorescent Magnetic Particles for Highly Sensitive Detection of Glycoconjugates.

I conducted this study to develop an improved method for glycome detection using fluorescent magnetic beads, whose surfaces were modified using lectins, for the highly sensitive detection of saccharides or glycoproteins via fluorescence quenching using a novel fluorescence emitter and quencher pair. The emitter (Cy3 fluorophore) was incorporated into magnetic beads, and a fluorescence quencher (cyanopyranyl group) was bound to glycomes via covalent bonding. The fluorescence intensities of fluorescent magnetic beads containing lectins decreased specifically in the presence of glycomes, which was a result of fluorescence quenching from Cy3 to cyanopyranyl groups due to the formation of a stable complex between lectins and glycome. Fluorescence intensities were plotted as a function of glycoprotein concentration, and good linear relationships were observed. This method enabled the fluorescent reading-out of a series of lectin-glycome interactions on the basis of recognition selectivity and affinity of immobilized lectins without tedious washing processes. Moreover, a simple profiling process was performed using this assay for diverse glycoconjugates, which not only included simple saccharides but also glycoproteins and glycome in cell lysates. These results clearly indicate that the combination of magnetic beads with the novel emitter-quencher pair enabled the highly sensitive detection of lectin-glycome interactions.

Emergence of Thymidine-Dependent Staphylococcus aureus Small-Colony Variants in Cystic Fibrosis Patients in Southern Brazil.

ABSTRACTWe characterized Staphylococcus aureus small-colony variant (SCV) strains isolated from cystic fibrosis (CF) patients in southern Brazil. Smaller colonies of S. aureus were isolated from respiratory samples collected consecutively from 225 CF patients from July 2013 to November 2016. Two phenotypic methods—the auxotrophic classification and a modified method of antimicrobial susceptibility testing—were employed. PCR was conducted to detect the mecA, ermA, ermB, ermC, msrA, and msrB resistance genes. Furthermore, DNA sequencing was performed to determine the mutations in the thyA gene, and multilocus sequence typing was used to identify the genetic relatedness. S. aureus strains were isolated from 186 patients (82%); suggestive colonies of SCVs were obtained in 16 patients (8.6%). The clones CC1 (ST1, ST188, and ST2383), CC5 (ST5 and ST221), and ST398 were identified. Among SCVs, antimicrobial susceptibility testing showed that 77.7% of the isolates were resistant to multiple drugs, and all of them were susceptible to vancomycin. mecA (2), ermA (1), ermB (1), ermC (3), and msrB (18) were distributed among the isolates. Phenotypically thymidine-dependent isolates had different mutations in the thyA gene, and frameshift mutations were frequently observed. Of note, revertants showed nonconservative or conservative missense mutations. SCVs are rarely identified in routine laboratory tests.IMPORTANCE Similar findings have not yet been reported in Brazil, emphasizing the importance of monitoring small-colony variants (SCVs). Altogether, our results highlight the need to improve detection methods and review antimicrobial therapy protocols in cystic fibrosis (CF) patients.

Breast cancer associated with pregnancy

Introduction. The problem of breast cancer associated with pregnancy for a long time attracts close attention. The incidence is relatively increasing, thanks in part to improved detection methods, but also to the growing trend towards late childbearing.Material and methods. The features of breast cancer treatment during pregnancy were studied, and the effect of pregnancy on the long-term prognosis and clinical course of breast cancer was evaluated.Results. All patients received treatment according to the clinical recommendations. Chemotherapeutic treatment was performed in 44 (77%) patients, surgical treatment in 54 (96%) cases, radiation therapy in 40(72%) patients, and hormone therapy in 24 (42%) women. Among the newborns, there were no significant malformations, one child (a girl) had an underdevelopment of the left lung tissue, and laterphysical development in accordance with age.Conclusion. The decision on the special treatment of breast cancer associated with pregnancy is made individually, the treatment has been planned together with obstetricians and gynecologists. Surgical and chemotherapeutic treatment (starting at 13 weeks) can be performed during pregnancy. Radiation therapy, hormone therapy, and targeted therapy should be postponed until delivery. Most patients have common forms of the disease and aggressive biological types of tumors. Currently, the features of the course of the disease, the prognosis, and the features of treatment in this category of patients are being studied.

Changing aetiology of paediatric septic arthritis.

The management of septic arthritis in children requires the prompt administration of antibiotic therapy and the identification of the causative pathogen. In the past, Staphylococcus aureus, Streptococcus pyogenes, Streptococcus pneumoniae and Haemophilus influenzae type b were considered the main causative agents of the disease, but a substantial fraction of presumptive joint infections remained unconfirmed by conventional bacteriologic cultures. In the last two decades, our knowledge of the aetiology of paediatric infectious arthritis has substantially changed as the result of the implementation of vaccination programmes against H. influenzae type b and pneumococci, and by the use of improved detection methods. In 1988, the inoculation of synovial fluid aspirates into blood culture vials revealed that Kingella kingae, a commensal member of the oropharyngeal microbiota, was the prime aetiology of skeletal system infections in children aged 6-48 months. The clinical presentation of K. kingae arthritis is subtle, and the disease is frequently missed by classic clinical and laboratory diagnostic criteria. Many children are afebrile, the acute phase reactants levels and the white blood cell counts in the blood and synovial fluid specimens are frequently normal, requiring a high clinical acumen. Increasing use of sensitive molecular methods in recent years, and particularly nucleic acid amplification tests that target K. kingae-specific genes, has further improved the detection of this elusive pathogen, demonstrated that it is responsible for 30-93% of all cases of septic arthritis below 4 years of age and reduced the fraction of culture-negative infections.

The Railway Detection via Adaptive Multi-scale Fusion Processing

One of the main problems for safe autonomous driving vehicles that have not been solved completely is the high-precision and timely lane detection. In this work, we present a novel operator for railway detection to settle these tasks based on lane detection for the first time, called adaptive multi-scale fusion Sobel operators. The new operators can eliminate the noises generated by the environment in the railway image and derive more integrated edge feature information from the 0°, 45°, 90°, and 135° detection via 4 matrixes of 3 * 3 operators for permutation and summation. The image processing for railway detection includes the preprocess for images, railway edge detection, and track line polynomial fitting. Our experiment has validated that this improved detection method has realized the high accuracy and efficiency for rail detection. The dynamic rail detection and identification in the video of the railway track prove that this method has a significant effect on the left and right curved railway detection. It has good robustness and applicability.

Fault Diagnosis of Power IoT System Based on Improved Q-KPCA-RF Using Message Data

As the power system develops from informatization to intelligence. Research on data services based on the Internet of Things (IoT) focuses more on application functions, but the research on the data quality of the IoT itself is insufficient. Long-term continuous operation of the big data IoT system has the risk of performance degradation or even partial fault, which leads to a decrease in the availability of collected data for intelligent analysis. In this article, based on the power IoT message data, the characteristics are established through a variety of improved detection methods, and then the abnormal data type is obtained through Q learning and fusion of the random forest (RF) identification features. Finally, the topology of the specific power user IoT system is combined with kernel principal component analysis (KPCA) + improved RF algorithm getting the abnormal location of the IoT. The results show that the research method has a significantly higher positioning accuracy (from 61% to 97%) than the traditional RF method, and the combination method has more advantages in parameter adjustment and classification accuracy than directly using a multilayer perceptron (MLP).

Haustorium formation and a distinct biotrophic transcriptome characterize infection of Nicotiana benthamiana by the tree pathogen Phytophthora kernoviae.

Phytophthora species cause some of the most serious diseases of trees and threaten forests in many parts of the world. Despite the generation of genome sequence assemblies for over 10 tree‐pathogenic Phytophthora species and improved detection methods, there are many gaps in our knowledge of how these pathogens interact with their hosts. To facilitate cell biology studies of the infection cycle we examined whether the tree pathogen Phytophthora kernoviae could infect the model plant Nicotiana benthamiana. We transformed P. kernoviae to express green fluorescent protein (GFP) and demonstrated that it forms haustoria within infected N. benthamiana cells. Haustoria were also formed in infected cells of natural hosts, Rhododendron ponticum and European beech (Fagus sylvatica). We analysed the transcriptome of P. kernoviae in cultured mycelia, spores, and during infection of N. benthamiana, and detected 12,559 transcripts. Of these, 1,052 were predicted to encode secreted proteins, some of which may function as effectors to facilitate disease development. From these, we identified 87 expressed candidate RXLR (Arg‐any amino acid‐Leu‐Arg) effectors. We transiently expressed 12 of these as GFP fusions in N. benthamiana leaves and demonstrated that nine significantly enhanced P. kernoviae disease progression and diversely localized to the cytoplasm, nucleus, nucleolus, and plasma membrane. Our results show that N. benthamiana can be used as a model host plant for studying this tree pathogen, and that the interaction likely involves suppression of host immune responses by RXLR effectors. These results establish a platform to expand the understanding of Phytophthora tree diseases.

Noninvasive Urine-Based Tests to Diagnose or Detect Recurrence of Bladder Cancer.

Simple SummaryBladder cancer (BC) is the tenth most common cancer worldwide, with approximatively 550,000 new cases and 200,000 deaths in 2018. BC is divided into two subgroups: non-muscle invasive bladder cancer, an early stage of the cancer, and muscle invasive bladder cancer, which is more aggressive. The crucial issue today is to be able to detect BC easily and early, with high sensitivity and specificity, in order to treat it sooner, using less invasive methods. Over the past decade, progress has been made to improve detection methods using novel urinary biomarkers. In this review, we discuss the present and future of noninvasive urine tests to diagnose or detect the recurrence of bladder cancer.Liquid biopsies are increasingly used for the diagnosis and follow-up of cancer patients. Urine is a body fluid that can be used to detect cancers and others diseases. It is noninvasive and easy to collect. To detect Bladder Cancer (BC), cytology is the first assay used. It is an effective way to detect high grade BC but has a high rate of equivocal results, especially for low grade BC. Furthermore, cystoscopy is used to confirm cytology results and to determine cancer status. Cystoscopy is also effective but highly invasive, and not well accepted by patients, especially for BC follow-up. In this review we survey the numerous assays recently developed in order to diagnose BC at an early stage, and to facilitate the follow-up of patients. We discuss their effectiveness, ease of use, and applications. Finally, we discuss assays that, in the future, could improve the diagnosis and management of BC patients.