Study on the Correlation between Human Papillomavirus and Mycoplasma genitalium Combined with TCT Detection

Objective: This study aims to explore the correlation between human papillomavirus (HPV) and Mycoplasma genitalium (CT) combined with TCT detection in cervical cancer screening. Method: A cross-sectional study design was adopted, and a total of 609 women who came to seek medical treatment were recruited as the study subjects. Combination testing was evaluated on cervical cancer screening by testing the women for HPV, CT with TCT detection and analyzing the relationship of cervical lesions with HPV and CT infection. Results: The study results showed that 21.57% of the subjects were infected with both HPV and CT, and 48.42% of the cases had abnormal TCT results at the same time. Further data analysis showed that HPV infection was significantly associated with abnormal TCT outcomes (p < 0.05), suggesting a possible synergistic effect of the two infections in cervical lesions. The combined sensitivity and specificity of HPV, CT and TCT detection were 21.57% and 48.42%, respectively, which were significantly higher than that of single detection. Conclusion: In summary, the results of this study support the importance of combined HPV, CT, and TCT testing in cervical cancer screening, and propose the hypothesis that combined testing may improve screening effectiveness. However, further large sample studies are needed to confirm this conclusion and explore the prospects of combined testing in clinical practice.

Antibody Detection on Mycoplasma capricolum subsp. Capripneumoniae in Qinghai Province

[Objective] The aim was to investigate the prevalence of Mycoplasma capricolum subsp. Capripneumoniae in Qinghai Province. [Method] By using indirect hemagglutination test kit for detecting Mycoplasma capricolum subsp. Capripneumoniae,208 goat serums were detected. [Result] The positive rate of goat sera was 16.3%,and the positive rate of sera from different regions ranged from 6.7% to 24.3%. [Conclusion] The infection rate of Mycoplasma capricolum subsp. Capripneumoniae was high in Qinghai Province,so it is necessary to strengthen the prevention and control of this disease.

Establishment of a Collection and Detection Technology of Mycoplasma hyopneumoniae in Aerosol

ObjectiveThis study was to establish a simple method for collecting and detecting Mycoplasma hyopneumoniae （Mhp） in aerosol. MethodBased on the mechanisms of liquid impinger and filtration sampler, a double concentration aerosol sampler was designed for collecting Mhp aerosol. Firstly, the collection was performed in a closed environment full of artificial aerosol of Mhp. Secondly, collection efficiency was detected by real-time PCR. Thereafter, the clinical feasibility of the designed equipment was tested by collecting aerosol samples in different pig herds. In one assay, the samples were collected at different times from one pig house challenged with Mhp. In another assay, the samples was collected from the delivery room, nursery and fattenning house of a MPS outbreak farm as well as a Mhp infection positive pig farm without obvious clinical symptoms. All the aerosol samples were then detected by real-time PCR or nested PCR. ResultThe collection efficiency of the designed bioaerosol sampler was （37.04±6.43） %, Mhp could be detected 7 d after intratracheal challenge with pneumonic lung homogenate suspension. Aerosol samples of 11 pig houses from the two Mhp positive pig farms with or without clinical symptoms all showed a positive result of PCR, the positivity rate was 100%. ConclusionA high sensitive collecting and detecting technology of aerosol was successfully established, which can be applied to clinical detection of Mhp in aerosol.

Advances in the Detection of Mycoplasma hyopneumoniae by Polymerase Chain Reaction (PCR) Technology

Mycoplasma hyopneumoniae is an important pathogen causing Mycoplasmal pneumonia of swine, which generally causes secondary infections and mixed infections, thus seriously threats the development of swine industry and resulting in huge economic losses. Using PCR technology has very important significance to the correct diagnosis of Mycoplasmal pneumonia at the early stage. In this paper, specific target genes of Mycoplasma hyopneumoniae, methods for clinical sample collection, key technical factors of DNA sample processing method, and the research progress, main advantages and disadvantages, and application of general PCR technology, multiple PCR technology, nested-PCR technology, real-time fluorescence quantitative PCR technology, gene chip detection technology and loop-mediated isothermal amplification in detection of Mycoplasma hyopneumoniae were summarized, which provided convenience for the effective diagnosis and prevention of Mycoplasmal pneumonia of swine.

Detection of Mycoplasma Contamination in Animal Cell Cultures

[ Objective ] To develop a rapid efficient method for detecting mycoplasma contamination in cell cultures. [ Method] A pair of primers was designed according to two highly conserved nucleotide sequences of the 16S RNA from six kinds of mycoplasma that commonly contaminated cells. Then the mycoplasma contamination of 25 cell samples was defected by PCR and DNA fluorescence staining. EResultl When these cell samples were detected by DNA fluorescence staining, the positive rate and probable positive rate were respectively 24% and 16%. And when they were detected by PCR, the positive rate was 36%. [ Condusion] The PCR method is more sensitive and specific than the DNA fluorescence staining, and combining these two methods is the optimal way to detect mycoplasma contamination in cell cultures.

Polymerase Chain Reaction for Detection of Mycoplasma Genitalium

Objective: To study the incidence of mycoplasmagenitalium infection in non-gonococcal urethritis(NGU)/mucopurulent cervicitis (MPC) patients.Method: Polymerase chain reaction (PCR) wasconducted to detect M. genitalium in the urogenitaltracts of 236 patients with NGU/MPC.Results: There was a specific M. genitalium band in42 out of 236 STD patients who were positive for M.genitalium by PCR.Conclusion: The results indicate that mycoplasmagenitalium exists among sexually transmitted diseasepatients. It may be one of the etiological agents of NGU/MPC.

Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is low.In recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean data.But it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean eddies.In this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center detection.To demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learningbased eddy detection method eddyNet.The results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

Comparative Analysis for the Detection and Monitoring of Mycoplasma hyopneumoniae Infection by Nested PCR(n-PCR) and Real time PCR(q-PCR) from Field Swine Herds

[Objective] 303 nasal swabs samples were collected from pigs in farms located in Taizhou city, Jiangsu Province, China from March to December 2012 for the purpose of detecting the presence of Mycoplasma hyopneumoniae, the primary agent of Enzootic porcine pneumonia （EPP） in pig herds using the nested PCR and Real time PCR techniques. [Method] Nasal swabs were collected from pigs of different ages＇ i.e. 7, 14, 21, 28, 30 and 35 days old, soaked in sterile 1 xPBS overnight at 4 ℃ and DNA extracted using the TIANamp（R） bacterial DNA kit. The DNA samples underwent amplification under the Mhyo 183 q-PCR and P36 primer Nested PCR systems. [Result] With the Nested PCR assay, 38 （12.5%） out of 303 samples tested positive for the presence of M. hyopneumoniae; with the real time PCR assay 152 （50.2%） tested positive for M. hyopneumoniae. The two assays matched to positively detect Mhyo in 22 （7.3%） samples and again matched in 127 （41.9%） samples negative for Mhyo infection. The pattern of infection in both assays was similar where 7- and 35-day-old piglets in both assays had the highest rates of infection i.e. 15.6% and 18.4% for n-PCR and 53.1% and 56.6% for q-PCR for 7- and 35-day-old piglets respectively. [Conclusion] The results highlight the suitability of both PCR assays in establishing the herd infection status of pigs in field conditions.

Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

Evaluation of a New Real-time PCR Assay for Detection of Mycoplasma Pneumoniae in Clinical Specimens

Objective To establish and evaluate a real-time PCR assay to detect Mycoplasma pneumoniae （M.pneumoniae） in clinical specimens. Methods By analysing the whole pl gene sequence of 60 M.pneurnoniae clinical isolates in Beijing of China, an optimized real-time PCR assay （MpP1） using pl gene conserved region was designed. The specificity and sensitivity of this assay were evaluated and compared with other two reported assays （RepMpl and Mp181） using 40 positive and 100 negative clinical specimens. Results The detection limit of the new assay was 8.1 fg （about 1-3CFU） M.pneumoniae DNA. The sensitivity of MpP1, RepMpl, and Mp181 assays appeared to be 100%, 100%, and 85%, respectively. Conclusion MpP1 assay is suitable for the detection of M.pneumoniae in Chinese clinical specimens.

Detection of Mycoplasmas in Patients with Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a progressive degenerative disease of the motor neurons and the cause is unknown. Diverse factors such as genetic defects, nutritional deficiencies, head trauma, environmental toxin, autoimmune responses and viral and bacterial infections are involved. Mycoplasmas have been implicated as causal agents of different illnesses in human. The purpose of this study was to investigate the presence of mycoplasmas in the bloodstream of patients with ALS. Patients with ALS and healthy individuals were included in the study. A blood sample was taken in tubes with or without anticoagulant. Mycoplasmas were detected by culture or direct PCR, and the presence of antibodies IgM and IgG against LAMPs of these microorganisms by Western blot. Cultures for aerobic facultative bacteria were also done. Blood samples from 13 patients and 44 healthy individuals were screened. All blood cultures for non-fermentative mycoplasmas and aerobic facultative bacteria were negative. Cultures for fermentative mycoplasmas were considered positive after identification of mycoplasmal DNA by PCR. Mycoplasma sp. was detected by culture or direct PCR in 6/13 (46%) patients and in 4/44 (9%) of healthy individuals. M. fermentans was detected by PCR using specific primers in six patients and in two healthy individuals. IgM against LAMPs of M. fermentans were detected in 6/13 (46%) blood samples from patients and in 13/44 (30%) from healthy individuals, while. IgG was detected in 4/13 (31%) patients and in 3/44 (7%) healthy individuals. The results of this study show that mycoplasmas cause a systemic infection and could play a role in the origin or progression of the ALS.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

Detection of Mycoplasma Hyorhinis Infection in Ovarian Cancer with in situ Hybridization and Immunohistochemistry

OBJECTIVE To detect Mycoplasma hyorhinis in ovarian cancer tissues and the relationship between mycoplasma infection and ovarian cancer. METHODS All specimens obtained from 109 cases with ovarian cancer were fixed in freshly prepared 10% neutral buffered formalin, embedded in paraffin, and cut into 4-μm sections for insitu hybridization （ISH） and then detected with immunohistochemistry （IHC）. The expressions of 16S rRNA and P37 protein from mycoplasma hyorhinis were detected respectively using ISH and IHC. SPSS 13.0 software was employed to analyze the relationship between the results of the study and clinical pathological materials. RESULTS The expression rate of mycoplasma hyorhinis 16S rRNA gene and P37 protein was 20.2% （22/109） and 43.1% （47/109 cases） in ovarian cancer tissues, respectively, but it was 0 （0/30 cases） in the normal ovarian tissues. The difference in mycoplasma infection ratio between ovarian cancer tissues and normal tissues was extremely significant （P 〈 0.001）. Anyhow, we didn＇t found any association between the mycoplasma infection and clinical pathological characters. CONCLUSION There was a mycoplasma infection in ovarian cancer tissues, which may play a role in oncogenesis of ovarian cancer.

Molecular Detection, Culture and Isolation of Mycoplasma Pneumoniae From Reproductive Tract of STD Patients

Objective: To confirm whether Mycoplasma pneumoniae (MP) are present in reproductive tract of STD patients inChina. Methods: Application of nested PCR (nPCR) and DNAsequencing to test samples of urethral/vaginal swabs withMP culture confirmation of several nPCR positive patients. Results: 74 of 786 STD patients were positive for MP bynPCR, with a rate of 9.4%. of the 484 male patients, 10.5%were positive, and among the 302 female patients, 7.6%were positive. There was no significant difference betweenthem (P<0.05). of 12 cases of MP positive samples by nPCR,4 cases were first generation culture-positive, and one ofthem passed to the next generation successfully. DNAsequencing was performed on the nPCR product of oneswab sample and one MP culture isolation. The determinedsequence was identical to the typical MP strain. Conclusion: In China, MP are present in reproductivetract of both male and female STD patients.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

Standardization of a Real-time PCR System for Quantitative Detection of Mycoplasma hyopneumoniae

This study was conducted to develop a method for accurate quantification of Mycoplasma hyopneumoniae during vaccine production or experimental research. Primer and probe concentration that gave the highest ΔRn and the lowest Ct were selected to establish the real-time PCR system for the detection of M. hyopneumoniae. Template DNA of M. hyopneumoniae was extracted by boiling under different conditions and detected by real-time PCR to determine the optimal conditions for DNA extraction. Thereafter, intra-and inter-batch reproducibility tests were carried out using a standard plasmid to evaluate the stability of the PCR system. Subsequently, the effect of medium composition on the quantitative detection was evaluated. Finally, the correlation between real-time PCR and CCU method was explored. The optimal primer and probe concentration for real-time PCR were 0.4 and 0.2 μmol/L, respectively. The intra-and inter-batch coefficients of variation(CV) in Ct value of 10~4-10~9 copies/μl standard plasmid were <5%, indicating good reproducibility of the real-time PCR system. Following incubation in a boiling water bath for 10 min, M. hyopneumoniae samples can be used directly as a template in subsequent real-time PCR assays,and good intra-batch and inter-batch reproducibility was observed. The working concentration of KM2 medium should be less than the 1/10 of the concentration of the stock solution to minimize its influence on the quantitative detection. Spearman's correlation analysis revealed that the log of CCU and the log of DNA copy number had a significant positive relationship(r=0.797,P=0.000). Thus, the two methods can be used in combination in the quantitative detection of M. hyopneumoniae. In summary, a rapid, stable and accurate quantitative PCR system for detecting M. hyopneumoniae culture was established in this study, which provides a technical means for accurate quantification of M. hyopneumoniae in vaccine production and laboratory tests.

Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.

A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7

Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important tasks.Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced,and traffic sign targets are small and have unclear features,which makes detection more difficult.Therefore,we propose aHybrid Feature Fusion Traffic Sign detection algorithmbased onYOLOv7(HFFTYOLO).First,a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales;Secondly,the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method.Design reuse paths at the end of the neck,paying particular attention to cross-scale fusion of highlevel features.In addition,we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters.In terms of training,a newregression lossCMPDIoUis proposed,which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different,but also enables the penalty term to dynamically change at different scales.Finally,our proposed improved method shows excellent results on the TT100K dataset.Compared with the baseline model,without increasing the number of parameters and computational complexity,AP0.5 and AP increased by 2.2%and 2.7%,respectively,reaching 92.9%and 58.1%.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.