Detection Rate Research Articles

Investigating the Influence of Gut Microbiota-related Metabolites in Gastrointestinal Cancer.

Gastrointestinal (GI) cancer is a major health concern due to its prevalence, impact on well-being, high mortality rate, economic burden, and potential for prevention and early detection. GI cancer research has made remarkable strides in understanding biology, risk factors, and treatment options. An emerging area of research is the gut microbiome's role in GI cancer development and treatment response. The gut microbiome, vital for digestion, metabolism, and immune function, is increasingly linked to GI cancers. Dysbiosis and alterations in gut microbe composition may contribute to cancer development. Scientists study how specific bacteria or microbial metabolites influence cancer progression and treatment response. Modulating the gut microbiota shows promise in enhancing treatment efficacy and preventing GI cancers. Gut microbiota dysbiosis can impact GI cancer through inflammation, metabolite production, genotoxicity, and immune modulation. Microbes produce metabolites like short-chain fatty acids, bile acids, and secondary metabolites. These affect host cells, influencing processes like cell proliferation, apoptosis, DNA damage, and immune regulation, all implicated in cancer development. This review explores the latest research on gut microbiota metabolites and their molecular mechanisms in GI cancers. The hope is that this attempt will help in conducting other relevant research to unravel the precise mechanism involved, identify microbial signatures associated with GI cancer, and develop targets.

A lightweight YOLOv5 for real-time dangerous weapons detection

Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When it comes to real-world applications, such as weapon detection, these methods are often not suitable for deployment on embedded devices. Because of the huge number of parameters and poor efficiency. The most recent object detection technique, which belongs to the YOLOv5 class, is commonly used for detecting weapons. However, it faces some difficulties such as high computational parameters and an unfavorable detection rate. to solve these shortcomings. an enhanced lightweight Yolov5s approach is suggested. Which consists of a combination of YOLOv5 and GhostNet modules. To evaluate the efficacy of the suggested technique, a set of experiments was performed on the Sohas weapon dataset., which is commonly used as a reference dataset in the field. Compared to the original YOLOv5, the results indicate a slight increase in the proposed model's mean Average Precision (mAP). Furthermore, there has been a reduction of 2.7 in GFLOPs and weights, and the number of model parameters has decreased by 1.42.

Ultrasensitive prostate cancer marker PCA3 detection with impedimetric biosensor based on specific label-free aptamers

Prostate cancer (PCa) appears among the most frequently diagnosed types of malignancies in males. Because of the high demand and increasing detection rate of early PCa, alongside the specificity limitations of the gold standard clinical tools available for the diagnosis and prognosis of prostate cancer, there is an urgent need for more reliable PCa markers and highly sensitive diagnostic tools to avoid under-treatment and over-diagnosis. PCA3, or prostate cancer antigen 3, is a potential prostate cancer biomarker that is more specific and useful for preventing unnecessary repeat biopsies, particularly in men with persistently high prostate-specific antigen indices after a negative biopsy. Additionally, an electrochemically based biosensor would prove to be a powerful diagnostic tool for PCA3 detection in urine because of its simplicity, sensitivity, and cost-effectiveness, in contrast to the more traditional PCa diagnostics that depend on blood testing. This paper aimed to design a novel and simple electrochemical impedimetric biosensor based on a label-free RNA-aptamer (CG3-PCA3) as the molecular recognition element for detecting PCA3. The proposed aptasensor for the detection of PCA3 has been developed using a screen-printed carbon electrode (SPCE) modified by gold nanoparticles (AuNPs), further improving sensitivity and allowing the immobilisation of thiolate aptamers on its surface. The findings presented here demonstrated a high sensitivity to PCA3, with a detection limit of 20 fM in artificial urine and 1 fM in buffer. These results indicate that the PCA3 aptasensor could be a promising tool for routine PCa diagnosis due to its high sensitivity and cost-effectiveness.

Implementation Paper on Dermatological Proriasis and other Skin Diseases Diagnosis Using Machine Learning

Abstract: This paper "Dermatological Psoriasis and Other Skin Diseases Diagnosis Using Machine Learning" aims to develop an efficient and accurate system for identifying psoriasis, a chronic skin condition, through the utilization of machine learning techniques. Psoriasis affects millions worldwide, presenting challenges in its diagnosis and treatment. Leveraging advanced algorithms, this project seeks to analyse various skin images and clinical data to create a robust model capable of distinguishing psoriatic lesions from other dermatological conditions with high precision. By employing machine learning algorithms such as convolutional neural networks (CNNs) and support vector machines (SVMs), alongside feature extraction methods, the system aims to automate and streamline the diagnostic process, potentially reducing both time and burdens on healthcare professionals. Ultimately, the implementation of such a tool could significantly enhance early detection rates, improve patient outcomes, and facilitate more targeted treatment strategies for individuals affected by psoriasis

Edoxaban and/or colchicine for patients with coronavirus disease 2019 managed in the out-of-hospital setting (CONVINCE): a randomized clinical trial.

The optimal pharmacological management of patients with Coronavirus disease 2019 (COVID-19) managed outside the hospital remains largely unsettled. In the investigator-initiated, open-label CONVINCE trial, 59 outpatients with COVID-19 were randomized (2 × 2 factorial design) to colchicine versus no treatment (anti-inflammatory comparison) or edoxaban versus no treatment (anticoagulation comparison). The study had two co-primary outcomes (one for each randomization): major vascular thrombotic events (MVTE, the composite of asymptomatic proximal deep vein thrombosis [DVT], symptomatic proximal or distal DVT, symptomatic pulmonary embolism or thrombosis, myocardial infarction, ischemic stroke, non-central nervous system embolism and death) at 25 ± 3 days for the anticoagulation comparison and the composite of SARS-CoV-2 detection rates or freedom from death or hospitalizations at 14 ± 3 days for the anti-inflammatory comparison. The trial was prematurely halted due to slow recruitment and availability of effective vaccines. Overall, 16 patients were randomized to edoxaban plus colchicine, 13 to edoxaban, 14 to colchicine and 16 to standard of care. The study showed no significant difference in the two co-primary outcomes with edoxaban and/or colchicine versus standard of care. However, these results should be interpreted in light of the low-risk profile of included patients and the premature termination of the trial.

Analysis of the Class 1 Integrons, Carbapenemase Genes and Biofilm Formation Genes Occurrence in Acinetobacter baumannii Clinical Isolates.

Acinetobacter baumannii is a non-fermentative Gram-negative bacterium that can cause nosocomial infections in critically ill patients. Carbapenem-resistant A. baumannii (CRAB) has spread rapidly in clinical settings and has become a key concern. The main objective of this study was to identify the distribution of integrons and biofilm-formation-related virulence genes in CRAB isolates. A total of 269 A. baumannii isolates (219 isolates of CRAB and 50 isolates of carbapenem-sensitive A. baumannii (CSAB)) were collected. Carbapenemase genes (bla KPC, bla VIM, bla IMP, bla NDM, and bla OXA-23-like) and biofilm-formation-related virulence genes (abal, bfms, bap, and cusE) were screened with PCR. Class 1 integron was screened with PCR, and common promoters and gene cassette arrays were determined with restriction pattern analysis combined with primer walking sequencing. Whole-genome sequencing was conducted, and data were analyzed for a bla OXA-23-like-negative isolate. All 219 CRAB isolates were negative for bla KPC, bla VIM, bla IMP, and bla NDM, while bla OXA-23-like was detected in 218 isolates. The detection rates for abal, bfms, bap, and cusE in 219 CRAB were 93.15%, 63.93%, 88.13%, and 77.63%, respectively. Class 1 integron was detected in 75 CRAB (34.25%) and in 3 CSAB. The single gene cassette array aacA4-catB8-aadA1 with relatively strong PcH2 promoter was detected in class 1 integrons. The bla OXA-23-like-negative CRAB isolate was revealed to be a new sequence type (Oxford 3272, Pasteur 2520) carrying bla OXA-72, bla OXA-259, and bla ADC-26. In conclusion, bla OXA-23-like was the main reason for CRAB's resistance to carbapenems. A new (Oxford 3272, Pasteur 2520) CRAB sequence type carrying the bla OXA-72, bla OXA-259, and bla ADC-26 was reported.

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% (P<0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% (P≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

Automated Evaluation of Environmental Coupling for Advanced LIGO Gravitational Wave Detections

Abstract The extreme sensitivity required for direct observation of gravitational waves by the Advanced LIGO detectors means that environmental noise is increasingly likely to contaminate Advanced LIGO gravitational wave signals if left unaddressed. Consequently, environmental monitoring efforts have been undertaken and novel noise mitigation techniques have been developed which have reduced environmental coupling and made it possible to analyze environmental artifacts with potential to affect the 90 gravitational wave events detected from 2015–2020 by the Advanced LIGO detectors. So far, there is no evidence for environmental contamination in gravitational wave detections. However, automated, rapid ways to monitor and assess the degree of environmental coupling between gravitational wave detectors and their surroundings are needed as the rate of detections continues to increase. We introduce a computational tool, PEMcheck, for quantifying the degree of environmental coupling present in gravitational wave signals using data from the extant collection of environmental monitoring sensors at each detector. We study its performance when applied to 79 gravitational waves detected in LIGO’s third observing run and test its performance in the case of extreme environmental contamination of gravitational wave data. We find that PEMcheck’s automated analysis identifies only a small number of gravitational waves that merit further study by environmental noise experts due to possible contamination, a substantial improvement over the manual vetting that occurred for every gravitational wave candidate in the first two observing runs. Building on a first attempt at automating environmental coupling assessments used in the third observing run, this tool represents an improvement in accuracy and interpretability of coupling assessments, reducing the time needed to validate gravitational wave candidates. With the validation provided herein; PEMcheck will play a critical role in event validation during LIGO’s fourth observing run as an integral part of the data quality report produced for each gravitational wave candidate.

Dense Outflowing Molecular Gas in Massive Star-forming Regions

Dense outflowing gas, traced by transitions of molecules with large dipole moment, is important for understanding mass loss and feedback of massive star formation. HCN 3-2 and HCO+ 3-2 are good tracers of dense outflowing molecular gas, which are closely related to active star formation. In this study, we present on-the-fly mapping observations of HCN 3-2 and HCO+ 3-2 toward a sample of 33 massive star-forming regions using the 10-m Submillimeter Telescope. With the spatial distribution of line wings of HCO+ 3-2 and HCN 3-2, outflows are detected in 25 sources, resulting in a detection rate of 76%. The optically thin H13CN and H13CO+ 3-2 lines are used to identify line wings as outflows and estimate core mass. The mass M out, momentum P out, kinetic energy E K , force F out, and mass-loss rate Ṁout of outflow and core mass, are obtained for each source. A sublinear tight correlation is found between the mass of dense molecular outflow and core mass, with an index of ∼0.8 and a correlation coefficient of 0.88.

Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study

BackgroundAutomated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications.Methods575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications.Results250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%).ConclusionsAB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.

End-to-end model for automatic seizure detection using supervised contrastive learning

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures, caused by abnormal electrical activity in cerebral neurons. Given that it is one of the most common neurological disorders globally, the efficient and accurate automatic seizure detection is urgently needed in the diagnosis of epilepsy to reduce the workload of continuous electroencephalogram (EEG) monitoring. Current deep learning based seizure detection approaches usually employ cross-entropy loss as objective function, which generally suffer from inadequate utilization of sample labels and poor classification margins, resulting in decreased performance in seizure detection. In this study, we propose an end-to-end automatic seizure detection framework based on supervised contrastive learning, which effectively utilizes labeled EEG to cluster similar samples while separating dissimilar ones. A supervised contrastive learning loss is employed to optimize classification boundaries by making full use of EEG labels. We employ long-term continuous EEG for evaluation. Given the presence of various noise and interferences, assessment on long-term continuous EEG proves to be more challenging. Post-processing techniques such as smoothing filter, threshold judgment, and collar technique are further adopted to diminish the artifact impacts on seizure detection performance. The proposed method is evaluated on the publicly available Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, achieving an event-based sensitivity of 99.71% and a false detection rate (FDR) of 0.35/h.

Robot-Assisted Radical Prostatectomy in PIRADS 5 Lesions Without Prior Biopsy: Is Biopsy Really Necessary in This Cohort?

Introduction: Conventionally, confirmation of clinically significant prostate cancer (csPCa) (Gleason grade group ≥ 2) involves an initial multiparametric magnetic resonance imaging (mpMRI) followed by biopsy. Prostate biopsy incurs inherent risks of infection, bleeding, patient discomfort, and a 6-week delay before robot-assisted laparoscopic radical prostatectomy (RALP). We explored the feasibility of immediate RALP in men with PIRADS 5 lesions without preceding biopsy. Methodology: After obtaining institutional review board approval, a prospective analysis was conducted on 235 patients with PIRADS 5 lesions on mpMRI from December 2018 to February 2023. Patients were divided into 2 groups as follows: Group NoBiopsy (biopsy not done before RALP, cases, n = 118) and Group YesBiopsy (biopsy done before RALP, controls, n = 117). Baseline preoperative, intraoperative, and postoperative parameters were analyzed. Functional outcomes were monitored at 1, 3-, 6-, 9-, and 12-months follow-up post-RALP. Statistical analysis was performed using SPSS and STATA. Results: Ninety-five percent of cases and 87.17% controls had csPCa on final pathology post-RALP. Multivariable analysis did not find significant association between biopsy status and csPCa. Abnormal digital rectal examination (DRE), family history, preoperative PSA, and MRI lesion volume predicted csPCa. Significant differences were observed in console time (NoBiopsy vs. YesBiopsy, 60 ± 10 vs. 70 ± 9 minutes, p < 0.001) and estimated blood loss (80 ± 20 vs. 100 ± 30 mL, p < 0.01) between groups. At 6 months post-RALP, 96% of men in Group NoBiopsy were continent, compared with 88% of men in Group YesBiopsy (p < 0.04). All men in the study cohort were continent (0 pads) at 12 months post-RALP. Ninety-eight percent of cases and 92% of controls at 9 months and 12 months, respectively, were able to have penetrative sex with or without PDE-5 inhibitors post-RALP. Conclusion: RALP without antecedent prostate biopsy in men with PIRADS 5 lesions demonstrated substantial csPCa detection rates and superior functional outcomes, warranting further validation.

Protocol for evaluating the fitness for purpose of an artificial intelligence product for radiology reporting in the BreastScreen New South Wales breast cancer screening programme

IntroductionRadiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has...

Self-Supervised Machine Learning Framework for Online Container Security Attack Detection

Container security has received much research attention recently. Previous work has proposed to apply various machine learning techniques to detect security attacks in containerized applications. On one hand, supervised machine learning schemes require sufficient labeled training data to achieve good attack detection accuracy. On the other hand, unsupervised machine learning methods are more practical by avoiding training data labeling requirements, but they often suffer from high false alarm rates. In this paper, we present a generic self-supervised hybrid learning (SHIL) framework for achieving efficient online security attack detection in containerized systems. SHIL can effectively combine both unsupervised and supervised learning algorithms but does not require any manual data labeling. We have implemented a prototype of SHIL and conducted experiments over 46 real world security attacks in 29 commonly used server applications. Our experimental results show that SHIL can reduce false alarms by 33-93% compared to existing supervised, unsupervised, or semi-supervised machine learning schemes while achieving a higher or similar detection rate.

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study.

Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning. A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups. The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001). The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.

First-trimester uterine artery Doppler and hypertensive disorders in twin pregnancies: Use of twin versus singleton references.

To determine the association of first-trimester uterine artery Doppler with hypertensive disorders of pregnancy in twin pregnancies. This was a retrospective cohort study of twin pregnancies followed at the University Hospital Center of Central Lisbon, Portugal, between January 2010 and December 2022. First-trimester uterine artery pulsatility index (UtA-PI) was determined and compared between twin pregnancies (n = 454) and singleton pregnancies (n = 908), matched to maternal and pregnancy characteristics. Maternal characteristics and mean UtA-PI were analyzed for gestational age, birth weight, gestational hypertension, early- and late-onset pre-eclampsia, HELLP (hemolysis, elevated liver enzymes, low platelets) syndrome, and preterm birth. Univariable and multivariable logistic regression models were used. The mean first-trimester UtA-PI was significantly lower in dichorionic twins than in singletons (P < 0.001). To study hypertensive disorders of pregnancy in twins, 390 pregnancies were included: 311 (79.7%) dichorionic and 79 (20.3%) monochorionic twins. The observed rates of early- and late-onset pre-eclampsia, gestational hypertension, and HELLP syndrome were 1.0%, 4.4%, 7.4%, and 1.5%, respectively. We achieved a 100% detection rate for early-onset pre-eclampsia using the UtA-PI 90th centile for twins. However, when singleton references were considered, the detection rate decreased to 50%. UtA-PI at or above the 95th centile was associated with increased odds for preterm birth before 32 weeks (adjusted odds ratio 4.1, 95% confidence interval 1.0-16.7, P = 0.043). Unless other major risk factors for hypertensive disorders are present, women with low UtA-PI will probably not benefit from aspirin prophylaxis. Close monitoring of all twin pregnancies for hypertensive disorders is still recommended.

Epidemiological characteristics of human metapneumovirus among children in Nanjing, China.

The objective of this study was to examine the molecular epidemiology and clinical characteristics of HMPV infection among children with ARIs in Nanjing. The respiratory samples were collected from 2078 children (≤ 14 years) with acute respiratory infections and were tested for HMPV using real-time RT-PCR. Amplification and sequencing of the HMPV G gene were followed by phylogenetic analysis using MEGA 7.0. The detection rate of HMPV among children was 4.7% (97/2078), with a concentration in those under 5 years of age. Notably, the peak season for HMPV prevalence was observed in winter. Among the 97 HMPV-positive samples, 51.5% (50/97) were available for characterization of the HMPV G protein gene. Phylogenetic analysis indicated that the sequenced HMPV strains were classified into three sublineages: A2c111nt - dup (84.0%), B1 (2.0%), and B2 (14.0%). There was an incidence of HMPV among hospitalized children during 2021-2022 in Nanjing with A2c111nt - dup being the dominant strain. This study demonstrated the molecular epidemiological characteristics of HMPV among children with respiratory infections in Nanjing, China.

Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies

Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.