Detection Rate Research Articles

Using four-point subcutaneous injection of lymphatic contrast-enhanced ultrasound to diagnose lymphedema of lower extremity.

The diagnosis of lymphedema primarily relies on the clinical symptoms, signs, medical history and imaging. Objective lymphatic imaging helps improving the diagnosis of lymphedema. This study aimed to develop an effective imaging tool to diagnose lymphedema. This is a single-center retrospective study. From September 2022 to November 2023, we enrolled thirty-two patients, involving 40 lower extremities who underwent lymphatic contrast-enhanced ultrasound (CEUS) following a subcutaneous injection of contrast agent at four points in the First Affiliated Hospital of Sun Yat-sen University. Cohen's kappa value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were calculated. Lymphoscintigraphy was the reference standard. Successful lymphatic-CEUS detection was defined as the situation that lymphatic drainage of medial or lateral lower limbs were observed. The successful detection rate was 100% (40 of 40). The diagnosis of lymphedema was based on the presence of either medial or lateral lymphatic obstructions, or subcutaneous lymphatic enhancement. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy for diagnosing lymphedema by lymphatic-CEUS were as follows: 91.2% (31 of 34), 100% (6 of 6), 100% (31 of 31), 66.7% (6 of 9) and 92.5% (37 of 40), respectively. The Cohen's Kappa value was 0.756. The area under the receiver operating characteristic curve (AUC) for the subcutaneous injection of four-point lymphatic-CEUS was 0.956. This study put forward a novel four-point lymphatic-CEUS method to detect the functions of the lymphatics of lower extremities and established a lymphatic-CEUS standard for diagnosing lymphedema of lower extremities. Four-point lymphatic-CEUS is a considerable option for diagnosing lymphedema of lower extremities.

Mobile Colistin Resistance and Plasmid-Mediated Quinolone Resistance Genes in Escherichia coli from China, 1993-2019.

Plasmid-mediated quinolone resistance (PMQR) genes and mobile colistin resistance (MCR) genes in Escherichia coli (E. coli) have been widely identified, which is considered a global threat to public health. In the present study, we conducted an analysis of MCR genes (mcr-1, mcr-2, mcr-3, mcr-4, and mcr-5) and PMQR genes [qnrA, qnrB, qnrC, qnrD, qnrE1, qnrVC, qnrS, aac(6')-Ib-cr, qepA, and oqxAB] in E. coli from China, 1993-2019. From the 3,663 E. coli isolates examined, 1,613 (44.0%) tested positive for PMQR genes, either individually or in combination. Meanwhile, 262 isolates (7.0%) carried the MCR genes. Minimum inhibitory concentration (MIC) analyses of 17 antibiotics for the MCR gene-carrying strains revealed universal multidrug resistance. Resistance to polymyxin varied between 4 μg/mL and 64 μg/mL, with MIC50 and MIC90 at 8 μg/mL and 16 μg/mL, respectively. In addition, fluctuations in the detection rates of these resistant genes correlated with the introduction of antibiotic policies, host origin, temporal trends, and geographical distribution. Continuous surveillance of PMQR and MCR variants in bacteria is required to implement control and prevention strategies.

Detection rates of hepatitis B surface and core-related antigens using novel highly sensitive assays in chronic hepatitis B patients with hepatitis B surface antigen seroclearance

Detection rates of hepatitis B surface and core-related antigens using novel highly sensitive assays in chronic hepatitis B patients with hepatitis B surface antigen seroclearance

Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data

Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

Clinical characteristics of community-onset Clostridioides difficile infections at a tertiary hospital in mainland China: A fourteen-year (2010–2023) retrospective study

BackgroundClostridioides difficile infection (CDI) is an increasingly common disease in healthcare facilities and community settings. However, there are limited reports of community-onset CDI (CO-CDI) in China. MethodsWe collected diarrheal stool samples from 3885 patients who went to outpatient department or emergency department in a tertiary hospital in China during 2010–2023, analyzed the correlation between patients’ basic information and the detection rate of CDI. Besides, all stool samples from 3885 outpatients included were tested by culturing. Moreover, we randomly selected 89 patients’ stools during the 14 years and isolated 126 C. difficile strains from them. The presence of toxin genes (tcdA, tcdB, cdtA, and cdtB) were confirmed by PCR. Toxigenic strains were typed using multilocus sequence typing (MLST). Susceptibility to 9 antimicrobials was evaluated using the E-test. Results528 of 3885 patients (13.6 %) with diarrhea were finally diagnosed as CDI. The median age of patients included was 51 years (6 months-95 years), while the median of patients with CDI was older than patients with negative results [55.5 years (6 months-93 years) vs. 50 years (9 months −95 years), p < 0.001]. In winter, patients with diarrhea might be more likely to have CDI. The detection rate of CDI of patients in emergency department was much higher than those in other outpatients (20.7 % vs. 12.4 %, p < 0.001), and did differ from each outpatient departments (p < 0.05). There were 95 isolated strains detected as toxigenic C. difficile. Among these strains, 82 (86.3 %) had the tcdA and tcdB genes (A+B+) and 5 of these 82 strains were positive for the binary toxin genes (cdtA and cdtB) (A+B+CDT+). There were 15 different sequence types (STs) by multilocus sequence typing (MLST), while the most ST was ST-54 (23.2 %). ST types composition was relatively stable over the time span of this study. Some strains had high resistance to ciprofloxacin, clindamycin, and erythromycin. Twenty-three isolates (24.2 %) were multidrug-resistant. ConclusionsOutpatients with CDI were common among patients having diarrhea during this period in our hospital. Elderly patients and patients went to emergency department may be susceptible to CDI. Based on MLST, the result revealed that the C. difficile isolates had high genetic diversity and maintained stability in this period. All isolates were susceptible to metronidazole and vancomycin, and nearly one quarter of all isolates had multidrug resistance.

Incipient fault detection based on dense feature ensemble net

With modern industrial processes becoming more and more complex, the occurrence of faults may cause unmitigated disaster. Therefore, incipient fault detection is very important and has attracted increasing attention Recently, a feature ensemble net (FENet) has been proposed based on a deep feature ensemble framework, which can detect partial notorious faults (such as faults 3, 9 and 15 in the Tennessee Eastman process (TEP)). However, the feature transformer layers in FENet can only mine the process information from the upper layer’s output rather than all features from previous layers. Therefore, a novel dense feature ensemble net (DenseFENet) is proposed to deeply extract the features of data for incipient fault detection in industrial process. DenseFENet firstly constructs base detectors with capabilities for extracting nonlinear features, such as one-class support vector machine, isolation forest, one-class back propagation neural network, one-class long short term memory network and one-class temporal convolutional network, etc. Then, a dense net structure with short paths between non-adjacent feature transformer layers is developed, improving the reutilization capability for shallow knowledge. In addition, a numerical simulation and TEP are adopted to demonstrate the performance of the proposed method. The fault detection rate of DenseFENet has improved by 5.05% and 3.27% correspondingly, revealing the superiority of this approach.

The genetic spectrum of NF1 variants in 10 unrelated Chinese families with neurofibromatosis type 1.

To investigate the clinical and genetic features in a cohort of Chinese families with neurofibromatosis type 1 (NF1). The clinical information of 21 patients with NF1 in 10 families was retrospectively analyzed. To broaden the genetic spectrum of NF1, multiplex ligation-dependent probe amplification analysis was performed first, followed by the whole-exome sequencing, in order to identify pathogenic or potentially pathogenic variants of NF1 gene in 10 unrelated Chinese families. Nine different NF1 variants were identified in all 10 families. Of these, 7 were known pathogenic variants and included the exon 1 deletion, exons 1-58 deletion, c.5401C>T (p.Q1801*), c.2291-2A>C, c.484C>T (p.Q162*), c.4922G>A (p.W1641*) and c.1019_1020del (p.S340Cfs*25). The 2 novel variants were c.5197T>C (p.S1733P) and c.783_797delinsC (p.K261Nfs*25). The p.S1733P variant was classified as a variant of uncertain significance, while p.K261Nfs*25 was classified as pathogenic. Hence, the positive detection rate of NF1 variants was 100% (10/10). While the truncating variants were responsible for 60.0% (6/10) of the cases, the splicing variant was responsible for 10% (1/10) of the cases. We identified 2 novel heterozygous variants (c.5197T>C and c.783_797delinsC) in the NF1 gene, which broadens the genetic spectrum of the NF1 gene.

Keratin 17 and Collagen type 1 genes: Esophageal cancer molecular marker discovery and evaluation.

One hundred eighty pairs of tissues of esophageal squamous cell carcinoma (ESCC) were tested by the transcriptome sequencing in order to explore etiology factors. The chi-square test and correlation analysis demonstrated that the relative expression levels of keratin 17 (KRT17) and collagen type I α1 chain (COL1A1) were significantly higher in EC with diabetes. Expression of KRT17 was correlated with blood glucose (r = 0.204, p = 0.001) and tumor size (r = -0.177, p = 0.038) in patients. COL1A1 correlated with age (r = -0.170, p = 0.029) and blood glucose levels (r = 0.190, p = 0.015). Experimental results of qRT-PCR: KRT17 and COL1A1 genes were highly expressed in ESCC (p < 0.05). When the two genes were used as a combination test, the positive detection rate of EC was 90.6%, and the ROC curve had greater power. The KRT17 and COL1A1 genes had the potential to be biomarkers for the diagnosis of ESCC.

Radiographs demonstrate poor accuracy for evaluation of postoperative osseous integration of osteochondral allografts in the knee

PurposeTo evaluate the interrater reliability and accuracy of radiographs in assessing healing of osteochondral allografts (OCA) in the knee compared to computed tomography (CT) imaging. MethodsA retrospective review patients who underwent OCA transplantation of the knee was conducted. Postoperatively, knee radiographs were obtained at three and six months, and CT scans were obtained at six months. Three board-eligible orthopaedic surgeons independently graded postoperative radiographs, utilizing the grading scale established by Oladeji et al 2017, and the CT scans utilizing the grading scale established by Gelber et al 2021. Their consensus measurements served as a comparative gold standard. ResultsAmong 44 prospectively imaged grafts, 13 (29.5%) had radiographs at three months and 28 (63.6%) had radiographs available for evaluation at six months. Assessment of radiographic integration demonstrated low reliability (ICC: -0.02 to 0.14). On CT scan, this improved to fair reliability (k=0.29, p<0.001). However, Spearman rho between CT and XR grading demonstrated poor correlation (ρ = -0.23, p=0.04). Among six-month postoperative radiographs, accuracy of cyst identification was negatively correlated to greater degree of cystic changes from 90.9% (30/33 correct; no cystic changes) to 19.4% (7/36 correct; small cystic changes) and 33.3% (5/15 correct; large cystic changes). Overall true positive cyst detection rates on radiographs were 27.8% to 46.7%. ConclusionRadiographs demonstrate poor interrater reliability and accuracy in evaluation of healing after OCA transplantation of the knee. Notably, there was a high rate of missed cystic changes on radiographic analysis, as well as poor correlation of integration assessment with CT scan findings.

Comparative study of phenotypic-based detection assays for carbapenemases in Acinetobacter baumannii

BackgroundAcinetobacter baumannii is a serious health concern worldwide, causing high mortality rates and limited medical therapy options. Carbapenem resistance is a significant problem in Acinetobacter baumannii isolates. The synthesis of acquired carbapenemases, such as oxacillinases, IMP, NDM, VIM, and KPC enzymes, causes carbapenem resistance. MethodsA total of 106 non-repetitive, Acinetobacter baumannii isolates were collected from four major hospitals in Bahrain including 78 carbapenem-resistant Acinetobacter baumannii (CRAB), and 28 carbapenem-susceptible Acinetobacter baumannii (CSAB) isolates. Three phenotypic tests were investigated in this study: including CARBA NP, modified carbapenem inactivation method (mCIM)/EDTA-CIM (eCIM), and modified Hodge test (MHT). ResultsCARBA NP was positive in 50 tested CRAB isolates (100%), and the sensitivity was 100%. The MHT was positive in 73/106 isolates (68.8%), while the sensitivity and specificity of the MHT were 77.6% and 100%. Moreover, only 38/106 (35.8%) isolates were positive for mCIM/eCIM. The sensitivity and specificity of mCIM were 40.4% and 100%. ConclusionCARBA NP was ideal for phenotypic detection of carbapenemase production, followed by MHT. The m/eCIM demonstrated a lower detection rate in CRAB. Consequently, combining tests would be more accurate. The mCIM/eCIM can easily distinguish between MBLs and serine-carbapenemases due to the frequent co-production of these enzymes in A. baumannii. In hospital setups where molecular characterization tests are not available, CARBA NP seems to be an alternative test in combination with MHT or mCIM/eCIM.

Real-time Application of ITS and D1-D3 Nanopore Amplicon Metagenomic Sequencing in Fungal Infections: Enhancing Fungal Infection Diagnostics

While fungal infections cause considerable morbidity and mortality, the performance of the current diagnostic tests for fungal infection is low. Even though fungal metagenomics or targeted next-generation sequencing have been investigated for various clinical samples, the real-time clinical utility of these methods still needs to be elucidated. In this study, we used internal transcribed spacer (ITS) and D1-D3 ribosomal DNA nanopore amplicon metagenomic sequencing to assess its utility in patients with fungal infections. Eighty-four samples from seventy-three patients were included and categorized into ‘Fungal infection,’ ‘Fungal colonization,’ and ‘Fungal contamination’ groups based on the judgement of infectious disease specialists. In the ‘Fungal infection’ group, forty-seven initial samples were obtained from forty-seven patients. Three fungal cases detected not by the sequencing but by conventional fungal assays were excluded from the analysis. In the remaining cases, the conventional fungal assay-negative/sequencing-positive group (n=11) and conventional fungal assay-positive/sequencing-positive group (n=33) were compared. Non-Candida and non-Aspergillus fungi infections were more frequent in the conventional-negative/sequencing-positive group (p-value = 0.031). We demonstrated the presence of rare human pathogens, such as Trichosporon asahii and Phycomyces blakesleeanus. In the ‘Fungal infection’ group and ‘Fungal colonization’ group, sequencing was faster than culturing (mean difference = 4.92 days, p-value < 0.001/ mean difference = 4.67, p-value <0.001). Compared to the conventional diagnostic methods including culture, nanopore amplicon sequencing showed a shorter turnaround time and a higher detection rate for uncommon fungal pathogens.

The super‐recogniser advantage extends to the detection of hyper‐realistic face masks

AbstractHyper‐realistic silicone masks provide a viable route to identity fraud. Over the last decade, more than 40 known criminal acts have been committed by perpetrators using this type of disguise. With the increasing availability and bespoke sophistication of these masks, research must now focus on ways to enhance their detection. In this study, we investigate whether super‐recognisers (SRs), people who excel at identity recognition, are more likely to detect this type of fraud, in comparison to typical‐recogniser controls. Across three tasks, we examined mask detection rates in the absence of a pre‐task prompt (covert task), and again after making participants aware of their use in criminal settings (explicit task). Finally, participants were asked to indicate which aspects of the masks could support their detection (regions of interest task). The findings show an SR advantage for the detection of hyper‐realistic masks across the covert and explicit mask detection tasks. In addition, the eye, mouth, and nose regions appear to be particularly indicative of the presence of a mask. The lack of natural skin texture, proportional features, expressiveness, and asymmetry are also salient cues. The theoretical and applied implications of these findings are discussed.

Complex evolutionary trajectories in vivo of two novel KPC variants conferring ceftazidime-avibactam resistance

More and more ceftazidime-avibactam resistant KPC-producing K. pneumoniae have been reported with its widespread use, and the detection rate of KPC variants has increased dramatically. However, the evolutionary mechanism and fitness effects during KPC mutation remained unknown. Here, we report the complex in vivo evolutionary trajectories of two novel KPC variants, KPC-155 (L169P/GT242A) and KPC-185 (D179Y/GT242A), from Klebsiella pneumoniae in the same patient. The novel variants were shown to confer ceftazidime-avibactam resistance but restore carbapenem susceptibility based on the results of plasmid transformation assays, cloning experiments, and enzyme kinetic measurements. In vitro competition experiments highlighted the adaptive advantage conferred by strains carrying these KPC variants, which could lead to the rapid spread of these ceftazidime-avibactam resistant strains. The growth curve indicated that blaKPC-185 had better growth conditions at lower avibactam concentration compared to blaKPC-155, which was consistent with ceftazidime-avibactam use in vivo. In addition, replicative transposition of the IS26-flanked translocatable unit (IS26-ISKpn6-blaKPC-ISKpn27-IS26) also contributes to the blaKPC amplification and formation of two copies (blaKPC-2 and blaKPC-185), conferring both carbapenem and ceftazidime-avibactam resistance. However, strains with double copies showed reduced competitive advantage and configuration stability. The comparative plasmid analysis of IS26 group (IS26-blaKPC-IS26) and Tn1721 group (Tn1721-blaKPC-IS26) revealed that IS26-insertion could influence the distribution of resistance genes and ability of self-conjugation. The dynamic changes in blaKPC configuration highlight the need for consistent monitoring including antimicrobial susceptibility testing and determination of blaKPC subtypes—during clinical treatment, especially when ceftazidime-avibactam is administered.

Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).

The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI). A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves. DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D. A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.

IoV-BCFL: An intrusion detection method for IoV based on blockchain and federated learning

In recent years, Internet of Vehicles (IoV) is in a booming stage. But at the same time, the methods of attack against IoV such as Denial of Service (DoS) and deception are great threats to personal and social security. Traditional IoV intrusion detection usually adopts a centralized detection model, which has the disadvantages of untimely detection results and is difficult to protect vehicle privacy in practical applications. Meanwhile, centralized computation requires a large amount of vehicle data transmission, which overloads the wireless bandwidth. Combined the distributed computing resources of Federated Learning (FL) and the decentralized features of blockchain, an IoV intrusion detection framework named IoV-BCFL is proposed, which is capable of distributed intrusion detection and reliable logging with privacy protection. FL is used for distributing training on vehicle nodes and aggregating the training models at Road Side Unit (RSU) to reduce data transmission, protect the privacy of training data, and ensure the security of the model. A blockchain-based intrusion logging mechanism is presented, which enhances vehicle privacy protection through Rivest-Shamir-Adleman (RSA) algorithm encryption and uses Inter Planetary File System (IPFS) to store the intrusion logs. The intrusion behavior can be faithfully recorded by logging smart contract after detecting the intrusion, which can be used to track intruders, analyze security vulnerabilities, and collect evidence. Experiments based on different open source datasets show that FL achieves a high detection rates on intrusion data and effectively reduce the communication overhead, the smart contract performs well on evaluation indicators such as sending rate, latency, and throughput.

A quantitative analysis of vertebrate environmental DNA degradation in soil in response to time, UV light, and temperature

AbstractEnvironmental DNA (eDNA) degradation influences the effectiveness of eDNA‐based biodiversity monitoring, but the factors that determine the rate of decay of eDNA in terrestrial environments are poorly understood. We assessed the persistence of vertebrate eDNA from a mock vertebrate community created with soil from zoo enclosures holding 10 target species from different taxonomic classes (reptiles, birds, and mammals) and of different biomass (little penguin and giraffe). We examined species detection rates resulting from eDNA metabarcoding, as well as relative eDNA concentrations via qPCR, from soil samples over eight time points (0–12 weeks), during exposure to three ambient temperatures (10, 25, and 40°C) and three levels of ultraviolet B (UV‐B) radiation (0%, 50%, and 100% intensity). We recorded considerable variation in detectability between species, independent of temperature, and UV‐B effects. Quantitative polymerase chain reaction (PCR) indicated degradation of eDNA over time for all temperature and UV treatments, although it was still possible to detect eDNA from some species after 12 weeks. Degradation rates were lowest for high UV‐B treatments, presumably due to UV‐B reducing bacterial metabolism. The temperatures investigated did not influence eDNA decay. Our results indicate that eDNA in soil can persist under a range of temperatures and high UV radiation for longer than expected. Sheltered sites with minimal UV‐B radiation, which have previously been considered ideal sites for terrestrial eDNA collection, may not be optimal for eDNA persistence in some cases due to microbial decay. A better understanding of eDNA degradation in terrestrial environments is needed to enhance the accuracy of eDNA metabarcoding for surveying terrestrial vertebrate communities.

Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data

Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

A comparative study of remotely sensed reservoir monitoring across multiple land cover types

Continuous monitoring of reservoirs and dams is essential for efficient water management. Synthetic Aperture Radar (SAR) imagery offers the potential for continuous monitoring of surface water through all-weather ground observation. The objective of this study is to enhance the accuracy of water body detection and water quantity estimation by applying 64 combinations of speckle filtering and object detection techniques to Sentinel-1 imagery. For speckle filtering, the Median, Gaussian, Lee, and Frost techniques were used with various window sizes (3, 5, 7, and 9). For water body detection, the Otsu, Kittler-Illingworth (KI), Chan-Vese (CV), and K-means methods were employed. The study area included three reservoirs and two dams in Korea, encompassing a variety of water surface sizes and types of land cover. To validate the accuracy of each water body detection combination, manual delineation-based water mask images from Sentinel-2 were employed. Furthermore, a regression equation (y=axb) between water surface area and storage was used to estimate water storage based on SAR imagery, followed by time-series validation using in-situ data. The research results indicate that the optimal detection technique varies significantly depending on the type of surrounding land cover and the size of the water body. The highest performance was observed for the CV technique combination for waterfront pixels, and for the KI technique combination for other land cover pixels. In speckle filtering techniques using a large window size, the false detection rate caused by vegetation and buildings was low; however, the boundaries of water bodies were blurred. Consequently, using smaller window sizes in SAR imagery and leveraging optimal water body detection combinations specific to land cover types, along with post-processing using masking data, would enhance the performance of water surface area and storage estimation.

Assessment of colonoscopy skill using machine learning to measure quality: Proof-of-concept and initial validation.

Background and study aims Low-quality colonoscopy increases cancer risk but measuring quality remains challenging. We developed an automated, interactive assessment of colonoscopy quality (AI-CQ) using machine learning (ML). Methods Based on quality guidelines, metrics selected for AI development included insertion time (IT), withdrawal time (WT), polyp detection rate (PDR), and polyps per colonoscopy (PPC). Two novel metrics were also developed: HQ-WT (time during withdrawal with clear image) and WT-PT (withdrawal time subtracting polypectomy time). The model was pre-trained using a self-supervised vision transformer on unlabeled colonoscopy images and then finetuned for multi-label classification on another mutually exclusive colonoscopy image dataset. A timeline of video predictions and metric calculations were presented to clinicians in addition to the raw video using a web-based application. The model was externally validated using 50 colonoscopies at a second hospital. Results The AI-CQ accuracy to identify cecal intubation was 88%. IT ( P = 0.99) and WT ( P = 0.99) were highly correlated between manual and AI-CQ measurements with a median difference of 1.5 seconds and 4.5 seconds, respectively. AI-CQ PDR did not significantly differ from manual PDR (47.6% versus 45.5%, P = 0.66). Retroflexion was correctly identified in 95.2% and number of right colon evaluations in 100% of colonoscopies. HQ-WT was 45.9% of, and significantly correlated with ( P = 0.85) WT time. Conclusions An interactive AI assessment of colonoscopy skill can automatically assess quality. We propose that this tool can be utilized to rapidly identify and train providers in need of remediation.