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A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG Boost are applied and evaluated on the Demographic and Health Surveys Program (DHSP), with thirteen features ultimately selected as the most predictive features. Classification and generalization experiments are conducted to test the accuracy, F1-score, precision, and area under the curve (AUC) performance of these four algorithms. Two imbalanced data solutions are also applied to reduce bias for classification performance improvement. The experimental results of these models demonstrate that the Random Forest algorithm yields the best results on HIV prediction, whereby the highest accuracy, and AUC are 0.99 and 0.99, respectively. The performance of the STI prediction achieves the best when the Synthetic Minority Oversampling Technique (SMOTE) is applied (Accuracy = 0.99, AUC = 0.99), which outperforms the state-of-the-art baselines. Two possible factors that may affect the classification and generalization performance are further analyzed. This automatic classification model helps to improve convenience and reduce the cost of HIV testing.

Abstract Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs), electromyograms (EMGs), and electroencephalograms (EEGs), to classify sleep stages. Recently, various state-of-the-art approaches based on a deep learning model have been demonstrated to have efficient and accurate outcomes in sleep stage classification. In this paper, a novel deep convolutional neural network (CNN) combined with a long short-time memory (LSTM) model is proposed for sleep scoring tasks. A key frequency domain feature named Mel-frequency Cepstral Coefficient (MFCC) is extracted from EEG and EMG signals. The proposed method can learn features from frequency domains on different bio-signal channels. It firstly extracts the MFCC features from multi-channel signals, and then inputs them to several convolutional layers and an LSTM layer. Secondly, the learned representations are fed to a fully connected layer and a softmax classifier for sleep stage classification. The experiments are conducted on two widely used sleep datasets, Sleep Heart Health Study (SHHS) and Vincent’s University Hospital/University College Dublin Sleep Apnoea (UCDDB) to test the effectiveness of the method. The results of this study indicate that the model can perform well in the classification of sleep stages using the features of the 2-dimensional (2D) MFCC feature. The advantage of using the feature is that it can be used to input a two-dimensional data stream, which can be used to retain information about each sleep stage. Using 2D data streams can reduce the time it takes to retrieve the data from the one-dimensional stream. Another advantage of this method is that it eliminates the need for deep layers, which can help improve the performance of the model. For instance, by reducing the number of layers, our seven layers of the model structure takes around 400 s to train and test 100 subjects in the SHHS1 dataset. Its best accuracy and Cohen’s kappa are 82.35% and 0.75 for the SHHS dataset, and 73.07% and 0.63 for the UCDDB dataset, respectively.

Abstract Background Low urine pH, which may be mediated by metabolic syndrome (MetS), is common in gout. Tart cherries are shown to improve MetS symptoms and possess anti-inflammatory properties. However, the efficacy of tart cherry supplements on urine pH has yet to be studied. Objectives This study aimed to investigate the efficacy and safety of tart cherry supplementary citrate (TaCCi) mixture on urine pH, serum urate (sUA), C-reactive protein (CRP), and gout flares in gout patients initiating urate-lowering therapy (ULT), in comparison to citrate mixture and sodium bicarbonate. Methods A prospective, randomized (1:1:1), open-label, parallel-controlled trial was conducted among 282 men with gout and fasting urine pH ≤ 6, who were initiating ULT with febuxostat (initially 20 mg daily, escalating to 40 mg daily if serum urate ≥ 360 μmol/L). Participants were randomized to groups taking either sodium bicarbonate, citrate mixture, or TaCCi mixture. All participants were followed every 4 weeks until week 12. Urine pH and sUA were co-primary outcomes, with various biochemical and clinical secondary endpoints. Results Urine pH increased to a similar extent in all three groups. SUA levels declined in all three groups as well, with no significant differences observed between the groups. At week 12, the TaCCi mixture group exhibited a greater reduction in the urine albumin/creatinine ratio (UACR) compared to the other two groups (p

ABSTRACT: Objectives: Inappropriate antimicrobial use (AMU) and healthcare-associated infections (HAIs) are important drivers of antimicrobial resistance, but data from the developing world are scarce. We conducted the first point prevalence survey (PPS) to determine the prevalence of AMU and HAIs and the suggested targeted interventions for appropriate AMU and HAI prevention in Shanxi Province, China. Methods: A multicentre PPS was performed in 18 hospitals in Shanxi. Detailed data on AMU and HAI were collected using the Global-PPS method developed by the University of Antwerp and the methodology developed by the European Centre for Disease Prevention and Control, respectively. Results: There were 2171 (28.2%) of the 7707 inpatients receiving at least one antimicrobial. The most commonly prescribed antimicrobials were levofloxacin (11.9%), ceftazidime (11.2%), and cefoperazone and beta-lactamase inhibitor (10.3%). Out of the total indications, 89.2% of antibiotics were prescribed for therapeutic, 8.0% for prophylaxis, and 2.8% for either unknown or other. Of the total surgical prophylaxis, 96.0% of antibiotics were given for more than one day. In general, antimicrobials were given mainly parenterally (95.4%) and empirically (83.3%). A total of 264 active HAIs were identified in 239 patients (3.1%), of which 139 (52.3%) were culture positive. The most common HAI was pneumonia (41.3%). Conclusions: This survey indicated the relatively low prevalence of AMU and HAIs in Shanxi Province. However, this study has also highlighted several priority areas and targets for quality improvement, and repeated PPSs in the future will be useful to gauge progress at controlling AMU and HAIs.

A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EEG), electromyogram (EMG), and electrooculogram (EOG) channels and fed into the 3D-CNN model to classify sleep stages. Intrinsic connections among different bio-signals and different frequency bands in time series and time-frequency are learned by 3D convolutional layers, while the frequency relations are learned by 2D convolutional layers. Partial dot-product attention layers help this model find the most important channels and frequency bands in different sleep stages. A long short-term memory unit is added to learn the transition rules among neighboring epochs. Classification experiments were conducted using both ISRUC-S3 datasets and ISRUC-S1, sleep-disorder datasets. The experimental results showed that the overall accuracy achieved 0.832 and the F1-score and Cohen’s kappa reached 0.814 and 0.783, respectively, on ISRUC-S3, which are a competitive classification performance with the state-of-the-art baselines. The overall accuracy, F1-score, and Cohen’s kappa on ISRUC-S1 achieved 0.820, 0.797, and 0.768, respectively, which also demonstrate its generality on unhealthy subjects. Further experiments were conducted on ISRUC-S3 subset to evaluate its training time. The training time on 10 subjects from ISRUC-S3 with 8549 epochs is 4493s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and $\text{U}^{2 }-$ Net architecture algorithms.

This study investigates covert communication in multi-sensor systems employing Intelligent Reflecting Surfaces (IRSs). Different from previous works, we focus on optimizing transmission amplitudes and phase angles for a 2-BPSK codebook in the presence of asymmetric noise over complex Gaussian channels. We adopt KL divergence as a covertness constraint and mutual information as a metric for transmission rate. We employ Taylor series expansion to approximate KL divergence and mutual information. Leveraging these approximations, we derive optimal phase angles through a proposed gradient descent algorithm. The numerical simulations validate the effectiveness and precision of our Taylor approximation method. Through validation in different scenarios, our algorithm demonstrates robust convergence, deriving all optimal phase angles. Comparing initial phase angles from prior works to those obtained via our algorithm, we observe a higher covert transmission rate.

We propose a secure user pairing (UP) and power allocation (PA) strategy for a downlink Non-Orthogonal Multiple Access (NOMA) system when there exists an external eavesdropper. The secure transmission of data through the downlink is constructed to optimize both UP and PA. This optimization aims to maximize the achievable sum secrecy rate (ASSR) while adhering to a limit on the rate for each user. However, this poses a challenge as it involves a mixed integer nonlinear programming (MINLP) problem, which cannot be efficiently solved through direct search methods due to its complexity. To handle this gracefully, we first divide the original problem into two smaller issues, i.e., an optimal PA problem for two paired users and an optimal UP problem. Next, we obtain the closed-form optimal solution for PA between two users and UP in a simplified NOMA system involving four users. Finally, the result is extended to a general 2K-user NOMA system. The proposed UP and PA method satisfies the minimum rate constraints with an optimal ASSR as shown theoretically and as validated by numerical simulations. According to the results, the proposed method outperforms random UP and that in a standard OMA system in terms of the ASSR and the average ASSR. It is also interesting to find that increasing the number of user pairs will bring more performance gain in terms of the average ASSR.

This research investigates the feasibility of covert and reliable communication between Alice and Bob in the presence of a vigilant adversary, Willie, employing a jammer in a complex Gaussian channel. Alice strategically manipulates the noise-power ratio along the real and imaginary axes to disrupt Willie’s detection capabilities. Covertness constraints are quantified using KL divergence, and transmission performance is assessed through mutual information. Additionally, an optimization method is introduced to enhance covert communication by fine-tuning the amplitude gain and noise-power ratio. Numerical results confirm the effectiveness and superiority of the proposed method, showcasing its ability to maximize covert transmission rates.

This study explores the covert transmission of surveillance videos using QAM (quadrature amplitude modulation) constellations. By analyzing transmission strategies used in surveillance video systems, we adopt KL divergence as the constraint for covertness performance and mutual information to characterize transmission rates. Utilizing the Taylor series expansion method, we simplify the complex integral calculations of mutual information and KL divergence into summation operations. This simplification not only reduces computational complexity but also ensures precise calculations. Theoretical derivations establish the necessary conditions for the covert transmission of surveillance videos, optimizing modulation probabilities for constellation points. Experimental results and simulation validations demonstrate the superior performance of our proposed method in terms of both information transfer and precision.

This work investigates wireless covert communication in a multi-sensor asymmetric noise scenario. We adopt KL (Kullback–Leibler) divergence as the covertness constraint metric and mutual information as the transmission rate metric. To accurately approximate KL divergence and mutual information in covert communication, we employ the Taylor series expansion technique. Analytical expressions for KL divergence and mutual information in covert communication are derived, and we optimize the amplitude gain and phase angles based on these analytical expressions. Our findings underscore the importance of phase angle selection in covert communication within asymmetric noise systems. We propose an effective method for optimizing the transmission amplitude gain and phase angles in scenarios with asymmetric noise. Numerical results validate the effectiveness and superiority of our proposed method.

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

Abstract Background Patients with gout frequently have low urinary pH, which is associated with the nephrolithiasis. However, the specific distribution of urinary pH and potential relationship of acidic urine pH to broader manifestations of kidney disease in gout are still poorly understood. Methods A 2016–2020 population-based cross-sectional study was conducted among 3565 gout patients in the dedicated gout clinic of the Affiliated Hospital of Qingdao University to investigate the association between low urinary pH and kidney disease. We studied patients that we defined to have “primary gout”, based on the absence of > stage 2 CKD. All subjects underwent 14 days of medication washout and 3-day standardized metabolic diet. We obtained general medical information, blood and urine biochemistries, and renal ultrasound examination on the day of the visit. The primary readouts were urine pH, eGFR, nephrolithiasis, renal cysts, microhematuria, and proteinuria. Patients were assigned into 5 subgroups (urine pH ≤5.0, 5.0 6.9), aligning with the clinical significance of urine pH. Results Overall, the median urine pH and eGFR of all patients was 5.63 (IQR 5.37~6.09), and 98.32 (IQR 86.03~110.6), with acidic urine in 46.5% of patients. The prevalence of nephrolithiasis, microhematuria, and proteinuria were 16.9%, 49.5%, and 6.9%, respectively. By univariate analysis, eGFR was significantly associated with age, sex, duration of gout, tophus, body mass index, systolic blood pressure, diastolic blood pressure, fasting blood glucose, total cholesterol, serum utare, hypertension, diabetes, and urine pH. On multivariable analysis, eGFR was associated with age, sex, diastolic blood pressure, serum uric acid, hypertension, diabetes, and urine pH. Acidic urine pH, especially urine pH < 5.0, was significantly associated with the prevalence of kidney disease, including > stage 1 CKD, nephrolithiasis, kidney cyst, and microhematuria. Patients with 6.2 ≤ urine pH ≤ 6.9 and SU ≤ 480 μmol/L had the highest eGFR with the lowest prevalence of nephrolithiasis, microhematuria, and proteinuria. Conclusions Approximately half of gout subjects had acidic urine pH. Urine pH < 5.0 was associated with significantly increased nephrolithiasis, renal cyst, microhematuria, and proteinuria. The results support prospective clinical investigation of urinary alkalinization in selected gout patients with acidic urine pH.

A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN model to extract spatial features from the graph convolutions efficiently and temporal features are extracted from its common standard convolutions to learn the transition rules among sleep stages. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively.

Monopole communication towers play an irreplaceable role in modern communication systems. Because of its high flexibility, lateral loads control the deformation of a monopole communication tower. Dynamic displacement can be used to describe structural vibration characteristics and real-time deformation situations. Therefore, this article proposes a monopole communication tower deformation monitoring method based on multi-source data fusion using dynamic displacement as the evaluation indicator. This method calculates the strain displacement using the strain-mode superposition methodology. Then, the strain displacement and acceleration obtained are processed using Kalman filtering technology to reconstruct the real-time displacements of the monopole communication tower. The effectiveness of this method was verified using numerical simulations and model experiments, respectively. In addition, parametric analysis shows that this method is suitable for processing multi-rate data, has good noise resistance in strong noise environments, and can effectively reconstruct the displacement near the tower bottom. The results indicate that the method has favorable robustness and can accurately reconstruct low-frequency and high-frequency displacements of the monopole communication tower.

Recovering human pose from RGB images and videos has drawn increasing attention in recent years owing to minimum sensor requirements and applicability in diverse fields such as human-computer interaction, robotics, video analytics, and augmented reality. Although a large amount of work has been devoted to this field, 3D human pose estimation based on monocular images or videos remains a very challenging task due to a variety of difficulties such as depth ambiguities, occlusion, background clutters, and lack of training data. In this survey, we summarize recent advances in monocular 3D human pose estimation. We provide a general taxonomy to cover existing approaches and analyze their capabilities and limitations. We also present a summary of extensively used datasets and metrics, and provide a quantitative comparison of some representative methods. Finally, we conclude with a discussion on realistic challenges and open problems for future research directions.

DNS (Domain Name System) tunnels almost obscure the true network activities of users, which makes it challenging for the gateway or censorship equipment to identify malicious or unpermitted network behaviors. An efficient way to address this problem is to conduct a temporal‐spatial analysis on the tunnel traffic. Nevertheless, current studies on this topic limit the DNS tunnel to those with a single protocol, whereas more than one protocol may be used simultaneously. In this paper, we concentrate on the refined identification of two protocols mixed in a DNS tunnel. A feature set is first derived from DNS query and response flows, which is incorporated with deep neural networks to construct a regression model. We benchmark the proposed method with captured DNS tunnel traffic, the experimental results show that the proposed scheme can achieve identification accuracy of more than 90%. To the best of our knowledge, the proposed scheme is the first to estimate the ratios of two mixed protocols in DNS tunnels.

Objective: Extrachromosomal circular DNA elements (eccDNAs) are known for their broad existence in cells and plasma, which may potentially play important roles in many biological processes. Our aim was to identify potentially functional or marked eccDNAs in gout patients.Methods: The Circle-Seq approach was applied for eccDNA detection from plasma in acute gout patients and healthy controls. Further analysis was performed on the distribution of genomic elements and eccDNA gene annotations in two groups.Results: We detected 57,216 and 109,683 eccDNAs from the acute gout and healthy control plasma, respectively. EccDNAs were mapped to the reference genome to identify diverse classes of genomic elements and there was no significant difference of eccDNAs on genomic element annotation between gout and control group. A total of 256 eccDNA-associated genes were detected as gout unique eccDNA genes, including COL1A1 and EPB42, which potentially contribute to hyperuricemia and gout, and a couple of genes involved in inflammation or immune response. Enrichment analysis showed that these eccDNA genes were highly correlated with defense response, stress response, and immune and inflammatory responses, including T cell receptor signaling pathway, Fc epsilon RI signaling pathway, and JAK-STAT signaling pathway.Conclusion: Our discovery reveals the novel potential biological roles of plasma eccDNAs in gouty arthritis.

Wireless covert communication is an emerging communication technique that prevents eavesdroppers. This paper presents a novel approach to wireless covert communication based on polar codes with dirty constellation polarization. The feasibility of dirty constellation polarization is analyzed, and the impact of covert transmission power on undetectability is investigated. We polarize the covert channel and decode the covert information using a successive-cancellation decoder. Simulation results demonstrate that polar codes can significantly improve the undetectability and reliability of covert communication when compared to BCH codes. Additionally, we find that polarized covert channels achieve better undetectability and reliability when using a successive-cancellation list decoder. The proposed algorithm shows significant improvement in the undetectability and reliability of wireless covert communication with dirty constellations. This research provides a promising direction for future development in the field of covert communication, with potential applications in secure communication systems.

As one of the most critical infrastructure, the power grid has been increasingly threatened by network attacks, especially advanced persistent threats (APTs). APT in the power grid is a continual and stealthy attack that analyzes the interaction between the cyber layer and the physical layer. The existing offensive and defensive processes for power grid using honeypots against APTs are modeled based on full rationality. Therefore, both the attacker and the defender make decisions to maximize their payoffs under full rationality. However, fully rational decisions made by end-users are not always conformed with the real cases, and prospect theory is a typical boundedly rational method to model these deviations. In this study, we propose a subjective APT-honeypot game model to study the offensive and defensive interactions between the attacker and the defender based on the prospect theory. In this model, we protect the power grid bus nodes by deploying honeypots, which consider both low- and high-interaction honeypot modes. We prove the existence of Bayesian-Nash equilibrium strategies in defense and attack strategies under bounded rationality. In addition, we used IEEE-30 Bus system to verify the proposed model in this paper. Experiment results show that bounded rationality affects strategy selection and reduces attacker's payoffs.

With the rapid development of mobile internet and cloud computing, numerous digital me-dia files in mobile social networking and media sharing software have become the important carriers of steganography. However, these digital media files may be resampled by the media server when being pushed to the intelligent mobile terminals. The resampling of digital media files is a transfor-mation which enlarges or shrinks objects by a scale factor that is the same in all dimensions. In order to reduce embedding distortion while ensuring the correct extraction of secret messages under resam-pling mechanism, a steganographic coding scheme based on dither convolutional trellis is proposed in this paper. The resampling mapping is estimated with finite sample pairs. The resampling stego media files with secret messages embedded are generated from the estimated resampling cover media files by syndrome-trellis codes (STCs). According to the estimated resampling mapping, the dither convolutional trellis for one dimensional resampling is constructed to generate the source stego me-dia files from source cover media files and resampling stego media files. The steganographic coding scheme is also extended to the circumstance of two dimensional resampling such as image scaling. The experimental results show that the proposed steganographic scheme can achieve less embedding dis-tortion while ensuring the accuracy of secret messages extraction under multi-dimensional resampling mechanism.

Rainstorms, insect swarms, and galloping horses produce “sound textures,”which are the resulting natural sounds of many similar acoustic events. With new achievements emerging regularly for generative models, the deep convolutional neural network (CNN) has proven to be a tremendously successful approach for image and sound synthesis. Existing state-of-the-art sound texture generative models simply treat sound texture signals as 1-D images while discarding the difference between the human vision and auditory systems. This paper considers mel-frequency statistical features, which are designed according to the human auditory system and have been viewed as the dominant features for sound identification. We first construct a CNN structure for extracting mel-frequency features from sounds losslessly. This structure is called mel-frequency CNN (MF-CNN). Next, we investigate a novel sound texture generative model by incorporating the MF-CNN into a convolutional generative network composed of cascading upsampling groups. A jointly alternating back propagation algorithm is proposed to train the overall network. The feedback of the MF-CNN is used to advise the gradients in the inferential and learning back propagation to make the mel-frequency features of the synthesized sounds more similar to the natural ones. Moreover, the proposed generative model can also be extended to other sound synthesis tasks.

The benefits of physical activities on depressive symptoms have increasingly been reported in the literature, but the effect through which a Web-based physical activity promotion program alleviates depressive symptoms is not clearly known, especially among ethnic minority midlife women. The purpose of this pilot randomized control study is to examine the preliminary efficacy of the Web-based physical activity promotion program in enhancing the depressive symptoms of Asian American midlife women through increasing physical activity. This study adopted a randomized repeated measures pretest/posttest control group design. This study consisted of two groups of research participants: 18 in an intervention group and 15 in a control group. By using multiple instruments, the participants’ background and health status, depressive symptom experience, and physical activity experience were measured at three time points (pre-, post 1-month, and post 3-months). The data were analyzed using a modified intent-to-treat linear mixed-model growth curve analysis. After controlling for covariates, random intercept, and random slope, only discrimination stress showed statistical significances in the group effect (0.18, p = .08 for control) and time effect (-0.04, p = .04), but not in the group × time effect (p = .51). The active living habits scores showed statistical significances in the group effect (0.82, p < 0.01 for control), time effect (0.29, p < 0.01), and group × time effect (-0.31, p = 0.03 for control). Findings support the significant effect of the Web-based physical activity promotion program on the women’s discrimination stress and active living habits.

In highly sophisticated network attacks, command-and-control (C&C) servers always use domain generation algorithms (DGAs) to dynamically produce several candidate domains instead of static hard-coded lists of IP addresses or domain names. Distinguishing the domains generated by DGAs from the legitimate ones is critical for finding out the existence of malware or further locating the hidden attackers. The word-based DGAs disclosed in recent network attack events have shown significantly stronger stealthiness when compared with traditional character-based DGAs. In word-based DGAs, two or more words are randomly chosen from one or more specific dictionaries to form a dynamic domain, these regularly generated domains aim to mimic the characteristics of a legitimate domain. Existing DGA detection schemes, including the state-of-the-art one based on deep learning, still cannot find out these domains accurately while maintaining an acceptable false alarm rate. In this study, we exploit the inter-word and inter-domain correlations using semantic analysis approaches, word embedding and the part-of-speech are taken into consideration. Next, we propose a detection framework for word-based DGAs by incorporating the frequency distribution of the words and that of part-of-speech into the design of the feature set. Using an ensemble classifier constructed from Naive Bayes, Extra-Trees, and Logistic Regression, we benchmark the proposed scheme with malicious and legitimate domain samples extracted from public datasets. The experimental results show that the proposed scheme can achieve significantly higher detection accuracy for word-based DGAs when compared with three state-of-the-art DGA detection schemes.

To investigate the value of prospective ECG-gated high-pitch 128-slice dual-source CT (DSCT) angiography in the diagnosis of congenital extracardiac vascular anomalies in infants and children in comparison with transthoracic echocardiography (TTE).Eighty consecutive infants or children clinically diagnosed of congenital heart disease and suspected with extracardiac vascular anomaly were enrolled, and 75 patients were finally included in this prospective study. All patients underwent prospective ECG-gated high-pitch DSCT angiography after TTE with an interval of 1-7 days. The diagnostic accuracy and sensitivity of high-pitch DSCT angiography and TTE were compared according to the surgical/CCA findings. The image quality of DSCT was assessed using a five-point scale. The effective radiation dose (ED) was calculated.A total of 17 congenital heart diseases and 162 separate extracardiac vascular anomalies were confirmed by surgical/CCA findings in 75 patients. The diagnostic accuracy of high-pitch DSCT angiography and TTE was 99.67% and 97.89%, respectively. The sensitivity of high-pitch DSCT angiography and TTE was 97.53% and 79.62%, respectively. There was significant difference regarding to the diagnostic accuracy and the sensitivity between high-pitch DSCT angiography and TTE (χ2 = 23.561 and 28.013, P

To explore the clinical value of low-dose prospectively electrocardiogram-gated axial dual-source CT angiography (low-dose PGA scanning, CTA) in patients with pulsatile bilateral bidirectional Glenn shunt (bBDG) as an alternative noninvasive method for postoperative morphological estimation.Twenty patients with pulsatile bBDG (mean age 4.2±1.6 years) underwent both low-dose PGA scanning and conventional cardiac angiography (CCA) for the morphological changes. The morphological evaluation included the anatomy of superior vena cava (SVC) and pulmonary artery (PA), the anastomotic location, thrombosis, aorto-pulmonary collateral circulation, pulmonary arteriovenous malformations, etc. Objective and subjective image quality was assessed. Bland-Altman analysis and linear regression analyses were used to evaluate the correlation on measurements between CTA and CCA. Effective radiation dose of both modalities was calculated.The CT attenuation value of bilateral SVC and PA was higher than 300 HU. The average subjective image quality score was 4.05±0.69. The morphology of bilateral SVC and PA was displayed completely and intuitively by CTA images. There were 24 SVC above PA and 15 SVC beside PA. Thrombosis was found in 1 patient. Collateral vessels were detected in 13 patients. No pulmonary arteriovenous malformation was found in our study. A strong correlation (R2>0.8, P0).The mean effective dose of CTA and CCA was 0.50±0.17 mSv and 4.85±1.34 mSv respectively.CT angiography with a low-dose PGA scanning is an accurate and reliable noninvasive examination in the assessment of morphological changes in patients with pulsatile bBDG.

To compare the image quality and diagnostic accuracy between sinogram affirmed iterative reconstruction (SAFIRE) algorithm and filtered back projection (FBP) reconstruction algorithm at 70 kVp-tube-voltage DSCT angiography in children with congenital heart disease (CHD).Twenty-eight patients (mean age: 13 months; range: 2-48 months; male: 16; female: 12; mean weight: 8 kg) with CHD underwent 70 kVp DSCT angiography. Imaging data were reconstructed with both FBP and SAFIRE algorithms. Subjective image quality was evaluated on a five-point scale. The parameters of image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) on the objective image quality were compared for the two reconstruction algorithms. Surgery was performed in 20 patients, whereas conventional cardiac angiography (CCA) was performed in 8 patients. The diagnostic accuracy was evaluated on the surgical and/or CCA findings. The effective radiation doses were calculated.Compared to FBP algorithm, SAFIRE algorithm had significantly higher scores for subjective image quality (P0.05). The mean effective dose for 70 kVp DSCT angiography was 0.30±0.13 mSv.The SAFIRE algorithm can significantly reduce image noise and improve the image quality at 70 kVp DSCT angiography for the assessment of CHD in children.

OBJECTIVES: To evaluate the feasibility of dose-modulated retrospective ECG-gated thoracoabdominal aorta CT angiography (CTA) assessing abdominal aortic intimal flap motion and investigate the motion characteristics of intimal flap in acute aortic dissection (AAD). MATERIALS AND METHODS: 49 patients who had thoracoabdominal aorta retrospective ECG-gated CTA scan were enrolled. 20 datasets were reconstructed in 5% steps between 0 and 95% of the R-R interval in each case. The aortic intimal flap motion was assessed by measuring the short axis diameters of the true lumen and false lumen 2 cm above of celiac trunk ostium in different R-R intervals. Intimal flap motion and configuration was assessed by two independent observers. RESULTS: In these 49 patients, 37 had AAD, 7 had intramural hematoma, and 5 had negative result for acute aortic disorder. 620 datasets of 31 patients who showed double lumens in abdominal aorta were enrolled in evaluating intimal flap motion. The maximum and minimum true lumen diameter were 12.2 ± 4.1 mm (range 2.6 ∼ 17.4) and 6.7 ± 4.1 mm (range 0 ∼ 15.3) respectively. The range of intimal flap motion in all patients was 5.5 ± 2.6 mm (range 1.8 ∼ 10.2). The extent of maximum true lumen diameter decreased during a cardiac cycle was 49.5% ± 23.5% (range 12% ∼ 100%). The maximum motion phase of true lumen diameter was in systolic phase (5% ∼ 40% of R-R interval). Maximum and minimum intimal flap motion was at 15% and 75% of the R-R interval respectively. Intimal flap configuration had correlation with the phase of cardiac cycle. CONCLUSIONS: Abdominal intimal flap position and configuration varied greatly during a cardiac cycle. Retrospective ECG-gated thoracoabdominal aorta CTA can reflect the actual status of the true lumen and provide more information about true lumen collapse. This information may be helpful to diagnosis and differential diagnosis of dynamic abstraction.

OBJECTIVES: To assess the feasibility of low-dose prospective ECG-gated dual-source CT (DSCT) in detecting airway anomalies in pediatric patients with cardiovascular anomalies compared with flexible tracheobronchoscopy (FTB). METHODS: 33 pediatrics with respiratory symptoms who had been revealed cardiovascular anomalies by transthoracic echocardiography underwent FTB and contrast material-enhanced prospective ECG-triggering CT were enrolled. The study was approved by our institution review board and written informed consent was obtained from all patients' guardian. DSCT examinations were performed to detect cardiovascular abnormalities using weight-adjusted low-dose protocol. Two radiologists independently performed CT image analysis. The FTB reports were reviewed by an experienced pulmonologist. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of DSCT in the detection of airway anomalies were assessed. The tracheobronchial stenoses revealed on FTB were graded. Effective radiation dose was calculated. RESULTS: Thirty cases were diagnosed with tracheobronchial narrowing and/or abnormality in 33 patients by FTB, while 3 patients had normal FTB findings. Twenty-eight cases were diagnosed with airway anomalies by CT, of which 27 were correct positive. 3 patients with normal findings at CT had findings of tracheobronchial narrowing due to tracheobronchomalacia at inspiration at FTB. Sensitivity and specificity of CT were 90.0% (95% CI: 72.3%, 97.4%) and 66.7% (95% CI: 12.5 %, 98.2 %), respectively. PPV and NPV were 96.4% (95% CI: 79.8 %, 99.8%) and 40.0% (95% CI: 7.3%, 83.0%), respectively. Overall accuracy of DSCT in detecting airway anomalies in pediatrics with cardiovascular anomalies was 87.9% (95% CI: 74.5%, 97.6%). In grading of tracheobronchial stenosis, images from CT correlated closely (r = 0.89) with those of FTB. Mean effective dose was 0.60 ± 0.20 mSv. CONCLUSION: In pediatric patients, ECG-triggered CT to evaluate congenital cardiovascular anomalies can also be used to diagnose and characterize fixed airway involvement in relation to the vascular structures.