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Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods. [ABSTRACT FROM AUTHOR]

Within population health information systems, indicators are commonly presented as independent, cross-sectional measures, neglecting the multivariate, longitudinal nature of disease progression, health care use, and profiles of performance. We use administrative claims data of Montreal, Canada to identify patterns across indicators and over time in chronic obstructive pulmonary disease patients. We first cluster regions based on four health service indicators. Our second approach discovers individual-level trajectories based on a hidden Markov model using the same four indicators. Both approaches offer additional insights by facilitating the discovery and interpretation of indicators, such as a dual interpretation of low use of general practitioner services. These approaches to the analysis and visualization of health indicators can provide a foundation for information displays that will help decision makers identify areas of concern, predict future disease burden, and implement appropriate policies. [ABSTRACT FROM AUTHOR]

The classification of EEG tasks has drawn much attention in recent years. In this paper, a novel classification model based on independent component analysis (ICA) and Extreme learning machine (ELM) is proposed to detect lying. Firstly, ICA and its topography information were used to automatically identify the P300 ICs. Then, time and frequency-domain features were extracted from the reconstructed P3 waveforms. Finally, two classes of feature samples were used to train ELM, Back-propagation network (BPNN) and support vector machine (SVM) classifiers for comparison. The optimal number of P3 ICs and the values of classifier parameter were optimized by the cross-validation procedures. Experimental results show that the presented method (ICA_ELM) achieves the highest training accuracy of 95.40% with extremely less training and testing time on detecting P3 components for the guilty and the innocent subjects. The results indicate that the proposed method can be applied in lie detection. [ABSTRACT FROM AUTHOR]

Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods. [ABSTRACT FROM AUTHOR]

This study investigated the degradation of clofibric acid (CFA), bezafibrate (BZF), and sulfamethoxazole (SMX) in synthetic human urine using a novel mesoporous iron powder-activated persulfate system (mFe-PS system), and identified the factors limiting their degradation in synthetic human urine. A kinetic model was established to expose the radical production in various reaction conditions, and experiments were conducted to verify the modeling results. In the phosphate-containing mFe-PS system, the 120 min removal efficiency of CFA decreased from 95.1% to 76.6% as the phosphate concentration increased from 0.32 to 6.45 mmol/L, but recovered to 90.5% when phosphate concentration increased to 16.10 mmol/L. Meanwhile, the increased concentration of phosphate from 0.32 to 16.10 mmol/L reduced the BZF degradation efficacy from 91.5% to 79.0%, whereas SMX removal improved from 37.3% to 62.9%. The mFe-PS system containing (bi)carbonate, from 4.20 to 166.70 mmol/L, reduced CFA and BZF removal efficiencies from 100% to 76.8% and 80.4%, respectively, and SMX from 83.5% to 56.7% within a 120-min reaction time. In addition, alkaline conditions (pH ≥ 8.0) inhibited CFA and BZF degradations, while nonacidic pH (pH ≥ 7.0) remarkably inhibited SMX degradation. Results of the kinetic model indicated the formation of phosphate (H 2 PO 4 · /HPO 4 ·- ) and/or carbonate radicals (CO 3 ·- ) could limit pharmaceutical removal. The transformation products (TPs) of the pharmaceuticals revealed more incompletely oxidized TPs occurred in the phosphate- and (bi)carbonate-containing mFe-PS systems, and indicated that H 2 PO 4 · /HPO 4 ·- mainly degraded pharmaceuticals via a benzene ring-opening reaction while CO 3 ·- preferentially oxidized pharmaceuticals via a hydroxylation reaction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of concern because of their ubiquitous presence in surface and ground water; analytical methods that can be used for rapid comprehensive exposure assessment and fingerprinting of PFAS are needed. Following the fires at the Intercontinental Terminals Company (ITC) in Deer Park, TX in 2019, large quantities of PFAS-containing firefighting foams were deployed. The release of these substances into the Houston Ship Channel/Galveston Bay (HSC/GB) prompted concerns over the extent and level of PFAS contamination. A targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based study of temporal and spatial patterns of PFAS associated with this incident revealed presence of 7 species; their levels gradually decreased over a 6-month period. Because the targeted LC-MS/MS analysis was focused on about 30 PFAS molecules, it may have missed other PFAS compounds present in firefighting foams. Therefore, we utilized untargeted LC-ion mobility spectrometry-mass spectrometry (LC-IMS-MS)-based analytical approach for a more comprehensive characterization of PFAS in these water samples. We analyzed 31 samples from 9 sites in the HSC/GB that were collected over 5 months after the incident. Our data showed that additional 19 PFAS were detected in surface water of HSC/GB, most of them decreased gradually after the incident. PFAS features detected by LC-MS/MS correlated well in abundance with LC-IMS-MS data; however, LC-IMS-MS identified a number of additional PFAS, many known to be components of firefighting foams. These findings therefore illustrate that untargeted LC-IMS-MS improved our understanding of PFAS presence in complex environmental samples., (Copyright © 2021. Published by Elsevier B.V.)

Understanding the progression of chronic diseases, such as chronic obstructive pulmonary disease (COPD), is important to inform early diagnosis, personalized care, and health system management. Data from clinical and administrative systems have the potential to advance this understanding, but traditional methods for modelling disease progression are not well-suited to analyzing data collected at irregular intervals, such as when a patient interacts with a healthcare system. We applied a continuous-time hidden Markov model to irregularly-spaced healthcare utilization events and patient-level characteristics in order to analyze the progression through discrete states of 76,888 patients with COPD. A 4-state model allowed classification of patients into interpretable states of disease progression and generated insights about the role of comorbidities, such as cardiovascular diseases, in accelerating severe trajectories. These results can improve the understanding of the evolution of COPD and point to new hypotheses about chronic disease management and comorbidity.

Deregulation of microRNAs (miRNAs) is essential to tumor development, and serum miRNA profiles have been reported in several cancers. However, the serum miRNA profile in oral squamous cell carcinoma (OSCC) remained unclear. The present study aimed to explore abnormal miRNA profile in sera samples from OSCC patients and the association of miR-483-5p with patient prognosis. Microarray analysis was performed in sera from OSCC patients versus healthy controls. miR-483-5p expression was measured by reverse transcription (RT)-PCR and correlated to clinicopathological characteristics of OSCC patients. The prognostic significance was then evaluated with a Kaplan-Meier curve and log-rank tests, using a Cox proportional hazard model. According to microRNA array, 16 miRNAs were upregulated and 10 were downregulated in OSCC patient sera. miR-483-5p expression was significantly increased in OSCC patients (3.23-fold, p < 0.01), and this was significantly correlated with tumor nodal metastasis (TNM) stage and lymph nodal metastases (p < 0.01, p < 0.01). For predicting OSCC, receiver operating characteristic (ROC)/area under the curve (AUC) analysis confirmed a AUC of 0.85 (sensitivity of 0.853 and specificity of 0.746). OSCC patients with high serum miR-483-5p had lower survival than those with low expression, and multivariate analyses for overall survival revealed that high serum miR-483-5p expression was an independent prognostic factor for OSCC (HR = 2.32, 95 %CI 1.20-4.48). miR-483-5p expression increased in OSCC patient sera, and this may be a novel diagnostic and prognostic biomarker for OSCC.