Efficient Detection of Gastric Cancer Marker miR-185 Using Graphene Oxide and Nuclease DSN

Since miR-185 has been identified as a prognostic biomarker to forecast the course of survival and relapse in gastric cancer (GC), quantitative detection of miR-185 features in developing personalized strategies for GC treatment. In this study, a highly sensitive method for miR-185 detection was rationally designed with the characteristic of fluorescent signal amplification and it was based on constructing graphene oxide sensor and utilizing duplex specific nuclease (DSN). In detail, the cleavage of many DNA signal probes was successfully triggered by the miR-185 target which contributed to the target-recycling mechanism. The protocol exhibited a prominent ability to analyze miR-185 in solution, and it can detect miR-185 at different concentrations as low as 476 pM with a linear range of 0–50 nM. Moreover, this method has gained its prominence in distinguishing the target miRNA from various sequences with one to three base mismatches or other miRNAs. Taken together, it presented the prominent potential to be a candidate tool in the field of clinical diagnosis considering its precise and efficient ability to detect miR-185.

Patterns of Saliency and Semantic Features Distinguish Gaze of Expert and Novice Viewers of Surveillance Footage

When viewing the actions of others, we not only see patterns of body movements, but we also "see" the intentions and social relations of people, enabling us to understand the surrounding social environment. Previous research has shown that experienced forensic examiners, Closed Circuit Television (CCTV) operators, convey superior performance in identifying and predicting hostile intentions from surveillance footages than novices. However, it remains largely unknown what visual content CCTV operators actively attend to when viewing surveillance footage, and whether CCTV operators develop different strategies for active information seeking from what novices do. In this study, we conducted computational analysis for the gaze-centered stimuli captured by experienced CCTV operators and novices' eye movements when they viewed the same surveillance footage. These analyses examined how low-level visual features and object-level semantic features contribute to attentive gaze patterns associated with the two groups of participants. Low-level image features were extracted by a visual saliency model, whereas object-level semantic features were extracted by a deep convolutional neural network (DCNN), AlexNet, from gaze-centered regions. We found that visual regions attended by CCTV operators versus by novices can be reliably classified by patterns of saliency features and DCNN features. Additionally, CCTV operators showed greater inter-subject correlation in attending to saliency features and DCNN features than did novices. These results suggest that the looking behavior of CCTV operators differs from novices by actively attending to different patterns of saliency and semantic features in both low-level and high-level visual processing. Expertise in selectively attending to informative features at different levels of visual hierarchy may play an important role in facilitating the efficient detection of social relationships between agents and the prediction of harmful intentions.

Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white noise. This paper presents a novel approach using clustering for data cleaning and feature extraction of phase-resolved partial discharge (PRPD) plots derived from live operational data. A total of 452 PRPD 2D plots collected from distribution substations over a six-month period were used to test the proposed technique. The output of the clustering technique is evaluated on different types of machine learning classification techniques and the accuracy is compared using balanced accuracy score. The proposed technique extends the measurement abilities of a portable PD measurement tool for diagnostics of switchgear condition, helping utilities to quickly detect potential PD activities with minimal human manual analysis and higher accuracy.

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

A Recombinase Polymerase Amplification-Coupled Cas12a Mutant-Based Module for Efficient Detection of Streptomycin-Resistant Mutations in Mycobacterium tuberculosis

Drug-resistant tuberculosis (TB) is a serious public health problem and threat to global TB prevention and control. Streptomycin (STR) is the earliest and classical anti-TB drug, and it is the earliest drug that generated resistance to anti-TB treatment, which limits its use in treating TB and impedes TB control efforts. The rapid, economical, and highly sensitive detection of STR-resistant TB may help reduce disease transmission and morbimortality. CRISPR/CRISPR-associated protein (Cas) is a new-generation pathogen detection method that can detect single-nucleotide polymorphisms with high sensitivity and good specificity. In this study, a Cas12a RR detection system that can recognize more non-traditional protospacer-adjacent motif-targeting sequences was developed based on Cas12a combined with recombinase polymerase amplification technology. This system detects 0.1% of the target substance, and the entire detection process can be completed within 60 min. Its sensitivity and specificity for detecting clinical STR-resistant Mycobacterium tuberculosis were both 100%. Overall, the Cas12 RR detection system provides a novel alternative for the rapid, simple, sensitive, and specific detection of STR-resistant TB, which may contribute to the prompt treatment and prevention of disease transmission in STR-resistant TB.