18 α-Glycyrrhetinic Acid Aggregation-Induced Emission Probes for Visual Fluorescence Detection of Explosive as well Multi-functional Application

With the aggravation of the international situation and the frequency of local wars, a variety of new types of explosives have been put into use and accurate detection of these...

RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

Accurate detection of circulating tumor DNA using nanopore consensus sequencing

Levels of circulating tumor DNA (ctDNA) in liquid biopsies may serve as a sensitive biomarker for real-time, minimally-invasive tumor diagnostics and monitoring. However, detecting ctDNA is challenging, as much fewer than 5% of the cell-free DNA in the blood typically originates from the tumor. To detect lowly abundant ctDNA molecules based on somatic variants, extremely sensitive sequencing methods are required. Here, we describe a new technique, CyclomicsSeq, which is based on Oxford Nanopore sequencing of concatenated copies of a single DNA molecule. Consensus calling of the DNA copies increased the base-calling accuracy ~60×, enabling accurate detection of TP53 mutations at frequencies down to 0.02%. We demonstrate that a TP53-specific CyclomicsSeq assay can be successfully used to monitor tumor burden during treatment for head-and-neck cancer patients. CyclomicsSeq can be applied to any genomic locus and offers an accurate diagnostic liquid biopsy approach that can be implemented in clinical workflows.

Validation of the intolerance of uncertainty scale as a screening tool for perinatal anxiety

Background To date, there is a significant lack of research validating clinical tools for early and accurate detection of anxiety disorders in perinatal populations. Intolerance of uncertainty was recently identified as a significant risk factor for postpartum anxiety symptoms and is a key trait of non-perinatal anxiety disorders. The present study aimed to validate the Intolerance of Uncertainty Scale (IUS) in a perinatal population and evaluate its use as a screening tool for anxiety disorders. Methods Psychiatric diagnoses were assessed in a sample of perinatal women (n = 198), in addition to completing a self-report battery of questionnaires. Psychometric properties including internal consistency and convergent and discriminant validity were assessed. Determination of an optimal clinical cut-off score was measured through a ROC analysis in which the area under the curve, sensitivity, specificity, as well as positive and negative predictive values were calculated. Results The IUS demonstrated excellent internal consistency (α = 0.95) and an optimal clinical cut-off score of 64 or greater was established, yielding a sensitivity of 89%. The IUS also demonstrated very good positive (79%) and negative (80%) predictive values. Conclusions These findings suggest that the IUS represents a clinically useful screening tool to be used as an aid for the early and accurate detection of perinatal anxiety.