Application of nucleic acid aptamer-based lateral flow assay in microbe detection

Microbial contamination and infection are global issues in the food and environmental fields that seriously threaten human health. Bacteria and fungi can easily cause food spoilage, resulting in diarrhea and vomiting;viruses can infect humans through different transmission routes, causing severe or even fatal harm. Hence, rapid analysis and identification of pathogenic microorganisms and simultaneous detection of multiple types of microbes have become hot research topics in biochemical analysis and molecular diagnosis. The lateral flow assay (LFA) is a simple, rapid, economical, and efficient detection technology with high sensitivity, simple operation, and environmental friendliness. It can provide instant test results under non-laboratory circumstances, hence becoming an ideal choice for point-of-care testing, which has been applied to rapidly detect various targets. The current conventional principle of the LFA is still based on the specific recognition of the antigen by the antibody. However, as a commonly used target recognition molecule in conventional biochemical and medical detection, the application of antibodies also has certain limitations for rapid and accurate identification of certain targets due to strict control of the production and purification process, as well as susceptibility to the interference of the operating environment, pH, temperature, and other conditions, such as long production cycle, high cost, poor stability, and cross-reactivity. Aptamers are a class of single-stranded DNA (ssDNA) or RNA obtained through the systematic evolution of ligands by exponential enrichment (SELEX), which can usually form a stable secondary structure. Aptamers can be folded into a three-dimensional structure through conformational change and interact with the target through conformation complementarity, π-π stacking between aromatic rings, base stacking, electrostatic interaction, and hydrogen bonding. So far, nearly 300 kinds of aptamers have been discovered. As alternatives, aptamers are easy and facile to modify and label with high sensitivity and specificity. Accordingly, the innovative rapid detection technique can be developed by combining the LFA with an aptamer. This aptamer-based LFA technology can be widely used in qualitative, semi-quantitative, and quantitative detection in food safety, environment, clinical, and other fields. Nowadays, most microbe detection methods are constructed based on this approach. The common strategies of aptamer-based LFAs include the sandwich method, competitive method, and adsorption–desorption method. Diverse ingenious materials such as gold nanoparticles and quantum dots have also been proposed for signal read-out. Different signal capture models, such as colorimetric and fluorescence methods, have been applied for sensitive and accurate detection of a single or multiple target microbe. Furthermore, in view of the unique properties of nucleic acid aptamers, several signal amplification methods can be further involved in the LFA to enhance the sensitivity for target detection. This review introduces the use of aptamers with different structural patterns and labeling types in recent years, as well as a variety of methods to detect microbes, especially for the rapid detection of pathogenic bacteria. Based on the excellent characteristics, the aptamer-based LFA presents more flexibility and selectivity for microbe detection with good applicability, specificity, and sensitivity and can better achieve low-cost, rapid detection. This study is expected to provide a reference for developing nucleic acid aptamer-based LFA technologies, especially for efficient and accurate diagnosis of corona virus disease 2019 (COVID-19), exploiting the novel application scope of LFA technology. © 2022 Chinese Academy of Sciences. All rights reserved.

On the Performance of Automatic Exposure Determination Using Bluetooth-based Proximity Estimation

The proximity detection mechanism in current automatic exposure notification systems is typically based on the Bluetooth signal strength from the individual's mobile phone. However, there is an underlying error in this proximity detection methodology that could result in wrong exposure decisions i.e., false negatives and false positives. A false negative error happens if a truly exposed individual is mistakenly identified as not exposed. This misidentification could result in further spread of the virus by the exposed (yet undetected) individual. Likewise, when a non-exposed individual is incorrectly identified as exposed, a false positive error occurs. This could lead to unnecessary quarantine of the individual;and therefore, incurring further economic cost. In this paper, using a simulation platform and a notion of proximity detection error, we investigate the performance of the system in terms of false exposure determinations. Knowledge of how the Bluetooth-based proximity detection error impacts such false determinations and identification of methodologies that can reduce this impact will be helpful to enhance the effectiveness of an automatic contact tracing system. Our preliminary results indicate the substantial impact of the proximity estimation error on the exposure detection accuracy. The results also suggest how proper filtering of distance measurements may reduce this impact. © 2022 IEEE.

Mispronunciation Detection and Diagnosis with Articulatory-Level Feedback Generation for Non-Native Arabic Speech

A high-performance versatile computer-assisted pronunciation training (CAPT) system that provides the learner immediate feedback as to whether their pronunciation is correct is very helpful in learning correct pronunciation and allows learners to practice this at any time and with unlimited repetitions, without the presence of an instructor. In this paper, we propose deep learning-based techniques to build a high-performance versatile CAPT system for mispronunciation detection and diagnosis (MDD) and articulatory feedback generation for non-native Arabic learners. The proposed system can locate the error in pronunciation, recognize the mispronounced phonemes, and detect the corresponding articulatory features (AFs), not only in words but even in sentences. We formulate the recognition of phonemes and corresponding AFs as a multi-label object recognition problem, where the objects are the phonemes and their AFs in a spectral image. Moreover, we investigate the use of cutting-edge neural text-to-speech (TTS) technology to generate a new corpus of high-quality speech from predefined text that has the most common substitution errors among Arabic learners. The proposed model and its various enhanced versions achieved excellent results. We compared the performance of the different proposed models with the state-of-the-art end-to-end technique of MDD, and our system had a better performance. In addition, we proposed using fusion between the proposed model and the end-to-end model and obtained a better performance. Our best model achieved a 3.83% phoneme error rate (PER) in the phoneme recognition task, a 70.53% F1-score in the MDD task, and a detection error rate (DER) of 2.6% for the AF detection task.

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

Intelligent traffic detection with millimeter-wave radar and camera

With the impact of COVID-19, more people are choosing to travel by private cars, which will cause problems such as traffic congestion. It is essential for traffic engineers to have real-time traffic volume, speed, and individual vehicle length. In this study, the ACC7350 millimeter-wave radar was tested, and its advantages and disadvantages were analyzed in vehicle speed, distance from the radar, and vehicle trajectory. The speed detection error between MWR and GPS was within ±6%, and the distance detection error was ±20%. Then the traffic flow detection results of the camera and millimeter-wave radar were compared and analyzed. Results show that the mistakes of traffic flow detection based on vision and MWR are ±4% and ±13%, respectively. Finally, we proposed a traffic data processing method combined with a camera-based target tracking algorithm. © 2021 SPIE.

Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error.

Accidentally clicking on a link is a type of human error known as a slip in which a user unintentionally performs an unintended task. The risk magnitude is the probability of occurrences of such error with a possible substantial effect to which even experienced individuals are susceptible. Phishing attacks take advantage of slip-based human error by attacking psychological aspects of the users that lead to unintentionally clicking on phishing links. Such actions may lead to installing tracking software, downloading malware or viruses, or stealing private, sensitive information, to list a few. Therefore, a system is needed that detects whether a click on a link is intentional or unintentional and, if unintentional, can then prevent it. This paper proposes a micro-behavioral accidental click detection system (ACDS) to prevent slip-based human error. A within-subject-based experiment was conducted with 20 participants to test the potential of the proposed system. The results reveal the statistical significance between the two cases of intentional vs. unintentional clicks using a smartphone. Random tree, random forest, and support vector machine classifiers were used, exhibiting 82.6%, 87.2%, and 91.6% accuracy in detecting unintentional clicks, respectively.

Distance Motor Learning during the COVID-19 Induced Confinement: Video Feedback with a Pedagogical Activity Improves the Snatch Technique in Young Athletes.

The purpose of the present study was to investigate which of two strategies, Video Feedback with Pedagogical Activity (VF-PA) or Video Feedback (VF), would be more beneficial for the remote error correction of the snatch weightlifting technique during the confinement period. Thirty-five school aged children with at least three months of weightlifting experience were randomized to one of three training conditions: VF-PA, VF or the Control group (CONT). Subjects underwent test sessions one week before (T0) and one day after (T1) a six-session training period and a retention test session a week later (T2). During each test session, the Kinovea version 0.8.15 software measured the kinematic parameters of the snatch performance. Following distance learning sessions (T1), the VF-PA improved various kinematic parameters (i.e., barbell horizontal displacements, maximum height, looping and symmetry) compared with T0 (p < 0.5; Cohen's d = 0.58-1.1). Most of these improvements were maintained during the retention test (T2) (p<0.01, Cohen's d = 1.2-1.3) when compared withT0. However, the VF group improved only twoparameters (i.e., barbell symmetry and horizontal displacement) at T1 (p < 0.05; Cohen's d = 0.9), which were not maintained at T2. Better horizontal displacement and looping values were registered during the retention test in the VF-PA group compared with theCONT group (p < 0.05, Cohen's d = 1.49-1.52). The present findings suggest combining video feedback with pedagogical activity during the pandemic induced online coaching or physical education to improve movement learning in school aged children.