RNA-based detection of genetically modified plants via current-voltage characteristic measurement.

The widespread adoption of genetically modified (GM) crops has escalated concerns about their safety and ethical implications, underscoring the need for efficient GM crop detection methods. Conventional detection methods, such as polymerase chain reaction, can be costly, lab-bound, and time-consuming. To overcome these challenges, we have developed RapiSense, a cost-effective, portable, and sensitive biosensor platform. This sensor generates a measurable voltage shift (0.1-1 V) in the system's current-voltage characteristics, triggered by an increase in membrane's negative charge upon hybridization of DNA/RNA targets with a specific DNA probe. Probes designed to identify the herbicide resistance gene hygromycin phosphotransferase show a detection range from ∼1 nM to ∼10 µM and can discriminate between complementary, non-specific, and mismatched nucleotide targets. The incorporation of a small membrane sensor to detect fragmented RNA samples substantially improve the platform's sensitivity. In this study, RapiSense has been effectively used to detect specific DNA and fragmented RNA in transgenic variants of Arabidopsis, sweet potato, and rice, showcasing its potential for rapid, on-site GM crop screening.

Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning.

BACKGROUND: Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations. METHODS: In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head's yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer. RESULTS: After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878. CONCLUSIONS: We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.