An exogenous encoding sequence based on DNA data storage technology and its application in assisted reproductive technology.

Safety and ethical issues are the primary concerns for assisted reproductive technology (ART). However, confusion and contamination of samples are common problems in embryo laboratories, preimplantation genetic test (PGT) laboratories, and third-party medical testing laboratories due to large sample numbers and complex procedures. Once these problems occur, they are often difficult to trace, posing risks and ethical challenges to hospital reproductive centers, third-party medical testing laboratories, and patient families.

Comparing ECG Lead Subsets for Heart Arrhythmia/ECG Pattern Classification: Convolutional Neural Networks and Random Forest.

Background: Despite the growth in popularity of deep learning (DL), limited research has compared the performance of DL and conventional machine learning (CML) methods in heart arrhythmia/electrocardiography (ECG) pattern classification. In addition, the classification of heart arrhythmias/ECG patterns is often dependent on specific ECG leads for accurate classification, and it remains unknown how DL and CML methods perform on reduced subsets of ECG leads. In this study, we sought to assess the accuracy of convolutional neural network (CNN) and random forest (RF) models for classifying arrhythmias/ECG patterns using reduced ECG lead subsets representing DL and CML methods.