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The COVID-19 pandemic has intensified the need for home-based cardiac health monitoring systems. Despite advancements in electrocardiograph (ECG) and phonocardiogram (PCG) wearable sensors, accurate heart sound segmentation algorithms remain understudied. Existing deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), struggle to segment noisy signals using only PCG data. We propose a two-step heart sound segmentation algorithm that analyzes synchronized ECG and PCG signals. The first step involves heartbeat detection using a CNN-LSTM-based model on ECG data, and the second step focuses on beat-wise heart sound segmentation with a 1D U-Net that incorporates multi-modal inputs. Our method leverages temporal correlation between ECG and PCG signals to enhance segmentation performance. To tackle the label-hungry issue in AI-supported biomedical studies, we introduce a segment-wise contrastive learning technique for signal segmentation, overcoming the limitations of traditional contrastive learning methods designed for classification tasks. We evaluated our two-step algorithm using the PhysioNet 2016 dataset and a private dataset from Bayland Scientific, obtaining a 96.43 F1 score on the former. Notably, our segment-wise contrastive learning technique demonstrated effective performance with limited labeled data. When trained on just 1% of labeled PhysioNet data, the model pre-trained on the full unlabeled dataset only dropped 2.88 in the F1 score, outperforming the SimCLR method. Overall, our proposed algorithm and learning technique present promise for improving heart sound segmentation and reducing the need for labeled data. 

Surface-enhanced Raman scattering was used to resolve the chemical species, including chloride ions, on the surface of Ag nanocrystals in their original reaction solution, avoiding changes to the surface while eliminating possible artifacts. 

Abstract It remains a challenge to accomplish colloidal synthesis of noble‐metal nanocrystals marked by high quality, large quantity, and batch‐to‐batch consistency. Here we report a self‐airtight setup for achieving robust, reproducible, and scalable production of Ag nanocubes with uniform and controlled sizes from 18 to 60 nm. Different from the conventional open‐to‐air setup, the self‐airtight system makes it practical to stabilize the reaction condition by minimizing the loss of volatile reagents. The new setup also allows us to easily optimize the amount of O₂(from air) trapped in the system, ensuring burst nucleation of single‐crystal seeds, followed by their slow growth into nanocubes. Most significantly, the new setup allows for the production of Ag nanocubes at gram quantities without sacrificing uniformity, corner/edge sharpness, controlled size, and high purity across different batches. The availability of high‐quality Ag nanocubes in such a large quantity is anticipated to substantially boost their use in applications related to plasmonics, catalysis, and biomedicine.