Young children spend substantial portions of their waking hours in noisy preschool classrooms. In these environments, children's vocal interactions with teachers are critical contributors to their language outcomes, but manually transcribing these interactions is prohibitive. Using audio from child- and teacher-worn recorders, we propose an automated framework that uses open source software both to classify speakers (ALICE) and to transcribe their utterances (Whisper). We compare results from our framework to those from a human expert for 110 minutes of classroom recordings, including 85 minutes from child-word microphones (n=4 children) and 25 minutes from teacher-worn microphones (n=2 teachers). The overall proportion of agreement, that is, the proportion of correctly classified teacher and child utterances, was .76, with an error-corrected kappa of .50 and a weighted F1 of .76. The word error rate for both teacher and child transcriptions was .15, meaning that 15% of words would need to be deleted, added, or changed to equate the Whisper and expert transcriptions. Moreover, speech features such as the mean length of utterances in words, the proportion of teacher and child utterances that were questions, and the proportion of utterances that were responded to within 2.5 seconds were similar when calculated separately from expert and automated transcriptions. The results suggest substantial progress in analyzing classroom speech that may support children's language development. Future research using natural language processing is underway to improve speaker classification and to analyze results from the application of the automated it framework to a larger dataset containing classroom recordings from 13 children and 4 teachers observed on 17 occasions over one year.

Several artificial neural networks (ANNs) have been developed recently to predict the prognosis of different types of cancer based on the tumor transcriptome. However, they have not demonstrated significantly better performance than the regularized Cox proportional hazards regression model. Training an ANN is challenging with a limited number of data samples and a high-dimensional feature space. Recent advancements in image classification have shown that contrastive learning (CL) can facilitate further learning tasks by learning good feature representation from a limited number of data samples. In this paper, we applied supervised CL to tumor gene expression and clinical data to learn feature representations in a low-dimensional space. We then used these learned features to train a Cox model for predicting cancer prognosis. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that our CL-based Cox model (CLCox) significantly outperformed existing methods in predicting the prognosis of 19 types of cancer considered. We also developed CL-based classifiers to classify tumors into different risk groups and showed that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) of greater than 0.8 for 14 types of cancer and and an AUC of greater than 0.9 for 2 types of cancer. CLCox models and CL-based classifiers trained with TCGA lung cancer and prostate cancer data were validated with the data of two independent cohorts.