The low dose of 1100 MBq radioiodine activity is sufficient for thyroid remnant ablation as compared to 3700 MBq radioiodine activity with similar quality of life, less common adverse effects, and a shorter hospital stay.

Speech is the most effective way for people to exchange complex information. Recognition of emotional information contained in speech is one of the important challenges in the field of artificial intelligence. To better acquire emotional features in speech signals, a parallelized convolutional recurrent neural network (PCRN) with spectral features is proposed for speech emotion recognition. First, frame-level features are extracted from each utterance and, a long short-term memory is employed to learn these features frame by frame. At the same time, the deltas and delta-deltas of the log Mel-spectrogram are calculated and reconstructed into three channels (static, delta, and delta-delta); these 3-D features are learned by a convolutional neural network (CNN). Then, the two learned high-level features are fused and batch normalized. Finally, a SoftMax classifier is used to classify emotions. Our PCRN model simultaneously processes two different types of features in parallel to better learn the subtle changes in emotion. The experimental results on four public datasets show the superiority of our proposed method, which is better than the previous works.INDEX TERMS Speech emotion recognition, parallelized convolutional recurrent neural network, convolutional neural network, long short-term memory.

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In Chinese patients with differentiated thyroid carcinoma, the low dose of 1850 MBq radioiodine activity is as effective as a high dose of 3700 MBq for thyroid remnant ablation.