Primary synovial sarcoma of the heart

Primary synovial sarcoma of the heart is very rare, accounting for 5% of cardiac malignancies. Of the few cases reported in the literature to date, nearly all have had a very poor outcome. We present a further case. This uncommon malignancy has no specific symptoms during its development, which results in delayed diagnosis. Echocardiography, chest computed tomography, and magnetic resonance imaging can provide effective information about this tumor. With the identification of the characteristic and diagnostic chromosomal abnormality t(X; 18), this malignancy will become increasingly recognized. Synovial sarcoma of the heart requires surgical intervention to improve the prognosis. Adjuvant and/or genetic therapy pre- or postoperation can help prolong life. Chemotherapy is usually recommended as it may benefit the patients. The key to treatment in the future is to find new therapeutic agents. Further elucidation of the effects of this chromosomal abnormality may lead to better-directed therapies in future. (Cardiol J 2011; 18, 2: 128-133

Probabilistic Generative Transformer Language models for Generative Design of Molecules

Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformerComment: 13 page