We report three heterozygous missense mutations of the skeletal muscle alpha actin gene (ACTA1) in three unrelated cases of congenital fiber type disproportion (CFTD) from Japan and Australia. This represents the first genetic cause of CFTD to be identified and confirms that CFTD is genetically heterogeneous. The three mutations we have identified Leucine221Proline, Aspartate292Valine, and Proline332Serine are novel. They have not been found previously in any cases of nemaline, actin, intranuclear rod, or rod-core myopathy caused by mutations in ACTA1. It remains unclear why these mutations cause type 1 fiber hypotrophy but no nemaline bodies. The three mutations all lie on one face of the actin monomer on the surface swept by tropomyosin during muscle activity, which may suggest a common pathological mechanism. All three CFTD cases with ACTA1 mutations had severe congenital weakness and respiratory failure without ophthalmoplegia. There were no clinical features specific to CFTD cases with ACTA1 mutations, but the presence of normal eye movements in a severe CFTD patient may be an important clue for the presence of a mutation in ACTA1.

Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. This suggests that techniques used in neural machine translation, such as Long Short-Term Memory (LSTM), can be used for source code summarization. However, there is a considerable difference between source code and natural language: Source code is essentially structured, having loops and conditional branching, etc. Therefore, there is some obstacle to apply known machine translation models to source code.Abstract syntax trees (ASTs) capture these structural properties and play an important role in recent machine learning studies on source code. Tree-LSTM is proposed as a generalization of LSTMs for tree-structured data. However, there is a critical issue when applying it to ASTs: It cannot handle a tree that contains nodes having an arbitrary number of children and their order simultaneously, which ASTs generally have such nodes. To address this issue, we propose an extension of Tree-LSTM, which we call Multi-way Tree-LSTM and apply it for source code summarization. As a result of computational experiments, our proposal achieved better results when compared with several state-of-the-art techniques.

No abstract

No abstract