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Congenital acorea is a rare disease with the absence of a pupil in the eye. To date, only one family and two isolated cases with congenital acorea have been reported. The gene associated with acorea has not been identified. In this study, we recruited a Chinese family acorea-microphthalmia-cataract syndrome. By analyzing the whole-exome sequencing (WES) data of this Chinese family, we revealed the association of a novel heterozygous variant, NM_005267.5:c.137G>A (p.G46E) in the gap junction protein alpha 8 (GJA8) gene encoding connexin 50 or CX50, with familial acorea-microphthalmia-cataract syndrome. Additionally, another variant, NM_005267.5:c.151G>A (p.D51N) in GJA8, was identified to co-segregate with this syndrome in an unrelated Japanese family. Ectopic expression of p.G46E and p.D51N mutant GJA8genes in cultured cells caused protein mislocalization, suggesting that the p.G46E and p.D51N mutations in GJA8impaired the function of the gap junction channels. These results established GJA8as the first gene associated with familial acorea-microphthalmia-cataract syndrome.

Question answering requiring numerical reasoning, which generally involves symbolic operations such as sorting, counting, and addition, is a challenging task. To address such a problem, existing mixture-of-experts (MoE)-based methods design several specific answer predictors to handle different types of questions and achieve promising performance. However, they ignore the modeling and exploitation of fine-grained reasoning-related operations to support numerical reasoning, encountering the inadequacy in reasoning capability and interpretability. To alleviate this issue, we propose OPERA, an operation-augmented numerical reasoning framework. Concretely, we systematically define a scalable operation set to model numerical reasoning. We first identify reasoning-related operations based on context and then softly execute them to imitate the answer reasoning procedure via an operation-aware cross-attention mechanism. Finally, we utilize the operation-augmented semantic representation of execution results to support answer prediction. We verify the effectiveness and generalization of OPERA in two scenarios with different knowledge sources and reasoning capabilities. Specifically, we conduct extensive experiments on two textual datasets, DROP and RACENum, and a table-text hybrid dataset TAT-QA. Experiment results show that OPERA outperforms previous strong methods on the DROP, RACENum, and TAT-QA datasets. Further, we statistically and visually analyze its interpretability.