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Joubert syndrome (JS) is a rare autosomal recessive neurodevelopmental condition characterized by congenital mid-hindbrain abnormalities and a variety of clinical manifestations. This article describes a case of Joubert syndrome type 21 with microcephaly, seizures, developmental delay and language regression, caused by a CSPP1 gene variant and examines the contributing variables. This paper advances the understanding of JS by summarizing the literature and offering detection patterns for practitioners with clinical suspicions of JS.

Type 50 early infantile epileptic encephalopathy, or EIEE-50 for short, is an autosomal recessive genetic disorder resulting from CAD mutations. So far, little has been reported on the disease. In this article, we will discuss the case of a male infant who is 8 years and 5 months old. A whole-exome sequencing of the boy revealed CAD compound heterozygous mutations. He suffered from global developmental delay and regression, refractory epilepsy, and anemia. After his diagnosis, we used uridine treatment and gained encouraging results. In this article, we will analyze our case studies in the context of the literature, so as to improve pediatricians' understanding of the disease.

As urban traffic pollution continues to increase, there is an urgent need to build traffic emission monitoring and forecasting system for the urban traffic construction. The traffic emission monitoring and forecasting system's core is the prediction of traffic emission's evolution. And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution. Due to the complex non-Euclidean topological structure of traffic networks and dynamic heterogeneous spatial-temporal correlations of traffic conditions, it is difficult to obtain satisfactory prediction results with less computation cost. To figure these issues out, a novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks (EAGTCN). More specifically, each component of our model contains two major blocks: (1) the global spatial patterns are captured by the spatial blocks which are fused by the Graph Convolution Network (GCN) and spatial ensemble attention layer; (2) the temporal patterns are captured by the temporal blocks which are composed by the Time Convolution Net (TCN) and temporal ensemble attention layers. Experiments on two real-world datasets demonstrate that our model obtains more accurate prediction results than the state-of-the-art baselines at less computation expense especially in the long-term prediction situation.