Forebrain excitatory neuron-specific loss of Brpf1 attenuates excitatory synaptic transmission and impairs spatial and fear memory

Bromodomain and plant homeodomain(PHD)finger containing protein 1(Brpf1)is an activator and scaffold protein of a multiunit complex that includes other components involving lysine acetyltransferase(KAT)6A/6B/7.Brpf1,KAT6A,and KAT6B mutations were identified as the causal genes of neurodevelopmental disorders leading to intellectual disability.Our previous work revealed strong and specific expression of Brpf1 in both the postnatal and adult forebrain,especially the hippocampus,which has essential roles in learning and memory.Here,we hypothesized that Brpf1 plays critical roles in the function of forebrain excitatory neurons,and that its deficiency leads to learning and memory deficits.To test this,we knocked out Brpf1 in forebrain excitatory neurons using CaMKIIa-Cre.We found that Brpf1 deficiency reduced the frequency of miniature excitatory postsynaptic currents and downregulated the expression of genes Pcdhgb1,Slc16a7,Robo3,and Rho,which are related to neural development,synapse function,and memory,thereby damaging spatial and fear memory in mice.These findings help explain the mechanisms of intellectual impairment in patients with BRPF1 mutation.

Short-Term Traffic Flow Prediction Based on Road Network Topology

Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).