目的:探讨地震灾区急性胰腺炎患者的疾病特征、治疗及预防。方法:回顾性分析“5·12”汶川大地震后一个月内从灾区转送到我科的14例急性胰腺炎患者的临床资料。结果:14例患者中轻症8例,重症6例;发病相关因素主要有:脂肪餐10例、胆囊结石6例、饮酒4例、高脂血症4例,其中合并两种因素者10例;经中西医结合非手术治疗尤其注重对患者的心理治疗和护理后,痊愈12例,好转2例。结论: 地震灾区急性胰腺炎的发生有其不同特征,饮食成为首发因素;心理因素可能影响疾病的发生和发展演变;防治方面应注重对患者的心理治疗和护理。这有助于今后对地震灾区急性胰腺炎的预防和处理。
With inherent sparse spike-based coding and asynchronous event-driven computation, spiking neural network (SNN) is naturally suitable for processing event stream data of event cameras. In order to improve the feature extraction and classification performance of bio-inspired hierarchical SNNs, in this paper an event camera object recognition system based on biological synaptic plasticity is proposed. In our system input event streams were firstly segmented adaptively using spiking neuron potential to improve computational efficiency of the system. Multi-layer feature learning and classification are implemented by our bio-inspired hierarchical SNN with synaptic plasticity. After Gabor filter-based event-driven convolution layer which extracted primary visual features of event streams, we used a feature learning layer with unsupervised spiking timing dependent plasticity (STDP) rule to help the network extract frequent salient features, and a feature learning layer with reward-modulated STDP rule to help the network learn diagnostic features. The classification accuracies of the network proposed in this paper on the four benchmark event stream datasets were better than the existing bio-inspired hierarchical SNNs. Moreover, our method showed good classification ability for short event stream input data, and was robust to input event stream noise. The results show that our method can improve the feature extraction and classification performance of this kind of SNNs for event camera object recognition.