Changes in retinal nerve fiber layer (RNFL) thickness have been reported in patients with mild cognitive impairment (MCI), the pre-dementia stage of Alzheimer's disease (AD). However, whether RNFL thickness is associated with specific cognitive impairment of MCI patients remains unknown. Therefore, we set out to investigate the potential association between RNFL thickness and episodic memory in MCI patients. Seventy five older adults (mean age 74 ± 3 years, 55% men) were included in the study. Fifty-two participants had normal cognition (NC), and 23 participants were diagnosed with MCI. RNFL thickness was obtained by optical coherence tomography measurement. Cognitive function was evaluated by the Repeatable Battery for the Assessment of Neuropsychological Status on the same day of the optical examination. We found that nasal quadrant RNFL thickness was positively associated with episodic memory scores in the participants with normal cognition: word list learning (r=0.392, p=0.004) and story recall (r=0.307, p=0.027). In the participants with MCI, however, the inferior quadrant RNFL thickness was inversely associated with the episodic memory score: word list learning (r=-0.652, p=0.001), story memory (r=-0.429, p=0.041), and story recall (r=-0.502, p=0.015,). The findings from this pilot study suggest that the inferior quadrant RNFL thickness was associated with specific episodic memory in MCI patients and could serve as a biomarker of MCI and AD. These findings would promote more studies to determine the potential application of RNFL as an AD biomarker.

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Stability and security are the key directions of VANET (vehicular ad hoc network) research. In order to solve the related problems in VANET, an improved AODV (ad hoc on-demand distance vector) routing protocol based on fuzzy neural network, namely, GSS-AODV (AODV with genetic simulated annealing, security and stability), is proposed. The improved scheme of the protocol analyzes the data in the movement process of the mobile node in VANET, extracts the parameters that affect the link stability, and uses the fuzzy neural network optimized by genetic simulated annealing to calculate the node stability. The improved scheme extracts the main parameters that affect the security of the nodes. After normalization, the fuzzy neural network based on genetic simulated annealing algorithm is used for fuzzy processing, and the node trust value of each node is evaluated. The improved scheme uses node stability and node trust value to control each routing process and dynamically adjusts parameters of the algorithm. The experimental results show that the improved scheme is stable and secure.

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