Lockdowns during the COVID-19 pandemic increased the risk for loneliness. We tested whether nostalgia counteracts loneliness via rises in happiness. We conducted surveys in China ( N = 1,546), the United States ( N = 1,572), and the United Kingdom ( N = 603). Although feeling lonely was associated with unhappiness, it was also associated with nostalgia, which in turn conduced to increased happiness. We complemented these findings with three experiments testing MTurk workers (Study 4, N = 209; Study 5, N = 196; Study 6, N = 190), where we manipulated nostalgia and assessed happiness. Nostalgia increased happiness immediately after the manipulation (Studies 4–6) and, following an induction booster, up to 2 days later (Studies 4–5). Nostalgia is a psychological resource that can be harnessed to raise happiness and help combat loneliness.

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Optical coherence tomography (OCT) is a promising high-speed, non-invasive imaging modality providing high-resolution retinal scans. However, a variety of external factors such as light occlusion and patient movement can seriously degrade OCT image quality, which complicates manual retinopathy detection and computer-aided diagnosis. As such, this study first presents an OCT image quality assessment (OCT-IQA) system, capable of automatic classification based on signal completeness, location, and effectiveness. Four CNN architectures (VGG-16, Inception-V3, ResNet-18, and ResNet-50) from the ImageNet classification task were used to train the proposed OCT-IQA system via transfer learning. The ResNet-50 with the best performance was then integrated into the final OCT-IQA network. The usefulness of this approach was evaluated using retinopathy detection results. A retinopathy classification network was first trained by fine-tuning Inception-V3 model. The model was then applied to two test datasets, created randomly from the original dataset, one of which was screened by the OCT-IQA system and only included high quality images while the other was mixed by high and low quality images. Results showed that retinopathy detection accuracy and area under curve (AUC) were 3.75% and 1.56% higher, respectively, for the filtered data (compared with the unfiltered data). These experimental results demonstrate the effectiveness of the proposed OCT-IQA system and suggest that deep learning could be applied to the design of computer-aided systems (CADSs) for automatic retinopathy detection.

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