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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Rapidly demographic aging substantially affects socioeconomic development [1][2][3][4] , presents grand challenges for food security and agricultural sustainability [5][6][7][8] , which have so far not been well understood. Here, by using over 30,000 households survey across China, we show that rural population aging lowers average education level of farmers by 3% (0-11% across different provinces) and reduces farm size by 4% (2-11%) due to land transfer-out and abandonment in 2019. These changes further led to a reduction of agricultural inputs, including fertilizers and machinery, which decrease agricultural output by 4% and labor productivity by 9%. Meanwhile, fertilizer use efficiency is reduced by 3%, while increasing fertilizer-related pollutants emission to the environment. New farming models, such as cooperative farming, tend to have larger farm sizes and being operated by younger farmers, who have a higher average education level, hence reducing total labor requirement. Without policy interventions, agricultural output, labor productivity and fertilizer use efficiency would decrease by 3-16% as a consequence of population aging by 2100, compared to 2019 levels. With policy measures, such as new farming models, this decrease could be reversed and in fact an increase by approximately one-third achieved in the same time period. Our analyses suggest that population aging effects on agriculture could be effectively addressed through labor saving in large-scale new farming models, further contributing to a widespread transformation of smallholder farming to sustainable agriculture in China.As life expectancy increases and population fertility rates decline, populations are aging at an accelerating rate globally 9 . Population aging brings grand challenges on multiple global sustainable development goals (SDGs), mainly in relation to no poverty, zero hunger, education, gender equality, decent work and economic growth, responsible consumption and production, etc 1-4, 10, 11 . Labor shortage and innovation constraints may become severe in a society with an aging population, especially in labor-intense economic sectors 3 . Policy interventions to increase birth rates seems to be largely 2 ineffective in many countries where aging issues have been considered serious 12,13 .Alternative strategies urgently need to be developed to achieve and maintain sustainable development paths for human society. Agriculture as a typical labor-intensive industry could be one of the sectors substantially affected by population aging, especially in countries where smallholder farming is prevalent 8, 14 . However, how aging affects agriculture and rural livelihoods has not been well researched or understood to date. This sector is facing substantial challenges in squaring the circle between maintaining food security and improving environmental protection through sustainable intensification at many levels. However, it is indispensable to identify integrated, comprehensive measures to ensure global food security, while protecting the e...

ObjectiveTo determine the correlations between dietary and blood inflammation indices in elderly Americans and their effects on cognitive function.MethodsThis research extracted data from the 2011–2014 National Health and Nutrition Examination Survey for 2,479 patients who were ≥60 years old. Cognitive function was assessed as a composite cognitive function score (Z-score) calculated from the results of the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning and Delayed Recall tests, the Animal Fluency test, and the Digit Symbol Substitution Test. We used a dietary inflammatory index (DII) calculated from 28 food components to represent the dietary inflammation profile. Blood inflammation indicators included the white blood cell count (WBC), neutrophil count (NE), lymphocyte count (Lym), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), neutrophil–albumin ratio (NAR), systemic immune-inflammation index [SII, calculated as (peripheral platelet count) × NE/Lym], and systemic inflammatory response index [SIRI, calculated as (monocyte count) × NE/Lym]. WBC, NE, Lym, NLR, PLR, NAR, SII, SIRI, and DII were initially treated as continuous variables. For logistic regression, WBC, NE, Lym, NLR, PLR, NAR, SII, and SIRI were divided into quartile groups, and DII was divided into tertile groups.ResultsAfter adjusting for covariates, WBC, NE, NLR, NAR, SII, SIRI, and DII scores were markedly higher in the cognitively impaired group than in the normal group (p < 0.05). DII was negatively correlated with the Z-score when combined with WBC, NE, and NAR (p < 0.05). After adjusting for all covariates, DII was positively correlated with SII in people with cognitive impairment (p < 0.05). Higher DII with NLR, NAR, SII, and SIRI all increased the risk of cognitive impairment (p < 0.05).ConclusionDII was positively correlated with blood inflammation indicators, and higher DII and blood inflammation indicators increased the risk of developing cognitive impairment.

Lesion detection is a critical component of disease diagnosis, but the manual segmentation of lesions in medical images is time-consuming and experience-demanding. These issues have recently been addressed through deep learning models. However, most of the existing algorithms were developed using supervised training, which requires time-intensive manual labeling and prevents the model from detecting unaware lesions. As such, this study proposes a weakly supervised learning network based on CycleGAN for lesions segmentation in full-width optical coherence tomography (OCT) images. The model was trained to reconstruct underlying normal anatomic structures from abnormal input images, then the lesions can be detected by calculating the difference between the input and output images. A customized network architecture and a multi-scale similarity perceptual reconstruction loss were used to extend the CycleGAN model to transfer between objects exhibiting shape deformations. The proposed technique was validated using an open-source retinal OCT image dataset. Image-level anomaly detection and pixel-level lesion detection results were assessed using area-under-curve (AUC) and the Dice similarity coefficient, producing results of 96.94% and 0.8239, respectively, higher than all comparative methods. The average test time required to generate a single full-width image was 0.039 s, which is shorter than that reported in recent studies. These results indicate that our model can accurately detect and segment retinopathy lesions in real-time, without the need for supervised labeling. And we hope this method will be helpful to accelerate the clinical diagnosis process and reduce the misdiagnosis rate.

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