Reinforcement learning of altruistic punishment differs between cultures and across the lifespan

Altruistic punishment is key to establishing cooperation and maintaining social order, yet its developmental trends across cultures remain unclear. Using computational reinforcement learning models, we provided the first evidence of how social feedback dynamically influences group-biased altruistic punishment across cultures and the lifespan. Study 1 (n = 371) found that Chinese participants exhibited higher learning rates than Americans when socially incentivized to punish unfair allocations. Additionally, Chinese adults showed slower learning and less exploration when punishing ingroups than outgroups, a pattern absent in American counterparts, potentially reflecting a tendency towards ingroup favoritism that may contribute to reinforcing collectivist values. Study 2 (n = 430, aged 12–52) further showed that such ingroup favoritism develops with age. Chinese participants’ learning rates for ingroup punishment decreased from adolescence into adulthood, while outgroup rates stayed constant, implying a process of cultural learning. Our findings highlight cultural and age-related variations in altruistic punishment learning, with implications for social reinforcement learning and culturally sensitive educational practices promoting fairness and altruism

Selenium intake help prevent age-related cataract formation: Evidence from NHANES 2001–2008

IntroductionCataract is one of the leading causes of blindness and visual impairment, about 16 million people around the world. Trace elements play an important role in a variety of the processes in human body. This study aimed to investigate the association between daily dietary intake of trace elements and age-related cataract incidence based on data from the National Health and Nutrition Examination Survey (NHANES) 2001–2008.MethodsIron, zinc, copper, and selenium were conducted in this study among subjects aged 50 years and older for African Americans and 55 and older in US adults. Multivariate logistic regression analysis was used in different models to investigate the association of trace elements intake and cataract.ResultsAfter screening, 7,525 subjects were ultimately included in this study. A significant negative association was found between selenium intake and cataract incidence in adjusted models using multivariate logistic regression analysis (model 1: OR = 0.998, 95% CI = 0.997–1.000; model 2: OR = 0.997, 95% CI = 0.995–1.000; and model 3: OR = 0.998, 95% CI = 0.995–1.000). After dividing selenium intake into quintiles, significant negative associations between selenium intake and cataract were observed in the first quintile of model 3, the fourth and fifth quintiles of all models. In subgroup analyses adjusted for age and sex, a significant negative association was observed only in women aged 65–74 years.DiscussionOur study points out that maintaining daily dietary selenium intake at higher levels is helpful for cataract prevention, and that increasing daily dietary selenium intake in American women aged 65–74 years may contribute to the prevention of age-related cataract. The intakes of iron, zinc, copper may not be associated with age-related cataract

Relationship between high dose intake of vitamin B12 and glaucoma: Evidence from NHANES 2005–2008 among United States adults

ObjectiveGlaucoma has currently become the second leading cause of blindness in the world. Serum vitamin B12 level has been found to be involved in the development and progression of glaucoma. We performed the present study to confirm this association.MethodsThis cross-sectional study included 594 participants aged 40 years and older in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2008. Retinal imaging was performed using the Ophthalmic Digital Imaging system (Retinography) to assess the retina for the presence of features of glaucomatous lesions. Logistic regression models were used to assess the association between dietary vitamin intake and glaucoma.ResultsAfter screening, 594 subjects were finally included. Among all vitamin intakes, we observed significant differences between the two groups for vitamin B12 intake (5.93 vs. 4.77 mg, p = 0.033). According to the logistic regression results, the intake of vitamin B12 was significantly positively associated with glaucoma (model 1: OR = 1.078, 95% CI = 1.019–1.141; model 2: OR = 1.092, 95% CI = 1.031–1.158; model 3: OR = 1.092, 95% CI = 1.029–1.158). After performing a quantile regression, we observed a significant positive association between vitamin B12 intake and incident glaucoma in the fourth quartile (model 1: OR = 1.133, 95% CI = 1.060–1.210; model 2: OR = 1.141, 95% CI = 1.072–1.215; model 3: OR = 1.146, 95% CI = 1.071–1.226).ConclusionsTherefore, the above results, high-dose intake of vitamin B12 may promote the development of glaucoma

Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies

OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its promise, recent studies and internal reviews highlight its underperformance in specialized medical tasks. This paper explores the boundary of GPT-4V's capabilities in medicine, particularly in processing complex imaging data from endoscopies, CT scans, and MRIs etc. Leveraging open-source datasets, we assessed its foundational competencies, identifying substantial areas for enhancement. Our research emphasizes prompt engineering, an often-underutilized strategy for improving AI responsiveness. Through iterative testing, we refined the model's prompts, significantly improving its interpretative accuracy and relevance in medical imaging. From our comprehensive evaluations, we distilled 10 effective prompt engineering techniques, each fortifying GPT-4V's medical acumen. These methodical enhancements facilitate more reliable, precise, and clinically valuable insights from GPT-4V, advancing its operability in critical healthcare environments. Our findings are pivotal for those employing AI in medicine, providing clear, actionable guidance on harnessing GPT-4V's full diagnostic potential

DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

We present DiffBIR, which leverages pretrained text-to-image diffusion models for blind image restoration problem. Our framework adopts a two-stage pipeline. In the first stage, we pretrain a restoration module across diversified degradations to improve generalization capability in real-world scenarios. The second stage leverages the generative ability of latent diffusion models, to achieve realistic image restoration. Specifically, we introduce an injective modulation sub-network -- LAControlNet for finetuning, while the pre-trained Stable Diffusion is to maintain its generative ability. Finally, we introduce a controllable module that allows users to balance quality and fidelity by introducing the latent image guidance in the denoising process during inference. Extensive experiments have demonstrated its superiority over state-of-the-art approaches for both blind image super-resolution and blind face restoration tasks on synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR

Do Narcissists Enjoy Visiting Social Networking Sites? It Depends on How Adaptive They Are

Previous evidence suggests that narcissistic people tend to visit social networking sites (SNS) frequently, but the emotions accompanying their engagement on such sites has not been a significant subject of study. Therefore, we examined the relationship between narcissism and the affective experience on SNS in two different samples. To do so, we not only examined narcissism as a whole but also distinguished between adaptive and maladaptive narcissism. Results of the two studies consistently showed that: (1) narcissism as a whole was not correlated with the SNS affective experience; (2) maladaptive narcissism was predictive of a worse affective experience on SNS; and (3) partly due to a positive correlation with self-esteem, adaptive narcissism was associated with a better SNS affective experience. In addition, these findings held with SNS activities considered in simultaneity. The present research extends our understanding of the relationship between narcissism and social networking as well as that between emotion and social networking

MicroRNAs in spermatogenesis dysfunction and male infertility: clinical phenotypes, mechanisms and potential diagnostic biomarkers

Infertility affects approximately 10–15% of couples worldwide who are attempting to conceive, with male infertility accounting for 50% of infertility cases. Male infertility is related to various factors such as hormone imbalance, urogenital diseases, environmental factors, and genetic factors. Owing to its relationship with genetic factors, male infertility cannot be diagnosed through routine examination in most cases, and is clinically called ‘idiopathic male infertility.’ Recent studies have provided evidence that microRNAs (miRNAs) are expressed in a cell-or stage-specific manner during spermatogenesis. This review focuses on the role of miRNAs in male infertility and spermatogenesis. Data were collected from published studies that investigated the effects of miRNAs on spermatogenesis, sperm quality and quantity, fertilization, embryo development, and assisted reproductive technology (ART) outcomes. Based on the findings of these studies, we summarize the targets of miRNAs and the resulting functional effects that occur due to changes in miRNA expression at various stages of spermatogenesis, including undifferentiated and differentiating spermatogonia, spermatocytes, spermatids, and Sertoli cells (SCs). In addition, we discuss potential markers for diagnosing male infertility and predicting the varicocele grade, surgical outcomes, ART outcomes, and sperm retrieval rates in patients with non-obstructive azoospermia (NOA)

STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions. Further scaling them up to higher orders of magnitude is rarely explored. An overarching goal of exploring large-scale models is to train them on large-scale medical segmentation datasets for better transfer capacities. In this work, we design a series of Scalable and Transferable U-Net (STU-Net) models, with parameter sizes ranging from 14 million to 1.4 billion. Notably, the 1.4B STU-Net is the largest medical image segmentation model to date. Our STU-Net is based on nnU-Net framework due to its popularity and impressive performance. We first refine the default convolutional blocks in nnU-Net to make them scalable. Then, we empirically evaluate different scaling combinations of network depth and width, discovering that it is optimal to scale model depth and width together. We train our scalable STU-Net models on a large-scale TotalSegmentator dataset and find that increasing model size brings a stronger performance gain. This observation reveals that a large model is promising in medical image segmentation. Furthermore, we evaluate the transferability of our model on 14 downstream datasets for direct inference and 3 datasets for further fine-tuning, covering various modalities and segmentation targets. We observe good performance of our pre-trained model in both direct inference and fine-tuning. The code and pre-trained models are available at https://github.com/Ziyan-Huang/STU-Net

Multi-source remote sensing identification of underground coal fires based on the fusion of surface temperature and deformation.

Underground coal fires have strong concealment and great harm, not only damaging vegetation and ecological environment, causing serious air pollution, but also easily inducing geological disasters, threatening the safety of life and property of surrounding people, as well as the safety of coal production. Therefore, accurate identification and monitoring of underground coal fire disasters is of great significance. To address the issue of difficulty in accurately identifying underground coal fires using a single remote sensing method, multiple Landsat-8 and Sentinel-1 A images from 2017 to 2019 were used. Long term surface temperature and surface deformation of Shuixigou coalfield were obtained using generalized single channel algorithm and DS–InSAR （Distributed Scatterer Inter fabric Synthetic Aperture Radar） technology, respectively. On this basis, research on multi-source remote sensing recognition of underground coal fires by integrating surface temperature and deformation was carried out based on methods such as threshold segmentation and spatiotemporal coupling superposition analysis. The results indicate that the spatiotemporal collaborative analysis of surface long-term temperature and deformation can effectively overcome the impact of complex abnormal signals such as non coal fire high temperature or deformation, and basically accurately restore the response characteristics of underground coal fire signals in the two dimensions of surface temperature and deformation. Moreover, subtle differences were found in the spatial distribution patterns of surface temperature anomalies and deformation anomalies in underground coal fire areas. The deformation anomaly benefits from the resolution of SAR images and fewer external interference factors, which have a stronger indicating effect on underground coal fire identification. However, the range of coal fire areas correctly identified based on temperature anomalies has better spatial consistency with the actual coal fire boundaries. In addition, there is a small deviation between the temperature and deformation peak spatial position of underground coal fire disasters. However, there is a clear consistency in the response between temperature and deformation in the time dimension, which is characterized by stable abnormal high temperatures and continuous subsidence in the coal fire area. It can be seen that compared to a single remote sensing method, the method of integrating temperature and deformation can more accurately identify underground coal fire areas, providing good technical support for the wide area survey and timely prevention and control of underground coal fire disasters