Health disparity among older adults in urban China: The role of local fiscal conditions.

This study explores the disparities in older adults' self-rated health within the urban landscape of China. Drawing on the 1% national population survey of China in 2015, it highlights how variations in city development contribute to geographical health disparities among older residents. In the era of the decentralized fiscal system, a crucial mechanism identified is the role of cities' local fiscal revenue in connecting their socioeconomic development and the health status among older adults. Despite efforts by cities in lower socioeconomic positions to increase fiscal expenditure and address deficits through central transfer payments, they prove inadequate in effectively mitigating population health disparities. The prioritization of economic growth and neglect of public service provision responsibilities are fundamental causes within this fiscal framework. The findings underscore the urgent need for increased central transfer payments in public services to address the growing disparities in older adults' health.

MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning.

BACKGROUND AND OBJECTIVE: The classification of diabetic retinopathy (DR) aims to utilize the implicit information in images for early diagnosis, to prevent and mitigate the further worsening of the condition. However, existing methods are often limited by the need to operate within large, annotated datasets to show significant advantages. Additionally, the number of samples for different categories within the dataset needs to be evenly distributed, because the characteristic of sample imbalance distribution can lead to an excessive focus on high-frequency disease categories, while neglecting the less common but equally important disease categories. Therefore, there is an urgent need to develop a new classification method that can effectively alleviate the issue of sample distribution imbalance, thereby enhancing the accuracy of diabetic retinopathy classification. METHODS: In this work, we propose MediDRNet, a dual-branch network model based on prototypical contrastive learning. This model adopts prototype contrastive learning, creating prototypes for different levels of lesions, ensuring they represent the core features of each lesion level. It classifies by comparing the similarity between data points and their category prototypes. Our dual-branch network structure effectively resolves the issue of category imbalance and improves classification accuracy by emphasizing subtle differences in retinal lesions. Moreover, our approach combines a dual-branch network with specific lesion-level prototypes for core feature representation and incorporates the convolutional block attention module for enhanced lesion feature identification. RESULTS: Our experiments using both the Kaggle and UWF classification datasets have demonstrated that MediDRNet exhibits exceptional performance compared to other advanced models in the industry, especially on the UWF DR classification dataset where it achieved state-of-the-art performance across all metrics. On the Kaggle DR classification dataset, it achieved the highest average classification accuracy (0.6327) and Macro-F1 score (0.6361). Particularly in the classification tasks for minority categories of diabetic retinopathy on the Kaggle dataset (Grades 1, 2, 3, and 4), the model reached high classification accuracies of 58.08%, 55.32%, 69.73%, and 90.21%, respectively. In the ablation study, the MediDRNet model proved to be more effective in feature extraction from diabetic retinal fundus images compared to other feature extraction methods. CONCLUSIONS: This study employed prototype contrastive learning and bidirectional branch learning strategies, successfully constructing a grading system for diabetic retinopathy lesions within imbalanced diabetic retinopathy datasets. Through a dual-branch network, the feature learning branch effectively facilitated a smooth transition of features from the grading network to the classification learning branch, accurately identifying minority sample categories. This method not only effectively resolved the issue of sample imbalance but also provided strong support for the precise grading and early diagnosis of diabetic retinopathy in clinical applications, showcasing exceptional performance in handling complex diabetic retinopathy datasets. Moreover, this research significantly improved the efficiency of prevention and management of disease progression in diabetic retinopathy patients within medical practice. We encourage the use and modification of our code, which is publicly accessible on GitHub: https://github.com/ReinforceLove/MediDRNet.

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning.

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research.

Atomically Dispersed Manganese on Carbon Substrate for Aqueous and Aprotic CO2 Electrochemical Reduction.

CO2 utilization and conversion are of great importance in alleviating the rising CO2 concentration in the atmosphere. Here, a single-atom catalyst (SAC) is reported for electrochemical CO2 utilization in both aqueous and aprotic electrolytes. Specifically, atomically dispersed Mn-N4 sites are embedded in bowl-like mesoporous carbon particles with the functionalization of epoxy groups in the second coordination spheres. Theoretical calculations suggest that the epoxy groups near the Mn-N4 site adjust the electronic structure of the catalyst with reduced reaction energy barriers for the electrocatalytic reduction of CO2 to CO. The resultant Mn-single-atom carbon with N and O doped catalyst (MCs-(N,O)) exhibits extraordinary electrocatalytic performance with a high CO faradaic efficiency of 94.5%, a high CO current density of 13.7 mA cm-2 , and a low overpotential of 0.44 V in the aqueous environment. Meanwhile, as a cathode catalyst for aprotic Li-CO2 batteries, the MCs-(N,O) with well-regulated active sites and unique mesoporous bowl-like morphology optimizes the nucleation behavior of discharge products. MCs-(N,O)-based batteries deliver a low overpotential and excellent cyclic stability of 1000 h. The findings in this work provide a new avenue to design and fabricate SACs for various electrochemical CO2 utilization systems.

ZmIAA5 regulates maize root growth and development by interacting with ZmARF5 under the specific binding of ZmTCP15/16/17.

Background: The auxin indole-3-acetic acid (IAA) is a type of endogenous plant hormone with a low concentration in plants, but it plays an important role in their growth and development. The AUX/IAA gene family was found to be an early sensitive auxin gene with a complicated way of regulating growth and development in plants. The regulation of root growth and development by AUX/IAA family genes has been reported in Arabidopsis, rice and maize. Results: In this study, subcellular localization indicated that ZmIAA1-ZmIAA6 primarily played a role in the nucleus. A thermogram analysis showed that AUX/IAA genes were highly expressed in the roots, which was also confirmed by the maize tissue expression patterns. In maize overexpressing ZmIAA5, the length of the main root, the number of lateral roots, and the stalk height at the seedling stage were significantly increased compared with those of the wild type, while the EMS mutant zmiaa5 was significantly reduced. The total number of roots and the dry weight of maize overexpressing ZmIAA5 at the mature stage were also significantly increased compared with those of the wild type, while those of the mutant zmiaa5 was significantly reduced. Yeast one-hybrid experiments showed that ZmTCP15/16/17 could specifically bind to the ZmIAA5 promoter region. Bimolecular fluorescence complementation and yeast two-hybridization indicated an interaction between ZmIAA5 and ZmARF5. Conclusions: Taken together, the results of this study indicate that ZmIAA5 regulates maize root growth and development by interacting with ZmARF5 under the specific binding of ZmTCP15/16/17.

Maize factors ZmUBP15, ZmUBP16 and ZmUBP19 play important roles for plants to tolerance the cadmium stress and salt stress.

Ubiquitin-Specific Protease16 (UBP16) has been described involved in cadmium stress and salt stress in Arabidopsis, however nothing is known about the functions of its homologs in maize. In this study, we investigate the functions of ZmUBP15, ZmUBP16 and ZmUBP19, three Arabidopsis UBP16 homologs in maize. Our results indicate that ZmUBP15, ZmUBP16 and ZmUBP19 are ubiquitously expressed throughout plant development, and ZmUBP15, ZmUBP16 and ZmUBP19 proteins are mainly localized in plasma membrane. Complementation analyses show that over-expression of ZmUBP15 or ZmUBP16 can rescue the defective phenotype of ubp16-1 in cadmium stress. In addition, over-expression of ZmUBP15, ZmUBP16 or ZmUBP19 can increase the plant tolerance to cadmium stress. These results indicate that ZmUBP15, ZmUBP16 and ZmUBP19 are required for plant to tolerance the cadmium stress. Consistent with this point, cadmium-related genes are markedly up-regulated in seedlings over-expressing ZmUBP15, ZmUBP16 or ZmUBP19. Furthermore, our data indicate that ZmUBP15, ZmUBP16 and ZmUBP19 partially rescue the salt-stress phenotype of ubp16-1. Thus, our research uncover the functions of three novel maize proteins, ZmUBP15, ZmUBP16 and ZmUBP19, which are required for plants in response to cadmium stress and salt stress.