Utilizing spontaneous plants for sustainable development in residential green spaces: Insights from environmental drivers and niche analysis in Fuzhou City, China.

This study, aimed at exploring low-maintenance, high-diversity, and sustainable greening strategies for residential areas, conducted a comprehensive survey and analysis of spontaneous plants in residential green spaces in Fuzhou City, documenting 361 species. Employing methods such as variance partitioning, Canonical Correspondence Analysis (CCA), and ecological niche analysis, we investigated the environmental factors influencing the distribution and composition of these plants, as well as their interrelationships. The study found that the composition of spontaneous plants in residential green spaces differs from other urban environments, with a high proportion of alien species (43.77%) due to influences such as resident activities, including a large number of ornamental and edible plants. Maintenance level, urbanization gradient, and green space ratio are common factors affecting the composition and distribution of spontaneous plants in urban environments, while unique residential socio-economic factors like building age, housing prices, and population density significantly affect the spontaneous plants in residential green spaces. The overall dominant plant community shows a significant positive association, indicating a relatively stable stage of succession. Although competition among most species is not significant and interspecific connectivity is weak, the presence of seven dominant invasive species intensifies competition. Based on these findings, the study proposes several specific sustainable management measures: adopting the concept of New Naturalistic Ecological Planting Design, selecting native spontaneous plants with strong adaptability, and constructing plant communities that are ecologically stable and have ornamental value by mimicking natural ecosystems. Additionally, specific methods for managing specific invasive species in residential green spaces using competitive replacement control methods are proposed. These measures aim to promote the health and sustainable development of urban residential green spaces.

Development and Optimization of A Human Hepatocellular Carcinoma Patient-Derived Organoid Model for Potential Target Identification and Drug Discovery.

Hepatocellular carcinoma (HCC) is a highly prevalent and lethal tumor worldwide and its late discovery and lack of effective specific therapeutic agents necessitate further research into its pathogenesis and treatment. Organoids, a novel model that closely resembles native tumor tissue and can be cultured in vitro, have garnered significant interest in recent years, with numerous reports on the development of organoid models for liver cancer. In this study, we have successfully optimized the procedure and established a culture protocol that enables the formation of larger-sized HCC organoids with stable passaging and culture conditions. We have comprehensively outlined each step of the procedure, covering the entire process of HCC tissue dissociation, organoid plating, culture, passaging, cryopreservation, and resuscitation, and provided detailed precautions in this paper. These organoids exhibit genetic similarity to the original HCC tissues and can be utilized for diverse applications, including the identification of potential therapeutic targets for tumors and subsequent drug development.

Deep Learning-Based Football Player Detection in Videos.

The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.