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Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions. [ABSTRACT FROM AUTHOR]

With rapid economic expansion, China is faced with environmental challenges like air pollution and greenhouse gas emissions. Shifting from conventional fossil fuels to renewable energy (REN) sources is critical to facilitate sustainable development in China. Compared to coal and oil, REN such as solar and wind energy emit less carbon emissions. Fostering innovation of REN technologies is thus essential for China's green transition. This study aims to analyze the impact of REN technology innovation on China's economic growth using panel data models. The results demonstrate that advancing REN technologies significantly promotes GDP increase in China. Targeted policy incentives must be implemented to accelerate REN technology progression and adoption across the country. Transitioning towards REN systems will be instrumental for China to achieve environmental sustainability while maintaining economic growth. [ABSTRACT FROM AUTHOR]