E-Bicycle as a Green and Physically Active Mode of Transport from the Aspect of Students: TPB and Financial Incentives.

The positive effects of e-bikes on physical activity, health, and the environment have been confirmed in many studies. Their choice, as well as of cycling in general, was previously considered from, among others, the socio-psychological aspect (often by use of the theory of planned behavior (TPB)) or the financial aspect (in the context of financial incentives). In addition, the question of physical activity can be especially relevant for the student population, since their level of physical activity usually declines. Starting from the previous framework, the aim of this research was to consider the intention to use e-bikes by the student population in the context of their attitudes, subjective norms, perceived behavioral control, and financial incentives. It is, according to the authors' knowledge, the first research that combines all those variables when studying e-bikes. The research was conducted in 2022 on a convenience sample of 332 students from the University of Novi Sad (Republic of Serbia). The results show that the strongest predictor of the intention to use e-bikes can be attributed to financial incentives, followed by attitudes and subjective norms, while perceived behavioral control is not significant. Besides considerations in the context of previous research, additional recommendations for increasing e-bikes' use were provided.

TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning.

Recent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20-30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image regions contribute to classification. Computational requirements to train TDAExplore models are modest and a standard PC can perform training with minimal user input. TDAExplore is therefore an accessible, powerful option for obtaining quantitative information about imaging data in a wide variety of applications.