Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population

Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. Our proposed approach, which has the feature to generate pseudo-nuclear stained images by simple DNN, showed remarkable advantages over existing approaches in the cell-detection and the detection of the relative position of cells for various cell densities, as well as in counting the exact cell numbers. Pseudo-nuclear staining of cells by deep learning will improve the accuracy of automated cell counting in a label-free cellular population on phase contrast images.

Uncovering effects of self-control and stimulus-driven action selection on the sense of agency

The sense of agency refers to feelings of causing one's own action and resulting effect. Previous research indicates that voluntary action selection is an important factor in shaping the sense of agency. Whereas the volitional nature of the sense of agency is well documented, the present study examined whether agency is modulated when action selection shifts from self-control to a more automatic stimulus-driven process. Seventy-two participants performed an auditory Simon task including congruent and incongruent trials to generate automatic stimulus-driven vs. more self-control driven action, respectively. Responses in the Simon task produced a tone and agency was assessed with the intentional binding task - an implicit measure of agency. Results showed a Simon effect and temporal binding effect. However, temporal binding was independent of congruency. These findings suggest that temporal binding, a window to the sense of agency, emerges for both automatic stimulus-driven actions and self-controlled actions.