First-person view of one's body in immersive virtual reality: Influence on episodic memory.

Episodic memories (EMs) are recollections of contextually rich and personally relevant past events. EM has been linked to the sense of self, allowing one to mentally travel back in subjective time and re-experience past events. However, the sense of self has recently been linked to online multisensory processing and bodily self-consciousness (BSC). It is currently unknown whether EM depends on BSC mechanisms. Here, we used a new immersive virtual reality (VR) system that maintained the perceptual richness of life episodes and fully controlled the experimental stimuli during encoding and retrieval, including the participant's body. Our data reveal a classical EM finding, which shows that memory for complex real-life like scenes decays over time. However, here we also report a novel finding that delayed retrieval performance can be enhanced when participants view their body as part of the virtual scene during encoding. This body effect was not observed when no virtual body or a moving control object was shown, thereby linking the sense of self, and BSC in particular, to EMs. The present VR methodology and the present behavioral findings will enable to study key aspects of EM in healthy participants and may be especially beneficial for the restoration of self-relevant memories in future experiments.

Reducing uncertainties in energy dissipation measurements in atomic force spectroscopy of molecular networks and cell-adhesion studies.

Atomic force microscope (AFM) based single molecule force spectroscopy (SMFS) is a valuable tool in biophysics to investigate the ligand-receptor interactions, cell adhesion and cell mechanics. However, the force spectroscopy data analysis needs to be done carefully to extract the required quantitative parameters correctly. Especially the large number of molecules, commonly involved in complex networks formation; leads to very complicated force spectroscopy curves. One therefore, generally characterizes the total dissipated energy over a whole pulling cycle, as it is difficult to decompose the complex force curves into individual single molecule events. However, calculating the energy dissipation directly from the transformed force spectroscopy curves can lead to a significant over-estimation of the dissipated energy during a pulling experiment. The over-estimation of dissipated energy arises from the finite stiffness of the cantilever used for AFM based SMFS. Although this error can be significant, it is generally not compensated for. This can lead to significant misinterpretation of the energy dissipation (up to the order of 30%). In this paper, we show how in complex SMFS the excess dissipated energy caused by the stiffness of the cantilever can be identified and corrected using a high throughput algorithm. This algorithm is then applied to experimental results from molecular networks and cell-adhesion measurements to quantify the improvement in the estimation of the total energy dissipation.