Finding event structure in time: What recurrent neural networks can tell us about event structure in mind.

Under a theory of event representations that defines events as dynamic changes in objects across both time and space, as in the proposal of Intersecting Object Histories (Altmann & Ekves, 2019), the encoding of changes in state is a fundamental first step in building richer representations of events. In other words, there is an inherent dynamic that is captured by our knowledge of events. In the present study, we evaluated the degree to which this dynamic was inferable from just the linguistic signal, without access to visual, sensory, and embodied experience, using recurrent neural networks (RNNs).

A Novel Soluble Peptide with pH-Responsive Membrane Insertion.

Several diseases, such as cancer, are characterized by acidification of the extracellular environment. Acidosis can be employed as a target to specifically direct therapies to the diseased tissue. We have used first principles to design an acidity-triggered rational membrane (ATRAM) peptide with high solubility in solution that is able to interact with lipid membranes in a pH-dependent fashion.

The negative charge of the membrane has opposite effects on the membrane entry and exit of pH-low insertion peptide.

The pH-low insertion peptide (pHLIP) targets acidic diseases such as cancer. The acidity of the environment causes key aspartic acids in pHLIP to become protonated, causing the peptide to insert into membranes. Here we investigate how the negative charge of the membrane influences how pHLIP enters and exits the lipid bilayer.