The parkinsonian LRRK2 R1441G mutation shows macroautophagy-mitophagy dysregulation concomitant with endoplasmic reticulum stress

Cell Biology and Toxicology - Autophagy is a mechanism responsible for the degradation of cellular components to maintain their homeostasis. However, autophagy is commonly altered and compromised...     

A guide to choosing and implementing reference models for social network analysis

Analysing social networks is challenging. Key features of relational data require the use of non-standard statistical methods such as developing system-specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.     

Physical exosome:exosome interactions

Exosomes are extracellular nanovesicles that mediate a number of cellular processes, including intracellular signalling. There are many published examples of exosome–exosome dimers; however, their relevance has not been explored. Here, we propose that cells release exosomes to physically interact with incoming exosomes, forming dimers that we hypothesize attenuate incoming exosome‐mediated signalling. We discuss experiments to test this hypothesis and potential relevance in health and disease.