Open pathways for cerebrospinal fluid outflow at the cribriform plate along the olfactory nerves.

BACKGROUND: Routes along the olfactory nerves crossing the cribriform plate that extend to lymphatic vessels within the nasal cavity have been identified as a critical cerebrospinal fluid (CSF) outflow pathway. However, it is still unclear how the efflux pathways along the nerves connect to lymphatic vessels or if any functional barriers are present at this site. The aim of this study was to anatomically define the connections between the subarachnoid space and the lymphatic system at the cribriform plate in mice. METHODS: PEGylated fluorescent microbeads were infused into the CSF space in Prox1-GFP reporter mice and decalcification histology was utilized to investigate the anatomical connections between the subarachnoid space and the lymphatic vessels in the nasal submucosa. A fluorescently-labelled antibody marking vascular endothelium was injected into the cisterna magna to demonstrate the functionality of the lymphatic vessels in the olfactory region. Finally, we performed immunostaining to study the distribution of the arachnoid barrier at the cribriform plate region. FINDINGS: We identified that there are open and direct connections from the subarachnoid space to lymphatic vessels enwrapping the olfactory nerves as they cross the cribriform plate towards the nasal submucosa. Furthermore, lymphatic vessels adjacent to the olfactory bulbs form a continuous network that is functionally connected to lymphatics in the nasal submucosa. Immunostainings revealed a discontinuous distribution of the arachnoid barrier at the olfactory region of the mouse. INTERPRETATION: Our data supports a direct bulk flow mechanism through the cribriform plate allowing CSF drainage into nasal submucosal lymphatics in mice. FUNDING: This study was supported by the Swiss National Science Foundation (310030_189226), Dementia Research Switzerland-Synapsis Foundation, the Heidi Seiler Stiftung and the Fondation Dr. Corinne Schuler.

A model-based approach to Spotify data analysis: a Beta GLMM.

Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web-based data. This paper deals with the management of songs audio features from a statistical point of view. In particular, it explores the data catching mechanisms enabled by Spotify Web API and suggests statistical tools for the analysis of these data. Special attention is devoted to songs popularity and a Beta model, including random effects, is proposed in order to give the first answer to questions like: which are the determinants of popularity? The identification of a model able to describe this relationship, the determination within the set of characteristics of those considered most important in making a song popular is a very interesting topic for those who aim to predict the success of new products.