Predicting the replicability of social and behavioural science claims in COVID-19 preprints.

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources.

Piloting an intraoral pH device to track gastroesophageal reflux in adults.

This study evaluated in vivo, an intraoral device against the gold standard esophageal device for gastroesophageal reflux disease (GERD) monitoring. Subjects scheduled for a catheter-based esophageal pH/impedance testing at a gastroenterology clinic were recruited. They were screened using the GerdQ questionnaire, demographics and dental conditions recorded.

De novo lipid synthesis and polarized prenylation drive cell invasion through basement membrane.

To breach the basement membrane, cells in development and cancer use large, transient, specialized lipid-rich membrane protrusions. Using live imaging, endogenous protein tagging, and cell-specific RNAi during Caenorhabditis elegans anchor cell (AC) invasion, we demonstrate that the lipogenic SREBP transcription factor SBP-1 drives the expression of the fatty acid synthesis enzymes POD-2 and FASN-1 prior to invasion. We show that phospholipid-producing LPIN-1 and sphingomyelin synthase SMS-1, which use fatty acids as substrates, produce lysosome stores that build the AC's invasive protrusion, and that SMS-1 also promotes protrusion localization of the lipid raft partitioning ZMP-1 matrix metalloproteinase.

Antibody glycan quality predicted from CHO cell culture media markers and machine learning.

N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media.