Search for: All records

Abstract The outer heliosphere is profoundly influenced by nonthermal energetic pickup ions (PUIs), which dominate the internal pressure of the solar wind beyond ~10 au, surpassing both solar wind and magnetic pressures. PUIs are formed mostly through charge exchange between interstellar neutral atoms and solar wind ions. This study examines the apparent heating of PUIs in the distant supersonic solar wind before reaching the heliospheric termination shock. New Horizons’ SWAP observations reveal an unexpected PUI temperature change between 2015 and 2020, with a notable bump in PUI temperature. Concurrent observations from the ACE and Wind spacecraft at 1 au indicate a ~50% increase in solar wind dynamic pressure at the end of 2014. Our simulation suggests that the bump observed in the PUI temperature by New Horizons is largely associated with the enhanced solar wind dynamic pressure observed at 1 au. Additional PUI temperature enhancements imply the involvement of other heating mechanisms. Analysis of New Horizons data reveals a correlation between shocks and PUI heating during the declining phase of the solar cycle. Using a PUI-mediated plasma model, we explore shock structures and PUI heating, finding that shocks preferentially heat PUIs over the thermal solar wind in the outer heliosphere. We also show that the broad shock thickness observed by New Horizons is due to the large diffusion coefficient associated with PUIs. Shocks and compression regions in the distant supersonic solar wind lead to elevated PUI temperatures and thus they can increase the production of energetic neutral atoms with large energy. 

Abstract Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved.