Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state

Idiopathic Parkinson's disease is characterized by a progressive loss of dopaminergic neurons, but the exact disease etiology remains largely unknown. To date, Parkinson's disease research has mainly focused on nigral dopaminergic neurons, although recent studies suggest disease-related changes also in non-neuronal cells and in midbrain regions beyond the substantia nigra. While there is some evidence for glial involvement in Parkinson's disease, the molecular mechanisms remain poorly understood. The aim of this study was to characterize the contribution of all cell types of the midbrain to Parkinson's disease pathology by single-nuclei RNA sequencing and to assess the cell type-specific risk for Parkinson's disease employing the latest genome-wide association study. We profiled >41 000 single-nuclei transcriptomes of postmortem midbrain from six idiopathic Parkinson's disease patients and five age-/sex-matched controls. To validate our findings in a spatial context, we utilized immunolabeling of the same tissues. Moreover, we analyzed Parkinson's disease-associated risk enrichment in genes with cell type-specific expression patterns. We discovered a neuronal cell cluster characterized by CADPS2 overexpression and low TH levels, which was exclusively present in IPD midbrains. Validation analyses in laser-microdissected neurons suggest that this cluster represents dysfunctional dopaminergic neurons. With regard to glial cells, we observed an increase in nigral microglia in Parkinson's disease patients. Moreover, nigral idiopathic Parkinson's disease microglia were more amoeboid, indicating an activated state. We also discovered a reduction in idiopathic Parkinson's disease oligodendrocyte numbers with the remaining cells being characterized by a stress-induced upregulation of S100B. Parkinson's disease risk variants were associated with glia- and neuron-specific gene expression patterns in idiopathic Parkinson's disease cases. Furthermore, astrocytes and microglia presented idiopathic Parkinson's disease-specific cell proliferation and dysregulation of genes related to unfolded protein response and cytokine signaling. While reactive patient astrocytes showed CD44 overexpression, idiopathic Parkinson's disease-microglia revealed a pro-inflammatory trajectory characterized by elevated levels of IL1B, GPNMB, and HSP90AA1. Taken together, we generated the first single-nuclei RNA sequencing dataset from the idiopathic Parkinson's disease midbrain, which highlights a disease-specific neuronal cell cluster as well as 'pan-glial' activation as a central mechanism in the pathology of the movement disorder. This finding warrants further research into inflammatory signaling and immunomodulatory treatments in Parkinson's disease

Increased light, moderate, and severe clear-air turbulence in response to climate change

Anthropogenic climate change is expected to strengthen the vertical wind shears at aircraft cruising altitudes within the atmospheric jet streams. Such a strengthening would increase the prevalence of shear instabilities, which generate clear-air turbulence. Climate modelling studies have indicated that the amount of moderate-or-greater clear-air turbulence on transatlantic flight routes in winter will increase significantly in future as the climate changes. However, the individual responses of light, moderate, and severe clear-air turbulence have not previously been studied, despite their importance for aircraft operations. Here we use climate model simulations to analyse the transatlantic wintertime clear-air turbulence response to climate change in five aviation-relevant turbulence strength categories. We find that the probability distributions for an ensemble of 21 clear-air turbulence diagnostics generally gain probability in their right-hand tails when the atmospheric carbon dioxide concentration is doubled. By converting the diagnostics into equivalent eddy dissipation rates, we find that the ensemble-average airspace volume containing light clear-air turbulence increases by 59% (with an intra-ensemble range of 43–68%), light-to-moderate by 75% (39–96%), moderate by 94% (37–118%), moderate-to-severe by 127% (30–170%), and severe by 149% (36–188%). These results suggest that the prevalence of transatlantic wintertime clear-air turbulence will increase significantly in all aviation-relevant strength categories as the climate changes

Aviation turbulence: dynamics, forecasting, and response to climate change

Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviation-affecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed—ideally in collaboration with the aviation industry—to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future

Social navigation support for groups in a community-based educational portal

This work seeks to enhance a user's experience in a digital library using group-based social navigation. Ensemble is a portal focusing on computing education as part of the US National Science Digital Library providing access to a large amount of learning materials and resources for education in Science, Technology, Engineering and Mathematics. With so many resources and so many contributing groups, we are seeking an effective way to guide users to find the right resource(s) by using group-based social navigation. This poster demonstrates how group-based social navigation can be used to extend digital library portals and how it can be used to guide portal users to valuable resources. © 2013 Springer-Verlag