Influence of magnetic cycles on stellar prominences and their mass loss rates

Funding: The authors acknowledge support from STFC consolidated grant number ST/R000824/1.Observations of rapidly-rotating cool stars often show coronal “slingshot” prominences that remove mass and angular momentum when they are ejected. The derived masses of these prominences show a scatter of some two orders of magnitude. In order to investigate if this scatter could be intrinsic, we use a full magnetic cycle of solar magnetograms to model the coronal structure and prominence distribution in a young Sun, where we scale the field strength in the magnetograms with angular velocity according to B∝Ω−1.32. We reproduce both the observed prominence masses and their scatter. We show that both the field strength and the field geometry contribute to the prominence masses that can be supported and to the rate at which they are ejected. Predicted prominence masses follow the magnetic cycle, but with half the period, peaking both at cycle maximum and at cycle minimum. We show that mass loss rates in prominences are less than those predicted for the stellar wind. We also investigate the role of small-scale field that may be unresolved in typical stellar magnetograms. This provides only a small reduction in the predicted total prominence mass, principally by reducing the number of large magnetic loops that can support slingshot prominences. We conclude that the observed scatter in prominence masses can be explained by underlying magnetic cycles.PostprintPeer reviewe

Progressive Activation of CD127+132− Recent Thymic Emigrants into Terminally Differentiated CD127−132+ T-Cells in HIV-1 Infection

AIM: HIV infection is associated with distortion of T-cell homeostasis and the IL-7/IL7R axis. Progressive infection results in loss of CD127+132- and gains in CD127-132+ CD4+ and CD8+ T-cells. We investigated the correlates of loss of CD127 from the T-cell surface to understand mechanisms underlying this homeostatic dysregulation. METHODS: Peripheral and cord blood mononuclear cells (PBMCs; CBMC) from healthy volunteers and PBMC from patients with HIV infection were studied. CD127+132-, CD127+132+ and CD127-132+ T-cells were phenotyped by activation, differentiation, proliferation and survival markers. Cellular HIV-DNA content and signal-joint T-cell receptor excision circles (sjTRECs) were measured. RESULTS: CD127+132- T-cells were enriched for naïve cells while CD127-132+ T-cells were enriched for activated/terminally differentiated T-cells in CD4+ and CD8+ subsets in health and HIV infection. HIV was associated with increased proportions of activated/terminally differentiated CD127-132+ T-cells. In contrast to CD127+132- T-cells, CD127-132+ T-cells were Ki-67+Bcl-2(low) and contained increased levels of HIV-DNA. Naïve CD127+132- T-cells contained a higher proportion of sjTRECs. CONCLUSION: The loss of CD127 from the T-cell surface in HIV infection is driven by activation of CD127+132- recent thymic emigrants into CD127-132+ activated/terminally differentiated cells. This process likely results in an irreversible loss of CD127 and permanent distortion of T-cell homeostasis

Deep Sequencing of the Oral Microbiome Reveals Signatures of Periodontal Disease

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (∼2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ∼90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery