Predicting geostationary 40–150 keV electron flux using ARMAX (an autoregressive moving average transfer function), RNN (a Recurrent Neural Network), and logistic regression: a comparison of models

We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES-13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. Value-predicting models developed using ARMAX (autoregressive moving average transfer function), RNN (recurrent neural network), or stepwise-reduced regression produced roughly similar results. Including magnetic local time as a categorical variable to describe both the differing levels of flux and the differing influence of parameters improved the models (r as high as 0.814; Heidke Skill Score (HSS) as high as 0.663), however value-predicting models did a poor job at predicting highs and lows. Diagnostic tests are introduced (cubic fit to observation-prediction relationship and Lag1 correlation) that better assess predictions of extremes than single metrics such as root mean square error, mean absolute error, or median symmetric accuracy. Classifier models (RNN and logistic regression) were equally able to predict flux rise above the 75th percentile (HSS as high as 0.667). Logistic regression models were improved by the addition of multiplicative interaction and quadratic terms. Only predictors from 1 or 3 hr before were necessary and a detailed description of flux time series behavior was not needed. Stepwise selection of these variables trimmed non-contributing parameters for a more parsimonious and portable logistic regression model that predicted as well as neural network-derived models. We provide a logistic regression model (LL3: LogisticLag3) based on inputs measured 3 hr previous, along with optimal probability thresholds, for future predictions

Comparison of multiple and logistic regression analyses of relativistic electron flux enhancement at geosynchronous orbit following storms.

Many factors influence relativistic outer radiation belt electron fluxes, such as waves in the ultra low frequency (ULF) Pc5, very low frequency (VLF), and electromagnetic ion cyclotron (EMIC) frequency bands, seed electron flux, Dst disturbance levels, substorm occurrence, and solar wind inputs. In this work we compared relativistic electron flux post storm vs. pre‐storm using three methods of analysis: 1) multiple regression to predict flux values following storms, 2) multiple regression to predict the size and direction of the change in electron flux, and 3) multiple logistic regression to predict only the probability of the flux rising or falling. We determined which is the most predictive model, and which factors are most influential. We found that a linear regression predicting the difference in pre‐storm and post storm flux (Model 2) results in the highest validation correlations. The logistic regression used in Model 3 had slightly weaker predictive abilities than the other two models, but had most value in providing a prediction of the probability of the electron flux increasing after a storm. Of the variables used (ULF Pc5 and VLF waves, seed electrons, substorm activity, and EMIC waves), the most influential in the final model were ULF Pc5 waves and the seed electrons. IMF Bz, Dst, and solar wind number density, velocity, and pressure did not improve any of the models, and were deemed unnecessary for effective predictions

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Long-term control of recurrent or refractory viral infections after allogeneic HSCT with third-party virus-specific T cells

Donor-derived adoptive T-cell therapy is a safe and effective treatment of viral infection posttransplant, but it is limited by donor serostatus and availability and by its personalized nature. Off-the-shelf, third-party virus-specific T cells (VSTs) appear promising, but the long-term safety and durability of responses have yet to be established. We conducted a prospective study of 30 allogeneic hemopoietic stem cell transplant (HSCT) patients with persistent or recurrent cytomegalovirus (CMV) (n = 28), Epstein-Barr virus (n = 1), or adenovirus (n = 1) after standard therapy. Patients were treated with infusions of partially HLA-matched, third-party, ex vivo-expanded VSTs (total = 50 infusions) at a median of 75 days post-HSCT (range, 37 to 349 days). Safety, viral dynamics, and immune recovery were monitored for 12 months. Infusions were safe and well tolerated. Acute graft versus host disease occurred in 2 patients, despite a median HLA match between VSTs and the recipient of 2 of 6 antigens. At 12 months, the cumulative incidence of overall response was 93%. Virological control was durable in the majority of patients; the reintroduction of antiviral therapy after the final infusion occurred in 5 patients. CMV-specific T-cell immunity rose significantly and coincided with a rise in CD8+ terminal effector cells. PD-1 expression was elevated on CD8+ lymphocytes before the administration of third-party T cells and remained elevated at the time of viral control. Third-party VSTs show prolonged benefit, with virological control achieved in association with the recovery of CD8+ effector T cells possibly facilitated by VST infusion. This trial was registered at www.clinicaltrials.gov as #NCT02779439 and www.anzctr.org.au as #ACTRN12613000603718.Barbara Withers, Emily Blyth, Leighton E. Clancy, Agnes Yong, Chris Fraser, Jane Burgess ... et al