Wave-induced loss of ultra-relativistic electrons in the Van Allen radiation belts.

The dipole configuration of the Earth's magnetic field allows for the trapping of highly energetic particles, which form the radiation belts. Although significant advances have been made in understanding the acceleration mechanisms in the radiation belts, the loss processes remain poorly understood. Unique observations on 17 January 2013 provide detailed information throughout the belts on the energy spectrum and pitch angle (angle between the velocity of a particle and the magnetic field) distribution of electrons up to ultra-relativistic energies. Here we show that although relativistic electrons are enhanced, ultra-relativistic electrons become depleted and distributions of particles show very clear telltale signatures of electromagnetic ion cyclotron wave-induced loss. Comparisons between observations and modelling of the evolution of the electron flux and pitch angle show that electromagnetic ion cyclotron waves provide the dominant loss mechanism at ultra-relativistic energies and produce a profound dropout of the ultra-relativistic radiation belt fluxes

Feasibility of a registry for standardized assessment of long-term and late-onset health events in survivors of childhood and adolescent cancer

Childhood and adolescent cancer survivors are at risk for chronic medical conditions. Longitudinal studies help to understand their development and course. We hypothesize that collecting follow-up data according to the modified CTCAE criteria and embedded in regular care, is feasible and results in a rich database. We recruited 50 Swiss survivors treated at our institution between 1992 and 2015, who completed their treatment and are still alive. Information on cancer diagnosis, treatment, and medical conditions from follow-up visits, graded according to the modified CTCAE criteria, were added in the database. We described the cohort, assessed the prevalence of medical conditions at the most recent visits and the time needed for data entry. Survivors had a median age of 10 years at diagnosis with 16 years of follow-up. 94% of survivors suffered from at least one medical condition. We registered 25 grade 3 or 4 conditions in 18 survivors. The time needed for data entry at enrollment was < 60 min in most survivors and much less for follow-up visits. Standardized assessment of medical conditions is feasible during regular clinical care. The database provides longitudinal real-time data to be used for clinical care, survivor education and research

Supervised and unsupervised language modelling in Chest X-Ray radiological reports

Chest radiography (CXR) is the most commonly used imaging modality and deep neural network (DNN) algorithms have shown promise in effective triage of normal and abnormal radiograms. Typically, DNNs require large quantities of expertly labelled training exemplars, which in clinical contexts is a major bottleneck to effective modelling, as both considerable clinical skill and time is required to produce high-quality ground truths. In this work we evaluate thirteen supervised classifiers using two large free-text corpora and demonstrate that bi-directional long short-term memory (BiLSTM) networks with attention mechanism effectively identify Normal, Abnormal, and Unclear CXR reports in internal (n = 965 manually-labelled reports, f1-score = 0.94) and external (n = 465 manually-labelled reports, f1-score = 0.90) testing sets using a relatively small number of expert-labelled training observations (n = 3,856 annotated reports). Furthermore, we introduce a general unsupervised approach that accurately distinguishes Normal and Abnormal CXR reports in a large unlabelled corpus. We anticipate that the results presented in this work can be used to automatically extract standardized clinical information from free-text CXR radiological reports, facilitating the training of clinical decision support systems for CXR triage

Good engraftment after reduced intensity targeted busulfan‐based conditioning and matched related donor hematopoietic cell transplantation in hemoglobinopathies

Background Hematopoietic cell transplantation (HCT) is an established curative therapy for transfusion‐dependent thalassemia (TDT) and sickle cell disease (SCD). The latest American Society of Hematology guidelines recommend myeloablative preparative regimen in patients under 18 years of age. Procedure The objective was to demonstrate safety and efficacy of a reduced intensity conditioning (RIC) regimen including high‐dose fludarabine, anti‐thymocyte globulin, and targeted busulfan as a single alkylator to sub‐myeloablative exposures. Results Between 2012 and 2021, 11 patients with SCD and five patients with TDT and matched related donor (MRD) HCT were included. The median age at transplantation was 8.3 years (range: 3.7–18.8 years). The median administered busulfan AUC was 67.4 mg/L×h (range: 60.7–80 mg/L×h). Overall survival was 93.8% and event‐free survival 87.5% with one engrafted SCD patient with pre‐existing moyamoya disease succumbing after drainage of a subdural hematoma. One SCD patient developed a secondary graft failure and was treated with a second HCT. Myeloid chimerism was full in all other patients with a median follow‐up time of 4.1 years (range: 2.0–11.1 years), whereas T‐cell donor chimerism was frequently mixed. Conclusion This RIC conditioning followed by MRD HCT is sufficiently myeloablative to cure pediatric patients with hemoglobinopathies without the need for additional total body irradiation or thiotepa

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Prediction of plasmaspheric hiss spectral classes

We present a random forests machine learning model for prediction of plasmaspheric hiss spectral classes from the Van Allen Probes dataset. The random forests model provides accurate prediction of plasmaspheric hiss spectral classes obtained by the self organizing map (SOM) unsupervised machine learning classification technique. The high predictive skill of the random forests model is largely determined by the distinct and different locations of a given spectral class (&ldquo;no hiss&rdquo;, &ldquo;regular hiss&rdquo;, and &ldquo;low-frequency hiss&rdquo;) in (MLAT, MLT, L) coordinate space, which are the main predictors of the simplest and most accurate base model. Adding to such a base model any other single predictor among different magnetospheric, geomagnetic, and solar wind conditions provides only minor and similarly incremental improvements in predictive skill, which is comparable to the one obtained when including all possible predictors, and thus confirming major role of spatial location for accurate prediction. &nbsp;</p

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

Measurement-induced entanglement and teleportation on a noisy quantum processor

Measurement has a special role in quantum theory: by collapsing the wavefunction it can enable phenomena such as teleportation and thereby alter the "arrow of time" that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time that go beyond established paradigms for characterizing phases, either in or out of equilibrium. On present-day NISQ processors, the experimental realization of this physics is challenging due to noise, hardware limitations, and the stochastic nature of quantum measurement. Here we address each of these experimental challenges and investigate measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases -- from entanglement scaling to measurement-induced teleportation -- in a unified way. We obtain finite-size signatures of a phase transition with a decoding protocol that correlates the experimental measurement record with classical simulation data. The phases display sharply different sensitivity to noise, which we exploit to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors

Timing of surgery following SARS-CoV-2 infection:an international prospective cohort study

Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. From 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odd ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odd ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay.</p