The Driving Magnetic Field and Reconnection in CME/Flare Eruptions and Coronal Jets

Signatures of reconnection in major CME (coronal mass ejection)/flare eruptions and in coronal X-ray jets are illustrated and interpreted. The signatures are magnetic field lines and their feet that brighten in flare emission. CME/flare eruptions are magnetic explosions in which: 1. The field that erupts is initially a closed arcade. 2. At eruption onset, most of the free magnetic energy to be released is not stored in field bracketing a current sheet, but in sheared field in the core of the arcade. 3. The sheared core field erupts by a process that from its start or soon after involves fast "tether-cutting" reconnection at an initially small current sheet low in the sheared core field. If the arcade has oppositely-directed field over it, the eruption process from its start or soon after also involves fast "breakout" reconnection at an initially small current sheet between the arcade and the overarching field. These aspects are shown by the small area of the bright field lines and foot-point flare ribbons in the onset of the eruption. 4. At either small current sheet, the fast reconnection progressively unleashes the erupting core field to erupt with progressively greater force. In turn, the erupting core field drives the current sheet to become progressively larger and to undergo progressively greater fast reconnection in the explosive phase of the eruption, and the flare arcade and ribbons grow to become comparable to the pre-eruption arcade in lateral extent. In coronal X-ray jets: 1. The magnetic energy released in the jet is built up by the emergence of a magnetic arcade into surrounding unipolar "open" field. 2. A simple jet is produced when a burst of reconnection occurs at the current sheet between the arcade and the open field. This produces a bright reconnection jet and a bright reconnection arcade that are both much smaller in diameter that the driving arcade. 3. A more complex jet is produced when the arcade has a sheared core field and undergoes an ejective eruption in the manner of a miniature CME/flare eruption. The jet is then a combination of a miniature CME and the products of more widely distributed reconnection of the erupting arcade with the open field than in simple jets

Quantitative trait loci analysis of a RIL soybean population to determine chromosomal regions governing seed protein, oil, and linolenic acid content

180 RILs (recombinant inbred lines) segregating for protein, oil, and fatty acids were produced from a cross between TN12-4098 and TN13-4303. These lines were grown across three locations spread horizontally across Tennessee: Research Education Center at Milan (RECM), Highland Rim Research and Education Center (HRREC), and East Tennessee Research and Education Center (ETREC) in 2018 and 2019. 21 quantitative trait loci (QTLs) spanning 7 chromosomes were found using WinQTLCart2.5

ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data

Sepsis is a deadly condition affecting many patients in the hospital. Recent studies have shown that patients diagnosed with sepsis have significant mortality and morbidity, resulting from the body's dysfunctional host response to infection. Clinicians often rely on the use of Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the Modified Early Warning Score (MEWS) to identify early signs of clinical deterioration requiring further work-up and treatment. However, many of these tools are manually computed and were not designed for automated computation. There have been different methods used for developing sepsis onset models, but many of these models must be trained on a sufficient number of patient observations in order to form accurate sepsis predictions. Additionally, the accurate annotation of patients with sepsis is a major ongoing challenge. In this paper, we propose the use of Active Learning Recurrent Neural Networks (ALRts) for short temporal horizons to improve the prediction of irregularly sampled temporal events such as sepsis. We show that an active learning RNN model trained on limited data can form robust sepsis predictions comparable to models using the entire training dataset.Comment: 11 pages, 5 figures, 2 table