Solar Energetic Particle Forecasting Algorithms and Associated False Alarms.
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ABSTRACT: Solar energetic particle (SEP) events are known to occur following solar flares and coronal mass ejections (CMEs). However, some high-energy solar events do not result in SEPs being detected at Earth, and it is these types of event which may be termed "false alarms". We define two simple SEP forecasting algorithms based upon the occurrence of a magnetically well-connected CME with a speed in excess of 1500kms-1 (a "fast" CME) or a well-connected X-class flare and analyse them with respect to historical datasets. We compare the parameters of those solar events which produced an enhancement of >40MeV protons at Earth (an "SEP event") and the parameters of false alarms. We find that an SEP forecasting algorithm based solely upon the occurrence of a well-connected fast CME produces fewer false alarms (28.8%) than an algorithm which is based solely upon a well-connected X-class flare (50.6%). Both algorithms fail to forecast a relatively high percentage of SEP events (53.2% and 50.6%, respectively). Our analysis of the historical datasets shows that false-alarm X-class flares were either not associated with any CME, or were associated with a CME slower than 500kms-1 ; false-alarm fast CMEs tended to be associated with flare classes lower than M3. A better approach to forecasting would be an algorithm which takes as its base the occurrence of both CMEs and flares. We define a new forecasting algorithm which uses a combination of CME and flare parameters, and we show that the false-alarm ratio is similar to that for the algorithm based upon fast CMEs (29.6%), but the percentage of SEP events not forecast is reduced to 32.4%. Lists of the solar events which gave rise to >40MeV protons and the false alarms have been derived and are made available to aid further study. Electronic Supplementary Material:The online version of this article (doi:10.1007/s11207-017-1196-y) contains supplementary material, which is available to authorized users.
Project description:An interval of exceptional solar activity was registered in early September 2017, late in the decay phase of solar cycle 24, involving the complex Active Region 12673 as it rotated across the western hemisphere with respect to Earth. A large number of eruptions occurred between 4 and 10 September, including four associated with X-class flares. The X9.3 flare on 6 September and the X8.2 flare on 10 September are currently the two largest during cycle 24. Both were accompanied by fast coronal mass ejections and gave rise to solar energetic particle (SEP) events measured by near-Earth spacecraft. In particular, the partially occulted solar event on 10 September triggered a ground-level enhancement (GLE), the second GLE of cycle 24. A further, much less energetic SEP event was recorded on 4 September. In this work we analyze observations by the Advanced Composition Explorer (ACE) and the Geostationary Operational Environmental Satellites (GOES), estimating the SEP event-integrated spectra above 300 keV and carrying out a detailed study of the spectral shape temporal evolution. Derived spectra are characterized by a low-energy break at few/tens of MeV; the 10 September event spectrum, extending up to ~1 GeV, exhibits an additional rollover at several hundred MeV. We discuss the spectral interpretation in the scenario of shock acceleration and in terms of other important external influences related to interplanetary transport and magnetic connectivity, taking advantage of multipoint observations from the Solar Terrestrial Relations Observatory. Spectral results are also compared with those obtained for the 17 May 2012 GLE event.
Project description:Soon after the discovery that insulin regulates blood glucose by Banting and Best in 1922, the symptoms and risks associated with hypoglycemia became widely recognized. This article reviews devices to warn individuals of impending hypo- and hyperglycemia; biosignals used by these devices include electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and continuous glucose monitors (CGMs). While systems based on other technology are increasing in performance and decreasing in size, CGM technology remains the best method for both reactive and predictive alarming of hypo- or hyperglycemia.
Project description:The hippocampus supports distinctive encoding, enabling discrimination of perceptions from similar memories. Here, an experimental and individual differences approach examined the role of encoding quality in the classification of similar lures. An object recognition task included thought probes during study and similar lures at test. On-task study reports were associated with lure discrimination in within-subject and between-subjects analyses. Within-subject on-task reports were also associated with false classifications of lures as studied objects. These results are compatible with the view that quality encoding supports memory-based rejection of lures but also engenders false alarms when perceptions and memories are inaccurately compared.
Project description:Solar energetic particles are an integral part of the physical processes related with space weather. We present a review for the acceleration mechanisms related to the explosive phenomena (flares and/or coronal mass ejections, CMEs) inside the solar corona. For more than 40 years, the main two-dimensional cartoon representing our understanding of the explosive phenomena inside the solar corona remained almost unchanged. The acceleration mechanisms related to solar flares and CMEs also remained unchanged and were part of the same cartoon. In this review, we revise the standard cartoon and present evidence from recent global magnetohydrodynamic simulations that support the argument that explosive phenomena will lead to the spontaneous formation of current sheets in different parts of the erupting magnetic structure. The evolution of the large-scale current sheets and their fragmentation will lead to strong turbulence and turbulent reconnection during solar flares and turbulent shocks. In other words, the acceleration mechanism in flares and CME-driven shocks may be the same, and their difference will be the overall magnetic topology, the ambient plasma parameters, and the duration of the unstable driver. This article is part of the theme issue 'Solar eruptions and their space weather impact'.
Project description:Despite its widespread use in North America and many other parts of the world, the safety of etomidate as an induction agent for rapid sequence intubation in septic patients is still debated. In this article, we evaluate the current literature on etomidate, review its clinical history, and discuss the controversy regarding its use, especially in sepsis. We address eight questions: (i) When did concern over the safety of etomidate first arise? (ii) What is the mechanism by which etomidate is thought to affect the adrenal axis? (iii) How has adrenal insufficiency in relation to etomidate use been defined or identified in the literature? (iv) What is the evidence that single dose etomidate is associated with subsequent adrenal-cortisol dysfunction? (v) What is the clinical significance of adrenal insufficiency or dysfunction associated with single dose etomidate, and where are the data that support or refute the contention that single-dose etomidate is associated with increased mortality or important post emergency department (ED) clinical outcomes? (vi) How should etomidate's effects in septic patients best be measured? (vii) What are alternative induction agents and what are the advantages and disadvantages of these agents relative to etomidate? (viii) What future work is needed to further clarify the characteristics of etomidate as it is currently used in patients with sepsis? We conclude that the observational nature of almost all available data suggesting adverse outcomes from etomidate does not support abandoning its use for rapid sequence induction. However, because we see a need to balance theoretical harms and benefits in the presence of data supporting the non-inferiority of alternative agents without similar theoretical risks associated with them, we suggest that the burden of proof to support continued widespread use may rest with the proponents of etomidate. We further suggest that practitioners become familiar with the use of more than one agent while awaiting further definitive data.
Project description:We have entered an era of direct-to-consumer (DTC) genomics. Patients have relayed many success stories of DTC genomics about finding causal mutations of genetic diseases before showing any symptoms and taking precautions. However, consumers may also take unnecessary medical actions based on false alarms of "pathogenic alleles". The severity of this problem is not well known. Using publicly available data, we compared DTC microarray genotyping data with deep-sequencing data of 5 individuals and manually checked each inconsistently reported single nucleotide variants (SNVs). We estimated that, on average, a person would have ~5 "pathogenic" alleles reported due to wrongly reported genotypes if using a 23andMe genotyping microarray. We also found that the number of wrongly classified "pathogenic" alleles per person is at least as significant as those due to wrongly reported genotypes. We show that the scale of the false alarm problem could be large enough that the medical costs will become a burden to public health.
Project description:Bioinformatic amino acid sequence searches are used, in part, to assess the potential allergenic risk of newly expressed proteins in genetically engineered crops. Previous work has demonstrated that the searches required by government regulatory agencies falsely implicate many proteins from rarely allergenic crops as an allergenic risk. However, many proteins are found in crops at concentrations that may be insufficient to cause allergy. Here we used a recently developed set of high-abundance non-allergenic proteins to determine the false-positive rates for several algorithms required by regulatory bodies, and also for an alternative 1:1 FASTA approach previously found to be equally sensitive to the official sliding-window method, but far more selective. The current investigation confirms these earlier findings while addressing dietary exposure.
Project description:This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric "score" from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.
Project description:NASA's Parker Solar Probe mission1 recently plunged through the inner heliosphere of the Sun to its perihelia, about 24 million kilometres from the Sun. Previous studies farther from the Sun (performed mostly at a distance of 1 astronomical unit) indicate that solar energetic particles are accelerated from a few kiloelectronvolts up to near-relativistic energies via at least two processes: 'impulsive' events, which are usually associated with magnetic reconnection in solar flares and are typically enriched in electrons, helium-3 and heavier ions2, and 'gradual' events3,4, which are typically associated with large coronal-mass-ejection-driven shocks and compressions moving through the corona and inner solar wind and are the dominant source of protons with energies between 1 and 10 megaelectronvolts. However, some events show aspects of both processes and the electron-proton ratio is not bimodally distributed, as would be expected if there were only two possible processes5. These processes have been very difficult to resolve from prior observations, owing to the various transport effects that affect the energetic particle population en route to more distant spacecraft6. Here we report observations of the near-Sun energetic particle radiation environment over the first two orbits of the probe. We find a variety of energetic particle events accelerated both locally and remotely including by corotating interaction regions, impulsive events driven by acceleration near the Sun, and an event related to a coronal mass ejection. We provide direct observations of the energetic particle radiation environment in the region just above the corona of the Sun and directly explore the physics of particle acceleration and transport.
Project description:PurposeClinical genome sequencing (cGS) followed by orthogonal confirmatory testing is standard practice. While orthogonal testing significantly improves specificity, it also results in increased turnaround time and cost of testing. The purpose of this study is to evaluate machine learning models trained to identify false positive variants in cGS data to reduce the need for orthogonal testing.MethodsWe sequenced five reference human genome samples characterized by the Genome in a Bottle Consortium (GIAB) and compared the results with an established set of variants for each genome referred to as a truth set. We then trained machine learning models to identify variants that were labeled as false positives.ResultsAfter training, the models identified 99.5% of the false positive heterozygous single-nucleotide variants (SNVs) and heterozygous insertions/deletions variants (indels) while reducing confirmatory testing of nonactionable, nonprimary SNVs by 85% and indels by 75%. Employing the algorithm in clinical practice reduced overall orthogonal testing using dideoxynucleotide (Sanger) sequencing by 71%.ConclusionOur results indicate that a low false positive call rate can be maintained while significantly reducing the need for confirmatory testing. The framework that generated our models and results is publicly available at https://github.com/HudsonAlpha/STEVE .