5 research outputs found
Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies
Flare frequency distributions represent a key approach to addressing one of
the largest problems in solar and stellar physics: determining the mechanism
that counter-intuitively heats coronae to temperatures that are orders of
magnitude hotter than the corresponding photospheres. It is widely accepted
that the magnetic field is responsible for the heating, but there are two
competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To
date, neither can be directly observed. Nanoflares are, by definition,
extremely small, but their aggregate energy release could represent a
substantial heating mechanism, presuming they are sufficiently abundant. One
way to test this presumption is via the flare frequency distribution, which
describes how often flares of various energies occur. If the slope of the power
law fitting the flare frequency distribution is above a critical threshold,
as established in prior literature, then there should be a
sufficient abundance of nanoflares to explain coronal heating. We performed
600 case studies of solar flares, made possible by an unprecedented number
of data analysts via three semesters of an undergraduate physics laboratory
course. This allowed us to include two crucial, but nontrivial, analysis
methods: pre-flare baseline subtraction and computation of the flare energy,
which requires determining flare start and stop times. We aggregated the
results of these analyses into a statistical study to determine that . This is below the critical threshold, suggesting that Alfv\'en
waves are an important driver of coronal heating.Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The
Astrophysical Journal on 2023-05-09, volume 948, page 7
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset
Recommended from our members
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different
degrees of aggressiveness, variable prognosis and various heterogeneous
histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic
core, active and non-enhancing core. This intrinsic heterogeneity is also
portrayed in their radio-phenotype, as their sub-regions are depicted by
varying intensity profiles disseminated across multi-parametric magnetic
resonance imaging (mpMRI) scans, reflecting varying biological properties.
Their heterogeneous shape, extent, and location are some of the factors that
make these tumors difficult to resect, and in some cases inoperable. The amount
of resected tumor is a factor also considered in longitudinal scans, when
evaluating the apparent tumor for potential diagnosis of progression.
Furthermore, there is mounting evidence that accurate segmentation of the
various tumor sub-regions can offer the basis for quantitative image analysis
towards prediction of patient overall survival. This study assesses the
state-of-the-art machine learning (ML) methods used for brain tumor image
analysis in mpMRI scans, during the last seven instances of the International
Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we
focus on i) evaluating segmentations of the various glioma sub-regions in
pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue
of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO
criteria, and iii) predicting the overall survival from pre-operative mpMRI
scans of patients that underwent gross total resection. Finally, we investigate
the challenge of identifying the best ML algorithms for each of these tasks,
considering that apart from being diverse on each instance of the challenge,
the multi-institutional mpMRI BraTS dataset has also been a continuously
evolving/growing dataset