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"Wang, Jason"
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Operational prediction of solar flares using a transformer-based framework
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a
γ
-class flare within the next 24 to 72 h. We consider three
γ
classes, namely the
≥
M5.0 class, the
≥
M class and the
≥
C class, and build three transformers separately, each corresponding to a
γ
class. Each transformer is used to make predictions of its corresponding
γ
-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.
Journal Article
Characterizing electronic health record usage patterns of inpatient medicine residents using event log data
Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.
Journal Article
The protein folding rate and the geometry and topology of the native state
Proteins fold in 3-dimensional conformations which are important for their function. Characterizing the global conformation of proteins rigorously and separating secondary structure effects from topological effects is a challenge. New developments in applied knot theory allow to characterize the topological characteristics of proteins (knotted or not). By analyzing a small set of two-state and multi-state proteins with no knots or slipknots, our results show that 95.4% of the analyzed proteins have non-trivial topological characteristics, as reflected by the second Vassiliev measure, and that the logarithm of the experimental protein folding rate depends on both the local geometry and the topology of the protein’s native state.
Journal Article
Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601–0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to ‘black-box’ methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.
Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features.
Journal Article
FlareDB: A Database of Significant Flares in Solar Cycles 24 and 25 with SDO/HMI and SDO/AIA Observations
We present FlareDB, a database that provides comprehensive magnetic field information, ultraviolet/extreme ultraviolet (UV/EUV) emissions, and white light continuum images for solar active regions (ARs) associated with 151 significant flares from May 2010 to May 2025. The data, sourced from the Solar Dynamics Observatory (SDO) via the Joint Science Operations Center (JSOC), were processed with SunPy and stored in standardized JSOC FITS format. FlareDB includes all M5.0 and larger flares within 50° of the solar disk center. Key features include (1) Atmospheric Imaging Assembly (AIA) AR patches in Helioprojective Cartesian(HPC) and Lambert Cylindrical Equal-Area (CEA) projections, aligned with corresponding HMI magnetogram patches; (2) quick-look movies with uniform value ranges that ensure consistent visualization, maintain data uniformity, and enhance readiness for machine learning studies; (3) a supplementary web interface that allows the entire dataset of a flare to be downloaded for large flare analysis. One of FlareDB’s primary objectives is to support scientists in predicting and understanding the onset of solar eruptions, including flares and coronal mass ejections. The data set is machine-learning ready for this purpose.
Journal Article
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Image super-resolution is an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network for solar image super-resolution. Our method, named SolarCNN, aims to enhance the quality of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). The ground-truth labels used for training SolarCNN are the LOS magnetograms collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Solar ARs consist of strong magnetic fields in which magnetic energy can suddenly be released to produce extreme space-weather events, such as solar flares, coronal mass ejections, and solar energetic particles. SOHO/MDI covers Solar Cycle 23, which is stronger with more eruptive events than Cycle 24. Enhanced SOHO/MDI magnetograms allow for better understanding and forecasting of violent events of space weather. Experimental results show that SolarCNN improves the quality of SOHO/MDI magnetograms in terms of the structural similarity index measure, Pearson’s correlation coefficient, and the peak signal-to-noise ratio.
Journal Article
Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data with Deep Learning
Solar activity is often caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions (ARs) have been used to analyze and forecast eruptive events, such as solar flares and coronal mass ejections. Unfortunately, the most recent Solar Cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the
Helioseismic and Magnetic Imager
(HMI) on board the
Solar Dynamics Observatory
(SDO) since its launch in 2010. In this work, we look into another major instrument, namely the
Michelson Doppler Imager
(MDI) on board the
Solar and Heliospheric Observatory
(SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers a more active Solar Cycle 23 with many large flares. However, SOHO/MDI only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms,
B
x
and
B
y
, taken by SDO/HMI, along with H
α
observations collected by the
Big Bear Solar Observatory
(BBSO), and to generate synthetic vector components
B
x
′
and
B
y
′
of ARs. These generated vector components, together with observational LOS data, would form vector magnetograms for SOHO/MDI. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the MagNet method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of ARs for SOHO/MDI using SDO/HMI and H
α
data.
Journal Article
Molecular mechanism of GPCR-mediated arrestin activation
Despite intense interest in discovering drugs that cause G-protein-coupled receptors (GPCRs) to selectively stimulate or block arrestin signalling, the structural mechanism of receptor-mediated arrestin activation remains unclear
1
,
2
. Here we reveal this mechanism through extensive atomic-level simulations of arrestin. We find that the receptor’s transmembrane core and cytoplasmic tail—which bind distinct surfaces on arrestin—can each independently stimulate arrestin activation. We confirm this unanticipated role of the receptor core, and the allosteric coupling between these distant surfaces of arrestin, using site-directed fluorescence spectroscopy. The effect of the receptor core on arrestin conformation is mediated primarily by interactions of the intracellular loops of the receptor with the arrestin body, rather than the marked finger-loop rearrangement that is observed upon receptor binding. In the absence of a receptor, arrestin frequently adopts active conformations when its own C-terminal tail is disengaged, which may explain why certain arrestins remain active long after receptor dissociation. Our results, which suggest that diverse receptor binding modes can activate arrestin, provide a structural foundation for the design of functionally selective (‘biased’) GPCR-targeted ligands with desired effects on arrestin signalling.
Molecular dynamics simulations and site-directed fluorescence spectroscopy show that the transmembrane core and cytoplasmic tail of G-protein-coupled receptors independently and cooperatively activate arrestin.
Journal Article
Mitochondria in Retinal Ganglion Cells: Unraveling the Metabolic Nexus and Oxidative Stress
This review explored the role of mitochondria in retinal ganglion cells (RGCs), which are essential for visual processing. Mitochondrial dysfunction is a key factor in the pathogenesis of various vision-related disorders, including glaucoma, hereditary optic neuropathy, and age-related macular degeneration. This review highlighted the critical role of mitochondria in RGCs, which provide metabolic support, regulate cellular health, and respond to cellular stress while also producing reactive oxygen species (ROS) that can damage cellular components. Maintaining mitochondrial function is essential for meeting RGCs’ high metabolic demands and ensuring redox homeostasis, which is crucial for their proper function and visual health. Oxidative stress, exacerbated by factors like elevated intraocular pressure and environmental factors, contributes to diseases such as glaucoma and age-related vision loss by triggering cellular damage pathways. Strategies targeting mitochondrial function or bolstering antioxidant defenses include mitochondrial-based therapies, gene therapies, and mitochondrial transplantation. These advances can offer potential strategies for addressing mitochondrial dysfunction in the retina, with implications that extend beyond ocular diseases.
Journal Article