In this study, we present the application of the Convolutional Neural Network (CNN) to the forecast of solar flare occurrence. For this, we consider three CNN models (two pretrained models, AlexNet ...and GoogLeNet, and one newly proposed model). Our inputs are SOHO/Michelson Doppler Imager (from 1996 May to 2010 December) and SDO/Helioseismic and Magnetic Imager (from 2011 January to 2017 June) full-disk magnetograms at 00:00 UT. Model outputs are the "Yes or No" of daily flare occurrence (C, M, and X classes) and they are compared with GOES observations. We train the models using the input data and observations from 1996 to 2008, covering the entire solar cycle 23, and test them using the data sets from 2009 to 2017, covering solar cycle 24. Then we compare the results of the CNN models with those of three previous flare forecast models in view of statistical scores. The major results from this study are as follows. First, we successfully apply CNN to the full-disk solar magnetograms without any preprocessing or feature extraction. Second, the results of our CNN models are slightly better in Heidke skill score and true skill statistics, and considerably better in false alarm ratio (FAR) and critical success index than the previous solar flare forecasting models. Third, our proposed model has better values of all statistical scores except for FAR, than the other two pretrained models. Our results indicate a sufficient possibility that deep learning methods can improve the capability of the solar flare forecast as well as similar types of forecast problems.
Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic ...single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR, KRAS, PIK3CA, and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay.
To elucidate the effects of neoadjuvant chemotherapy (NAC), we conduct whole transcriptome profiling coupled with histopathology analyses of a longitudinal breast cancer cohort of 146 patients ...including 110 pairs of serial tumor biopsies collected before treatment, after the first cycle of treatment and at the time of surgery. Here, we show that cytotoxic chemotherapies induce dynamic changes in the tumor immune microenvironment that vary by subtype and pathologic response. Just one cycle of treatment induces an immune stimulatory microenvironment harboring more tumor infiltrating lymphocytes (TILs) and up-regulation of inflammatory signatures predictive of response to anti-PD1 therapies while residual tumors are immune suppressed at end-of-treatment compared to the baseline. Increases in TILs and CD8+ T cell proportions in response to NAC are independently associated with pathologic complete response. Further, on-treatment immune response is more predictive of treatment outcome than immune features in paired baseline samples although these are strongly correlated.
Glioblastoma (GBM) is the most common and aggressive primary brain tumor. To better understand how GBM evolves, we analyzed longitudinal genomic and transcriptomic data from 114 patients. The ...analysis shows a highly branched evolutionary pattern in which 63% of patients experience expression-based subtype changes. The branching pattern, together with estimates of evolutionary rate, suggests that relapse-associated clones typically existed years before diagnosis. Fifteen percent of tumors present hypermutation at relapse in highly expressed genes, with a clear mutational signature. We find that 11% of recurrence tumors harbor mutations in LTBP4, which encodes a protein binding to TGF-β. Silencing LTBP4 in GBM cells leads to suppression of TGF-β activity and decreased cell proliferation. In recurrent GBM with wild-type IDH1, high LTBP4 expression is associated with worse prognosis, highlighting the TGF-β pathway as a potential therapeutic target in GBM.
In this study, we forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hr using a deep learning model based on multilayer perceptron. The input data of the ...model are solar wind parameters (temperature, density and speed), interplanetary magnetic field (|B| and Bz), geomagnetic indices (Kp and Dst), and electron fluxes themselves. All input data are hourly averaged ones for the preceding 72 consecutive hours. We use electron flux data from Geostationary Operational Environmental Satellite (GOES)‐15 and ‐16, and perform a mapping for matching these two data. Total period of the data is from 2011 January to 2021 March (GOES‐15 data for 2011–2017 and GOES‐16 data for 2018–2021). We divide the data into training set (January–August), validation set (September), and test set (October–December) to consider the solar cycle effect. Our main results are as follows. First, our model successfully predicts hourly electron fluxes for the next 72 hr. Second, root‐mean‐square error of our model is from 0.18 (for 1 hr prediction) to 0.68 (for 72 hr prediction), and prediction efficiency is from 0.97 to 0.53, which are much better than those of the previous studies. Third, our model well predicts both diurnal variation and sudden increases of electron fluxes associated with fast solar winds and interplanetary magnetic fields. Our study implies that the deep learning model can be applied to forecasting long‐term sequential space weather events.
Plain Language Summary
Relativistic electron fluxes (>2 MeV) can damage satellites, resulting in loss of function. Thus, forecasting electron fluxes is a necessary task to minimize the loss. We develop a deep learning model to perform time‐series forecasting of hourly relativistic electron fluxes 3 days ahead. For this, we use solar wind parameters, interplanetary magnetic field, geomagnetic indices, and electron fluxes from Geostationary Operational Environmental Satellite‐15 and ‐16. Our model shows outstanding performances for time series forecasting of electron fluxes in view of metrics. In addition, our model successfully predicts the change of electron fluxes such as diurnal variation and sudden increase.
Key Points
A deep learning model based on multilayer perceptron is presented to forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hr
The performance of our model is much better than that of previous studies in view of metrics such as prediction efficiency, root‐mean‐square error, and correlation coefficient
Our model successfully predicts the change of electron fluxes such as diurnal variation and sudden increase
In this Letter, we apply deep-learning methods to the image-to-image translation from solar magnetograms to solar ultraviolet (UV) and extreme UV (EUV) images. For this, We consider two convolutional ...neural network models with different loss functions, one (Model A) is with L1 loss (L1), and the other (Model B) is with L1 and cGAN loss (LcGAN). We train the models using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) nine-passband (94, 131, 171, 193, 211, 304, 335, 1600, and 1700 ) UV/EUV images and their corresponding SDO/Helioseismic and Magnetic Imager (HMI) line-of-sight (LOS) magnetograms from 2011 to 2016. We evaluate the models by comparing pairs of SDO/AIA images and the corresponding ones generated in 2017. Our main results from this study are as follows. First, the models successfully generate SDO/AIA-like solar UV and EUV images from SDO/HMI LOS magnetograms. Second, in view of three metrics (pixel-to-pixel correlation coefficient, relative error, and the percentage of pixels having errors less than 10%), the results from Model A are mostly comparable or slightly better than those from Model B. Third, in view of the rms contrast measure, the generated images by Model A are much more blurred than those by Model B because of LcGAN specialized for generating realistic images.
In this Letter, we present the application of a couple of novel deep learning models to the forecast of major solar X-ray flare flux profiles. These models are based on a sequence-to-sequence ...framework using long short-term memory cell and an attention mechanism. For this, we use Geostationary Operational Environmental Satellite 10 X-ray flux data from 1998 August to 2006 April. Seven hundred sixty events are used for training and 85 for testing. The models forecast 30 minutes of X-ray flux profiles during the rise phase of the solar flare with a minute time cadence. We evaluate the models using the 10-fold cross-validation and rms error (RMSE) based on flux profiles and RMSE based on its peak flux. For comparison we consider two simple deep learning models and four conventional regression models. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of solar flare X-ray flux profiles, without any preprocessing to extract features from data. Second, our proposed models outperform the other models. Third, our models achieve better performance for forecasting X-ray flux profiles with low-peak fluxes than those with high-peak fluxes. Fourth, our models successfully predict flare duration with high correlations for both all cases and cases at peak times. Our study indicates that our deep learning models can be useful for forecasting time-series data in astronomy and space weather, even for impulsive events such as major flares.
Increased levels and non-telomeric roles have been reported for shelterin proteins, including RAP1 in cancers. Herein using Rap1 null mice, we provide the genetic evidence that mammalian Rap1 plays a ...major role in hematopoietic stem cell survival, oncogenesis and response to chemotherapy. Strikingly, this function of RAP1 is independent of its association with the telomere or with its known partner TRF2. We show that RAP1 interacts with many members of the DNA damage response (DDR) pathway. RAP1 depleted cells show reduced interaction between XRCC4/DNA Ligase IV and DNA-PK, and are impaired in DNA Ligase IV recruitment to damaged chromatin for efficient repair. Consistent with its role in DNA damage repair, RAP1 loss decreases double-strand break repair via NHEJ in vivo, and consequently reduces B cell class switch recombination. Finally, we discover that RAP1 levels are predictive of the success of chemotherapy in breast and colon cancer.
Aim
Nonalcoholic fatty liver disease (NAFLD) is currently the most common form of chronic liver disease and some studies have documented its link with cardiovascular risk factors. This study aimed to ...investigate the association between arterial stiffness and NAFLD.
Methods
Among 1,442 health check-up subjects (955 men, 487 women), we examined the association between brachial-ankle pulse wave velocity (baPWV) as a measurement of arterial stiffness and the presence of NAFLD based on abdominal sonographic findings. Multivariate linear and logistic regression analyses were conducted to examine the independent association between baPWV and the presence of NAFLD in gender-specific manners.
Results
In multivariate regression analysis, NAFLD was found to be independently associated with baPWV in both men and women. Moreover, in multivariate logistic regression analysis, a graded independent relation was found between higher levels of baPWV and the prevalence risk of NAFLD. Odds ratios (95% CI) for the highest vs. the lowest quartile of baPWV were 1.85 (range, 1.13–2.62) in men and 3.32 (1.45–7.62) in women after adjusting for age, smoking status, regular exercise, body mass index, blood pressure, fasting plasma glucose, triglyceride, HDL-cholesterol, hypertension and diabetes.
Conclusion
Arterial stiffness was independently associated with the prevalence risk for NAFLD regardless of classical CVD risk factors.
Abstract
This study is the first attempt to generate a three-dimensional (3D) coronal electron density distribution based on the pix2pixHD model, whose computing time is much shorter than that of the ...magnetohydrodynamic (MHD) simulation. For this, we consider photospheric solar magnetic fields as input, and electron density distribution simulated with the MHD Algorithm outside a Sphere (MAS) at a given solar radius is taken as output. We consider 155 pairs of Carrington rotations as inputs and outputs from 2010 June to 2022 April for training and testing. We train 152 deep-learning models for 152 solar radii, which are taken up to 30 solar radii. The artificial intelligence (AI) generated 3D electron densities from this study are quite consistent with the simulated ones from lower radii to higher radii, with an average correlation coefficient 0.97. The computing time of testing data sets up to 30 solar radii of 152 deep-learning models is about 45.2 s using the NVIDIA TITAN XP graphics-processing unit, which is much less than the typical simulation time of MAS. We find that the synthetic coronagraphic images estimated from the deep-learning models are similar to the Solar Heliospheric Observatory (SOHO)/Large Angle and Spectroscopic Coronagraph C3 coronagraph data, especially during the solar minimum period. The AI-generated coronal density distribution from this study can be used for space weather models on a near-real-time basis.
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