Evolution of solar subsurface meridional flows in the declining phase of cycle 23

[[abstract]]We study the evolution of meridional flows in the solar convection zone extending to a depth of 0.793 R⊙ in the period 2000-2003 with helioseismic data taken with the Taiwan Oscillation Network (TON) using the technique of time-distance helioseismology. The meridional flows of each hemisphere formed a single-cell pattern in the convection zone at the solar minimum. An additional divergent flow was created at active latitudes in both hemispheres as the activity developed. The amplitude of this divergent flow correlates with the sunspot number: it increased from solar minimum to maximum (from 1996 to 2000), and then decreased from 2000 to 2003 with the sunspot number. The amplitude of the divergent flow increases with depth from 0.987 R⊙ to a depth of about 0.9 R⊙, and then decreases with depth at least down to 0.793 R⊙[[fileno]]2010104010029[[department]]物理

Measurements of absorption, emissivity reduction, and local suppression of solar acoustic waves in sunspots

[[abstract]]The power of solar acoustic waves in magnetic regions is lower relative to the quiet Sun. Absorption, emissivity reduction, and local suppression of acoustic waves contribute to the observed power reduction in magnetic regions. We propose a model for the energy budget of acoustic waves propagating through a sunspot in terms of the coefficients of absorption, emissivity reduction, and local suppression of the sunspot. Using the property that the waves emitted along the wave path between two points have no correlation with the signal at the starting point, we can separate the effects of these three mechanisms. Applying this method to helioseismic data filtered with direction and phase-velocity filters, we measure the fraction of the contribution of each mechanism to the power deficit in the umbra of the leading sunspot of NOAA 9057. The contribution from absorption is 23.3 ± 1.3%, emissivity reduction 8.2 ± 1.4%, and local suppression 68.5 ± 1.5%, for a wave packet corresponding to a phase velocity of 6.98 × 10–5 rad s–1.[[fileno]]2010104010033[[department]]物理

Solar Cycle Variations of p-Mode Frequencies

Observations show that the solar p-mode frequencies change with the solar cycle. The horizontal-phase-velocity dependence of the relative frequency change, scaled by mode mass, provides depth information on the perturbation in the solar interior. We find that the smoothed scaled relative frequency change varies along the solar cycle for horizontal phase velocities higher than a critical value, which corresponds to a depth near the base of the convection zone. This phenomenon suggests that the physical conditions in a region near the base of the convection zone change with the solar cycle

Taiwan Oscillation Network

The Taiwan Oscillation Network (TON) is a ground-based network to measure solar intensity oscillations to study the internal structure of the Sun. K-line full-disk images of 1000 pixels diameter are taken at a rate of one image per minute. Such data would provide information onp-modes withl as high as 1000. The TON will consist of six identical telescope systems at proper longitudes around the world. Three telescope systems have been installed at Teide Observatory (Tenerife), Huairou Solar Observing Station (near Beijing), and Big Bear Solar Observatory (California). The telescopes at these three sites have been taking data simultaneously since October of 1994. Anl – v diagram derived from 512 images is included to show the quality of the data

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

The RNA-Binding Protein KSRP Promotes Decay of β-Catenin mRNA and Is Inactivated by PI3K-AKT Signaling

β-catenin plays an essential role in several biological events including cell fate determination, cell proliferation, and transformation. Here we report that β-catenin is encoded by a labile transcript whose half-life is prolonged by Wnt and phosphatidylinositol 3-kinase–AKT signaling. AKT phosphorylates the mRNA decay-promoting factor KSRP at a unique serine residue, induces its association with the multifunctional protein 14-3-3, and prevents KSRP interaction with the exoribonucleolytic complex exosome. This impairs KSRP's ability to promote rapid mRNA decay. Our results uncover an unanticipated level of control of β-catenin expression pointing to KSRP as a required factor to ensure rapid degradation of β-catenin in unstimulated cells. We propose KSRP phosphorylation as a link between phosphatidylinositol 3-kinase–AKT signaling and β-catenin accumulation