Impact of Radio Occultation Data on the Prediction of Typhoon Haishen (2020) with WRFDA Hybrid Assimilation

FORMOSAT-7/COSMIC-2 (FS7/C2) satellite was successfully launched in June 2019. The satellite provides about 5000 radio occultation (RO) soundings daily over the tropical and partial subtropical regions. Such plentiful RO soundings with high accuracy and vertical resolution could be used to improve model initial analysis for typhoon prediction. In this study, assimilation experiments with and without the RO data were conducted with the WRFDA hybrid system for the prediction of Typhoon Haishen (2020). The experimental results show a remarkable improvement in typhoon track prediction with RO data assimilation, especially when using a nonlocal refractivity operator. Results in cycling DA and forecast are analyzed and verified for the RO data impact. Diagnostics of potential vorticity (PV) tendency budget helps explain the typhoon translation induced by different physical processes in the budget. The typhoon translation is essentially dominated by horizontal PV advection, but the track deviation can increase due to the vertical PV advection with opposite effects in the absence of RO data. Sensitivity experiments for different model initial times, physics schemes, and RO observation amounts show positive RO data impacts on typhoon prediction, mainly contributed from FS7. Complementary, an improved forecast of Typhoon Hagupit (2020) is also illustrated for the RO data impact.

Trust and Trade-Offs in Sharing Data for Precision Medicine: A National Survey of Singapore

Background: Precision medicine (PM) programs typically use broad consent. This approach requires maintenance of the social license and public trust. The ultimate success of PM programs will thus likely be contingent upon understanding public expectations about data sharing and establishing appropriate governance structures. There is a lack of data on public attitudes towards PM in Asia. Methods: The aim of the research was to measure the priorities and preferences of Singaporeans for sharing health-related data for PM. We used adaptive choice-based conjoint analysis (ACBC) with four attributes: uses, users, data sensitivity and consent. We recruited a representative sample of n = 1000 respondents for an in-person household survey. Results: Of the 1000 respondents, 52% were female and majority were in the age range of 40–59 years (40%), followed by 21–39 years (33%) and 60 years and above (27%). A total of 64% were generally willing to share de-identified health data for IRB-approved research without re-consent for each study. Government agencies and public institutions were the most trusted users of data. The importance of the four attributes on respondents’ willingness to share data were: users (39.5%), uses (28.5%), data sensitivity (19.5%), consent (12.6%). Most respondents found it acceptable for government agencies and hospitals to use de-identified data for health research with broad consent. Our sample was consistent with official government data on the target population with 52% being female and majority in the age range of 40–59 years (40%), followed by 21–39 years (33%) and 60 years and above (27%). Conclusions: While a significant body of prior research focuses on preferences for consent, our conjoint analysis found consent was the least important attribute for sharing data. Our findings suggest the social license for PM data sharing in Singapore currently supports linking health and genomic data, sharing with public institutions for health research and quality improvement; but does not support sharing with private health insurers or for private commercial use.

Enhancing GDPR compliance through data sensitivity and data hiding tools

Since the emergence of GDPR, several industries and sectors are setting informatics solutions for fulfilling these rules. The Health sector is considered a critical sector within the Industry 4.0 because it manages sensitive data, and National Health Services are responsible for managing patients’ data. European NHS are converging to a connected system allowing the exchange of sensitive information cross different countries. This paper defines and implements a set of tools for extending the reference architectural model industry 4.0 for the healthcare sector, which are used for enhancing GDPR compliance. These tools are dealing with data sensitivity and data hiding tools A case study illustrates the use of these tools and how they are integrated with the reference architectural model.

3D Seismic Wave Simulations of Mantle Heterogeneity and Shear Wave Splitting Phases

<p>Constraining the pattern and properties of seismic anisotropy in the Earth can help reveal relationships between mineral physics, mantle convection, and seismology. Sources of anisotropy in the lithosphere as frozen-in anisotropy, transition zone, and D" complicate shear wave splitting measurements, resulting in shear wave splitting that can differ from plate motion. If we better understand seismic anisotropy sourced in the lithosphere, we could also better constrain D" anisotropy, which requires correcting for the upper mantle to some extent. The goal of this work is to investigate the effect of 3D mantle and crustal structure on waveforms based on 3D wave simulations and adjoint data sensitivity kernels. We will explore the common phases (SKS, SKKS, S, ScS, PKS, etc.) and the common distance ranges used for mantle shear wave splitting with a resolution down to 9 s by conducting numerical simulations via 3D global wave propagation solver SPECFEM3D_GLOBE. We show results for a 1D mantle model (i.e., PREM [Dziewonski and Anderson, 1981]) and at least three 3D mantle models (S20RTS [Ritsema et al., 2011], GLAD-M15 [Bozdag et al., 2015], GLAD-M25 [Lei et al., 2020]). We calculate a number of data sensitivity kernels for travel time, amplitude, and anisotropy for our phases of interest over a variety of event depths and distance ranges. This work will help improve the measurements of shear wave splitting. The long-running goal is to use shear wave splitting in global full waveform inversion by addressing appropriate parameterization to describe body-wave anisotropy in the mantle during the inversion process. All simulations were conducted on a Research Allocation on the high-performance computing environment of XSEDE resources (TACC Stampede2).</p>

An optimized MRI and PET based clinical protocol for improving the differential diagnosis of Geriatric Depression and Alzheimer’s Disease

OBJECTIVEMRI derived hippocampal volume (HV) and amyloid PET may be useful clinical biomarkers for differentiating between geriatric depression and Alzheimer’s Disease (AD). Here we investigated the incremental value of HV and 18F-flutemetmol PET in tandem and sequentially to improve discrimination in unclassified participants.METHODTwo approaches were compared in 41 participants with geriatric depression and 27 participants with probable AD: (1) amyloid and HV combined in one model and (2) HV first and then amyloid.RESULTSBoth HV(χ2(1) = 6.46: p= 0.011) and amyloid (χ2(1) =11.03: p=0.0009) were significant diagnostic predictors of depression (sensitivity: 95%, specificity: 89%). (2) 51% of participants were correctly classified according to clinical diagnosis based on HV alone, increasing to 87% when adding amyloid data (sensitivity: 94%, specificity: 78%).CONCLUSIONHippocampal volume may be a useful gatekeeper for identifying depressed individuals at risk for AD who would benefit from additional amyloid biomarkers when available.