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Spatio-temporal deep learning has drawn a lot of attention since many downstream real-world applications can benefit from accurate predictions. For example, accurate prediction of heavy rainfall events is essential for effective urban water usage, flooding warning, and mitigation. In this paper, we propose a strategy to leverage spatially connected real-world features to enhance prediction accuracy. Specifically, we leverage spatially connected real-world climate data to predict heavy rainfall risks in a broad range in our case study. We experimentally ascertain that our Trans-Graph Convolutional Network (TGCN) accurately predicts heavy rainfall risks and real estate trends, demonstrating the advantage of incorporating external spatially-connected real-world data to improve model performance, and it shows that this proposed study has a significant potential to enhance spatio-temporal prediction accuracy, aiding in efficient urban water usage, flooding risk warning, and fair housing in real estate. 

Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future. 

Abstract We present an analysis of the first 20 second cadence light curves obtained by the TESS space telescope during its extended mission. We find improved precision of 20 second data compared to 2 minute data for bright stars when binned to the same cadence (≈10%–25% better forT≲ 8 mag, reaching equal precision atT≈ 13 mag), consistent with pre-flight expectations based on differences in cosmic-ray mitigation algorithms. We present two results enabled by this improvement. First, we use 20 second data to detect oscillations in three solar analogs (γPav,ζTuc, andπMen) and use asteroseismology to measure their radii, masses, densities, and ages to ≈1%, ≈3%, ≈1%, and ≈20% respectively, including systematic errors. Combining our asteroseismic ages with chromospheric activity measurements, we find evidence that the spread in the activity–age relation is linked to stellar mass and thus the depth of the convection zone. Second, we combine 20 second data and published radial velocities to recharacterizeπMen c, which is now the closest transiting exoplanet for which detailed asteroseismology of the host star is possible. We show thatπMen c is located at the upper edge of the planet radius valley for its orbital period, confirming that it has likely retained a volatile atmosphere and that the “asteroseismic radius valley” remains devoid of planets. Our analysis favors a low eccentricity forπMen c (<0.1 at 68% confidence), suggesting efficient tidal dissipation (Q/k_2,1≲ 2400) if it formed via high-eccentricity migration. Combined, these early results demonstrate the strong potential of TESS 20 second cadence data for stellar astrophysics and exoplanet science. 

This white paper is on the HMCS Firefly mission concept study. Firefly focuses on the global structure and dynamics of the Sun's interior, the generation of solar magnetic fields, the deciphering of the solar cycle, the conditions leading to the explosive activity, and the structure and dynamics of the corona as it drives the heliosphere.