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With its extreme axial tilt, Uranus' radiant energy budget (REB) and internal heat flux remain among the most intriguing mysteries in our solar system. By combining observations with modeling, we present the global REB over a complete orbital period (1946–2030), revealing significant seasonal variations. Despite these fluctuations, the global average emitted thermal power consistently exceeds absorbed solar power, indicating a net energy loss. Assuming no significant seasonal variation in emitted power, we estimate an internal heat flux of 0.078 ± 0.018 W/m2 by analyzing the energy budget over one orbital period. The combination of internal heat and radiant energies indicates substantial global and hemispheric imbalances, with excesses or deficits exceeding 85% of emitted power at the hemispheric scale. These findings are crucial for understanding Uranus' interior and atmosphere. A future flagship mission to Uranus would provide critical observations to address more unresolved questions of this enigmatic ice giant. 

Abstract Motivation:Clustering patients into subgroups based on their microbial compositions can greatly enhance our understanding of the role of microbes in human health and disease etiology. Distance-based clustering methods, such as partitioning around medoids (PAM), are popular due to their computational efficiency and absence of distributional assumptions. However, the performance of these methods can be suboptimal when true cluster memberships are driven by differences in the abundance of only a few microbes, a situation known as the sparse signal scenario. Results:We demonstrate that classical multidimensional scaling (MDS), a widely used dimensionality reduction technique, effectively denoises microbiome data and enhances the clustering performance of distance-based methods. We propose a two-step procedure that first applies MDS to project high-dimensional microbiome data into a low-dimensional space, followed by distance-based clustering using the low-dimensional data. Our extensive simulations demonstrate that our procedure offers superior performance compared to directly conducting distance-based clustering under the sparse signal scenario. The advantage of our procedure is further showcased in several real data applications. Availability and implementation:The R package MDSMClust is available at https://github.com/wxy929/MDS-project. 

Abstract The Hunga Tonga‐Hunga Ha'apai (Hunga) volcanic eruption in January 2022 injected a substantial amount of water vapor and a moderate amount of SO₂into the stratosphere. Both satellite observations in 2022 and subsequent chemistry‐climate model simulations forced by realistic Hunga perturbations reveal large‐scale cooling in the Southern Hemisphere (SH) tropical to subtropical stratosphere following the Hunga eruption. This study analyzes the drivers of this cooling, including the distinctive role of anomalies in water vapor, ozone, and sulfate aerosol concentration on the simulated climate response to the Hunga volcanic forcing, based on climate simulations with prescribed chemistry/aerosol. Simulated circulation and temperature anomalies based on specified‐chemistry simulations show good agreement with previous coupled‐chemistry simulations and indicate that each forcing of ozone, water vapor, and sulfate aerosol from the Hunga volcanic eruption contributed to the circulation and temperature anomalies in the SH stratosphere. Our results also suggest that (a) the large‐scale stratospheric cooling during the austral winter was mainly induced by changes in dynamical processes, not by radiative processes, and that (b) the radiative feedback from negative ozone anomalies contributed to the prolonged cold temperature anomalies in the lower stratosphere (∼70 hPa level) and hence to long lasting cold conditions of the polar vortex. 

Abstract The global energy budget is pivotal to understanding planetary evolution and climate behaviors. Assessing the energy budget of giant planets, particularly those with large seasonal cycles, however, remains a challenge without long-term observations. Evolution models of Saturn cannot explain its estimated Bond albedo and internal heat flux, mainly because previous estimates were based on limited observations. Here, we analyze the long-term observations recorded by the Cassini spacecraft and find notably higher Bond albedo (0.41 ± 0.02) and internal heat flux (2.84 ± 0.20 Wm⁻²) values than previous estimates. Furthermore, Saturn’s global energy budget is not in a steady state and exhibits significant dynamical imbalances. The global radiant energy deficit at the top of the atmosphere, indicative of the planetary cooling of Saturn, reveals remarkable seasonal fluctuations with a magnitude of 16.0 ± 4.2%. Further analysis of the energy budget of the upper atmosphere including the internal heat suggests seasonal energy imbalances at both global and hemispheric scales, contributing to the development of giant convective storms on Saturn. Similar seasonal variabilities of planetary cooling and energy imbalance exist in other giant planets within and beyond the Solar System, a prospect currently overlooked in existing evolutional and atmospheric models. 

Abstract MotivationGenome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS. ResultsThis work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We instantiate our approach using logistic regression, a commonly used statistical method for identifying associations between genetic markers and the phenotype of interest. We evaluate our proposed methods using two real genomic datasets and demonstrate their robustness in the presence of between-study heterogeneity and skewed phenotype distributions using a variety of experimental settings. The empirical results show the efficiency and applicability of the proposed method and the promise for its application for large-scale collaborative GWAS. Availability and implementationThe source code and data are available at https://github.com/amioamo/TDS.