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Abstract MotivationForecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. ResultsWe present a novel deep hypergraph learning method named Heterogeneous Entity Representation for MEdicinal Synergy (HERMES) prediction to predict the synergistic effects of anti-cancer drugs. Heterogeneous data sources, including drug chemical structures, gene expression profiles, and disease clinical semantics, are integrated into hypergraph neural networks equipped with a gated residual mechanism to enhance high-order relationship modeling. HERMES demonstrates state-of-the-art performance on two benchmark datasets, significantly outperforming existing methods in predicting the synergistic effects of drug combinations, particularly in cases involving unseen drugs. Availability and implementationThe source code is available at https://github.com/Christina327/HERMES. 

Abstract Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km²across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems. 

Abstract Photo‐responsive semiconductors can facilitate nitrogen activation and ammonia production, but the high recombination rate of photogenerated carriers represents a significant barrier. Ferroelectric photocatalysts show great promise in overcoming this challenge. Herein, by adopting a low‐temperature hydrothermal procedure with varying concentrations of glyoxal as the reducing agent, oxygen vacancies (Vo) are effectively produced on the surface of ferroelectric SrBi₄Ti₄O₁₅(SBTO) nanosheets, which leads to a considerable increase in photocatalytic activity toward nitrogen fixation under simulated solar light with an ammonia production rate of 53.41 µmol g⁻¹h⁻¹, without the need of sacrificial agents or photosensitizers. This is ascribed to oxygen vacancies that markedly enhance the self‐polarization and internal electric field of ferroelectric SBTO, and hence, facilitate the separation of photogenerated charge carriers and light trapping as well as N₂adsorption and activation, as compared to pristine SBTO. Consistent results are obtained in theoretical studies. Results from this study highlight the significance of surface oxygen vacancies in enhancing the performance of photocatalytic nitrogen fixation by ferroelectric catalysts.