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Achieving Sustainable Development Goal 7 (SDG 7) and SDG 13 together requires a holistic and integrated approach to simultaneously address the challenges of clean energy and climate action. In order to find integrated policy strategies, this study offers a comparative analysis using the case of China and the world regarding energy access, energy intensity, clean cooking, renewable energy, global warming gases, and investment in energy by the private sector to advance SDGs 7 and 13, applying a principal component regression (PCR) and forecasting models for the period 1990 to 2021. Overall, these findings indicate that China is making significant progress towards meeting the goals of the Paris Agreement. This progress is evident in the notable variations observed in key variables such as access to clean cooking solutions, private sector investments in energy, renewable energy generation, and enhanced energy efficiency. In contrast, the global landscape exhibits only minimal fluctuations in these aspects within its framework. The PCR proves that all the components are significant regarding China, whereas, for the world, seven components are significant out of eight. Furthermore, the global temperature projection indicates that the world is nearing the 1-degree Celsius threshold, with the current temperature standing at 0.558 degrees Celsius. This suggests that the goal of limiting global warming to 1.5 degrees Celsius by 2030 remains attainable. Notably, China's projected average temperature for 2030 is 7.2 degrees Celsius, marking a 12% decrease from the 2021 temperature level. This trajectory aligns with China's commitment to achieving the 1.5-degree Celsius target by 2030. This study makes a valuable contribution to the field of energy transition, offering insights into the path to maintaining global warming at 1.5 degrees Celsius as stipulated by the Paris Agreement by 2030. [ABSTRACT FROM AUTHOR]

The discovery of putative transcription factor binding sites (TFBSs) is important for understanding the underlying binding mechanism and cellular functions. Recently, many computational methods have been proposed to jointly account for DNA sequence and shape properties in TFBSs prediction. However, these methods fail to fully utilize the latent features derived from both sequence and shape profiles and have limitation in interpretability and knowledge discovery. To this end, we present a novel Deep Convolution Attention network combining Sequence and Shape, dubbed as D-SSCA, for precisely predicting putative TFBSs. Experiments conducted on 165 ENCODE ChIP-seq datasets reveal that D-SSCA significantly outperforms several state-of-the-art methods in predicting TFBSs, and justify the utility of channel attention module for feature refinements. Besides, the thorough analysis about the contribution of five shapes to TFBSs prediction demonstrates that shape features can improve the predictive power for transcription factors-DNA binding. Furthermore, D-SSCA can realize the cross-cell line prediction of TFBSs, indicating the occupancy of common interplay patterns concerning both sequence and shape across various cell lines. The source code of D-SSCA can be found at https://github.com/MoonLord0525/. [ABSTRACT FROM AUTHOR]

Developing active and durable electrocatalysts for the ethylene glycol electrooxidation reaction (EGOR) is vital for the corresponding direct fuel cells with ethylene glycol as the energy resource. As elements located at nearby positions in the periodic table, Pd and Rh often exhibit catalytic performance similar to that of Pt. Thus, Pd- and Rh-based catalysts are emerging as alternative catalysts to replace Pt-based catalysts in many catalytic reactions. To explore EGOR electrocatalysts with high performance, research focused on the optimization of the structure and composition of the electrocatalysts, including Pd- and Rh-based electrocatalysts, is continually being carried out. Despite a few recent advances, it remains a great challenge to synthesize Pd- and Rh-based electrocatalysts with definite shape and composition, such as one-dimensional nanowires, and their electrocatalytic performance remains unexplored. Herein, we successfully developed a solvothermal-assisted approach to prepare PdRhTe nanowires with tunable Pd/Rh ratios. The resultant PdRhTe nanowires present excellent EGOR performances. In particular, the Pd45Rh28Te27 nanowire catalyst delivers the highest catalytic activity of 42.1 mA cm−2 as well as the best tolerance over the 12 hour stability test. Electrochemical studies reveal that the improved electrooxidation kinetics upon the introduction of Rh are conducive to improvement of the EGOR performance. This work not only demonstrates a new approach for the fabrication of Pd- and Rh-based nanowires, but also inspires a new strategy for developing high-performance EGOR electrocatalysts. [ABSTRACT FROM AUTHOR]

The discovery of putative transcription factor binding sites (TFBSs) is important for understanding the underlying binding mechanism and cellular functions. Recently, many computational methods have been proposed to jointly account for DNA sequence and shape properties in TFBSs prediction. However, these methods fail to fully utilize the latent features derived from both sequence and shape profiles and have limitation in interpretability and knowledge discovery. To this end, we present a novel Deep Convolution Attention network combining Sequence and Shape, dubbed as D-SSCA, for precisely predicting putative TFBSs. Experiments conducted on 165 ENCODE ChIP-seq datasets reveal that D-SSCA significantly outperforms several state-of-the-art methods in predicting TFBSs, and justify the utility of channel attention module for feature refinements. Besides, the thorough analysis about the contribution of five shapes to TFBSs prediction demonstrates that shape features can improve the predictive power for transcription factors-DNA binding. Furthermore, D-SSCA can realize the cross-cell line prediction of TFBSs, indicating the occupancy of common interplay patterns concerning both sequence and shape across various cell lines. The source code of D-SSCA can be found at https://github.com/MoonLord0525/., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)