Natural products, with their remarkable structural and biological diversity, have historically served as a vital bridge between chemistry, the life sciences, and medicine. They not only provide essential scaffolds for drug discovery but also inspire innovative strategies in drug development. The biomimetic synthesis of natural products employs principles from biomimicry, applying inspiration from biogenetic processes to design synthetic strategies that mimic biosynthetic processes. Biomimetic synthesis is a highly efficient approach in synthetic chemistry, as it addresses critical challenges in the synthesis of structurally complex natural products with significant biological and medicinal importance. It has gained widespread attention from researchers in chemistry, biology, pharmacy, and related fields, underscoring its interdisciplinary impact. In this perspective, we present recent advances and challenges in the biomimetic synthesis of natural products, along with the significance and prospects of this field, highlighting the transformative potential of biomimetic synthesis strategies for both chemical and biosynthetic approaches to natural product synthesis in the pursuit of novel therapeutic agents.
Intracortical neural interfaces directly connect brain neurons with external devices to achieve high temporal resolution and spatially precise sampling of neural activity. When applied to freely moving animals, this technology provides in-depth insight into the underlying neural mechanisms for their movement and cognition in real-world scenarios. However, the application of implanted devices in freely moving animals is limited by restrictions on their behavioral freedom and physiologic impact. In this paper, four technological directions for ideal implantable neural interface devices are analyzed: higher spatial density, improved biocompatibility, enhanced multimodal detection of electrical/neurotransmitter signals, and more effective neural modulation. Finally, we discuss how these technological developments have been applied to freely moving animals to provide better insight into neuroscience and clinical medicine.
Large language models (LLMs) have significantly advanced artificial intelligence (AI) by excelling in tasks such as understanding, generation, and reasoning across multiple modalities. Despite these achievements, LLMs have inherent limitations including outdated information, hallucinations, inefficiency, lack of interpretability, and challenges in domain-specific accuracy. To address these issues, this survey explores three promising directions in the post-LLM era: knowledge empowerment, model collaboration, and model co-evolution. First, we examine methods of integrating external knowledge into LLMs to enhance factual accuracy, reasoning capabilities, and interpretability, including incorporating knowledge into training objectives, instruction tuning, retrieval-augmented inference, and knowledge prompting. Second, we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging, functional model collaboration, and knowledge injection. Third, we delve into model co-evolution, in which multiple models collaboratively evolve by sharing knowledge, parameters, and learning strategies to adapt to dynamic environments and tasks, thereby enhancing their adaptability and continual learning. We illustrate how the integration of these techniques advances AI capabilities in science, engineering, and society—particularly in hypothesis development, problem formulation, problem-solving, and interpretability across various domains. We conclude by outlining future pathways for further advancement and applications.
With the future substantial increase in coverage and network heterogeneity, emerging networks will encounter unprecedented security threats. Covert communication is considered a potential enhanced security and privacy solution for safeguarding future wireless networks, as it can enable monitors to detect the transmitter’s transmission behavior with a low probability, thereby ensuring the secure transmission of private information. Due to its favorable security, it is foreseeable that covert communication will be widely used in various wireless communication settings such as medical, financial, and military scenarios. However, existing covert communication methods still present many challenges toward practical applications. In particular, it is difficult to guarantee the effectiveness of covert schemes based on the randomness of eavesdropping environments, and it is challenging for legitimate users to detect weak covert signals. Considering that emerging artificial-intelligence-aided transmission technologies can open up entirely new opportunities to address the above challenges, we provide a comprehensive review of recent advances and potential research directions in the field of intelligent covert communications in this work. First, the basic concepts and performance metrics of covert communications are introduced. Then, existing effective covert communication techniques in the time, frequency, spatial, power, and modulation domains are reviewed. Finally, this paper discusses potential implementations and challenges for intelligent covert communications in future networks.
Composed of natural materials but constructed using artificial structures through ingenious design, metamaterials possess properties beyond nature. Unlike traditional materials studies, metamaterials research requires great human creativity in order to realize the desired properties and thereby the required functionalities through design. Such properties and functionalities are not necessarily available in nature, and their design can break through the existing materials ideology. This paper reviews progress in metamaterials research over the past 20 years in terms of the materials innovations that have achieved the designation of “meta.” In particular, we discuss future trends in metamaterials in the fields of both fundamental science and engineering.
This paper proposes that China, under the challenge of balancing its development and security, can aim for the Paris Agreement’s goal to limit global warming to no more than 2 °C by actively seeking carbon-peak and carbon-neutrality pathways that align with China’s national conditions, rather than following the idealized path toward the 1.5 °C target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage (DACCS). This work suggests that pursuing a 1.5 °C target is increasingly less feasible for China, as it would potentially incur 3-4 times the cost of pursuing the 2 °C target. With China being likely to achieve a peak in its emissions around 2028, at about 12.8 billion tonnes of anthropogenic CO2, and become carbon neutral, projected global warming levels may be less severe after the 2050s than previously estimated. This could reduce the risk potential of climate tipping points and extreme events, especially considering that the other two major carbon emitters in the world (Europe and North America) have already passed their carbon peaks. While natural carbon sinks will contribute to China’s carbon neutrality efforts, they are not expected to be decisive in the transition stages. This research also addresses the growing focus on climate overshoot, tipping points, extreme events, loss and damage, and methane reductions in international climate cooperation, emphasizing the need to balance these issues with China’s development, security, and fairness considerations. China’s pursuit of carbon neutrality will have significant implications for global emissions scenarios, warming levels, and extreme event projections, as well as for climate change hotspots of international concern, such as climate tipping points, the climate crisis, and the notion that the world has moved from a warming to a boiling era. Possible research recommendations for global emissions scenarios based on China’s 2 °C target pathway are also summarized.
The security of the seed industry is crucial for ensuring national food security. Currently, developed countries in Europe and America, along with international seed industry giants, have entered the breeding 4.0 era. This era integrates biotechnology, artificial intelligence (AI), and big data information technology. In contrast, China is still in a transition period between stages 2.0 and 3.0, which primarily relies on conventional selection and molecular breeding. In the context of increasingly complex international situations, accurately identifying core issues in China’s seed industry innovation and seizing the frontier of international seed technology are strategically important. These efforts are essential for ensuring food security and revitalizing the seed industry. This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding. It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives. These include high-throughput phenotype acquisition and analysis, multiomics big data database and management system construction, AI-based multiomics integrated analysis, and the development of intelligent breeding software tools based on biological big data and AI technology. Based on an in-depth analysis of the current status and challenges of China’s seed industry technology development, we propose strategic goals and key tasks for China’s new generation of AI and big data-driven intelligent design breeding. These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining, efficient gene manipulation, engineered variety design, and systematized biobreeding. This study provides a theoretical basis and practical guidance for the development of China’s seed industry technology.