The Innovation最新文献

Cyanobacteria are constructors of biological soil crusts (BSCs); their motility is thought to be crucial for surviving burial and BSC expansion. In this study, X-ray computed microtomography in combination with machine-learning-based image processing was employed to investigate cyanobacteria-dominated BSCs. The structural changes in these BSCs, as well as the behaviors of the dominant cyanobacterium Microcoleus vaginatus therein, in response to changes in water availability and particle burial were visualized and quantitatively analyzed. Hygroscopic swelling of cyanobacteria biomaterials increased pore-network complexity and reduced the porosity and hydraulic radius. Trichomes of M. vaginatus inside BSCs were connected to the surface by tunnel-like structures made of extracellular polymeric substances (EPSs), through which the trichomes migrated to and from the surface in bundles. Despite the generally negative effects of EPSs on hydraulic conductivity, EPS tunnels have the potential to enhance water transfer to the trichomes. Extensive hydration and particle burial led to the spreading migration of individual trichomes, forming net-like structures inside the newly deposited layer. The results highlight the significance of the structural organization of EPSs within BSCs and the importance of cyanobacterial migration in BSC expansion.

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities-such as vision, language, and sensory data-into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.

The peer-review process, which serves as the quality-control mechanism of scientific knowledge production, has been criticized for its bias, unreliability, and inefficiency. Academic conferences and journals typically rely on a centralized mechanism for reviewer assignment and paper assessment. We argue that this centralization is a major factor contributing to the unreliability of the review process, leading to deficiencies in the current knowledge-assessment systems. To address this, we propose a novel decentralized model that democratizes peer review by shifting decision-making rights from centralized authorities to all scholars participating in a scholarly community. Our model includes a dual-rewarding incentive mechanism that motivates scholars to actively participate in peer review by recognizing both their effort and scientific contributions. This model transforms peer review from passive judgment to active collaboration. We simulated the model in conference settings and demonstrated its potential to revolutionize knowledge production and dissemination.

Analyzing the geometric relationships among genomic sequences from a mathematical perspective and revealing the laws hidden within these relationships is a crucial challenge in bioinformatics. The natural vector method constructs a genome space by extracting statistical moments of k-mers to illuminate the relationships among genomes. This approach highlights a fundamental law in biology known as the convex hull principle, which states that natural vectors corresponding to different types of biological sequences form distinct, non-overlapping convex hulls. Previous studies have validated this important principle across various datasets. However, they often focused on specific kingdoms and did not thoroughly analyze the significance of the dimensions required for the convex hull separation. In this study, we integrate all reliable sequences from different kingdoms to construct the grand biological universe, within which we comprehensively validate the multi-level convex hull principle. We demonstrate that the separation of convex hulls arises from biological properties rather than mathematical characteristics of high-dimensional spaces. Furthermore, we develop suitable metrics within the grand biological universe to facilitate efficient sequence classification. This research advances the convex hull principle through both theoretical development and experimental validation, making significant contributions to the understanding of the geometric structure of genome space.