The Innovation最新文献

{"title":"Heat requirement, not warming, governs the urban-rural disparity in spring phenology advancement.","authors":"Can He, Xiaoyue Wang, Josep Peñuelas, Xuezhen Zhang, Chaoyang Wu","doi":"10.1016/j.xinn.2026.101343","DOIUrl":"10.1016/j.xinn.2026.101343","url":null,"abstract":"<p><p>Urbanization has profoundly altered local climates and vegetation structures, resulting in pronounced differences in spring phenology between urban and rural areas. However, the mechanistic drivers of this urban-rural divergence, specifically regarding the rates of phenological advancement, remain poorly understood. Using satellite-derived phenology observations across 354 cities in China from 2001 to 2020, we show that the advancement of the spring green-up date (GUD) was significantly more rapid in urban areas than in their rural surroundings under continuous warming. This urban-rural divergence was observed in 47.7% of the studied cities, where urban advancement trends outpaced rural ones by an average of 0.37 ± 0.24 days/year, particularly in temperate warm-summer regions. By integrating chilling accumulation and thermal requirement models with interpretable machine learning, we identify accumulated heat demand as the primary driver of this divergence. Partial correlation analysis reveals that thermal requirements dominate GUD shifts, explaining 74% of the variance in urban areas and surpassing the influence of chilling accumulation (51%). Moreover, we find that vegetation structure modulates these shifts, as increased species richness amplifies urban GUD trends while tree density buffers rural shifts. Notably, current empirical models largely failed to reproduce both the spatial patterns and the magnitude of the urban-rural divergence in GUD trends. Our results redefine the hierarchy of controls on spring phenology in human-dominated landscapes, with critical implications for predicting ecosystem responses under accelerating urbanization.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 9","pages":"101343"},"PeriodicalIF":39.5,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13556322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision-powered generative paradigm for robust battery health prediction in real-world electric vehicles.","authors":"Zicheng Fei, Minghe Li, Zijun Zhang, Kwok-Leung Tsui","doi":"10.1016/j.xinn.2026.101342","DOIUrl":"10.1016/j.xinn.2026.101342","url":null,"abstract":"<p><p>Predicting the state of health (SOH) of electric vehicle (EV) batteries under real-world operating conditions remains challenging due to noisy measurements, irregular charging behaviors, and highly variable environments, which differ substantially from controlled laboratory settings. This study proposes a vision-powered, multi-modal generative paradigm for accurate EV battery SOH prediction, which reformulates numerical SOH signals into image-based visual representations and leverages pixel-level spatial correlations learned from clean data to reconstruct underlying degradation patterns from noisy EV measurements. The paradigm comprises four key stages: image synthesis, generative model construction, pixel mapping, and SOH prediction. The proposed generative framework integrates a U-Net-based visual channel with a numerical temporal channel, allowing local spatial degradation patterns to be effectively fused with multivariate temporal dynamics. Advanced attention mechanisms are further incorporated to selectively enhance degradation-related representations and to facilitate coherent cross-modal information fusion. The proposed method is evaluated on one laboratory dataset and three real-world EV datasets encompassing diverse battery chemistries, vehicle types, and operational conditions. Experimental results demonstrate that the proposed paradigm effectively mitigates data quality limitations inherent in field measurements and achieves highly accurate SOH prediction, attaining a minimum root mean-square error of 0.0013. Comprehensive benchmarking against 15 state-of-the-art methods further confirms its consistently superior generalization performance across heterogeneous datasets. Interpretability analyses further reveal that both visual and numerical modalities capture physics-consistent aging patterns. These findings highlight the potential of the proposed vision-powered paradigm to extract stable degradation signatures from noisy EV operational data and its applicability to broader health prognostics tasks.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 8","pages":"101342"},"PeriodicalIF":39.5,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential plastic biodegradation in lakes worldwide.","authors":"Qi Zhang, Zhenyan Zhang, Ziyao Zhang, Guoyan Qin, Mingkang Jin, Bingfeng Chen, Yitian Yu, Tingzhang Wang, Meixia Wang, Tao Lu, Dong Zhu, Li Cui, Haifeng Qian, Matthias C Rillig, Yong-Guan Zhu","doi":"10.1016/j.xinn.2026.101338","DOIUrl":"10.1016/j.xinn.2026.101338","url":null,"abstract":"<p><p>Plastic pollution is ubiquitous, yet the biodegradation of plastic waste remains poorly understood due to limited knowledge of microbial plastic degradation potential. Here, we demonstrate that plastic waste shapes the global distribution of plastic-degrading potential across 182,661 lakes worldwide using integrated metagenomic and machine learning analyses and identify a tipping point (≥7.44 particles/m³) for effective <i>in situ</i> bioremediation. We constructed, for the first time, a catalog of candidate plastic-degrading bacteria, including 15,715 nonredundant enzyme homologs and 4,856 metagenome-assembled genomes. To facilitate future applications, we developed a computational approach to categorizing candidate plastic-degrading bacteria according to their degradation potential, ecological risk, environmental adaptation, and competition capacity. Furthermore, we customized eight template culture media based on the growth factor biosynthesis profiles of high-priority candidate plastic-degrading bacteria. Using these media, we successfully enriched the plastic-degrading microbial communities and isolated a high-priority strain, <i>Serratia ficaria</i> HfyG-1, from Xiazhu Lake, which harbors a wide variety of previously uncharacterized putative degrading enzymes that effectively degrade polylactic acid and polyethylene terephthalate. Our study provides a molecular resource for the bioremediation of plastic-polluted environments worldwide and highlights a proof-of-concept framework for identifying, investigating, and exploiting unknown functional microorganisms for practical applications.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 8","pages":"101338"},"PeriodicalIF":39.5,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>N</i>-nitroamine: Open a new era for direct deaminative functionalization.","authors":"Hao-Yuan Li, Can-Ming Zhu, Jun-Li Li, Qing-Yuan Meng","doi":"10.1016/j.xinn.2026.101337","DOIUrl":"10.1016/j.xinn.2026.101337","url":null,"abstract":"","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 5","pages":"101337"},"PeriodicalIF":39.5,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13147977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A soft perceptive multimodal locomotion robot via double-helix fiber-reinforced artificial muscles.","authors":"Yufeng Wang, Chenchen Li, Yulian Peng, Yingao Xu, Jinxing Wang, Zhipeng Wei, Zhengyan Wang, Houping Wu, Hongbo Wang","doi":"10.1016/j.xinn.2026.101320","DOIUrl":"10.1016/j.xinn.2026.101320","url":null,"abstract":"<p><p>Pneumatic artificial muscle has a high payload-to-weight ratio and rapid response with rich form factors and great design flexibility, making it a promising candidate for muscle-driven bioinspired robots. Here, we propose a perceptive pneumatic artificial muscle (PPAM) with self-length sensing via a seamlessly integrated double-helix conductive fiber coil. The double-helix coil fiber restricts the radial expansion of the silicone tube to achieve axial extension in actuation and measures its own length with a resolution of 10 μm by monitoring its self-inductance. A perceptive multimodal locomotion robot using three PPAMs as a bundle was developed, with crawling, turning, lateral movement, and rolling capabilities. By applying machine learning to analyze the three-channel inductance changes caused by body deformation under different loading conditions, the PPAM-based soft robot can recognize different external stimuli with an average accuracy of 99.28%. It is demonstrated that the soft robot can recognize a deep pit and bypass it automatically, change its gait amplitude to crawl through a low-roof tunnel, and sense its body posture change when a side flip occurs and automatically rolls back to its original position. In summary, the robot can perceive its own state and environment like a biological worm and can actively adjust its locomotion gait/mode to navigate itself through diverse terrains.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 7","pages":"101320"},"PeriodicalIF":39.5,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13343437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structured illumination microscopy for high SNR 3D imaging from millimeter-thick tissues to cellular dynamics.","authors":"Mengrui Wang, Manming Shu, Jiajing Yan, Chang Liu, Xiangda Fu, Jingxiang Zhang, Yuchen Lin, Hu Zhao, Yuwei Huang, Dingbang Ma, Yifan Ge, Huiwen Hao, Tianyu Zhao, Yansheng Liang, Shaowei Wang, Ming Lei","doi":"10.1016/j.xinn.2026.101321","DOIUrl":"10.1016/j.xinn.2026.101321","url":null,"abstract":"<p><p>Three-dimensional (3D) optical imaging of intact tissues with high spatiotemporal resolution is a highly desirable goal, as it offers visualization of biological structures and processes in their physiological context. Optical sectioning structured illumination microscopy (OS-SIM) stands out among various 3D imaging techniques for its comprehensive superiority, as it offers high spatial resolution, rapid imaging speed, and low phototoxicity. However, under the challenging conditions of dominant background and increased fluorescence scattering in thick tissues, which leads to a decrease in modulation contrast, OS-SIM faces the problem of low signal-to-noise ratio (SNR) and limited imaging depth. We have developed a high SNR OS-SIM strategy for 3D imaging of biological samples ranging from millimeter-thick tissues to cellular dynamics. By enhancing the SNR by ∼10 dB with rapid processing, our method enables imaging of tissue-cleared sample with thickness >2 mm and low light dose for live-cell recording, while also doubling the imaging penetration depth. With our OS-SIM system, we achieve high-quality 3D images of various biological samples, including mouse brains, <i>Drosophila</i> clock neurons, organoids, and live cells. We anticipate that this approach will render OS-SIM a powerful technique for the research of cellular organelles or thick tissues in 3D morphology.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 8","pages":"101321"},"PeriodicalIF":39.5,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Far-red-emitting Nile red with boosted solvatochromism enables multidimensional imaging of membrane heterogeneity and dynamics.","authors":"Mingqiao Liu, Yunzhe Fu, Xiaopeng Li, Haolin Zhang, Yumiao Ma, Meiqi Li, Chunyan Shan, Hongxi Zheng, Xiaoyi Lu, Yan Zhang, Li Yu, Peng Xi, Zhixing Chen","doi":"10.1016/j.xinn.2026.101319","DOIUrl":"10.1016/j.xinn.2026.101319","url":null,"abstract":"<p><p>The heterogeneity of lipid membranes plays an essential role in regulating the biological functions of organelles. As a solvatochromic probe, Nile red exhibits optical responses to varying membrane environments; however, its limited solvatochromism restricts its ability to detect subtle changes in membrane polarity and lipid order. In this work, we rationally design the auxochrome of Nile red by incorporating an oxygenated julolidine moiety and develop oxygenated julolidine-based Nile red (JONR), which exhibits significantly enhanced sensitivity to environmental polarity, along with strong fluorogenicity from protic to aprotic solvents, large Stokes shifts, and far-red emission in polar environments. Compared to Nile red, JONR demonstrates 3-fold greater accuracy in identifying various organelles in live cells and enables real-time tracking of their dynamic interactions through ratiometric imaging. Furthermore, JONR can distinguish lipid environments through polarization modulation depth and fluorescence lifetime variations. By integrating these properties with its superior spectral response, we present the first comprehensive mapping of subtle lipid heterogeneity within migration-associated structures in L929 cells. The high sensitivity to subtle differences in lipid environments of JONR enables fine resolution of subcellular membrane structures in live cells and provides a new approach for high-throughput analysis of intracellular microenvironments.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 8","pages":"101319"},"PeriodicalIF":39.5,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From mahjong tables to meta-capabilities: What East-West contrasts really teach us about cognition.","authors":"The Innovation Editorial Team","doi":"10.1016/j.xinn.2026.101318","DOIUrl":"https://doi.org/10.1016/j.xinn.2026.101318","url":null,"abstract":"","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 6","pages":"101318"},"PeriodicalIF":25.7,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trade protectionism undermines sustainable development in the Global South.","authors":"Junyi Liang, Shaojian Wang","doi":"10.1016/j.xinn.2026.101302","DOIUrl":"10.1016/j.xinn.2026.101302","url":null,"abstract":"","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 4","pages":"101302"},"PeriodicalIF":39.5,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"More papers, fewer breakthroughs: Can AI reshape scientific discovery?","authors":"Kaihua Chen, Siqing Fang, Heyang Li, Boqiang Li, Ji Dai","doi":"10.1016/j.xinn.2026.101316","DOIUrl":"10.1016/j.xinn.2026.101316","url":null,"abstract":"","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"7 5","pages":"101316"},"PeriodicalIF":39.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13147985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}