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Knowledge complexity affects the heterogeneity of knowledge production and flow and has a considerable impact on the resilience evolution of regional innovation ecosystems. To explore the impact mechanism of knowledge complexity on regional innovation ecosystem resilience, we constructed a framework for measuring regional innovation ecosystem resilience from three dimensions: stability, adaptability, and transformability. We also explored the spatial evolution of innovation ecosystem resilience in the Pearl River Delta from 2010 to 2020 and the impact of knowledge complexity on regional innovation ecosystem resilience. Research has shown that: 1) knowledge complexity affects system resilience through the diversity of knowledge subset types, interdependence between subsets, and knowledge space stickiness, absorption, and competition effects; 2) from 2010 to 2020, the resilience of the innovation ecosystem in the Pearl River Delta has significantly improved, gradually forming a spatial pattern with Shenzhen and Guangzhou as the core, with high resilience in the central region and low resilience in the eastern and western regions. The difference in resilience between the central, western, and eastern regions is becoming increasingly significant; 3) The spatial pattern of different resilience dimensions shows higher stability and adaptability in the central region, higher transformability in the eastern region, and lower resilience in all three dimensions in the western region. The stability shows a stable structure of "high in the central region and low in the eastern and western regions," the adaptability shows a stable structure of "high in the central north and central eastern regions, low in the central southern, eastern and western regions," the transformability shows a development trend from "high in the central and eastern regions, and low in the western region" to "high in the central eastern and eastern regions, and low in the central western and western regions"; and 4) Knowledge complexity has a significant positive effect on the innovation ecosystem resilience in the Pearl River Delta, with a significant positive and negative impact on system's stability and adaptability and transformability, respectively. Knowledge complexity has a significant positive impact on innovation ecosystem resilience in the Pearl River Delta by promoting industrial structure upgrading, technological progress, and policy environment optimization. Analyzing the resilience differences and imbalanced evolution of the innovation ecosystem in the Pearl River Delta from the perspective of knowledge complexity can provide new methods and empirical evidence for exploring the impact of heterogeneity in knowledge production and flow on regional resilience evolution and theoretical construction. It can also provide a practical reference for local governments to introduce resilience governance goals for innovative ecosystems based on regional knowledge development capabilities and formulate targeted resilience enhancement strategies.

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth's surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth's albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth's climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth's surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goals and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity.

Objective To investigate the predictive value of depth of invasion (DOI) of tongue squamous cell carcinoma (TSCC) for cervical lymph node metastasis and prognosis. Methods We retrospectively analyzed the clinical and pathological data of 73 patients with T1/2 TSCC. ROC curve was used to determine the optimal cut-off value of DOI for predicting cervical lymph node metastasis, and logistic regression analysis was performed to analyze the related factors affecting cervical lymph node metastasis of TSCC. Kaplan-Meier method and Cox regression analysis were used for survival analysis. Results Among 73 patients, 18 patients were with lymph node metastasis and 55 patients were without lymph node metastasis. The median DOI with and without lymph node metastasis were 8.00 and 5.00 mm, respectively (P=0.003). The optimal cut-off value for DOI was 6.15 mm, with AUC 0.75 (95%CI: 64.1%~87.1%, P=0.001), sensitivity 77.8% and specificity 63.6%. DOI and pathological differentiation were independent prognostic factors for cervical lymph node metastasis in multivariate analysis. DOI, nerve invasion and pathological differentiation were independent prognostic factors of survival in Cox regression analysis. Conclusion DOI of TSCC patients has important predictive value for both cervical lymph node metastasis and prognosis. Neck lymph node dissection is recommended for patients with DOI > 6.15 mm to improve survival rate and reduce recurrence rate.

'Qiuchi' [phrase omitted] ('Qiu Pool') recurs as an enigmatic proper name in Su Shi's i'i [phrase omitted] (1037-1101) later poetry. While scholars have treated Qiuchi primarily as the poet's name for strange rocks, 1 argue that it is in the first place a metaphorical lopos tbr him--it is a heterotopia, a counter-site to reality for this nonconformist. As a poetic image, Su Shi's Qiuchi is a composite motif that allows for diverse interpretations. It is a hallucinatory dreamscape at the outset; the vision then turns out to be Mount Qiuchi, a historical spot and Daoist 'grotto heaven' near Su's hometown in Sichuan so that Qiuchi becomes a macro-micro-equation of landscape: finally, the poet analogizes his Qiuchi to Tao Qian's [phrase omitted] (372?.427) Peach Blossom Spring while denying its transcendency by bringing it down to earth. Qiuchi also inhabits various roles in Su Shi's different life stages. It appears in matching poems among scholar-officials as the antithesis of bureaucratic service and an expression of longing for seclusion when the poet was in office. During his banishment, the inaccessible Qiuchi is the prototypical home topos and projected on other sites of exile as mirror images, which I call 'heterotopia and its para-topia.' In this way, the Qiuchi trope contributes to Su Shi's self-identity and public image, crystallized in the poet's posthumous appellation., Su Shi [phrase omitted] (1037-1101), the greatest poet of the Song dynasty (960-1279) has successfully built a series of self-referential images relating to 'places' with imprints of his life experience, [...]

As natural living substances, microorganisms have emerged as useful resources in medicine for creating microbe-material hybrids ranging from nano to macro dimensions. The engineering of microbe-involved nanomedicine capitalizes on the distinctive physiological attributes of microbes, particularly their intrinsic "living" properties such as hypoxia tendency and oxygen production capabilities. Exploiting these remarkable characteristics in combination with other functional materials or molecules enables synergistic enhancements that hold tremendous promise for improved drug delivery, site-specific therapy, and enhanced monitoring of treatment outcomes, presenting substantial opportunities for amplifying the efficacy of disease treatments. This comprehensive review outlines the microorganisms and microbial derivatives used in biomedicine and their specific advantages for therapeutic application. In addition, we delineate the fundamental strategies and mechanisms employed for constructing microbe-material hybrids. The diverse biomedical applications of the constructed microbe-material hybrids, encompassing bioimaging, anti-tumor, anti-bacteria, anti-inflammation and other diseases therapy are exhaustively illustrated. We also discuss the current challenges and prospects associated with the clinical translation of microbe-material hybrid platforms. Therefore, the unique versatility and potential exhibited by microbe-material hybrids position them as promising candidates for the development of next-generation nanomedicine and biomaterials with unique theranostic properties and functionalities. [ABSTRACT FROM AUTHOR]

Driving fatigue has been one of the major causes of traffic accident. Efficient and accurate detection of driving fatigue are a legitimate public concern. In this paper, we conduct the simulated driving experiments and an EEG-based driving fatigue detection framework integrating multilayer brain network and convolutional neural network (CNN) is developed. This lightweight attention-based multi-frequency topology learning (AMFTL) framework first captures the fatigue-related multi-frequency brain topological information and then feeds it into a CNN-based topology feature extraction (TFE) module to fully explore and integrate the critical topological features. The quantitative analysis results show that there are significant differences in brain topologies between the alert and fatigue states. And experimental results show that our proposed framework achieves an average detection accuracy of 94.71% for driving fatigue, which outperforms the current state-of-the-art methods. This proposed framework is expected to open new venues for EEG-based brain state analysis, and holds promising practical application potential. [ABSTRACT FROM AUTHOR]