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Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method's effectiveness in identifying cracks in complex environments. [ABSTRACT FROM AUTHOR]

Macroautophagy/autophagy is a catabolic process crucial for degrading cytosolic components and damaged organelles to maintain cellular homeostasis, enabling cells to survive in extreme extracellular environments. ENAH/MENA, a member of the Ena/VASP protein family, functions as a highly efficient actin elongation factor. In this study, our objective was to explore the role of ENAH in the autophagy process. Initially, we demonstrated that depleting ENAH in cancer cells inhibits autophagosome formation. Subsequently, we observed ENAH's colocalization with MAP1LC3/LC3 during tumor cell starvation, dependent on actin cytoskeleton polymerization and the interaction between ENAH and BECN1 (beclin 1). Additionally, mammalian ATG9A formed a ring-like structure around ENAH-LC3 puncta during starvation, relying on actin cytoskeleton polymerization. Furthermore, ENAH's EVH1 and EVH2 domains were found to be indispensable for its colocalization with LC3 and BECN1, while the PRD domain played a crucial role in the formation of the ATG9A ring. Finally, our study revealed ENAH-led actin comet tails in autophagosome trafficking. In conclusion, our findings provide initial insights into the regulatory role of the mammalian actin elongation factor ENAH in autophagy. Abbreviations: 3-MA 3-methyladenine; ABPs actin-binding proteins; ATG autophagy related; ATG9A autophagy related 9A; Baf A1 bafilomycin A1; CM complete medium; CytERM endoplasmic reticulum signal-anchor membrane protein; Cyto D cytochalasin D; EBSS Earl's balanced salt solution; ENAH/MENA ENAH actin regulator; EVH1 Ena/VASP homology 1 domain; EVH2 Ena/VASP homology 2 domain; GAPDH glyceraldehyde-3-phosphate dehydrogenase; Lat B latrunculin B; LC3-I unlipidated form of LC3; LC3-II phosphatidylethanolamine-conjugated form of LC3; MAP1LC3/LC3 microtubule associated protein 1 light chain 3; mEGFP monomeric enhanced green fluorescent protein; mTagBFP2 monomeric Tag blue fluorescent protein 2; OSER organized smooth endoplasmic reticulum; PRD proline-rich domain; PtdIns3K class III phosphatidylinositol 3-kinase; WM wortmannin. [ABSTRACT FROM AUTHOR]

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population, infrastructure, and socioeconomic activities. Scaling laws are basic but effective rules for evaluating a city's internal growth logic and assessing its efficiency by investigating whether urban indicators scale with population. To date, only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few meters. Using multiple urban-sensed and human-sensed data, this study proposes a thorough framework for quantifying the scaling laws in a city. To begin, urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking data. To demonstrate the spatiotemporal variability of urban mobility, various network-based mobility measures are proposed. Following that, three different features of urban mobility laws are exposed, explaining spatial agglomeration, spatial hierarchical structures, and the temporal growth process. The scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed data. Applying this framework to the case study of Shenzhen, China revealed (a) spatial travel heterogeneity, hierarchical spatial structures, and mobility growth, and (b) not only a robust sub-linear relationship between infrastructure volume and population, but also a sub-linear relationship for socioeconomic activity. The identified scaling laws, both in terms of mobility measures and urban indicators, provide a multi-faceted portrait of the spatio-temporal variations of urban settings, allowing us to better understand intra-urban developments and, consequently, provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future. [ABSTRACT FROM AUTHOR]