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Progressive collapse is a phenomenon where initial local failure of a component spreads to surrounding components, resulting in the collapse of the entire structure or most of it. This research is based on the component method (joint simplification model) and the hierarchical shell element macromodeling approach (composite floor simplification model). A three-dimensional spatial composite structural model was established using the finite-element (FE) software ANSYS/LS-DYNA. Experimental results were used to validate the reliability of the FE model. Based on these methods, a typical prototype structural model designed by the National Institute of Standards and Technology (NIST) was established, and a computational analysis of progressive collapse due to component removal was conducted. The results showed that the occurrence of structural collapse depends on the type of beam-column connection, floor level, and type of component removed. Also, it was found that structural collapse is more likely to occur when interior columns, gravity columns (nonresisting lateral column), and low-floor columns are removed. After column failure, the path of vertical loads is mainly transmitted to the columns within the area of direct collapse effect. The area within the potential collapse effect is also exposed to a severe impact phenomenon due to falling floors. Based on the collapse modes and internal force transfer path, an innovative collapse-prevention design method (structural response-based design, or SRBD) is proposed. The SRBD method predicts the collapse response of structure at a theoretical level and avoids redundant modeling and nonlinear calculations in collapse-prevention design. A comparison with collapse response of the NIST model shows that the prediction accuracy is approximately 90%. The SRBD method provides guidance for structural engineers in progressive collapse-prevention design. [ABSTRACT FROM AUTHOR]

Background: The spatial context of tumor-infiltrating immune cells (TIICs) is important in predicting colorectal cancer (CRC) patients' clinical outcomes. However, the prognostic value of the TIIC spatial distribution is unknown. Thus, we aimed to investigate the association between TIICs in situ and patient prognosis in a large CRC sample. Methods: We implemented multiplex immunohistochemistry staining technology in 190 CRC samples to quantify 14 TIIC subgroups in situ. To delineate the spatial relationship of TIICs to tumor cells, tissue slides were segmented into tumor cell and microenvironment compartments based on image recognition technology, and the distance between immune and tumor cells was calculated by implementing the computational pipeline phenoptr. Results: MPO+ neutrophils and CD68+IDO1+ tumor-associated macrophages (TAMs) were enriched in the epithelial compartment, and myeloid lineage cells were located nearest to tumor cells. Except for CD68+CD163+ TAMs, other cells were all positively associated with favorable prognosis. The prognostic predictive power of TIICs was highly related to their distance to tumor cells. Unsupervised clustering analysis divided colorectal cancer into three subtypes with distinct prognostic outcomes, and correlation analysis revealed the synergy among B cells, CD68+IDO1+TAMs, and T lineage cells in producing an effective immune response. Conclusions: Our study suggests that the integration of spatial localization with TIIC abundance is important for comprehensive prognostic assessment. [ABSTRACT FROM AUTHOR]

Phytoremediation is an effective solution to treat pollution with antibiotic compounds in aquatic environments; however, the underlying mechanisms for plants to cope with antibiotic pollutants are obscure. Here we used cell suspension culture to investigate the distribution and transformation of ciprofloxacin (CIP) in common reed (Phragmites australis) plants, as well as the accompanying phenotypic and metabolic responses of plants. By means of radioactive isotope labelling, we found that in total 68 % of CIP was transformed via intracellular Phase I transformation (reduction and methylation), Phase Ⅱ conjugation (glycosylation), and Phase Ⅲ compartmentalization (cell-bound residue formation mainly in cell walls, 23 %). The reduction and glycosylation products were secreted by the cells. To mitigate stress induced by CIP and its transformation products, the cells activated the defense system by up-regulating both intra- and extra-cellular antioxidant metabolites (e.g., catechin, l-cystine, and dehydroascorbic acid), anti-C/N metabolism disorder metabolites (e.g., succinic acid), secreting signaling (e.g., nicotinic acid), and anti-stress (e.g., allantoin) metabolites. Notably, the metabolic reprogramming could be involved in the CIP transformation process (e.g., glycosylation). Our findings reveal the strategy of wetland plants to cope with the stress from CIP by transforming the xenobiotic compound and reprogramming metabolism, and provide novel insights into the fate of antibiotics and plant defense mechanisms during phytoremediation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Colorectal cancer is one of the most common cancers worldwide. Lymph node metastasis is an important marker of colorectal cancer progression and plays a key role in the evaluation of patient prognosis. Accurate preoperative assessment of lymph node metastasis is crucial for devising appropriate treatment plans. However, current clinical imaging methods have limitations in many aspects. Therefore, the discovery of a method for accurately predicting lymph node metastasis is crucial clinical decision-making. DNA methylation is a common epigenetic modification that can regulate gene expression, which also has an important impact on the development of colorectal cancer. It is considered to be a promising biomarker with good specificity and stability and has promising application in predicting lymph node metastasis in patients with colorectal cancer. This article reviews the characteristics and limitations of currently available methods for predicting lymph node metastasis in patients with colorectal cancer and discusses the role of DNA methylation as a biomarker., (© 2024. The Author(s), under exclusive licence to Federación de Sociedades Españolas de Oncología (FESEO).)