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The clinical application of polymyxin B (PMB) is limited by its nephrotoxic effects, making the reduction of PMB‐induced nephrotoxicity has become a pressing concern for clinicians. Tetrahydrocurcumin (THC), known for its beneficial characteristics in biological functions, presents an attractive option for intervention therapy to mitigate PMB‐induced nephrotoxicity. However, the underlying mechanism of how THC mitigates PMB‐induced nephrotoxicity is still poorly understood. Here, we first evaluated the potential of THC intervention therapy to mitigate PMB‐induced nephrotoxicity in an in vitro model of PMB‐induced cell injury. Moreover, we demonstrated that THC effectively protected HK‐2 cells from PMB‐induced apoptosis by using cell counting kit‐8 and flow cytometry assay. THC could also suppress PMB‐induced endoplasmic reticulum (ER) stress via PERK/eIF2α/ATF4/CHOP pathway. In addition, using PERK inhibitor GSK2606414 to inhibit ER stress also alleviated PMB‐induced apoptosis. Taken together, these findings provide novel insights that THC possesses the ability to alleviate PMB‐induced nephrotoxicity by inhibiting the ER stress‐mediated PERK/eIF2α/ATF4/CHOP axis, which sheds light on the benefits of THC as an intervention strategy to reduce PMB‐induced nephrotoxicity, thus providing a potential avenue for improved clinical outcomes in patients receiving PMB treatment. [ABSTRACT FROM AUTHOR]

Representing building layouts as graphs can extract critical design patterns that would facilitate space syntax analyses as well as design mining and automation but traditional approaches (e.g., non-weighted adjacent graphs) encountered problems in modular buildings, as they are largely shaped under the principle of 'modularity' rather than freeform cast in-situ elements. This paper attempts to develop a novel analytical tool called ModularGraph to represent modular building layouts (MBLs) as graphs considering their unique adjacency, connectivity, and conjoint relationships in a triumvirate. It does so by developing a prototype then applying it to 36 modular buildings for iteration, finetuning, and finalizing. It is found that ModularGraph can effectively translate heterogeneous forms of MBLs into unified graph-based representations with rich graphic and semantic information. This study not only contributes an innovative graph analytic tool for design pattern mining, but also lays a stepping stone towards generative AI for modular building design. • Propose a graph triumvirate structure for modular building layout representation. • Incorporate a weight mechanism to indicate the strength of spatial relationships for better semantic interpretations. • Perform a quantitative analysis on the layouts of 36 modular buildings in Hong Kong using the proposed framework. • Uncover patterns in modular building layout designs exemplified by ModularGraph. • Develop a machine learning model to learn knowledge from layout designs represented by ModularGraph. [ABSTRACT FROM AUTHOR]

Quantifying truck-loaded materials is a problem in many industrial operations. In construction and demolition waste (CDW) management, inspectors at disposal facilities are often required to measure the amount of different waste components loaded by incoming trucks to determine admissibility. Due to the bulky and mixed nature of construction materials, accurate quantification of specific waste categories without sacrificing operability in the field is a challenge. This study proposes a CDW volume estimation algorithm based on monocular vision which can automatically quantify from a single image the amount of specific material components, e.g., rock, gravel, and wood, in waste mixtures. The algorithm achieves a relative error of 0.065 in calculating truck bucket dimensions, and a relative error of 0.169 in estimating material-level construction waste volume. It takes 3.3 s in average to process one image. In applying the algorithm to analyze 2,914 waste truckloads received by an off-site sorting facility in Hong Kong, we observe that the facility entrance received around 800.0 m3 CDW per day of which about 10.8 m3 were rejected. Since non-inert wood/cardboard accounts for the highest proportion among all material types, this may imply that many waste dumps accepted by the facility may have been in violation of the admissibility criteria. The study contributes to the knowledge body by providing a novel, non-destructive approach to quantifying CDW via monocular vision. It can be extended to address the general problem of truck payload quantification in scenarios such as road construction, warehouse inventory management, and logistics and supply chain management. [Display omitted] [ABSTRACT FROM AUTHOR]

There are various scenarios challenging human experts to judge the interior of something based on limited surface information. Likewise, at waste disposal facilities around the world, human inspectors are often challenged to gauge the composition of waste bulks to determine admissibility and chargeable levy. Manual approaches are laborious, hazardous, and prone to carelessness and fatigue, making unattended gauging of construction waste composition using simple surface information highly desired. This research attempts to contribute to automated waste composition gauging by harnessing a valuable dataset from Hong Kong. Firstly, visual features, called visual inert probability (VIP), characterizing inert and non-inert materials are extracted from 1127 photos of waste bulks using a fine-tuned convolutional neural network (CNN). Then, these visual features together with easy-to-obtain physical features (e.g., weight and depth) are fed to a tailor-made support vector machine (SVM) model to determine waste composition as measured by the proportions of inert and non-inert materials. The visual-physical feature hybrid model achieved a waste composition gauging accuracy of 94% in the experiments. This high performance implies that the model, with proper adaption and integration, could replace human inspectors to smooth the operation of the waste disposal facilities. • The gauging of construction waste composition is automated. • Visual features are extracted for inert/non-inert waste differentiation. • Hybrid use of visual-physical features outperforms standalone visual recognition. • The accuracy of the proposed approach is comparable to human inspectors. [ABSTRACT FROM AUTHOR]