Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)-a machine learning model-and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model's decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.
In the current two decades, dyslipidemia and increased blood glucose as metabolic abnormalities are the most common health threats with a high incidence among HIV/AIDS patients on antiretroviral (ARV) treatment. Scientific investigations and reports on lipid and glucose disorders among HIV infected communities are inadequate especially in those developing such as Malaysia. This cross-sectional survey was mainly aimed to evaluate the prevalence of metabolic abnormalities and associated risk factors among HIV infected population patients on ARV medication.
Aims: To evaluate the anti breast-cancer activity, biocompatibility and toxicity of poly(d,l)-lactic-co-glycolic acid (PLGA)-encapsulated quercetin nanoparticles (Q-PLGA-NPs). Materials & methods: Quercetin was nano-encapsulated by an emulsion-diffusion process, and the nanoparticles were fully characterized through Fourier transform infrared spectroscopy, x-ray diffractions, FESEM and zeta-sizer analysis. Activity against CAL51 and MCF7 cell lines were assessed by DNA fragmentation assays, fluorescence microscopy, and acridine-orange, and propidium-iodide double-stainings. Biocompatibility towards red blood cells and toxicity towards mice were also explored. Results: The Q-PLGA-NPs exhibited apoptotic activity against the cell lines. The murine in vivo studies showed no significant alterations in the liver and kidney's functional biomarkers, and no apparent abnormalities, or tissue damages were observed in the histological images of the liver, spleen, lungs, heart and kidneys. Conclusion: The study established the preliminary in vitro efficacy and in vivo safety of Q-PLGA-NPs as a potential anti-breast cancer formulation.