A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared

Long-term live-cell imaging techniques for visualizing pavement cell morphogenesis

Recent advancements in microscopy and biological technologies have allowed scientists to study dynamic plant developmental processes with high temporal and spatial resolution. Pavement cells, epidermal cells found on leaf tissue, form complex shapes with alternating regions of indentations and outgrowths that are postulated to be driven by the microtubule cytoskeleton. Given their complex shapes, pavement cells and the microtubule contribution towards morphogenesis have been of great interest in the field of developmental biology. Here, we focus on two live-cell imaging methods that allow for early and long-term imaging of the cotyledon (embryonic leaf-like tissue) and leaf epidermis with minimal invasiveness in order to study microtubules throughout pavement cell morphogenesis. The methods described in this chapter can be applied to studying other developmental processes associated with cotyledon and leaf tissue