2D-3D Fully convolutional neural networks for cardiac MR segmentation

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in terms of clinical parameters. A comparative analysis is provided by introducing a novel dice loss function and its combination with cross entropy loss. By exploring different network structures and comprehensive experiments, we discuss several key insights to obtain optimal model performance, which also is central to the theme of this challenge

Low-Power Wide-Area Networks: A Broad Overview of its Different Aspects

Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.publishedVersio

CHARACTERIZATION OF DIVERSE NONCODING RNAS IN THERAPEUTICS, BIOTECHNOLOGY AND CHROMATIN BIOLOGY

This study encompasses exploration of diverse noncoding RNAs (ncRNAs) across therapeutics, biotechnology, and cell biology, presented through three distinct yet interconnected projects. The first project delves into the 3D organization of the mammalian genome, particularly focusing on mitotic bookmarking and the potential role of RNA in maintaining genomic integrity and cellular memory during cell division. By employing advanced chromatin conformation capture techniques, notably the iMARGI method, this study generated a comprehensive RNA-DNA interactome across cell cycle phases, revealing significant cell type-specific differences in RNA-DNA interactions. These findings highlight the dynamic interplay between RNA and DNA and will contribute to a deeper understanding of genomic regulation and cellular memory. The second project investigates the conformational dynamics and target sequence influence on the catalytic activity of single versus dual RNA-guided CRISPR-Cas9 systems. Contrary to conventional wisdom, our research found that dual guide RNAs (dgRNAs) can perform as well as, or better than, single guide RNAs (sgRNAs) in genome editing efficiency. This discovery was supported by molecular dynamics simulations, revealing that dgRNAs and sgRNAs confer alternative structural dynamics to Cas9, impacting its target sequence preferences and catalytic activity. This study thus provides crucial insights into the structural underpinnings of CRISPR-Cas9 efficiency and paves the way for optimizing gene editing approaches. The third project focuses on the challenges of cloning and sequence validation of repetitive and high GC-content shRNAs, often used in gene knockdown and therapeutic applications. We present improved methods for efficient cloning of shRNAs targeting disease-associated repeat expansions into lentivectors, enhanced by design and preparation techniques, recombination-based cloning, and comprehensive sequencing-based validation. These advancements in shRNA technology underscore the importance of RNA-based tools in therapeutic development and gene research. In summary, this dissertation provides a comprehensive examination of the functional versatility of ncRNAs, from their role in chromatin organization and gene regulation to their applications in biotechnological innovations and therapeutic strategies. The findings collectively underscore the significance of ncRNAs in advancing our understanding of complex biological systems and their potential in developing innovative solutions to address diverse biomedical challenges