The expanding availability of large-scale genomic data and the growing interest in uncovering gene-disease associations call for efficient tools to visualize and evaluate gene expression and genetic variation data. Here, we developed a comprehensive pipeline that was implemented as an interactive Shiny application and a standalone desktop application. NeuroVar is a tool for visualizing genetic variation (single nucleotide polymorphisms and insertions/deletions) and gene expression profiles of biomarkers of neurological diseases. Data collection involved filtering biomarkers related to multiple neurological diseases from the ClinGen database. NeuroVar provides a user-friendly graphical user interface to visualize genomic data and is freely accessible on the project’s GitHub repository (https://github.com/omicscodeathon/neurovar).

Artificial intelligence (AI) has emerged as a powerful tool to address various challenges in the food industry, particularly in the development of eco-friendly processes of sustainable food systems. With the growing concern for environmental sustainability and food security, there is a need for the food industry to adopt sustainable practices that reduce wastes, preserve resources, and improve the overall efficiency of food production, processing, and packaging to meet the needs of the growing human populations without compromising the ability of future generations. Within this context, this chapter delves into the utilization of artificial intelligence to construct sustainable food systems. It emphasizes the new trends of AI applications to qualitatively and quantitatively optimize key elements of sustainable food systems. Request access from your librarian to read this chapter's full text.

RNA sequencing data manipulation workflows are complex and require various skills and tools. This creates the need for user-friendly and integrated genomic data analysis and visualization tools. We developed a novel R package using multiple Cran and Bioconductor packages to perform gene expression analysis and genetic variant calling from RNA sequencing data. Multiple public datasets were analyzed using the developed package to validate the pipeline for all the supported species. The developed R package, named “exvar”, includes multiple data analysis functions and three data visualization shiny apps integrated as functions. Also, it could be used to analyze several species’ data. The exvar package is available in the project’s GitHub repository (https://github.com/omicscodeathon/exvar).

Agriculture plays a crucial role in addressing global challenges such as food security, resource scarcity, and climate change. As the world population continues to grow, the need for sustainable and efficient farming practices becomes paramount. This chapter aims to explore the applications of artificial intelligence (AI) in agricultural sustainability, focusing on how AI technologies can enhance efficiency and resilience in agricultural practices. The chapter provides an overview of the potential benefits and challenges of integrating AI into agriculture, highlighting specific use cases, and discussing the implications for sustainable farming systems. It also examines the role of AI in addressing key sustainability issues including resource management, yield optimization, pest control, and adaptation to environmental conditions. By examining current trends and advancements in AI for agriculture, this chapter contributes to a comprehensive understanding of how AI can revolutionize farming practices and promote sustainable agricultural systems. Request access from your librarian to read this chapter's full text.

Background: The identification of differentially expressed genes and their associated biological processes, molecular function, and cellular components are essential for genetic disease studies because they present potential biomarkers and therapeutic targets. Methods: In this study, we developed an o²S²PARC template to instantiate an interactive pipeline for gene expression data visualization, ontological mapping, and statistical evaluation. To demonstrate the tool's usefulness, we performed a case study on a publicly available dataset. Results: The tool enables users to identify the differentially expressed genes (DEGs) and visualize them in a volcano plot format. Ontologies associated with the DEGs are assigned and visualized in barplots. Conclusions: The “Expression data visualization” template is publicly available on the o²S²PARC platform.

The agriculture industry is facing mounting enforcement to produce more food to nourish a growing population while simultaneously mitigating its environmental footprint. In response, the use of internet of things (IoT) technologies in agriculture has emerged as a promising approach to monitoring and improving the supply chain's environmental performance. This chapter provides a comprehensive overview of the use of IoT technologies in the context of environmental monitoring in agricultural supply chains. It begins by introducing the concept of environmental performance in agriculture, its importance, and its challenges. The chapter then explains the basics of IoT technology and how it can be used to monitor and track the environmental performance in agricultural supply chains. The chapter also discusses the benefits and challenges of IoT technologies implementation. Overall, by leveraging the power of IoT, it is possible to enhance sustainability and efficiency in food production, while also reducing environmental impact. Request access from your librarian to read this chapter's full text.