Biofeed through Bioconversion process with the Engineered Methylomicrobium buryatense Strain 5GBC1-RO1

Abdelmoumen Shad SERROUNE — 2024-11-01

This study presents a novel approach to mitigate methane emissions while simultaneously addressing the growing demand for sustainable animal feed through the development of an engineered methanotrophic strain, Methylomicrobium buryatense 5GB1C-RO1. Utilizing advanced genetic engineering techniques, including CRISPR/Cas9 and horizontal gene transfer, we have optimized the ribulose monophosphate (RuMP) cycle and enhanced oxidase activity in this strain. The bioconversion process is facilitated by innovative bioreactor designs, including Two-Phase Partitioning Bioreactors (TPPBs) and Inverse Membrane Bioreactors (IMBRs), which significantly improve methane solubility and mass transfer. Through metabolic flux analysis and computational modeling, we have achieved high biomass yields and efficient methane utilization. The resulting biofeed demonstrates a superior nutritional profile, with optimized macronutrient content and essential components. This integrated approach not only contributes to greenhouse gas mitigation but also offers a promising solution for sustainable animal nutrition. Our findings suggest that the 5GB1C-RO1 strain and associated bioprocesses have the potential to revolutionize both environmental protection and agricultural sustainability

Generative AI to Predict Breast Cancer: Current Approaches, Advancements, and Challenges

Alma Mohapatra — 2024-11-08

Breast cancer is one of the most prevalent cancers worldwide, with early detection playing a critical role in improving patient outcomes and survival rates. Traditional diagnostic methods, though effective, often face challenges in terms of accessibility, cost, and the need for highly skilled radiologists. Recent advancements in Artificial Intelligence (AI), particularly Generative AI (GAI), have opened new avenues in medical imaging and predictive analysis. Unlike conventional AI models, Generative AI can produce synthetic data that mimics real mammograms, providing a robust solution to data scarcity and enhancing model training.

This paper explores the application of Generative AI, especially Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in predicting breast cancer through synthetic imaging and data augmentation techniques. By generating high-quality synthetic breast cancer imaging data, GAI models can improve diagnostic accuracy and sensitivity. We review recent studies and case examples where Generative AI has demonstrated efficacy in predicting and detecting breast cancer at early stages, often outperforming traditional AI models. In addition, we provide a technical overview of the workflow involved in training and deploying GAI models for breast cancer prediction, highlighting steps from data acquisition and preprocessing to model evaluation.

The findings suggest that while Generative AI holds significant promise in predictive oncology, it also faces challenges related to model interpretability, data bias, and ethical considerations. We discuss these limitations and propose strategies to address them, focusing on the need for diversified data sources, model transparency, and collaboration between data scientists and healthcare professionals. The paper concludes with an outlook on future advancements in Generative AI, including the integration of newer models such as diffusion models, and emphasizes the potential of these technologies to revolutionize cancer diagnostics by providing cost-effective, accessible, and highly accurate predictive tools for breast cancer.

International Journal of Medical Science and Clinical Invention

Biofeed through Bioconversion process with the Engineered Methylomicrobium buryatense Strain 5GBC1-RO1

Generative AI to Predict Breast Cancer: Current Approaches, Advancements, and Challenges